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Lamba S, Das T, Srivastava PK. Impact of infectious density-induced additional screening and treatment saturation on COVID-19: Modeling and cost-effective optimal control. Infect Dis Model 2024; 9:569-600. [PMID: 38558959 PMCID: PMC10978547 DOI: 10.1016/j.idm.2024.03.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2023] [Revised: 02/18/2024] [Accepted: 03/11/2024] [Indexed: 04/04/2024] Open
Abstract
This study introduces a novel SI2HR model, where "I2" denotes two infectious classes representing asymptomatic and symptomatic infections, aiming to investigate and analyze the cost-effective optimal control measures for managing COVID-19. The model incorporates a novel concept of infectious density-induced additional screening (IDIAS) and accounts for treatment saturation. Furthermore, the model considers the possibility of reinfection and the loss of immunity in individuals who have previously recovered. To validate and calibrate the proposed model, real data from November-December 2022 in Hong Kong are utilized. The estimated parameters obtained from this calibration process are valuable for prediction purposes and facilitate further numerical simulations. An analysis of the model reveals that delays in screening, treatment, and quarantine contribute to an increase in the basic reproduction number R0, indicating a tendency towards endemicity. In particular, from the elasticity of R0, we deduce that normalized sensitivity indices of baseline screening rate (θ), quarantine rates (γ, αs), and treatment rate (α) are negative, which shows that delaying any of these may cause huge surge in R0, ultimately increases the disease burden. Further, by the contour plots, we note the two-parameter behavior of the infectives (both symptomatic and asymptomatic). Expanding upon the model analysis, an optimal control problem (OCP) is formulated, incorporating three control measures: precautionary interventions, boosted IDIAS, and boosted treatment. The Pontryagin's maximum principle and the forward-backward sweep method are employed to solve the OCP. The numerical simulations highlight that enhanced screening and treatment, coupled with preventive interventions, can effectively contribute to sustainable disease control. However, the cost-effectiveness analysis (CEA) conducted in this study suggests that boosting IDIAS alone is the most economically efficient and cost-effective approach compared to other strategies. The CEA results provide valuable insights into identifying specific strategies based on their cost-efficacy ranking, which can be implemented to maximize impact while minimizing costs. Overall, this research offers significant insights for policymakers and healthcare professionals, providing a framework to optimize control efforts for COVID-19 or similar epidemics in the future.
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Affiliation(s)
- Sonu Lamba
- Department of Mathematics, Indian Institute of Technology Patna Bihta – 801106, Patna, Bihar, India
| | - Tanuja Das
- Department of Mathematics, Indian Institute of Technology Patna Bihta – 801106, Patna, Bihar, India
- Department of Mathematics and Statistics, University of New Brunswick Fredericton, NB, E3B 5A3, Canada
| | - Prashant K. Srivastava
- Department of Mathematics, Indian Institute of Technology Patna Bihta – 801106, Patna, Bihar, India
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2
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Taube JC, Susswein Z, Colizza V, Bansal S. Respiratory disease contact patterns in the US are stable but heterogeneous. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.04.26.24306450. [PMID: 38712118 PMCID: PMC11071567 DOI: 10.1101/2024.04.26.24306450] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2024]
Abstract
Background Contact plays a critical role in infectious disease transmission. Characterizing heterogeneity in contact patterns across individuals, time, and space is necessary to inform accurate estimates of transmission risk, particularly to explain superspreading, predict age differences in vulnerability, and inform social distancing policies. Current respiratory disease models often rely on data from the 2008 POLYMOD study conducted in Europe, which is now outdated and potentially unrepresentative of behavior in the US. We seek to understand the variation in contact patterns across spatial scales and demographic and social classifications, whether there is seasonality to contact patterns, and what social behavior looks like at baseline in the absence of an ongoing pandemic. Methods We analyze spatiotemporal non-household contact patterns across 11 million survey responses from June 2020 - April 2021 post-stratified on age and gender to correct for sample representation. To characterize spatiotemporal heterogeneity in respiratory contact patterns at the county-week scale, we use generalized additive models. In the absence of pre-pandemic data on contact in the US, we also use a regression approach to produce baseline contact estimates to fill this gap. Findings Although contact patterns varied over time during the pandemic, contact is relatively stable after controlling for disease. We find that the mean number of non-household contacts is spatially heterogeneous regardless of disease. There is additional heterogeneity across age, gender, race/ethnicity, and contact setting, with mean contact decreasing with age and lower in women. The contacts of white individuals and contacts at work or social events change the most under increased national incidence. Interpretation We develop the first county-level estimates of non-pandemic contact rates for the US that can fill critical gaps in parameterizing disease models. Our results identify that spatiotemporal, demographic, and social heterogeneity in contact patterns is highly structured, informing the risk landscape of respiratory disease transmission in the US.
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Affiliation(s)
- Juliana C. Taube
- Department of Biology, Georgetown University, Washington, DC, USA
| | - Zachary Susswein
- Department of Biology, Georgetown University, Washington, DC, USA
| | | | - Shweta Bansal
- Department of Biology, Georgetown University, Washington, DC, USA
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3
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García Bulle Bueno B, Horn AL, Bell BM, Bahrami M, Bozkaya B, Pentland A, de la Haye K, Moro E. Effect of mobile food environments on fast food visits. Nat Commun 2024; 15:2291. [PMID: 38480685 PMCID: PMC10937966 DOI: 10.1038/s41467-024-46425-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Accepted: 02/26/2024] [Indexed: 03/17/2024] Open
Abstract
Poor diets are a leading cause of morbidity and mortality. Exposure to low-quality food environments saturated with fast food outlets is hypothesized to negatively impact diet. However, food environment research has predominantly focused on static food environments around home neighborhoods and generated mixed findings. In this work, we leverage population-scale mobility data in the U.S. to examine 62M people's visits to food outlets and evaluate how food choice is influenced by the food environments people are exposed to as they move through their daily routines. We find that a 10% increase in exposure to fast food outlets in mobile environments increases individuals' odds of visitation by 20%. Using our results, we simulate multiple policy strategies for intervening on food environments to reduce fast-food outlet visits. This analysis suggests that optimal interventions are informed by spatial, temporal, and behavioral features and could have 2x to 4x larger effect than traditional interventions focused on home food environments.
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Affiliation(s)
| | - Abigail L Horn
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, 90089, USA
- Department of Industrial and Systems Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, 90089, USA
- Information Sciences Institute, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, 90292, USA
| | - Brooke M Bell
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, 90089, USA
- Department of Chronic Disease Epidemiology, Yale School of Public Health, Yale University, New Haven, CT, 06510, USA
| | - Mohsen Bahrami
- Institute for Data, Systems, and Society, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Burçin Bozkaya
- Sabanci Business School, Sabanci University, 34956, Tuzla, Istanbul, Turkey
| | - Alex Pentland
- Institute for Data, Systems, and Society, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Kayla de la Haye
- Center for Economic and Social Research, University of Southern California, Los Angeles, CA, 90089, USA
| | - Esteban Moro
- Institute for Data, Systems, and Society, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.
- Grupo Interdisciplinar de Sistemas Complejos (GISC), Department of Mathematics and GISC, Universidad Carlos III de Madrid, 28911, Leganés, Spain.
- Network Science Institute, Northeastern University, Boston, MA, 02115, USA.
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4
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He K, Foerster S, Vora NM, Blaney K, Keeley C, Hendricks L, Varma JK, Long T, Shaman J, Pei S. Evaluating completion rates of COVID-19 contact tracing surveys in New York City. BMC Public Health 2024; 24:414. [PMID: 38331772 PMCID: PMC10854191 DOI: 10.1186/s12889-024-17920-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Accepted: 01/29/2024] [Indexed: 02/10/2024] Open
Abstract
IMPORTANCE Contact tracing is the process of identifying people who have recently been in contact with someone diagnosed with an infectious disease. During an outbreak, data collected from contact tracing can inform interventions to reduce the spread of infectious diseases. Understanding factors associated with completion rates of contact tracing surveys can help design improved interview protocols for ongoing and future programs. OBJECTIVE To identify factors associated with completion rates of COVID-19 contact tracing surveys in New York City (NYC) and evaluate the utility of a predictive model to improve completion rates, we analyze laboratory-confirmed and probable COVID-19 cases and their self-reported contacts in NYC from October 1st 2020 to May 10th 2021. METHODS We analyzed 742,807 case investigation calls made during the study period. Using a log-binomial regression model, we examined the impact of age, time of day of phone call, and zip code-level demographic and socioeconomic factors on interview completion rates. We further developed a random forest model to predict the best phone call time and performed a counterfactual analysis to evaluate the change of completion rates if the predicative model were used. RESULTS The percentage of contact tracing surveys that were completed was 79.4%, with substantial variations across ZIP code areas. Using a log-binomial regression model, we found that the age of index case (an individual who has tested positive through PCR or antigen testing and is thus subjected to a case investigation) had a significant effect on the completion of case investigation - compared with young adults (the reference group,24 years old < age < = 65 years old), the completion rate for seniors (age > 65 years old) were lower by 12.1% (95%CI: 11.1% - 13.3%), and the completion rate for youth group (age < = 24 years old) were lower by 1.6% (95%CI: 0.6% -2.6%). In addition, phone calls made from 6 to 9 pm had a 4.1% (95% CI: 1.8% - 6.3%) higher completion rate compared with the reference group of phone calls attempted from 12 and 3 pm. We further used a random forest algorithm to assess its potential utility for selecting the time of day of phone call. In counterfactual simulations, the overall completion rate in NYC was marginally improved by 1.2%; however, certain ZIP code areas had improvements up to 7.8%. CONCLUSION These findings suggest that age and time of day of phone call were associated with completion rates of case investigations. It is possible to develop predictive models to estimate better phone call time for improving completion rates in certain communities.
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Affiliation(s)
- Kaiyu He
- Department of Biostatistics, Mailman School of Public Health, Columbia University, New York, NY, 10032, USA
| | - Steffen Foerster
- New York City Department of Health and Mental Hygiene (DOHMH), Long Island City, NY, 11001, USA
| | - Neil M Vora
- New York City Department of Health and Mental Hygiene (DOHMH), Long Island City, NY, 11001, USA
| | - Kathleen Blaney
- New York City Department of Health and Mental Hygiene (DOHMH), Long Island City, NY, 11001, USA
| | | | | | - Jay K Varma
- Department of Population Health Sciences, Weill Cornell Medical College, New York, NY, 10065, USA
| | - Theodore Long
- NYC Health + Hospitals, New York, NY, USA
- Department of Population Health, New York University, New York, NY, 10016, USA
| | - Jeffrey Shaman
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, NY, 10032, USA
- Columbia Climate School, Columbia University, New York, NY, 10025, USA
| | - Sen Pei
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, NY, 10032, USA.
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5
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Bathe J, Renner HJ, Watzinger S, Olave-Rojas D, Hannappel L, Wnent J, Nickel S, Gräsner JT. [The SCATTER project: computer-based simulation in the strategic transfer of intensive care patients]. Bundesgesundheitsblatt Gesundheitsforschung Gesundheitsschutz 2024; 67:215-224. [PMID: 38153419 PMCID: PMC10834643 DOI: 10.1007/s00103-023-03811-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2023] [Accepted: 11/20/2023] [Indexed: 12/29/2023]
Abstract
BACKGROUND The need for a concept for the nationwide strategic transfer of critical care patients in Germany was highlighted during the COVID-19 (coronavirus disease 2019) pandemic. Despite the cloverleaf concept developed specifically for this purpose, the transfer of large numbers of critical care patients represents a major challenge. With the help of a computer simulation, the SCATTER research project uses a fictitious example to test, develop, and recommend transfer strategies. METHOD The simulation was programmed after collecting procedural and structural data on critical care transports within Germany. The simulation allows altering various parameters and testing different transfer scenarios. In a fictitious scenario, nationwide transfers starting from Schleswig-Holstein were simulated and evaluated using predetermined criteria. RESULTS In the case of ground-based transfers, it became apparent that, depending on the selected target region, not all patients could be transferred due to the limited range of ground-based vehicles. Although a higher number of patients can be transferred by air, this is associated with additional gurney changes and potential risk to the patient. A distance-dependent transport strategy led to the identical results as purely air-bound transport, since air-bound transport was always chosen due to the long distances. DISCUSSION The simulation can be used to develop recommendations and to draw important conclusions from different transfer strategies.
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Affiliation(s)
- Janina Bathe
- Institut für Rettungs- und Notfallmedizin, Campus Kiel und Campus Lübeck, Universitätsklinikum Schleswig-Holstein, Arnold-Heller-Str. 3, Haus 808, 24105, Kiel, Deutschland.
| | - Hanna-Joy Renner
- Institut für Rettungs- und Notfallmedizin, Campus Kiel und Campus Lübeck, Universitätsklinikum Schleswig-Holstein, Arnold-Heller-Str. 3, Haus 808, 24105, Kiel, Deutschland
| | - Sven Watzinger
- Institut für Operations Research - Diskrete Optimierung und Logistik, Karlsruher Institut für Technologie, Karlsruhe, Deutschland
| | - David Olave-Rojas
- Institut für Operations Research - Diskrete Optimierung und Logistik, Karlsruher Institut für Technologie, Karlsruhe, Deutschland
| | - Leonie Hannappel
- Institut für Rettungs- und Notfallmedizin, Campus Kiel und Campus Lübeck, Universitätsklinikum Schleswig-Holstein, Arnold-Heller-Str. 3, Haus 808, 24105, Kiel, Deutschland
- Fachgruppe Intensivmedizin, Infektiologie und Notfallmedizin (Fachgruppe COVRIIN), Fachgebiet ZBS 7 - Strategie und Einsatz, Koordination: Robert Koch-Institut, Berlin, Deutschland
| | - Jan Wnent
- Institut für Rettungs- und Notfallmedizin, Campus Kiel und Campus Lübeck, Universitätsklinikum Schleswig-Holstein, Arnold-Heller-Str. 3, Haus 808, 24105, Kiel, Deutschland
- Fachgruppe Intensivmedizin, Infektiologie und Notfallmedizin (Fachgruppe COVRIIN), Fachgebiet ZBS 7 - Strategie und Einsatz, Koordination: Robert Koch-Institut, Berlin, Deutschland
- School of Medicine, University of Namibia, Windhoek, Namibia
- Klinik f. Anästhesiologie und Operative Intensivmedizin, Campus Kiel, Universitätsklinikum Schleswig-Holstein, Kiel, Deutschland
| | - Stefan Nickel
- Institut für Operations Research - Diskrete Optimierung und Logistik, Karlsruher Institut für Technologie, Karlsruhe, Deutschland
| | - Jan-Thorsten Gräsner
- Institut für Rettungs- und Notfallmedizin, Campus Kiel und Campus Lübeck, Universitätsklinikum Schleswig-Holstein, Arnold-Heller-Str. 3, Haus 808, 24105, Kiel, Deutschland
- Fachgruppe Intensivmedizin, Infektiologie und Notfallmedizin (Fachgruppe COVRIIN), Fachgebiet ZBS 7 - Strategie und Einsatz, Koordination: Robert Koch-Institut, Berlin, Deutschland
- Klinik f. Anästhesiologie und Operative Intensivmedizin, Campus Kiel, Universitätsklinikum Schleswig-Holstein, Kiel, Deutschland
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Klein B, LaRock T, McCabe S, Torres L, Friedland L, Kos M, Privitera F, Lake B, Kraemer MUG, Brownstein JS, Gonzalez R, Lazer D, Eliassi-Rad T, Scarpino SV, Vespignani A, Chinazzi M. Characterizing collective physical distancing in the U.S. during the first nine months of the COVID-19 pandemic. PLOS DIGITAL HEALTH 2024; 3:e0000430. [PMID: 38319890 PMCID: PMC10846712 DOI: 10.1371/journal.pdig.0000430] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Accepted: 12/11/2023] [Indexed: 02/08/2024]
Abstract
The COVID-19 pandemic offers an unprecedented natural experiment providing insights into the emergence of collective behavioral changes of both exogenous (government mandated) and endogenous (spontaneous reaction to infection risks) origin. Here, we characterize collective physical distancing-mobility reductions, minimization of contacts, shortening of contact duration-in response to the COVID-19 pandemic in the pre-vaccine era by analyzing de-identified, privacy-preserving location data for a panel of over 5.5 million anonymized, opted-in U.S. devices. We define five indicators of users' mobility and proximity to investigate how the emerging collective behavior deviates from typical pre-pandemic patterns during the first nine months of the COVID-19 pandemic. We analyze both the dramatic changes due to the government mandated mitigation policies and the more spontaneous societal adaptation into a new (physically distanced) normal in the fall 2020. Using the indicators here defined we show that: a) during the COVID-19 pandemic, collective physical distancing displayed different phases and was heterogeneous across geographies, b) metropolitan areas displayed stronger reductions in mobility and contacts than rural areas; c) stronger reductions in commuting patterns are observed in geographical areas with a higher share of teleworkable jobs; d) commuting volumes during and after the lockdown period negatively correlate with unemployment rates; and e) increases in contact indicators correlate with future values of new deaths at a lag consistent with epidemiological parameters and surveillance reporting delays. In conclusion, this study demonstrates that the framework and indicators here presented can be used to analyze large-scale social distancing phenomena, paving the way for their use in future pandemics to analyze and monitor the effects of pandemic mitigation plans at the national and international levels.
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Affiliation(s)
- Brennan Klein
- Network Science Institute, Northeastern University, Boston, Massachusetts, United States of America
| | - Timothy LaRock
- Network Science Institute, Northeastern University, Boston, Massachusetts, United States of America
| | - Stefan McCabe
- Network Science Institute, Northeastern University, Boston, Massachusetts, United States of America
| | - Leo Torres
- Network Science Institute, Northeastern University, Boston, Massachusetts, United States of America
| | - Lisa Friedland
- Network Science Institute, Northeastern University, Boston, Massachusetts, United States of America
| | - Maciej Kos
- Network Science Institute, Northeastern University, Boston, Massachusetts, United States of America
| | | | - Brennan Lake
- Cuebiq Inc., New York, New York, United States of America
| | | | - John S. Brownstein
- Boston Children’s Hospital, Boston, Massachusetts, United States of America
- Harvard Medical School, Boston, Massachusetts, United States of America
| | - Richard Gonzalez
- University of Michigan, Ann Arbor, Michigan, United States of America
| | - David Lazer
- Network Science Institute, Northeastern University, Boston, Massachusetts, United States of America
| | - Tina Eliassi-Rad
- Network Science Institute, Northeastern University, Boston, Massachusetts, United States of America
| | - Samuel V. Scarpino
- Network Science Institute, Northeastern University, Boston, Massachusetts, United States of America
- Vermont Complex Systems Center, University of Vermont, Burlington, Vermont, United States of America
- Santa Fe Institute, Santa Fe, New Mexico, United States of America
| | - Alessandro Vespignani
- Network Science Institute, Northeastern University, Boston, Massachusetts, United States of America
- ISI Foundation, Turin, Italy
| | - Matteo Chinazzi
- Network Science Institute, Northeastern University, Boston, Massachusetts, United States of America
- The Roux Institute, Northeastern University, Portland, Maine, United States of America
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Pangallo M, Aleta A, Del Rio-Chanona RM, Pichler A, Martín-Corral D, Chinazzi M, Lafond F, Ajelli M, Moro E, Moreno Y, Vespignani A, Farmer JD. The unequal effects of the health-economy trade-off during the COVID-19 pandemic. Nat Hum Behav 2024; 8:264-275. [PMID: 37973827 PMCID: PMC10896714 DOI: 10.1038/s41562-023-01747-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Accepted: 10/05/2023] [Indexed: 11/19/2023]
Abstract
Despite the global impact of the coronavirus disease 2019 pandemic, the question of whether mandated interventions have similar economic and public health effects as spontaneous behavioural change remains unresolved. Addressing this question, and understanding differential effects across socioeconomic groups, requires building quantitative and fine-grained mechanistic models. Here we introduce a data-driven, granular, agent-based model that simulates epidemic and economic outcomes across industries, occupations and income levels. We validate the model by reproducing key outcomes of the first wave of coronavirus disease 2019 in the New York metropolitan area. The key mechanism coupling the epidemic and economic modules is the reduction in consumption due to fear of infection. In counterfactual experiments, we show that a similar trade-off between epidemic and economic outcomes exists both when individuals change their behaviour due to fear of infection and when non-pharmaceutical interventions are imposed. Low-income workers, who perform in-person occupations in customer-facing industries, face the strongest trade-off.
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Affiliation(s)
| | - Alberto Aleta
- Institute for Biocomputation and Physics of Complex Systems and Department of Theoretical Physics, University of Zaragoza, Zaragoza, Spain
| | | | | | - David Martín-Corral
- Department of Mathematics and GISC, Universidad Carlos III de Madrid, Leganes, Spain
| | - Matteo Chinazzi
- MOBS Lab, Northeastern University, Boston, MA, USA
- The Roux Institute, Northeastern University, Portland, ME, USA
| | - François Lafond
- Institute for New Economic Thinking at the Oxford Martin School, and Smith School of Enterprise and the Environment, University of Oxford, Oxford, UK
| | - Marco Ajelli
- Laboratory for Computational Epidemiology and Public Health, Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomington, IN, USA
| | - Esteban Moro
- Department of Mathematics and GISC, Universidad Carlos III de Madrid, Leganes, Spain
- Connection Science, Institute for Data Science and Society, MIT, Cambridge, MA, USA
| | - Yamir Moreno
- CENTAI Institute, Turin, Italy
- Institute for Biocomputation and Physics of Complex Systems and Department of Theoretical Physics, University of Zaragoza, Zaragoza, Spain
- Complexity Science Hub, Vienna, Austria
| | | | - J Doyne Farmer
- Institute for New Economic Thinking at the Oxford Martin School, and Smith School of Enterprise and the Environment, University of Oxford, Oxford, UK
- Santa Fe Institute, Santa Fe, NM, USA
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Alhomaid A, Alzeer AH, Alsaawi F, Aljandal A, Al-Jafar R, Albalawi M, Alotaibi D, Alabdullatif R, AlGhassab R, Mominkhan DM, Alharbi M, Alghamdi AA, Almoklif M, Alabdulaali MK. The impact of non-pharmaceutical interventions on the spread of COVID-19 in Saudi Arabia: Simulation approach. Saudi Pharm J 2024; 32:101886. [PMID: 38162709 PMCID: PMC10755097 DOI: 10.1016/j.jsps.2023.101886] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Accepted: 11/25/2023] [Indexed: 01/03/2024] Open
Abstract
Objectives This paper aims to measure the impact of the implemented nonpharmaceutical interventions (NPIs) in the Kingdom of Saudi Arabia (KSA) during the pandemic using simulation modeling. Methods To measure the impact of NPI, a hybrid agent-based and system dynamics simulation model was built and validated. Data were collected prospectively on a weekly basis. The core epidemiological model is based on a complex Susceptible-Exposed-Infectious-Recovered and Dead model of epidemic dynamics. Reverse engineering was performed on a weekly basis throughout the study period as a mean for model validation which reported on four outcomes: total cases, active cases, ICU cases, and deaths cases. To measure the impact of each NPI, the observed values of active and total cases were captured and compared to the projected values of active and total cases from the simulation. To measure the impact of each NPI, the study period was divided into rounds of incubation periods (cycles of 14 days each). The behavioral change of the spread of the disease was interpreted as the impact of NPIs that occurred at the beginning of the cycle. The behavioral change was measured by the change in the initial reproduction rate (R0). Results After 18 weeks of the reverse engineering process, the model achieved a 0.4 % difference in total cases for prediction at the end of the study period. The results estimated that NPIs led to 64 % change in The R0. Our breakdown analysis of the impact of each NPI indicates that banning going to schools had the greatest impact on the infection reproduction rate (24 %). Conclusion We used hybrid simulation modeling to measure the impact of NPIs taken by the KSA government. The finding further supports the notion that early NPIs adoption can effectively limit the spread of COVID-19. It also supports using simulation for building mathematical modeling for epidemiological scenarios.
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Affiliation(s)
- Ahmad Alhomaid
- Data Services Sector, Lean Business Services, Riyadh, Saudi Arabia
| | | | - Fahad Alsaawi
- Data Services Sector, Lean Business Services, Riyadh, Saudi Arabia
| | | | - Rami Al-Jafar
- Data Services Sector, Lean Business Services, Riyadh, Saudi Arabia
- School of Public Health, Imperial College London, London, UK
| | - Marwan Albalawi
- Department of Digital Health, Lean Business Services, Riyadh, Saudi Arabia
| | - Dana Alotaibi
- Data Services Sector, Lean Business Services, Riyadh, Saudi Arabia
| | | | - Razan AlGhassab
- Data Services Sector, Lean Business Services, Riyadh, Saudi Arabia
| | - Dalia M. Mominkhan
- National Health Command Center, Ministry of Health, Riyadh, Saudi Arabia
| | - Muaddi Alharbi
- National Health Command Center, Ministry of Health, Riyadh, Saudi Arabia
| | - Ahmad A. Alghamdi
- National Health Command Center, Ministry of Health, Riyadh, Saudi Arabia
| | - Maryam Almoklif
- National Health Command Center, Ministry of Health, Riyadh, Saudi Arabia
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9
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Wang K, Wang P, Jiang Z, Wang L, Zhou L, Qi D, Yin W, Meng P. Data-driven assessment of immune evasion and dynamic Zero-COVID policy on fast-spreading Omicron in Changchun. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2023; 20:21692-21716. [PMID: 38124616 DOI: 10.3934/mbe.2023960] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/23/2023]
Abstract
Due to its immune evasion capability, the SARS-CoV-2 Omicron variant was declared a variant of concern by the World Health Organization. The spread of Omicron in Changchun (i.e., the capital of Jilin province in northeast of China) during the spring of 2022 was successfully curbed under the strategy of a dynamic Zero-COVID policy. To evaluate the impact of immune evasion on vaccination and other measures, and to understand how the dynamic Zero-COVID measure stopped the epidemics in Changchun, we establish a compartmental model over different stages and parameterized the model with actual reported data. The model simulation firstly shows a reasonably good fit between our model prediction and the data. Second, we estimate the testing rate in the early stage of the outbreak to reveal the real infection size. Third, numerical simulations show that the coverage of vaccine immunization in Changchun and the regular nucleic acid testing could not stop the epidemic, while the 'non-pharmaceutical' intervention measures utilized in the dynamic Zero-COVID policy could play significant roles in the containment of Omicron. Based on the parameterized model, numerical analysis demonstrates that if one wants to achieve epidemic control by fully utilizing the effect of 'dynamic Zero-COVID' measures, therefore social activities are restricted to the minimum level, and then the economic development may come to a halt. The insight analysis in this work could provide reference for infectious disease prevention and control measures in the future.
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Affiliation(s)
- Kun Wang
- School of Mathematics and Statistics, Changchun University of Science and Technology, Changchun 130022, China
| | - Peng Wang
- Jilin Provincial Joint Key Labortory of Big Data Science and Engineering, Changchun University of Science and Technology, Changchun 130022, China
| | - Zhengang Jiang
- Jilin Provincial Joint Key Labortory of Big Data Science and Engineering, Changchun University of Science and Technology, Changchun 130022, China
| | - Lu Wang
- School of Mathematics and Statistics, Changchun University of Science and Technology, Changchun 130022, China
| | - Linhua Zhou
- School of Mathematics and Statistics, Changchun University of Science and Technology, Changchun 130022, China
| | - Dequan Qi
- School of Mathematics and Statistics, Changchun University of Science and Technology, Changchun 130022, China
| | - Weishi Yin
- School of Mathematics and Statistics, Changchun University of Science and Technology, Changchun 130022, China
| | - Pinchao Meng
- School of Mathematics and Statistics, Changchun University of Science and Technology, Changchun 130022, China
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10
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Zhang D, Ge Y, Wang J, Liu H, Zhang WB, Wu X, Heuvelink G, Wu C, Yang J, Ruktanonchai N, Qader S, Ruktanonchai C, Cleary E, Yao Y, Liu J, Nnanatu C, Wesolowski A, Cummings D, Tatem A, Lai S. Optimizing the detection of emerging infections using mobility-based spatial sampling. RESEARCH SQUARE 2023:rs.3.rs-3597070. [PMID: 38014322 PMCID: PMC10680910 DOI: 10.21203/rs.3.rs-3597070/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/29/2023]
Abstract
Background Timely and precise detection of emerging infections is crucial for effective outbreak management and disease control. Human mobility significantly influences infection risks and transmission dynamics, and spatial sampling is a valuable tool for pinpointing potential infections in specific areas. This study explored spatial sampling methods, informed by various mobility patterns, to optimize the allocation of testing resources for detecting emerging infections. Methods Mobility patterns, derived from clustering point-of-interest data and travel data, were integrated into four spatial sampling approaches to detect emerging infections at the community level. To evaluate the effectiveness of the proposed mobility-based spatial sampling, we conducted analyses using actual and simulated outbreaks under different scenarios of transmissibility, intervention timing, and population density in cities. Results By leveraging inter-community movement data and initial case locations, the proposed case flow intensity (CFI) and case transmission intensity (CTI)-informed sampling approaches could considerably reduce the number of tests required for both actual and simulated outbreaks. Nonetheless, the prompt use of CFI and CTI within communities is imperative for effective detection, particularly for highly contagious infections in densely populated areas. Conclusions The mobility-based spatial sampling approach can substantially improve the efficiency of community-level testing for detecting emerging infections. It achieves this by reducing the number of individuals screened while maintaining a high accuracy rate of infection identification. It represents a cost-effective solution to optimize the deployment of testing resources, when necessary, to contain emerging infectious diseases in diverse settings.
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11
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Kettlitz R, Harries M, Ortmann J, Krause G, Aigner A, Lange B. Association of known SARS-CoV-2 serostatus and adherence to personal protection measures and the impact of personal protective measures on seropositivity in a population-based cross-sectional study (MuSPAD) in Germany. BMC Public Health 2023; 23:2281. [PMID: 37978484 PMCID: PMC10657116 DOI: 10.1186/s12889-023-17121-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Accepted: 11/01/2023] [Indexed: 11/19/2023] Open
Abstract
BACKGROUND In 2020/2021 in Germany, several non-pharmacological interventions were introduced to lower the transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). We investigated to what extent knowledge of prior infection with SARS-CoV-2 or vaccination status influenced the use of personal protection measures (PPM). Further, we were interested in the effect of compliance with PPM on SARS-CoV-2 serostatus. METHODS Data was based on a sequential, multilocal seroprevalence study (MuSPAD), carried out in eight locations from July 2020 to August 2021. We estimated the association between a known SARS-CoV-2 serostatus (reported positive PCR test or vaccination) and self-reported PPM behavior (hand hygiene, physical distancing, wearing face mask), just as the association of PPM compliance with seropositivity against nucleocapsid (NC), receptor-binding domain (RBD), and spike protein (S) antigens. We identified relevant variables and deduced adjustment sets with directed acyclic graphs (DAG), and applied mixed logistic regression. RESULTS Out of the 22,297 participants (median age: 54 years, 43% male), 781 were classified as SARS-CoV-2-infected and 3,877 had a vaccinated immune response. Vaccinated individuals were less likely to keep 1.5 m distance [OR = 0.74 (95% CI: 0.57-0.97)] and only partly physically distanced [OR = 0.71 (95% CI: 0.58-0.87)]. Participants with self-reported positive PCR test had a lower chance of adhering partly to physical distancing [OR = 0.70 (95% CI: 0.50-0.99)] in comparison to the reference group. Higher odds of additionally wearing a face mask was observed in vaccinated [OR = 1.28 (95% CI: 1.08-1.51)] even if it was not obligatory. Overall, among unvaccinated participants, we found little evidence of lower odds of seropositivity given mask wearing [OR: 0.91 (95% CI: 0.71-1.16)], physical distancing [OR: 0.84 (95% CI: 0.59-1.20)] and no evidence for completely adhering to hand cleaning [OR: 0.97 (95% CI: 0.29-3.22)]. CONCLUSIONS A known confirmed prior infection and vaccination may have the potential to influence adherence to PPM.
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Affiliation(s)
- R Kettlitz
- Helmholtz Centre for Infection Research, Department Epidemiology, Brunswick, Lower Saxony, Germany.
| | - M Harries
- Helmholtz Centre for Infection Research, Department Epidemiology, Brunswick, Lower Saxony, Germany.
- Translational Infrastructure Epidemiology, German Centre for Infection Research, DZIF, Düsseldorf, North Rhine-Westphalia, Germany.
| | - J Ortmann
- Helmholtz Centre for Infection Research, Department Epidemiology, Brunswick, Lower Saxony, Germany
| | - G Krause
- Helmholtz Centre for Infection Research, Department Epidemiology, Brunswick, Lower Saxony, Germany
- Translational Infrastructure Epidemiology, German Centre for Infection Research, DZIF, Düsseldorf, North Rhine-Westphalia, Germany
- Institute for Infectious Disease Epidemiology, TWINCORE, Hannover, Lower Saxony, Germany
| | - A Aigner
- Institute of Biometry and Clinical Epidemiology, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität Zu Berlin, Berlin, Berlin, Germany
| | - B Lange
- Helmholtz Centre for Infection Research, Department Epidemiology, Brunswick, Lower Saxony, Germany
- Translational Infrastructure Epidemiology, German Centre for Infection Research, DZIF, Düsseldorf, North Rhine-Westphalia, Germany
- Institute for Infectious Disease Epidemiology, TWINCORE, Hannover, Lower Saxony, Germany
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12
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Heron L, Mugglin C, Zürcher K, Brumann E, Keune-Dübi B, Low N, Fenner L. Contact tracing for COVID-19 in a Swiss canton: analysis of key performance indicators. Swiss Med Wkly 2023; 153:40112. [PMID: 37955850 DOI: 10.57187/smw.2023.40112] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2023] Open
Abstract
BACKGROUND Contact tracing (CT) has played an important role in strategies to control COVID-19. However, there is limited evidence on the performance of digital tools for CT and no consensus on which indicators to use to monitor their performance. We aimed to describe the system and analyse outcomes of CT with a partially automated workflow in the Swiss canton of Solothurn, using key performance indicators (KPIs). METHODS We describe the process of CT used in the canton of Solothurn between November 2020 and February 2022, including forward and backward CT. We developed 16 KPIs representing CT structure (S1-2), process (P1-11) and outcome (O1-3) based on previous literature to analyse the relative performance of CT. We report the changes in the indicators over waves of SARS-CoV-2 infections caused by several viral variants. RESULTS The CT team in Solothurn processed 57,363 index cases and 71,809 contacts over a 15-month period. The CT team successfully contacted 99% of positive cases within 24 hours (KPI P7) throughout the pandemic and returned almost all test results on the same or next day (KPI P6), before the delta variant emerged. Three-quarters of contacts were notified within 24 hours of the CT interview with the index (KPI P8) before the emergence of the alpha, delta and omicron variants, when the proportions decreased to 64%, 36% and 54%, respectively. The percentage of new symptomatic cases tested and interviewed within 3 days of symptom onset was high at >70% (KPI P10) and contacts started quarantine within a median of 3 days of index case symptom onset (KPI P3). About a fifth of new index cases had already been in quarantine by the time of their positive test (KPI O1), before the delta variant emerged. The percentage of index cases in isolation by day of testing remained at almost 100% throughout the period of analysis (KPI O2). CONCLUSIONS The CT in Solothurn used a partially automated workflow and continued to perform well throughout the pandemic, although the relative performance of the CT system declined at higher caseloads. CT remains an important tool for controlling the spread of infectious diseases, but clearer standards should improve the performance, comparability and monitoring of infection in real time as part of pandemic preparedness efforts.
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Affiliation(s)
- Leonie Heron
- Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland
| | - Catrina Mugglin
- Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland
| | - Kathrin Zürcher
- Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland
| | - Erich Brumann
- Cantonal Physician's Office, Canton of Solothurn, Solothurn, Switzerland
| | - Bettina Keune-Dübi
- Cantonal Physician's Office, Canton of Solothurn, Solothurn, Switzerland
| | - Nicola Low
- Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland
| | - Lukas Fenner
- Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland
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13
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Kremer C, Willem L, Boone J, Arrazola de Oñate W, Hammami N, Faes C, Hens N. Key performance indicators of COVID-19 contact tracing in Belgium from September 2020 to December 2021. PLoS One 2023; 18:e0292346. [PMID: 37862313 PMCID: PMC10588862 DOI: 10.1371/journal.pone.0292346] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2022] [Accepted: 09/18/2023] [Indexed: 10/22/2023] Open
Abstract
The goal of tracing, testing, and quarantining contacts of infected individuals is to contain the spread of infectious diseases, a strategy widely used during the COVID-19 pandemic. However, limited research exists on the effectiveness of contact tracing, especially with regard to key performance indicators (KPIs), such as the proportion of cases arising from previously identified contacts. In our study, we analyzed contact tracing data from Belgium collected between September 2020 and December 2021 to assess the impact of contact tracing on SARS-CoV-2 transmission and understand its characteristics. Among confirmed cases involved in contact tracing in the Flemish and Brussels-Capital regions, 19.1% were previously identified as close contacts and were aware of prior exposure. These cases, referred to as 'known' to contact tracing operators, reported on average fewer close contacts compared to newly identified individuals (0.80 versus 1.05), resulting in fewer secondary cases (0.23 versus 0.28). Additionally, we calculated the secondary attack rate, representing infections per contact, which was on average lower for the 'known' cases (0.22 versus 0.25) between December 2020 and August 2021. These findings indicate the effectiveness of contact tracing in Belgium in reducing SARS-CoV-2 transmission. Although we were unable to quantify the exact number of prevented cases, our findings emphasize the importance of contact tracing as a public health measure. In addition, contact tracing data provide indications of potential shifts in transmission patterns among different age groups associated with emerging variants of concern and increasing vaccination rates.
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Affiliation(s)
- Cécile Kremer
- Interuniversity Institute for Biostatistics and statistical Bioinformatics, Data Science Institute, Hasselt University, Hasselt, Belgium
| | - Lander Willem
- Centre for Health Economics Research and Modelling Infectious Diseases, Vaccine and Infectious Disease Institute, University of Antwerp, Antwerp, Belgium
- Family Medicine and Population Health, University of Antwerp, Antwerp, Belgium
| | - Jorden Boone
- KPMG Advisory, Public Sector Practice, Zaventem, Belgium
| | - Wouter Arrazola de Oñate
- Belgian Lung and Tuberculosis Association, Brussels, Belgium
- Flemish Association for Respiratory Health and Tuberculosis, Leuven, Belgium
| | - Naïma Hammami
- Department of Infectious Disease Prevention and Control, Department of Care, Flemish Region, Brussels, Belgium
| | - Christel Faes
- Interuniversity Institute for Biostatistics and statistical Bioinformatics, Data Science Institute, Hasselt University, Hasselt, Belgium
| | - Niel Hens
- Interuniversity Institute for Biostatistics and statistical Bioinformatics, Data Science Institute, Hasselt University, Hasselt, Belgium
- Centre for Health Economics Research and Modelling Infectious Diseases, Vaccine and Infectious Disease Institute, University of Antwerp, Antwerp, Belgium
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14
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Erfani A, Frias-Martinez V. A fairness assessment of mobility-based COVID-19 case prediction models. PLoS One 2023; 18:e0292090. [PMID: 37851681 PMCID: PMC10584164 DOI: 10.1371/journal.pone.0292090] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2023] [Accepted: 09/12/2023] [Indexed: 10/20/2023] Open
Abstract
In light of the outbreak of COVID-19, analyzing and measuring human mobility has become increasingly important. A wide range of studies have explored spatiotemporal trends over time, examined associations with other variables, evaluated non-pharmacologic interventions (NPIs), and predicted or simulated COVID-19 spread using mobility data. Despite the benefits of publicly available mobility data, a key question remains unanswered: are models using mobility data performing equitably across demographic groups? We hypothesize that bias in the mobility data used to train the predictive models might lead to unfairly less accurate predictions for certain demographic groups. To test our hypothesis, we applied two mobility-based COVID infection prediction models at the county level in the United States using SafeGraph data, and correlated model performance with sociodemographic traits. Findings revealed that there is a systematic bias in models' performance toward certain demographic characteristics. Specifically, the models tend to favor large, highly educated, wealthy, young, and urban counties. We hypothesize that the mobility data currently used by many predictive models tends to capture less information about older, poorer, less educated and people from rural regions, which in turn negatively impacts the accuracy of the COVID-19 prediction in these areas. Ultimately, this study points to the need of improved data collection and sampling approaches that allow for an accurate representation of the mobility patterns across demographic groups.
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Affiliation(s)
- Abdolmajid Erfani
- Department of Civil, Environmental, and Geospatial Engineering, Michigan Technological University, Houghton, MI, United States of America
| | - Vanessa Frias-Martinez
- College of Information Studies, University of Maryland, College Park, MD, United States of America
- University of Maryland Institute for Advanced Computer Studies, University of Maryland, College Park, MD, United States of America
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15
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Zhang J, Tan S, Peng C, Xu X, Wang M, Lu W, Wu Y, Sai B, Cai M, Kummer AG, Chen Z, Zou J, Li W, Zheng W, Liang Y, Zhao Y, Vespignani A, Ajelli M, Lu X, Yu H. Heterogeneous changes in mobility in response to the SARS-CoV-2 Omicron BA.2 outbreak in Shanghai. Proc Natl Acad Sci U S A 2023; 120:e2306710120. [PMID: 37824525 PMCID: PMC10589641 DOI: 10.1073/pnas.2306710120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Accepted: 09/12/2023] [Indexed: 10/14/2023] Open
Abstract
The coronavirus disease 2019 (COVID-19) pandemic and the measures taken by authorities to control its spread have altered human behavior and mobility patterns in an unprecedented way. However, it remains unclear whether the population response to a COVID-19 outbreak varies within a city or among demographic groups. Here, we utilized passively recorded cellular signaling data at a spatial resolution of 1 km × 1 km for over 5 million users and epidemiological surveillance data collected during the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Omicron BA.2 outbreak from February to June 2022 in Shanghai, China, to investigate the heterogeneous response of different segments of the population at the within-city level and examine its relationship with the actual risk of infection. Changes in behavior were spatially heterogenous within the city and population groups and associated with both the infection incidence and adopted interventions. We also found that males and individuals aged 30 to 59 y old traveled more frequently, traveled longer distances, and their communities were more connected; the same groups were also associated with the highest SARS-CoV-2 incidence. Our results highlight the heterogeneous behavioral change of the Shanghai population to the SARS-CoV-2 Omicron BA.2 outbreak and the effect of heterogenous behavior on the spread of COVID-19, both spatially and demographically. These findings could be instrumental for the design of targeted interventions for the control and mitigation of future outbreaks of COVID-19, and, more broadly, of respiratory pathogens.
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Affiliation(s)
- Juanjuan Zhang
- Department of Epidemiology, School of Public Health, Key Laboratory of Public Health Safety, Ministry of Education, Fudan University, Shanghai200032, China
| | - Suoyi Tan
- College of Systems Engineering, National University of Defense Technology, Changsha410073, China
| | - Cheng Peng
- Department of Epidemiology, School of Public Health, Key Laboratory of Public Health Safety, Ministry of Education, Fudan University, Shanghai200032, China
| | - Xiangyanyu Xu
- Department of Epidemiology, School of Public Health, Key Laboratory of Public Health Safety, Ministry of Education, Fudan University, Shanghai200032, China
| | - Mengning Wang
- College of Systems Engineering, National University of Defense Technology, Changsha410073, China
| | - Wanying Lu
- Department of Epidemiology, School of Public Health, Key Laboratory of Public Health Safety, Ministry of Education, Fudan University, Shanghai200032, China
| | - Yanpeng Wu
- Department of Epidemiology, School of Public Health, Key Laboratory of Public Health Safety, Ministry of Education, Fudan University, Shanghai200032, China
| | - Bin Sai
- College of Systems Engineering, National University of Defense Technology, Changsha410073, China
| | - Mengsi Cai
- College of Systems Engineering, National University of Defense Technology, Changsha410073, China
| | - Allisandra G. Kummer
- Laboratory for Computational Epidemiology and Public Health, Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomington, IN47405
| | - Zhiyuan Chen
- Department of Epidemiology, School of Public Health, Key Laboratory of Public Health Safety, Ministry of Education, Fudan University, Shanghai200032, China
| | - Junyi Zou
- Department of Epidemiology, School of Public Health, Key Laboratory of Public Health Safety, Ministry of Education, Fudan University, Shanghai200032, China
| | - Wenxin Li
- Department of Epidemiology, School of Public Health, Key Laboratory of Public Health Safety, Ministry of Education, Fudan University, Shanghai200032, China
| | - Wen Zheng
- Department of Epidemiology, School of Public Health, Key Laboratory of Public Health Safety, Ministry of Education, Fudan University, Shanghai200032, China
| | - Yuxia Liang
- Department of Epidemiology, School of Public Health, Key Laboratory of Public Health Safety, Ministry of Education, Fudan University, Shanghai200032, China
| | - Yuchen Zhao
- Department of Epidemiology, School of Public Health, Key Laboratory of Public Health Safety, Ministry of Education, Fudan University, Shanghai200032, China
| | - Alessandro Vespignani
- Laboratory for the Modeling of Biological and Socio-technical Systems, Network Science Institute, Northeastern University, Boston, MA02115
| | - Marco Ajelli
- Laboratory for Computational Epidemiology and Public Health, Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomington, IN47405
| | - Xin Lu
- College of Systems Engineering, National University of Defense Technology, Changsha410073, China
| | - Hongjie Yu
- Department of Epidemiology, School of Public Health, Key Laboratory of Public Health Safety, Ministry of Education, Fudan University, Shanghai200032, China
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16
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Guo Y, Li T. Modeling the competitive transmission of the Omicron strain and Delta strain of COVID-19. JOURNAL OF MATHEMATICAL ANALYSIS AND APPLICATIONS 2023; 526:127283. [PMID: 37035507 PMCID: PMC10065814 DOI: 10.1016/j.jmaa.2023.127283] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Indexed: 06/19/2023]
Abstract
Since November 2021, there have been cases of COVID-19's Omicron strain spreading in competition with Delta strains in many parts of the world. To explore how these two strains developed in this competitive spread, a new compartmentalized model was established. First, we analyzed the fundamental properties of the model, obtained the expression of the basic reproduction number, proved the local and global asymptotic stability of the disease-free equilibrium. Then by means of the cubic spline interpolation method, we obtained the data of new Omicron and Delta cases in the United States of new cases starting from December 8, 2021, to February 12, 2022. Using the weighted nonlinear least squares estimation method, we fitted six time series (cumulative confirmed cases, cumulative deaths, new cases, new deaths, new Omicron cases, and new Delta cases), got estimates of the unknown parameters, and obtained an approximation of the basic reproduction number in the United States during this time period as R 0 ≈ 1.5165 . Finally, each control strategy was evaluated by cost-effectiveness analysis to obtain the optimal control strategy under different perspectives. The results not only show the competitive transmission characteristics of the new strain and existing strain, but also provide scientific suggestions for effectively controlling the spread of these strains.
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Affiliation(s)
- Youming Guo
- College of Science, Guilin University of Technology, Guilin, Guangxi 541004, PR China
- Guangxi Colleges and Universities Key Laboratory of Applied Statistics, Guilin University of Technology, Guilin, Guangxi 541004, PR China
| | - Tingting Li
- College of Science, Guilin University of Technology, Guilin, Guangxi 541004, PR China
- Guangxi Colleges and Universities Key Laboratory of Applied Statistics, Guilin University of Technology, Guilin, Guangxi 541004, PR China
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17
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Delussu F, Tizzoni M, Gauvin L. The limits of human mobility traces to predict the spread of COVID-19: A transfer entropy approach. PNAS NEXUS 2023; 2:pgad302. [PMID: 37811338 PMCID: PMC10558401 DOI: 10.1093/pnasnexus/pgad302] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Accepted: 08/17/2023] [Indexed: 10/10/2023]
Abstract
Mobile phone data have been widely used to model the spread of COVID-19; however, quantifying and comparing their predictive value across different settings is challenging. Their quality is affected by various factors and their relationship with epidemiological indicators varies over time. Here, we adopt a model-free approach based on transfer entropy to quantify the relationship between mobile phone-derived mobility metrics and COVID-19 cases and deaths in more than 200 European subnational regions. Using multiple data sources over a one-year period, we found that past knowledge of mobility does not systematically provide statistically significant information on COVID-19 spread. Our approach allows us to determine the best metric for predicting disease incidence in a particular location, at different spatial scales. Additionally, we identify geographic and demographic factors, such as users' coverage and commuting patterns, that explain the (non)observed relationship between mobility and epidemic patterns. Our work provides epidemiologists and public health officials with a general-not limited to COVID-19-framework to evaluate the usefulness of human mobility data in responding to epidemics.
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Affiliation(s)
- Federico Delussu
- ISI Foundation, via Chisola 5, 10126 Torino, Italy
- Department of Applied Mathematics and Computer Science, DTU, Richard Petersens Plads, DK-2800 Copenhagen, Denmark
| | - Michele Tizzoni
- ISI Foundation, via Chisola 5, 10126 Torino, Italy
- Department of Sociology and Social Research, University of Trento, via Verdi 26, I-38122 Trento, Italy
| | - Laetitia Gauvin
- ISI Foundation, via Chisola 5, 10126 Torino, Italy
- UMR 215 PRODIG, Institute for Research on Sustainable Development - IRD, 5 cours des Humanités, F-93 322 Aubervilliers Cedex, France
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18
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He M, Tang S, Xiao Y. Combining the dynamic model and deep neural networks to identify the intensity of interventions during COVID-19 pandemic. PLoS Comput Biol 2023; 19:e1011535. [PMID: 37851640 PMCID: PMC10584194 DOI: 10.1371/journal.pcbi.1011535] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Accepted: 09/20/2023] [Indexed: 10/20/2023] Open
Abstract
During the COVID-19 pandemic, control measures, especially massive contact tracing following prompt quarantine and isolation, play an important role in mitigating the disease spread, and quantifying the dynamic contact rate and quarantine rate and estimate their impacts remain challenging. To precisely quantify the intensity of interventions, we develop the mechanism of physics-informed neural network (PINN) to propose the extended transmission-dynamics-informed neural network (TDINN) algorithm by combining scattered observational data with deep learning and epidemic models. The TDINN algorithm can not only avoid assuming the specific rate functions in advance but also make neural networks follow the rules of epidemic systems in the process of learning. We show that the proposed algorithm can fit the multi-source epidemic data in Xi'an, Guangzhou and Yangzhou cities well, and moreover reconstruct the epidemic development trend in Hainan and Xinjiang with incomplete reported data. We inferred the temporal evolution patterns of contact/quarantine rates, selected the best combination from the family of functions to accurately simulate the contact/quarantine time series learned by TDINN algorithm, and consequently reconstructed the epidemic process. The selected rate functions based on the time series inferred by deep learning have epidemiologically reasonable meanings. In addition, the proposed TDINN algorithm has also been verified by COVID-19 epidemic data with multiple waves in Liaoning province and shows good performance. We find the significant fluctuations in estimated contact/quarantine rates, and a feedback loop between the strengthening/relaxation of intervention strategies and the recurrence of the outbreaks. Moreover, the findings show that there is diversity in the shape of the temporal evolution curves of the inferred contact/quarantine rates in the considered regions, which indicates variation in the intensity of control strategies adopted in various regions.
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Affiliation(s)
- Mengqi He
- School of Mathematics and Statistics, Shaanxi Normal University, Xi’an, China
| | - Sanyi Tang
- School of Mathematics and Statistics, Shaanxi Normal University, Xi’an, China
| | - Yanni Xiao
- School of Mathematics and Statistics, Xi’an Jiaotong University, Xi’an, China
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19
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Valgañón P, Useche AF, Soriano-Paños D, Ghoshal G, Gómez-Gardeñes J. Quantifying the heterogeneous impact of lockdown policies on different socioeconomic classes during the first COVID-19 wave in Colombia. Sci Rep 2023; 13:16481. [PMID: 37777581 PMCID: PMC10542364 DOI: 10.1038/s41598-023-43685-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Accepted: 09/27/2023] [Indexed: 10/02/2023] Open
Abstract
In the absence of vaccines, the most widespread reaction to curb the COVID-19 pandemic worldwide was the implementation of lockdowns or stay-at-home policies. Despite the reported usefulness of such policies, their efficiency was highly constrained by socioeconomic factors determining their feasibility and their associated outcome in terms of mobility reduction and the subsequent limitation of social activity. Here we investigate the impact of lockdown policies on the mobility patterns of different socioeconomic classes in the three major cities of Colombia during the first wave of the COVID-19 pandemic. In global terms, we find a consistent positive correlation between the reduction in mobility levels and the socioeconomic stratum of the population in the three cities, implying that those with lower incomes were less capable of adopting the aforementioned policies. Our analysis also suggests a strong restructuring of the mobility network of lowest socioeconomic strata during COVID-19 lockdown, increasing their endogenous mixing while hampering their connections with wealthiest areas due to a sharp reduction in long-distance trips.
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Affiliation(s)
- Pablo Valgañón
- Departament of Condensed Matter Physics, University of Zaragoza, 50009, Zaragoza, Spain
- GOTHAM lab, Institute for Biocomputation and Physics of Complex Systems, University of Zaragoza, 50018, Zaragoza, Spain
| | - Andrés F Useche
- Department of Industrial Engineering, School of Engineering, Universidad de Los Andes, 111711, Bogotá, Colombia
| | - David Soriano-Paños
- GOTHAM lab, Institute for Biocomputation and Physics of Complex Systems, University of Zaragoza, 50018, Zaragoza, Spain.
- Instituto Gulbenkian de Ciência, 2780-156, Oeiras, Portugal.
| | - Gourab Ghoshal
- Department of Physics and Astronomy, University of Rochester, Rochester, NY, 14627, USA
| | - Jesús Gómez-Gardeñes
- Departament of Condensed Matter Physics, University of Zaragoza, 50009, Zaragoza, Spain
- GOTHAM lab, Institute for Biocomputation and Physics of Complex Systems, University of Zaragoza, 50018, Zaragoza, Spain
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20
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Wang Z, Wu P, Wang L, Li B, Liu Y, Ge Y, Wang R, Wang L, Tan H, Wu CH, Laine M, Salje H, Song H. Marginal effects of public health measures and COVID-19 disease burden in China: A large-scale modelling study. PLoS Comput Biol 2023; 19:e1011492. [PMID: 37721947 PMCID: PMC10538769 DOI: 10.1371/journal.pcbi.1011492] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2023] [Revised: 09/28/2023] [Accepted: 09/05/2023] [Indexed: 09/20/2023] Open
Abstract
China had conducted some of the most stringent public health measures to control the spread of successive SARS-CoV-2 variants. However, the effectiveness of these measures and their impacts on the associated disease burden have rarely been quantitatively assessed at the national level. To address this gap, we developed a stochastic age-stratified metapopulation model that incorporates testing, contact tracing and isolation, based on 419 million travel movements among 366 Chinese cities. The study period for this model began from September 2022. The COVID-19 disease burden was evaluated, considering 8 types of underlying health conditions in the Chinese population. We identified the marginal effects between the testing speed and reduction in the epidemic duration. The findings suggest that assuming a vaccine coverage of 89%, the Omicron-like wave could be suppressed by 3-day interval population-level testing (PLT), while it would become endemic with 4-day interval PLT, and without testing, it would result in an epidemic. PLT conducted every 3 days would not only eliminate infections but also keep hospital bed occupancy at less than 29.46% (95% CI, 22.73-38.68%) of capacity for respiratory illness and ICU bed occupancy at less than 58.94% (95% CI, 45.70-76.90%) during an outbreak. Furthermore, the underlying health conditions would lead to an extra 2.35 (95% CI, 1.89-2.92) million hospital admissions and 0.16 (95% CI, 0.13-0.2) million ICU admissions. Our study provides insights into health preparedness to balance the disease burden and sustainability for a country with a population of billions.
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Affiliation(s)
- Zengmiao Wang
- State Key Laboratory of Remote Sensing Science, Center for Global Change and Public Health, Faculty of Geographical Science, Beijing Normal University, Beijing, China
| | - Peiyi Wu
- State Key Laboratory of Remote Sensing Science, Center for Global Change and Public Health, Faculty of Geographical Science, Beijing Normal University, Beijing, China
| | - Lin Wang
- Department of Genetics, University of Cambridge, Cambridge, United Kingdom
| | - Bingying Li
- State Key Laboratory of Remote Sensing Science, Center for Global Change and Public Health, Faculty of Geographical Science, Beijing Normal University, Beijing, China
| | - Yonghong Liu
- Beijing Center for Disease Prevention and Control, Beijing, China
| | - Yuxi Ge
- State Key Laboratory of Remote Sensing Science, Center for Global Change and Public Health, Faculty of Geographical Science, Beijing Normal University, Beijing, China
| | - Ruixue Wang
- State Key Laboratory of Remote Sensing Science, Center for Global Change and Public Health, Faculty of Geographical Science, Beijing Normal University, Beijing, China
| | - Ligui Wang
- Center of Disease Control and Prevention, PLA, Beijing, China
| | - Hua Tan
- Translational and Functional Genomics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland, United States of America
| | - Chieh-Hsi Wu
- Mathematical Sciences, University of Southampton, Southampton, United Kingdom
| | - Marko Laine
- Finnish Meteorological Institute, Meteorological Research Unit, Helsinki, Finland
| | - Henrik Salje
- Department of Genetics, University of Cambridge, Cambridge, United Kingdom
| | - Hongbin Song
- Center of Disease Control and Prevention, PLA, Beijing, China
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21
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Zhang K, Xia Z, Huang S, Sun GQ, Lv J, Ajelli M, Ejima K, Liu QH. Evaluating the impact of test-trace-isolate for COVID-19 management and alternative strategies. PLoS Comput Biol 2023; 19:e1011423. [PMID: 37656743 PMCID: PMC10501547 DOI: 10.1371/journal.pcbi.1011423] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2023] [Revised: 09/14/2023] [Accepted: 08/09/2023] [Indexed: 09/03/2023] Open
Abstract
There are many contrasting results concerning the effectiveness of Test-Trace-Isolate (TTI) strategies in mitigating SARS-CoV-2 spread. To shed light on this debate, we developed a novel static-temporal multiplex network characterizing both the regular (static) and random (temporal) contact patterns of individuals and a SARS-CoV-2 transmission model calibrated with historical COVID-19 epidemiological data. We estimated that the TTI strategy alone could not control the disease spread: assuming R0 = 2.5, the infection attack rate would be reduced by 24.5%. Increased test capacity and improved contact trace efficiency only slightly improved the effectiveness of the TTI. We thus investigated the effectiveness of the TTI strategy when coupled with reactive social distancing policies. Limiting contacts on the temporal contact layer would be insufficient to control an epidemic and contacts on both layers would need to be limited simultaneously. For example, the infection attack rate would be reduced by 68.1% when the reactive distancing policy disconnects 30% and 50% of contacts on static and temporal layers, respectively. Our findings highlight that, to reduce the overall transmission, it is important to limit contacts regardless of their types in addition to identifying infected individuals through contact tracing, given the substantial proportion of asymptomatic and pre-symptomatic SARS-CoV-2 transmission.
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Affiliation(s)
- Kun Zhang
- College of Computer Science, Sichuan University, Chengdu, China
| | - Zhichu Xia
- Glasgow College, University of Electronic Science and Technology of China, Chengdu, China
| | - Shudong Huang
- College of Computer Science, Sichuan University, Chengdu, China
| | - Gui-Quan Sun
- Department of Mathematics, North University of China, Taiyuan, China
- Complex Systems Research Center, Shanxi University, Taiyuan, China
| | - Jiancheng Lv
- College of Computer Science, Sichuan University, Chengdu, China
| | - Marco Ajelli
- Laboratory for Computational Epidemiology and Public Health, Department of Epidemiology and Biostatistics, School of Public Health, Indiana University Bloomington, Bloomington, Indiana, United States of America
| | - Keisuke Ejima
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
| | - Quan-Hui Liu
- College of Computer Science, Sichuan University, Chengdu, China
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22
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Ge Y, Wu X, Zhang W, Wang X, Zhang D, Wang J, Liu H, Ren Z, Ruktanonchai NW, Ruktanonchai CW, Cleary E, Yao Y, Wesolowski A, Cummings DAT, Li Z, Tatem AJ, Lai S. Effects of public-health measures for zeroing out different SARS-CoV-2 variants. Nat Commun 2023; 14:5270. [PMID: 37644012 PMCID: PMC10465600 DOI: 10.1038/s41467-023-40940-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Accepted: 08/15/2023] [Indexed: 08/31/2023] Open
Abstract
Targeted public health interventions for an emerging epidemic are essential for preventing pandemics. During 2020-2022, China invested significant efforts in strict zero-COVID measures to contain outbreaks of varying scales caused by different SARS-CoV-2 variants. Based on a multi-year empirical dataset containing 131 outbreaks observed in China from April 2020 to May 2022 and simulated scenarios, we ranked the relative intervention effectiveness by their reduction in instantaneous reproduction number. We found that, overall, social distancing measures (38% reduction, 95% prediction interval 31-45%), face masks (30%, 17-42%) and close contact tracing (28%, 24-31%) were most effective. Contact tracing was crucial in containing outbreaks during the initial phases, while social distancing measures became increasingly prominent as the spread persisted. In addition, infections with higher transmissibility and a shorter latent period posed more challenges for these measures. Our findings provide quantitative evidence on the effects of public-health measures for zeroing out emerging contagions in different contexts.
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Affiliation(s)
- Yong Ge
- State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China.
- Key Laboratory of Poyang Lake Wetland and Watershed Research Ministry of Education, Jiangxi Normal University, Nanchang, China.
- College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, China.
| | - Xilin Wu
- State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
- College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, China
| | - Wenbin Zhang
- State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
- College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, China
- WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton, UK
| | - Xiaoli Wang
- Beijing Center for Disease Prevention and Control, Beijing, China
| | - Die Zhang
- State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
- Key Laboratory of Poyang Lake Wetland and Watershed Research Ministry of Education, Jiangxi Normal University, Nanchang, China
| | - Jianghao Wang
- State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
- College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, China
| | - Haiyan Liu
- Marine Data Center, Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, China
| | - Zhoupeng Ren
- State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
| | | | | | - Eimear Cleary
- WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton, UK
| | - Yongcheng Yao
- WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton, UK
- School of Mathematics and Statistics, Zhengzhou Normal University, Zhengzhou, China
| | - Amy Wesolowski
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Derek A T Cummings
- Department of Biology and Emerging Pathogens Institute, University of Florida, Gainesville, FL, USA
| | - Zhongjie Li
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Andrew J Tatem
- WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton, UK
| | - Shengjie Lai
- WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton, UK.
- Institute for Life Sciences, University of Southampton, Southampton, UK.
- Shanghai Institute of Infectious Disease and Biosecurity, Fudan University, Shanghai, China.
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23
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Huang J, Zhao Y, Yan W, Lian X, Wang R, Chen B, Chen S. Multi-source dynamic ensemble prediction of infectious disease and application in COVID-19 case. J Thorac Dis 2023; 15:4040-4052. [PMID: 37559615 PMCID: PMC10407500 DOI: 10.21037/jtd-23-234] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Accepted: 06/18/2023] [Indexed: 08/11/2023]
Abstract
BACKGROUND The development of an epidemic always exhibits multiwave oscillation owing to various anthropogenic sources of transmission. Particularly in populated areas, the large-scaled human mobility led to the transmission of the virus faster and more complex. The accurate prediction of the spread of infectious diseases remains a problem. To solve this problem, we propose a new method called the multi-source dynamic ensemble prediction (MDEP) method that incorporates a modified susceptible-exposed-infected-removed (SEIR) model to improve the accuracy of the prediction result. METHODS The modified SEIR model is based on the compartment model, which is suitable for local-scale and confined spaces, where human mobility on a large scale is not considered. Moreover, compartmental models cannot be used to predict multiwave epidemics. The proposed MDEP method can remedy defects in the compartment model. In this study, multi-source prediction was made on the development of coronavirus disease 2019 (COVID-19) and dynamically assembled to obtain the final integrated result. We used the real epidemic data of COVID-19 in three cities in China: Beijing, Lanzhou, and Beihai. Epidemiological data were collected from 17 April, 2022 to 12 August, 2022. RESULTS Compared to the one-wave modified SEIR model, the MDEP method can depict the multiwave development of COVID-19. The MDEP method was applied to predict the number of cumulative cases of recent COVID-19 outbreaks in the aforementioned cities in China. The average accuracy rates in Beijing, Lanzhou, and Beihai were 89.15%, 91.74%, and 94.97%, respectively. CONCLUSIONS The MDEP method improved the prediction accuracy of COVID-19. With further application to other infectious diseases, the MDEP method will provide accurate predictions of infectious diseases and aid governments make appropriate directives.
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Affiliation(s)
- Jianping Huang
- Collaborative Innovation Centre for Western Ecological Safety (CIWES), College of Atmospheric Sciences, Lanzhou University, Lanzhou, China
- College of Atmospheric Sciences, Lanzhou University, Lanzhou, China
| | - Yingjie Zhao
- College of Atmospheric Sciences, Lanzhou University, Lanzhou, China
| | - Wei Yan
- College of Atmospheric Sciences, Lanzhou University, Lanzhou, China
| | - Xinbo Lian
- College of Atmospheric Sciences, Lanzhou University, Lanzhou, China
| | - Rui Wang
- College of Atmospheric Sciences, Lanzhou University, Lanzhou, China
| | - Bin Chen
- College of Atmospheric Sciences, Lanzhou University, Lanzhou, China
| | - Siyu Chen
- College of Atmospheric Sciences, Lanzhou University, Lanzhou, China
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24
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Li J, Bao W, Zhang X, Song Y, Lin Z, Zhu H. Modelling the transmission and control of COVID-19 in Yangzhou city with the implementation of Zero-COVID policy. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2023; 20:15781-15808. [PMID: 37919989 DOI: 10.3934/mbe.2023703] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/04/2023]
Abstract
In the fight against the COVID-19 pandemic, China has long adhered to the "Dynamic Zero COVID-19" strategy till the end of 2022. To understand the mechanism of this strategy, we used the case of the Yangzhou summer outbreak in 2021 and a multi-stage dynamical model incorporating city-wide and key area testing-trace-isolation (TTI) strategies. We defined two time-varying indexes for measuring the disease transmission risk and the public health prevention and control force, respectively, which allowed us to explore the mechanisms of TTI policies. Integrating with the historical data and literature parameter values, we first estimated the parameters and then quantified the relevant indexes over time. The findings showed that multiple rounds of rapid testing were one of the critical measures to overcome the outbreak in Yangzhou within one month. In addition, we compared the impact of the duration of the free transmission stage, tracking rate, testing interval and precise division of key areas on the epidemiological indicators, including the final sizes of infections and isolations, peak value, peak arrival time and epidemic duration and the minimum round of testing. Our results suggest that the early detection of the epidemic, an improved efficiency of tracking, and a reduced duration of each test play a positive role in restraining COVID-19; however, a considerable investment of resources was essential to achieve a significant effect quickly.
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Affiliation(s)
- Juan Li
- School of Computer Science and Technology (School of Artificial Intelligence), Zhejiang Sci-Tech University, Hangzhou 310018, China
| | - Wendi Bao
- College of Science, China University of Petroleum, Qingdao 266580, China
| | - Xianghong Zhang
- School of Mathematics and Statistics, Southwest University, Chongqing 400715, China
| | - Yongzhong Song
- Jiangsu Key Laboratory for NSLSCS, Institute of Mathematics School of Mathematics Science Nanjing Normal University, Nanjing 210023, China
| | - Zhigui Lin
- School of Mathematical Science, Yangzhou University, Yangzhou 225002, China
| | - Huaiping Zhu
- LAMPS and Center for Diseases Modeling (CDM), Department of Mathematics and Statistics, York University, Toronto M3J 1P3, ON, Canada
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25
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Zhang J, Heath LS. Adaptive group testing strategy for infectious diseases using social contact graph partitions. Sci Rep 2023; 13:12102. [PMID: 37495642 PMCID: PMC10372051 DOI: 10.1038/s41598-023-39326-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Accepted: 07/24/2023] [Indexed: 07/28/2023] Open
Abstract
Mass testing is essential for identifying infected individuals during an epidemic and allowing healthy individuals to return to normal social activities. However, testing capacity is often insufficient to meet global health needs, especially during newly emerging epidemics. Dorfman's method, a classic group testing technique, helps reduce the number of tests required by pooling the samples of multiple individuals into a single sample for analysis. Dorfman's method does not consider the time dynamics or limits on testing capacity involved in infection detection, and it assumes that individuals are infected independently, ignoring community correlations. To address these limitations, we present an adaptive group testing (AGT) strategy based on graph partitioning, which divides a physical contact network into subgraphs (groups of individuals) and assigns testing priorities based on the social contact characteristics of each subgraph. Our AGT aims to maximize the number of infected individuals detected and minimize the number of tests required. After each testing round (perhaps on a daily basis), the testing priority is increased for each neighboring group of known infected individuals. We also present an enhanced infectious disease transmission model that simulates the dynamic spread of a pathogen and evaluate our AGT strategy using the simulation results. When applied to 13 social contact networks, AGT demonstrates significant performance improvements compared to Dorfman's method and its variations. Our AGT strategy requires fewer tests overall, reduces disease spread, and retains robustness under changes in group size, testing capacity, and other parameters. Testing plays a crucial role in containing and mitigating pandemics by identifying infected individuals and helping to prevent further transmission in families and communities. By identifying infected individuals and helping to prevent further transmission in families and communities, our AGT strategy can have significant implications for public health, providing guidance for policymakers trying to balance economic activity with the need to manage the spread of infection.
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Affiliation(s)
- Jingyi Zhang
- Department of Computer Science, Virginia Tech, Blacksburg, VA, 24060, USA.
| | - Lenwood S Heath
- Department of Computer Science, Virginia Tech, Blacksburg, VA, 24060, USA
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26
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Parag KV, Obolski U. Risk averse reproduction numbers improve resurgence detection. PLoS Comput Biol 2023; 19:e1011332. [PMID: 37471464 PMCID: PMC10393178 DOI: 10.1371/journal.pcbi.1011332] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2022] [Accepted: 07/06/2023] [Indexed: 07/22/2023] Open
Abstract
The effective reproduction number R is a prominent statistic for inferring the transmissibility of infectious diseases and effectiveness of interventions. R purportedly provides an easy-to-interpret threshold for deducing whether an epidemic will grow (R>1) or decline (R<1). We posit that this interpretation can be misleading and statistically overconfident when applied to infections accumulated from groups featuring heterogeneous dynamics. These groups may be delineated by geography, infectiousness or sociodemographic factors. In these settings, R implicitly weights the dynamics of the groups by their number of circulating infections. We find that this weighting can cause delayed detection of outbreak resurgence and premature signalling of epidemic control because it underrepresents the risks from highly transmissible groups. Applying E-optimal experimental design theory, we develop a weighting algorithm to minimise these issues, yielding the risk averse reproduction number E. Using simulations, analytic approaches and real-world COVID-19 data stratified at the city and district level, we show that E meaningfully summarises transmission dynamics across groups, balancing bias from the averaging underlying R with variance from directly using local group estimates. An E>1generates timely resurgence signals (upweighting risky groups), while an E<1ensures local outbreaks are under control. We propose E as an alternative to R for informing policy and assessing transmissibility at large scales (e.g., state-wide or nationally), where R is commonly computed but well-mixed or homogeneity assumptions break down.
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Affiliation(s)
- Kris V Parag
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, United Kingdom
| | - Uri Obolski
- School of Public Health, Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
- Porter School of the Environment and Earth Sciences, Faculty of Exact Sciences, Tel Aviv University, Tel Aviv, Israel
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27
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Wang J, Huang Y, Dong Y, Wu B. Assessment of the impact of reopening strategies on the spatial transmission risk of COVID-19 based on a data-driven transmission model. Sci Rep 2023; 13:11146. [PMID: 37429885 DOI: 10.1038/s41598-023-37297-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2023] [Accepted: 06/19/2023] [Indexed: 07/12/2023] Open
Abstract
COVID-19 has dramatically changed people's mobility geste patterns and affected the operations of different functional spots. In the environment of the successful reopening of countries around the world since 2022, it's pivotal to understand whether the reopening of different types of locales poses a threat of wide epidemic transmission. In this paper, by establishing an epidemiological model based on mobile network data, combining the data handed by the Safegraph website, and taking into account the crowd inflow characteristics and the changes of susceptible and latent populations, the trends of the number of crowd visits and the number of epidemic infections at different functional points of interest after the perpetration of continuing strategies were simulated. The model was also validated with daily new cases in ten metropolitan areas in the United States from March to May 2020, and the results showed that the model fitted the evolutionary trend of realistic data more accurately. Further, the points of interest were classified into risk levels, and the corresponding reopening minimum standard prevention and control measures were proposed to be implemented according to different risk levels. The results showed that restaurants and gyms became high-risk points of interest after the perpetration of the continuing strategy, especially the general dine-in restaurants were at higher risk levels. Religious exertion centers were the points of interest with the loftiest average infection rates after the perpetration of the continuing strategy. Points of interest such as convenience stores, large shopping malls, and pharmacies were at a lower risk for outbreak impact after the continuing strategy was enforced. Based on this, continuing forestallment and control strategies for different functional points of interest are proposed to provide decision support for the development of precise forestallment and control measures for different spots.
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Affiliation(s)
- Jing Wang
- School of Economics and Management, Fuzhou University, Fuzhou, 350116, China.
- Emergency Management Research Center, Fuzhou University, Fuzhou, 350116, China.
| | - YuHui Huang
- School of Economics and Management, Fuzhou University, Fuzhou, 350116, China
| | - Ying Dong
- School of Economics and Management, Fuzhou University, Fuzhou, 350116, China
| | - BingYing Wu
- School of Economics and Management, Fuzhou University, Fuzhou, 350116, China
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28
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Katzmarzyk PT, Myers CA, Nelson MR, Denstel KD, Mire EF, Newton RL, Broyles ST, Kirwan JP. Exploring barriers to SARS-CoV-2 testing uptake in underserved black communities in Louisiana. Am J Hum Biol 2023; 35:e23879. [PMID: 36807397 PMCID: PMC10591290 DOI: 10.1002/ajhb.23879] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2022] [Revised: 01/27/2023] [Accepted: 02/02/2023] [Indexed: 02/22/2023] Open
Abstract
OBJECTIVE To collect qualitative data on approaches that can potentially reduce barriers to, and create strategies for, increasing SARS-CoV-2 testing uptake in underserved Black communities in Louisiana. METHODS A series of eight focus groups, including 41 participants, were conducted in primarily Black communities. The Nominal Group Technique (NGT) was used to determine perceptions of COVID-19 as a disease, access to testing, and barriers limiting testing uptake. RESULTS Common barriers to SARS-CoV-2 testing were identified as lack of transportation, misinformation/lack of information, lack of time/long wait times, fear of the test being uncomfortable and/or testing positive, the cost of testing, and lack of computer/smartphone/internet. The most impactful approaches identified to increase testing uptake included providing testing within the local communities; testing specifically in heavily traveled areas such as supermarkets, churches, schools, and so forth; providing incentives; engaging local celebrities; and providing information to the community through health fairs, or through churches and schools. The strategies that were deemed to be the easiest to implement revolved around communication about testing, with suggested strategies involving churches, local celebrities or expert leaders, social media, text messages, public service announcements, post cards, or putting up signs in neighborhoods. Providing transportation to testing sites, providing incentives, and bringing the testing to neighborhoods and schools were also identified as easy to implement strategies. CONCLUSIONS Several strategies to increase testing uptake were identified in this population. These strategies need to be tested for effectiveness in real-world settings using experimental and observational study designs.
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Affiliation(s)
| | - Candice A. Myers
- Pennington Biomedical Research Center, Baton Rouge, Louisiana, USA
| | - Michelle R. Nelson
- Surgeons Group of Baton Rouge, Our Lady of the Lake Regional Medical Center, Baton Rouge, Louisiana, USA
| | - Kara D. Denstel
- Pennington Biomedical Research Center, Baton Rouge, Louisiana, USA
| | - Emily F. Mire
- Pennington Biomedical Research Center, Baton Rouge, Louisiana, USA
| | - Robert L. Newton
- Pennington Biomedical Research Center, Baton Rouge, Louisiana, USA
| | | | - John P. Kirwan
- Pennington Biomedical Research Center, Baton Rouge, Louisiana, USA
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29
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Das T, Bandekar SR, Srivastav AK, Srivastava PK, Ghosh M. Role of immigration and emigration on the spread of COVID-19 in a multipatch environment: a case study of India. Sci Rep 2023; 13:10546. [PMID: 37385997 PMCID: PMC10310821 DOI: 10.1038/s41598-023-37192-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2022] [Accepted: 06/17/2023] [Indexed: 07/01/2023] Open
Abstract
Human mobility has played a critical role in the spread of COVID-19. The understanding of mobility helps in getting information on the acceleration or control of the spread of disease. The COVID-19 virus has been spreading among several locations despite all the best efforts related to its isolation. To comprehend this, a multi-patch mathematical model of COVID-19 is proposed and analysed in this work, where-in limited medical resources, quarantining, and inhibitory behaviour of healthy individuals are incorporated into the model. Furthermore, as an example, the impact of mobility in a three-patch model is studied considering the three worst-hit states of India, i.e. Kerala, Maharashtra and Tamil Nadu, as three patches. Key parameters and the basic reproduction number are estimated from the available data. Through results and analyses, it is seen that Kerala has a higher effective contact rate and has the highest prevalence. Moreover, if Kerala is isolated from Maharashtra or Tamil Nadu, the number of active cases will increase in Kerala but reduce in the other two states. Our findings indicate that the number of active cases will decrease in the high prevalence state and increase in the lower prevalence states if the emigration rate is higher than the immigration rate in the high prevalence state. Overall, proper travel restrictions are to be implemented to reduce or control the spread of disease from the high-prevalence state to other states with lower prevalence rates.
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Affiliation(s)
- Tanuja Das
- Department of Mathematics and Statistics, University of New Brunswick, Fredericton, Canada
| | | | - Akhil Kumar Srivastav
- Mathematical and Theoretical Biology, BCAM - Basque Center for Applied Mathematics, Bilbao, Spain
| | - Prashant K Srivastava
- Department of Mathematics, Indian Institute of Technology Patna, Patna, 801103, India
| | - Mini Ghosh
- Division of Mathematics, School of Advanced Sciences, Vellore Institute of Technology, Chennai, India.
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30
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Lajot A, Wambua J, Coletti P, Franco N, Brondeel R, Faes C, Hens N. How contact patterns during the COVID-19 pandemic are related to pre-pandemic contact patterns and mobility trends. BMC Infect Dis 2023; 23:410. [PMID: 37328811 DOI: 10.1186/s12879-023-08369-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Accepted: 06/02/2023] [Indexed: 06/18/2023] Open
Abstract
BACKGROUND Non-pharmaceutical interventions (NPIs) were adopted in Belgium in order to decrease social interactions between people and as such decrease viral transmission of SARS-CoV-2. With the aim to better evaluate the impact of NPIs on the evolution of the pandemic, an estimation of social contact patterns during the pandemic is needed when social contact patterns are not available yet in real time. METHODS In this paper we use a model-based approach allowing for time varying effects to evaluate whether mobility and pre-pandemic social contact patterns can be used to predict the social contact patterns observed during the COVID-19 pandemic between November 11, 2020 and July 4, 2022. RESULTS We found that location-specific pre-pandemic social contact patterns are good indicators for estimating social contact patterns during the pandemic. However, the relationship between both changes with time. Considering a proxy for mobility, namely the change in the number of visitors to transit stations, in interaction with pre-pandemic contacts does not explain the time-varying nature of this relationship well. CONCLUSION In a situation where data from social contact surveys conducted during the pandemic are not yet available, the use of a linear combination of pre-pandemic social contact patterns could prove valuable. However, translating the NPIs at a given time into appropriate coefficients remains the main challenge of such an approach. In this respect, the assumption that the time variation of the coefficients can somehow be related to aggregated mobility data seems unacceptable during our study period for estimating the number of contacts at a given time.
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Affiliation(s)
- Adrien Lajot
- Department of Epidemiology and Public Health, Sciensano, Brussels, Belgium.
- Data Science Institute, I-BioStat, University of Hasselt, Hasselt, Belgium.
| | - James Wambua
- Data Science Institute, I-BioStat, University of Hasselt, Hasselt, Belgium
| | - Pietro Coletti
- Data Science Institute, I-BioStat, University of Hasselt, Hasselt, Belgium
| | - Nicolas Franco
- Data Science Institute, I-BioStat, University of Hasselt, Hasselt, Belgium
- Namur Institute for Complex Systems (naXys) and Department of Mathematics, University of Namur, Namur, Belgium
| | - Ruben Brondeel
- Department of Epidemiology and Public Health, Sciensano, Brussels, Belgium
| | - Christel Faes
- Data Science Institute, I-BioStat, University of Hasselt, Hasselt, Belgium
| | - Niel Hens
- Data Science Institute, I-BioStat, University of Hasselt, Hasselt, Belgium
- Centre for Health Economic Research and Modelling Infectious Diseases, Vaccine and infectious disease institute, University of Antwerp, Antwerp, Belgium
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31
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Palma G, Caprioli D, Mari L. Epidemic Management via Imperfect Testing: A Multi-criterial Perspective. Bull Math Biol 2023; 85:66. [PMID: 37296314 PMCID: PMC10255952 DOI: 10.1007/s11538-023-01172-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Accepted: 05/27/2023] [Indexed: 06/12/2023]
Abstract
Diagnostic testing may represent a key component in response to an ongoing epidemic, especially if coupled with containment measures, such as mandatory self-isolation, aimed to prevent infectious individuals from furthering onward transmission while allowing non-infected individuals to go about their lives. However, by its own nature as an imperfect binary classifier, testing can produce false negative or false positive results. Both types of misclassification are problematic: while the former may exacerbate the spread of disease, the latter may result in unnecessary isolation mandates and socioeconomic burden. As clearly shown by the COVID-19 pandemic, achieving adequate protection for both people and society is a crucial, yet highly challenging task that needs to be addressed in managing large-scale epidemic transmission. To explore the trade-offs imposed by diagnostic testing and mandatory isolation as tools for epidemic containment, here we present an extension of the classical Susceptible-Infected-Recovered model that accounts for an additional stratification of the population based on the results of diagnostic testing. We show that, under suitable epidemiological conditions, a careful assessment of testing and isolation protocols can contribute to epidemic containment, even in the presence of false negative/positive results. Also, using a multi-criterial framework, we identify simple, yet Pareto-efficient testing and isolation scenarios that can minimize case count, isolation time, or seek a trade-off solution for these often contrasting epidemic management objectives.
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Affiliation(s)
- Giuseppe Palma
- Institute of Nanotechnology, National Research Council, Campus Ecotekne, Via Monteroni, 73100 Lecce, LE Italy
| | - Damiano Caprioli
- Department of Astronomy & Astrophysics, E. Fermi Institute, University of Chicago, 5640 South Ellis Avenue, Chicago, IL 60637 USA
| | - Lorenzo Mari
- Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Via Ponzio 34/5, 20133 Milano, MI Italy
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32
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Monti C, Pangallo M, De Francisci Morales G, Bonchi F. On learning agent-based models from data. Sci Rep 2023; 13:9268. [PMID: 37286576 DOI: 10.1038/s41598-023-35536-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Accepted: 05/19/2023] [Indexed: 06/09/2023] Open
Abstract
Agent-Based Models (ABMs) are used in several fields to study the evolution of complex systems from micro-level assumptions. However, a significant drawback of ABMs is their inability to estimate agent-specific (or "micro") variables, which hinders their ability to make accurate predictions using micro-level data. In this paper, we propose a protocol to learn the latent micro-variables of an ABM from data. We begin by translating an ABM into a probabilistic model characterized by a computationally tractable likelihood. Next, we use a gradient-based expectation maximization algorithm to maximize the likelihood of the latent variables. We showcase the efficacy of our protocol on an ABM of the housing market, where agents with different incomes bid higher prices to live in high-income neighborhoods. Our protocol produces accurate estimates of the latent variables while preserving the general behavior of the ABM. Moreover, our estimates substantially improve the out-of-sample forecasting capabilities of the ABM compared to simpler heuristics. Our protocol encourages modelers to articulate assumptions, consider the inferential process, and spot potential identification problems, thus making it a useful alternative to black-box data assimilation methods.
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33
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Romanyukha AA, Novikov KA, Avilov KK, Nestik TA, Sannikova TE. The trade-off between COVID-19 and mental diseases burden during a lockdown: Mathematical modeling of control measures. Infect Dis Model 2023; 8:403-414. [PMID: 37064013 PMCID: PMC10084665 DOI: 10.1016/j.idm.2023.04.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Revised: 04/07/2023] [Accepted: 04/07/2023] [Indexed: 04/18/2023] Open
Abstract
Background During the COVID-19 pandemic, many countries used lockdowns as a containment measure. While lockdowns successfully contributed to slowing down the contagion, the related mobility restrictions were reportedly associated with an increased risk of major depressive and anxiety disorders. We aimed to quantify the trade-off between the quality-adjusted life years (QALY) gain due to lower COVID-19 incidence as a result of a lockdown and QALY loss due to lockdown-induced mental disorders. Methods We developed an agent-based model of COVID-19 epidemic and coupled mental disorder development in the population of a large city. We used data sources on the places of living, studying and working, public health and census surveys. Modeling of mental disorders was based on diathesis-stress concept. We quantified mental and physical health burden in terms of QALY taking into account major depressive and anxiety disorder episodes, lethal and non-lethal cases of COVID-19, and immunization. Findings We evaluated the dynamics of new major depressive disorder (MDD) and anxiety disorder (AD) cases during the period between September 2020 and December 2021 in Moscow, Russia. We found that lockdown imposition increases the daily chances of getting MDD or ADD by a vulnerable person by 16.79% (95% CI [12.36%, 21.23%]). The QALY loss associated with COVID-19-induced and lockdown-induced mental disorders was estimated to be 18.93% (95% CI [16.94%, 19.73%]) of the total QALY loss caused by COVID-19, immunization, and all kinds of mental disorders. For a synthetic "strong" lockdown, it had been shown that QALY loss is minimized when about 70% of the population are isolated. Interpretation The burden associated with mental disorders amounts to a considerable part of COVID-19-related losses. Our findings demonstrate that cost-benefit analysis of mobility restriction should include a forecast of mental disorder development in the population.
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Affiliation(s)
- Alexei Alexeevich Romanyukha
- Marchuk Institute of Numerical Mathematics of the Russian Academy of Sciences, Gubkina str., 8, Moscow, 119333, Russia
| | | | | | | | - Tatiana Evgenevna Sannikova
- Marchuk Institute of Numerical Mathematics of the Russian Academy of Sciences, Gubkina str., 8, Moscow, 119333, Russia
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Chen M, Dong Y, Shi X, Zhuang J. Global analysis of the COVID-19 policy activity levels and evolution patterns: A cross-sectional study. Health Sci Rep 2023; 6:e1350. [PMID: 37342293 PMCID: PMC10277603 DOI: 10.1002/hsr2.1350] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2022] [Revised: 05/28/2023] [Accepted: 06/01/2023] [Indexed: 06/22/2023] Open
Abstract
Background and Aims Since the beginning of the coronavirus disease 2019 (COVID-19), a large number of government policies have been implemented worldwide in response to the global spread of COVID-19. This paper aims at developing a data-driven analysis to answer the three research questions: (a) Compared to the pandemic development, are the global government COVID-19 policies sufficiently active? (b) What are the differences and characteristics in the policy activity levels at the country level? (c) What types of COVID-19 policy patterns are forming? Methods Using the Oxford COVID-19 Government Response Tracker data set, we present a global analysis of the COVID-19 policy activity levels and evolution patterns from January 1, 2020 to June 30, 2022, based on the differential expression-sliding window analysis (DE-SWAN) algorithm and the clustering ensemble algorithm. Results Within the period under study, the results indicate that (a) the global government policy responses to COVID-19 are very active, and the policy activity levels are significantly higher than those of global pandemic developments; (b) a high activity of policy is positively correlated to pandemic prevention at the country level; and (c) a high human development index (HDI) score is negatively correlated to the country policy activity level. Furthermore, we propose to categorize the global policy evolution patterns into three categories: (i) Mainstream (152 countries); (ii) China; and (iii) Others (34 countries). Conclusion This work is one of the few studies that quantitatively explores the evolutionary characteristics of global government policies on COVID-19, and our results provide some new perspectives on global policy activity levels and evolution patterns.
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Affiliation(s)
- Meiqian Chen
- Center for Network Big Data and Decision‐Making, Business SchoolSichuan UniversityChengduChina
| | - Yucheng Dong
- Center for Network Big Data and Decision‐Making, Business SchoolSichuan UniversityChengduChina
- Xiangjiang LaboratoryChangshaChina
| | - Xiaoping Shi
- Irving K. Barber School of Arts and SciencesUniversity of British ColumbiaKelownaBritish ColumbiaCanada
| | - Jun Zhuang
- Department of Industrial and Systems EngineeringUniversity at BuffaloBuffaloNew YorkUSA
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35
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Luca M, Campedelli GM, Centellegher S, Tizzoni M, Lepri B. Crime, inequality and public health: a survey of emerging trends in urban data science. Front Big Data 2023; 6:1124526. [PMID: 37303974 PMCID: PMC10248183 DOI: 10.3389/fdata.2023.1124526] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Accepted: 05/10/2023] [Indexed: 06/13/2023] Open
Abstract
Urban agglomerations are constantly and rapidly evolving ecosystems, with globalization and increasing urbanization posing new challenges in sustainable urban development well summarized in the United Nations' Sustainable Development Goals (SDGs). The advent of the digital age generated by modern alternative data sources provides new tools to tackle these challenges with spatio-temporal scales that were previously unavailable with census statistics. In this review, we present how new digital data sources are employed to provide data-driven insights to study and track (i) urban crime and public safety; (ii) socioeconomic inequalities and segregation; and (iii) public health, with a particular focus on the city scale.
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Affiliation(s)
- Massimiliano Luca
- Mobile and Social Computing Lab, Bruno Kessler Foundation, Trento, Italy
- Faculty of Computer Science, Free University of Bolzano, Bolzano, Italy
| | | | | | - Michele Tizzoni
- Department of Sociology and Social Research, University of Trento, Trento, Italy
| | - Bruno Lepri
- Mobile and Social Computing Lab, Bruno Kessler Foundation, Trento, Italy
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36
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Yang Y, Pentland A, Moro E. Identifying latent activity behaviors and lifestyles using mobility data to describe urban dynamics. EPJ DATA SCIENCE 2023; 12:15. [PMID: 37220629 PMCID: PMC10193357 DOI: 10.1140/epjds/s13688-023-00390-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Accepted: 05/09/2023] [Indexed: 05/25/2023]
Abstract
Urbanization and its problems require an in-depth and comprehensive understanding of urban dynamics, especially the complex and diversified lifestyles in modern cities. Digitally acquired data can accurately capture complex human activity, but it lacks the interpretability of demographic data. In this paper, we study a privacy-enhanced dataset of the mobility visitation patterns of 1.2 million people to 1.1 million places in 11 metro areas in the U.S. to detect the latent mobility behaviors and lifestyles in the largest American cities. Despite the considerable complexity of mobility visitations, we found that lifestyles can be automatically decomposed into only 12 latent interpretable activity behaviors on how people combine shopping, eating, working, or using their free time. Rather than describing individuals with a single lifestyle, we find that city dwellers' behavior is a mixture of those behaviors. Those detected latent activity behaviors are equally present across cities and cannot be fully explained by main demographic features. Finally, we find those latent behaviors are associated with dynamics like experienced income segregation, transportation, or healthy behaviors in cities, even after controlling for demographic features. Our results signal the importance of complementing traditional census data with activity behaviors to understand urban dynamics. Supplementary Information The online version contains supplementary material available at 10.1140/epjds/s13688-023-00390-w.
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Affiliation(s)
- Yanni Yang
- Department of Computing, The Hong Kong Polytechnic University, Hong Kong, China
- Connection Science, Institute for Data Science and Society, Massachusetts Institute of Technology, Cambridge, MA United States
| | - Alex Pentland
- Connection Science, Institute for Data Science and Society, Massachusetts Institute of Technology, Cambridge, MA United States
| | - Esteban Moro
- Connection Science, Institute for Data Science and Society, Massachusetts Institute of Technology, Cambridge, MA United States
- Grupo Interdisciplinar de Sistemas Complejos (GISC), Department of Mathematics, Universidad Carlos III de Madrid, Leganés, Madrid, Spain
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37
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Otshudiema JO, Folefack GLT, Nsio JM, Kakema CH, Minikulu L, Bafuana A, Kosianza JB, Mfumu AK, Nkwembe E, Munyeku-Bazitama Y, Makiala-Mandanda S, Guinko N, Mbuyi G, Tshilumbu JMK, Saidi GN, Umba-di-Masiala MS, Ebondo AK, Mutonj JJ, Kalombo S, Kabeya J, Mawanda TK, Bile FN, Kasereka GK, Mbala-Kingebeni P, Ahuka-Mundeke S, Karamagi HC, Fai KN, Djiguimde AP. Community-based COVID-19 active case finding and rapid response in the Democratic Republic of the Congo: Improving case detection and response. PLoS One 2023; 18:e0278251. [PMID: 37200322 PMCID: PMC10194859 DOI: 10.1371/journal.pone.0278251] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2022] [Accepted: 04/25/2023] [Indexed: 05/20/2023] Open
Abstract
A community-based coronavirus disease (COVID-19) active case-finding strategy using an antigen-detecting rapid diagnostic test (Ag-RDT) was implemented in the Democratic Republic of Congo (DRC) to enhance COVID-19 case detection. With this pilot community-based active case finding and response program that was designed as a clinical, prospective testing performance, and implementation study, we aimed to identify insights to improve community diagnosis and rapid response to COVID-19. This pilot study was modeled on the DRC's National COVID-19 Response Plan and the COVID-19 Ag-RDT screening algorithm defined by the World Health Organization (WHO), with case findings implemented in 259 health areas, 39 health zones, and 9 provinces. In each health area, a 7-member interdisciplinary field team tested the close contacts (ring strategy) and applied preventive and control measures to each confirmed case. The COVID-19 testing capacity increased from 0.3 tests per 10,000 inhabitants per week in the first wave to 0.4, 1.6, and 2.2 in the second, third, and fourth waves, respectively. From January to November 2021, this capacity increase contributed to an average of 10.5% of COVID-19 tests in the DRC, with 7,110 positive Ag-RDT results for 40,226 suspected cases and close contacts who were tested (53.6% female, median age: 37 years [interquartile range: 26.0-50.0)]. Overall, 79.7% (n = 32,071) of the participants were symptomatic and 7.6% (n = 3,073) had comorbidities. The Ag-RDT sensitivity and specificity were 55.5% and 99.0%, respectively, based on reverse transcription polymerase chain reaction analysis, and there was substantial agreement between the tests (k = 0.63). Despite its limited sensitivity, the Ag-RDT has improved COVID-19 testing capacity, enabling earlier detection, isolation, and treatment of COVID-19 cases. Our findings support the community testing of suspected cases and asymptomatic close contacts of confirmed cases to reduce disease spread and virus transmission.
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Affiliation(s)
| | | | - Justus M. Nsio
- COVID-19 Response, Ministry of Health, Kinshasa, Democratic Republic of the Congo
| | - Cathy H. Kakema
- COVID-19 Response, World Health Organization, Kinshasa, Democratic Republic of the Congo
| | - Luigino Minikulu
- COVID-19 Response, Ministry of Health, Kinshasa, Democratic Republic of the Congo
| | - Aimé Bafuana
- COVID-19 Response, Ministry of Health, Kinshasa, Democratic Republic of the Congo
| | - Joel B. Kosianza
- COVID-19 Response, World Health Organization, Kinshasa, Democratic Republic of the Congo
| | - Antoine K. Mfumu
- COVID-19 Response, World Health Organization, Kinshasa, Democratic Republic of the Congo
| | - Edith Nkwembe
- COVID-19 Laboratory and Epidemiology Team, National Institute of Biomedical Research, Kinshasa, Democratic Republic of the Congo
| | - Yannick Munyeku-Bazitama
- COVID-19 Laboratory and Epidemiology Team, National Institute of Biomedical Research, Kinshasa, Democratic Republic of the Congo
| | - Sheila Makiala-Mandanda
- COVID-19 Laboratory and Epidemiology Team, National Institute of Biomedical Research, Kinshasa, Democratic Republic of the Congo
| | - Noé Guinko
- COVID-19 Response, World Health Organization, Kinshasa, Democratic Republic of the Congo
| | - Gisèle Mbuyi
- COVID-19 Response, Ministry of Health, Kinshasa, Democratic Republic of the Congo
| | | | - Guy N. Saidi
- COVID-19 Response, World Health Organization, Kinshasa, Democratic Republic of the Congo
| | | | - Amos K. Ebondo
- COVID-19 Response, World Health Organization, Kinshasa, Democratic Republic of the Congo
| | - Jean-Jacques Mutonj
- COVID-19 Response, World Health Organization, Kinshasa, Democratic Republic of the Congo
| | - Serge Kalombo
- COVID-19 Response, World Health Organization, Kinshasa, Democratic Republic of the Congo
| | - Jad Kabeya
- COVID-19 Response, World Health Organization, Kinshasa, Democratic Republic of the Congo
| | - Taty K. Mawanda
- COVID-19 Response, World Health Organization, Kinshasa, Democratic Republic of the Congo
| | - Faustin N. Bile
- COVID-19 Response, World Health Organization, Kinshasa, Democratic Republic of the Congo
| | - Gaby K. Kasereka
- COVID-19 Response, World Health Organization, Kinshasa, Democratic Republic of the Congo
| | - Placide Mbala-Kingebeni
- COVID-19 Laboratory and Epidemiology Team, National Institute of Biomedical Research, Kinshasa, Democratic Republic of the Congo
| | - Steve Ahuka-Mundeke
- COVID-19 Laboratory and Epidemiology Team, National Institute of Biomedical Research, Kinshasa, Democratic Republic of the Congo
| | - Humphrey Cyprian Karamagi
- Data Analytics and Knowledge Management, World Health Organization Regional Office for Africa, Brazzaville, Democratic Republic of Congo
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Xiao Y, Zhou J, Cheng Q, Yang J, Chen B, Zhang T, Xu L, Xu B, Ren Z, Liu Z, Shen C, Wang C, Liu H, Li X, Li R, Yu L, Guan D, Zhang W, Wang J, Hou L, Deng K, Bai Y, Xu B, Dou D, Gong P. Global age-structured spatial modeling for emerging infectious diseases like COVID-19. PNAS NEXUS 2023; 2:pgad127. [PMID: 37143866 PMCID: PMC10153731 DOI: 10.1093/pnasnexus/pgad127] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/07/2022] [Revised: 02/27/2023] [Accepted: 03/30/2023] [Indexed: 05/06/2023]
Abstract
Modeling the global dynamics of emerging infectious diseases (EIDs) like COVID-19 can provide important guidance in the preparation and mitigation of pandemic threats. While age-structured transmission models are widely used to simulate the evolution of EIDs, most of these studies focus on the analysis of specific countries and fail to characterize the spatial spread of EIDs across the world. Here, we developed a global pandemic simulator that integrates age-structured disease transmission models across 3,157 cities and explored its usage under several scenarios. We found that without mitigations, EIDs like COVID-19 are highly likely to cause profound global impacts. For pandemics seeded in most cities, the impacts are equally severe by the end of the first year. The result highlights the urgent need for strengthening global infectious disease monitoring capacity to provide early warnings of future outbreaks. Additionally, we found that the global mitigation efforts could be easily hampered if developed countries or countries near the seed origin take no control. The result indicates that successful pandemic mitigations require collective efforts across countries. The role of developed countries is vitally important as their passive responses may significantly impact other countries.
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Affiliation(s)
- Yixiong Xiao
- Business Intelligence Lab, Baidu Research, Beijing 100193, China
| | - Jingbo Zhou
- Business Intelligence Lab, Baidu Research, Beijing 100193, China
| | - Qu Cheng
- Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Jun Yang
- Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing 100084, China
| | - Bin Chen
- Division of Landscape Architecture, The University of Hong Kong, Hong Kong 999007, China
| | - Tao Zhang
- Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing 100084, China
| | - Lei Xu
- Vanke School of Public Health, Tsinghua University, Beijing 100084, China
| | - Bo Xu
- Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing 100084, China
| | - Zhehao Ren
- Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing 100084, China
| | - Zhaoyang Liu
- Center for Statistical Science, Tsinghua University, Beijing 100084, China
- Department of Industrial Engineering, Tsinghua University, Beijing 100084, China
| | - Chong Shen
- Center for Statistical Science, Tsinghua University, Beijing 100084, China
- Department of Industrial Engineering, Tsinghua University, Beijing 100084, China
| | - Che Wang
- Center for Statistical Science, Tsinghua University, Beijing 100084, China
- Department of Industrial Engineering, Tsinghua University, Beijing 100084, China
| | - Han Liu
- Business Intelligence Lab, Baidu Research, Beijing 100193, China
| | - Xiaoting Li
- Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing 100084, China
| | - Ruiyun Li
- School of Public Health (SPH), Nanjing Medical University, Nanjing 211166, China
| | - Le Yu
- Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing 100084, China
| | - Dabo Guan
- Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing 100084, China
| | - Wusheng Zhang
- Department of Computer Science and Technology, Institute of High Performance Computing, Tsinghua University, Beijing 100084, China
| | - Jie Wang
- State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
- AI for Earth Laboratory, Cross-Strait Institute, Tsinghua University, Beijing 100084, China
| | - Lin Hou
- Center for Statistical Science, Tsinghua University, Beijing 100084, China
- Department of Industrial Engineering, Tsinghua University, Beijing 100084, China
| | - Ke Deng
- Center for Statistical Science, Tsinghua University, Beijing 100084, China
- Department of Industrial Engineering, Tsinghua University, Beijing 100084, China
| | - Yuqi Bai
- Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing 100084, China
| | - Bing Xu
- Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing 100084, China
| | - Dejing Dou
- Business Intelligence Lab, Baidu Research, Beijing 100193, China
| | - Peng Gong
- To whom correspondence should be addressed:
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Chen K, Jiang X, Li Y, Zhou R. A stochastic agent-based model to evaluate COVID-19 transmission influenced by human mobility. NONLINEAR DYNAMICS 2023; 111:1-17. [PMID: 37361002 PMCID: PMC10148626 DOI: 10.1007/s11071-023-08489-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Accepted: 03/20/2023] [Indexed: 06/28/2023]
Abstract
The COVID-19 pandemic has created an urgent need for mathematical models that can project epidemic trends and evaluate the effectiveness of mitigation strategies. A major challenge in forecasting the transmission of COVID-19 is the accurate assessment of the multiscale human mobility and how it impacts infection through close contacts. By combining the stochastic agent-based modeling strategy and hierarchical structures of spatial containers corresponding to the notion of geographical places, this study proposes a novel model, Mob-Cov, to study the impact of human traveling behavior and individual health conditions on the disease outbreak and the probability of zero-COVID in the population. Specifically, individuals perform power law-type local movements within a container and global transport between different-level containers. It is revealed that frequent long-distance movements inside a small-level container (e.g., a road or a county) and a small population size reduce both the local crowdedness and disease transmission. It takes only half of the time to induce global disease outbreaks when the population increases from 150 to 500 (normalized unit). When the exponent c 1 of the long-tail distribution of distance k moved in the same-level container, p ( k ) ∼ k - c 1 · level , increases, the outbreak time decreases rapidly from 75 to 25 (normalized unit). In contrast, travel between large-level containers (e.g., cities and nations) facilitates global spread of the disease and outbreak. When the mean traveling distance across containers 1 d increases from 0.5 to 1 (normalized unit), the outbreak occurs almost twice as fast. Moreover, dynamic infection and recovery in the population are able to drive the bifurcation of the system to a "zero-COVID" state or to a "live with COVID" state, depending on the mobility patterns, population number and health conditions. Reducing population size and restricting global travel help achieve zero-COVID-19. Specifically, when c 1 is smaller than 0.2, the ratio of people with low levels of mobility is larger than 80% and the population size is smaller than 400, zero-COVID can be achieved within fewer than 1000 time steps. In summary, the Mob-Cov model considers more realistic human mobility at a wide range of spatial scales, and has been designed with equal emphasis on performance, low simulation cost, accuracy, ease of use and flexibility. It is a useful tool for researchers and politicians to apply when investigating pandemic dynamics and when planning actions against disease. Supplementary Information The online version contains supplementary material available at 10.1007/s11071-023-08489-5.
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Affiliation(s)
- Kejie Chen
- School of Optoelectric Engineering and Instrumental Science, Dalian University of Technology, Dalian, 116024 China
| | - Xiaomo Jiang
- Provincial Key Lab of Digital Twin for Industrial Equipment, Dalian, 116024 China
- School of Energy and Power Engineering, Dalian, 116024 China
| | - Yanqing Li
- School of Optoelectric Engineering and Instrumental Science, Dalian University of Technology, Dalian, 116024 China
| | - Rongxin Zhou
- School of Optoelectric Engineering and Instrumental Science, Dalian University of Technology, Dalian, 116024 China
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Yabe T, Bueno BGB, Dong X, Pentland A, Moro E. Behavioral changes during the COVID-19 pandemic decreased income diversity of urban encounters. Nat Commun 2023; 14:2310. [PMID: 37085499 PMCID: PMC10120472 DOI: 10.1038/s41467-023-37913-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Accepted: 04/04/2023] [Indexed: 04/23/2023] Open
Abstract
Diversity of physical encounters in urban environments is known to spur economic productivity while also fostering social capital. However, mobility restrictions during the pandemic have forced people to reduce urban encounters, raising questions about the social implications of behavioral changes. In this paper, we study how individual income diversity of urban encounters changed during the pandemic, using a large-scale, privacy-enhanced mobility dataset of more than one million anonymized mobile phone users in Boston, Dallas, Los Angeles, and Seattle, across three years spanning before and during the pandemic. We find that the diversity of urban encounters has substantially decreased (by 15% to 30%) during the pandemic and has persisted through late 2021, even though aggregated mobility metrics have recovered to pre-pandemic levels. Counterfactual analyses show that behavioral changes including lower willingness to explore new places further decreased the diversity of encounters in the long term. Our findings provide implications for managing the trade-off between the stringency of COVID-19 policies and the diversity of urban encounters as we move beyond the pandemic.
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Affiliation(s)
- Takahiro Yabe
- Institute for Data, Systems, and Society, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.
| | | | - Xiaowen Dong
- Department of Engineering Science, University of Oxford, Oxford, OX2 6ED, UK
- Media Lab, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Alex Pentland
- Institute for Data, Systems, and Society, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
- Media Lab, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Esteban Moro
- Institute for Data, Systems, and Society, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.
- Media Lab, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.
- Grupo Interdisciplinar de Sistemas Complejos (GISC), Departamento de Matemáticas, Universidad Carlos III de Madrid, 28911, Leganés, Madrid, Spain.
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Cui X, Wang Y, Zhai J, Xue M, Zheng C, Yu L. Future trajectory of SARS-CoV-2: Constant spillover back and forth between humans and animals. Virus Res 2023; 328:199075. [PMID: 36805410 PMCID: PMC9972147 DOI: 10.1016/j.virusres.2023.199075] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Revised: 02/05/2023] [Accepted: 02/14/2023] [Indexed: 02/23/2023]
Abstract
SARS-CoV-2, known as severe acute respiratory syndrome coronavirus 2, is causing a massive global public health dilemma. In particular, the outbreak of the Omicron variants of SARS-CoV-2 in several countries has aroused the great attention of the World Health Organization (WHO). As of February 1st, 2023, the WHO had counted 671,016,135 confirmed cases and 6,835,595 deaths worldwide. Despite effective vaccines and drug treatments, there is currently no way to completely and directly eliminate SARS-CoV-2. Moreover, frequent cases of SARS-CoV-2 infection in animals have also been reported. In this review, we suggest that SARS-CoV-2, as a zoonotic virus, may be frequently transmitted between animals and humans in the future, which provides a reference and warning for rational prevention and control of COVID-19.
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Affiliation(s)
- Xinhua Cui
- State Key Laboratory of Human-Animal Zoonotic infectious Diseases, Key Laboratory of Zoonosis Research, Ministry of Education, College of Veterinary Medicine, Center of Infectious Diseases and Pathogen Biology, Department of Infectious Diseases, First Hospital of Jilin University, Changchun, China
| | - Yang Wang
- State Key Laboratory of Human-Animal Zoonotic infectious Diseases, Key Laboratory of Zoonosis Research, Ministry of Education, College of Veterinary Medicine, Center of Infectious Diseases and Pathogen Biology, Department of Infectious Diseases, First Hospital of Jilin University, Changchun, China
| | - Jingbo Zhai
- Medical College, Inner Mongolia Minzu University, Tongliao, China; Key Laboratory of Zoonose Prevention and Control at Universities of Inner Mongolia Autonomous Region, Tongliao, China
| | - Mengzhou Xue
- Department of Cerebrovascular Diseases, The Second Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
| | - Chunfu Zheng
- Institute of Animal Health, Guangdong Academy of Agricultural Sciences, Key Laboratory of Livestock Disease Prevention of Guangdong Province, Scientific Observation and Experiment Station of Veterinary Drugs and Diagnostic Techniques of Guangdong Province, Ministry of Agriculture and Rural Affairs, Guangzhou, China; Department of Microbiology, Immunology and Infectious Diseases, University of Calgary, Calgary, Alberta, Canada.
| | - Lu Yu
- State Key Laboratory of Human-Animal Zoonotic infectious Diseases, Key Laboratory of Zoonosis Research, Ministry of Education, College of Veterinary Medicine, Center of Infectious Diseases and Pathogen Biology, Department of Infectious Diseases, First Hospital of Jilin University, Changchun, China.
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42
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Meng X, Lin J, Fan Y, Gao F, Fenoaltea EM, Cai Z, Si S. Coupled disease-vaccination behavior dynamic analysis and its application in COVID-19 pandemic. CHAOS, SOLITONS, AND FRACTALS 2023; 169:113294. [PMID: 36891356 PMCID: PMC9977628 DOI: 10.1016/j.chaos.2023.113294] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Revised: 01/20/2023] [Accepted: 02/19/2023] [Indexed: 06/18/2023]
Abstract
Predicting the evolutionary dynamics of the COVID-19 pandemic is a complex challenge. The complexity increases when the vaccination process dynamic is also considered. In addition, when applying a voluntary vaccination policy, the simultaneous behavioral evolution of individuals who decide whether and when to vaccinate must be included. In this paper, a coupled disease-vaccination behavior dynamic model is introduced to study the coevolution of individual vaccination strategies and infection spreading. We study disease transmission by a mean-field compartment model and introduce a non-linear infection rate that takes into account the simultaneity of interactions. Besides, the evolutionary game theory is used to investigate the contemporary evolution of vaccination strategies. Our findings suggest that sharing information with the entire population about the negative and positive consequences of infection and vaccination is beneficial as it boosts behaviors that can reduce the final epidemic size. Finally, we validate our transmission mechanism on real data from the COVID-19 pandemic in France.
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Affiliation(s)
- Xueyu Meng
- Department of Industrial Engineering, School of Mechanical Engineering, Northwestern Polytechnical University, Xi'an 710072, China
- Ministry of Industry and Information Technology Key Laboratory of Industrial Engineering and Intelligent Manufacturing, Northwestern Polytechnical University, Xi'an 710072, China
- Department of Physics, University of Fribourg, Fribourg 1700, Switzerland
| | - Jianhong Lin
- Department of Physics, University of Fribourg, Fribourg 1700, Switzerland
- Department of Management, Technology and Economics, ETH Zürich, Scheuchzerstrasse 7, CH-8092 Zürich, Switzerland
| | - Yufei Fan
- Department of Industrial Engineering, School of Mechanical Engineering, Northwestern Polytechnical University, Xi'an 710072, China
- Ministry of Industry and Information Technology Key Laboratory of Industrial Engineering and Intelligent Manufacturing, Northwestern Polytechnical University, Xi'an 710072, China
| | - Fujuan Gao
- Department of Physics, University of Fribourg, Fribourg 1700, Switzerland
| | | | - Zhiqiang Cai
- Department of Industrial Engineering, School of Mechanical Engineering, Northwestern Polytechnical University, Xi'an 710072, China
- Ministry of Industry and Information Technology Key Laboratory of Industrial Engineering and Intelligent Manufacturing, Northwestern Polytechnical University, Xi'an 710072, China
| | - Shubin Si
- Department of Industrial Engineering, School of Mechanical Engineering, Northwestern Polytechnical University, Xi'an 710072, China
- Ministry of Industry and Information Technology Key Laboratory of Industrial Engineering and Intelligent Manufacturing, Northwestern Polytechnical University, Xi'an 710072, China
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43
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Berec L, Diviák T, Kuběna A, Levínský R, Neruda R, Suchopárová G, Šlerka J, Šmíd M, Trnka J, Tuček V, Vidnerová P, Zajíček M. On the contact tracing for COVID-19: A simulation study ☆. Epidemics 2023; 43:100677. [PMID: 36989916 PMCID: PMC10019035 DOI: 10.1016/j.epidem.2023.100677] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Revised: 03/05/2023] [Accepted: 03/06/2023] [Indexed: 03/18/2023] Open
Abstract
Background: Contact tracing is one of the most effective non-pharmaceutical interventions in the COVID-19 pandemic. This study uses a multi-agent model to investigate the impact of four types of contact tracing strategies to prevent the spread of COVID-19. Methods: In order to analyse individual contact tracing in a reasonably realistic setup, we construct an agent-based model of a small municipality with about 60.000 inhabitants (nodes) and about 2.8 million social contacts (edges) in 30 different layers. Those layers reflect demographic, geographic, sociological and other patterns of the TTWA (Travel-to-work-area) Hodonín in Czechia. Various data sources such as census, land register, transport data or data reflecting the shopping behaviour, were employed to meet this purpose. On this multi-graph structure we run a modified SEIR model of the COVID-19 dynamics. The parameters of the model are calibrated on data from the outbreak in the Czech Republic in the period March to June 2020. The simplest type of contact tracing follows just the family, the second tracing version tracks the family and all the work contacts, the third type finds all contacts with the family, work contacts and friends (leisure activities). The last one is a complete (digital) tracing capable of recalling any and all contacts. We evaluate the performance of these contact tracing strategies in four different environments. First, we consider an environment without any contact restrictions (benchmark); second with strict contact restriction (replicating the stringent non-pharmaceutical interventions employed in Czechia in the spring 2020); third environment, where the measures were substantially relaxed, and, finally an environment with weak contact restrictions and superspreader events (replicating the situation in Czechia in the summer 2020). Findings: There are four main findings in our paper. 1. In general, local closures are more effective than any type of tracing. 2. In an environment with strict contact restrictions there are only small differences among the four contact tracing strategies. 3. In an environment with relaxed contact restrictions the effectiveness of the tracing strategies differs substantially. 4. In the presence of superspreader events only complete contact tracing can stop the epidemic. Interpretation: In situations, where many other non-pharmaceutical interventions are in place, the specific extent of contact tracing may not have a large influence on their effectiveness. In a more relaxed setting with few contact restrictions and larger events the effectiveness of contact tracing depends heavily on their extent.
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Affiliation(s)
- Luděk Berec
- Centre for Modelling of Biological and Social Processes, Na Břehu 497/15, 190 00 Praha 9, Czech Republic,Centre for Mathematical Biology, Institute of Mathematics, Faculty of Science, University of South Bohemia, Branišovská 1760, 37005 České Budějovice, Czech Republic,Czech Academy of Sciences, Biology Centre, Institute of Entomology, Department of Ecology, Branišovská 31, 37005, České Budějovice, Czech Republic
| | - Tomáš Diviák
- Centre for Modelling of Biological and Social Processes, Na Břehu 497/15, 190 00 Praha 9, Czech Republic,Department of Criminology and Mitchell Centre for Social Network Analysis, School of Social Sciences, University of Manchester, Oxford Rd, Manchester, UK
| | - Aleš Kuběna
- Centre for Modelling of Biological and Social Processes, Na Břehu 497/15, 190 00 Praha 9, Czech Republic,The Czech Academy of Sciences, Institute of Information Theory and Automation, Pod Vodárenskou věží 4, 18200 Praha 8, Czech Republic
| | - René Levínský
- Centre for Modelling of Biological and Social Processes, Na Břehu 497/15, 190 00 Praha 9, Czech Republic,CERGE-EI, Politických vězňů 7, 11121 Praha 1, Czech Republic,New Media Studies, Faculty of Arts, Charles University, Na Pří kopě 29, 110 00 Praha 1, Czech Republic
| | - Roman Neruda
- Centre for Modelling of Biological and Social Processes, Na Břehu 497/15, 190 00 Praha 9, Czech Republic,The Czech Academy of Sciences, Institute of Computer Science, Pod Vodárenskou věží 2, 18200 Praha 8, Czech Republic,Corresponding author at: The Czech Academy of Sciences, Institute of Computer Science, Pod Vodárenskou věží 2, 18200 Praha 8, Czech Republic
| | - Gabriela Suchopárová
- Centre for Modelling of Biological and Social Processes, Na Břehu 497/15, 190 00 Praha 9, Czech Republic,The Czech Academy of Sciences, Institute of Computer Science, Pod Vodárenskou věží 2, 18200 Praha 8, Czech Republic
| | - Josef Šlerka
- Centre for Modelling of Biological and Social Processes, Na Břehu 497/15, 190 00 Praha 9, Czech Republic,The Czech Academy of Sciences, Institute of Information Theory and Automation, Pod Vodárenskou věží 4, 18200 Praha 8, Czech Republic
| | - Martin Šmíd
- Centre for Modelling of Biological and Social Processes, Na Břehu 497/15, 190 00 Praha 9, Czech Republic,New Media Studies, Faculty of Arts, Charles University, Na Pří kopě 29, 110 00 Praha 1, Czech Republic
| | - Jan Trnka
- Centre for Modelling of Biological and Social Processes, Na Břehu 497/15, 190 00 Praha 9, Czech Republic,Department of Biochemistry, Cell and Molecular Biology, Third Faculty of Medicine, Charles University, Ruská 87, 100 00 Praha 10, Czech Republic
| | - Vít Tuček
- Centre for Modelling of Biological and Social Processes, Na Břehu 497/15, 190 00 Praha 9, Czech Republic,Department of Mathematics, University of Zagreb, Bijenička 30, 10000 Zagreb, Croatia
| | - Petra Vidnerová
- Centre for Modelling of Biological and Social Processes, Na Břehu 497/15, 190 00 Praha 9, Czech Republic,The Czech Academy of Sciences, Institute of Computer Science, Pod Vodárenskou věží 2, 18200 Praha 8, Czech Republic
| | - Milan Zajíček
- Centre for Modelling of Biological and Social Processes, Na Břehu 497/15, 190 00 Praha 9, Czech Republic,The Czech Academy of Sciences, Institute of Information Theory and Automation, Pod Vodárenskou věží 4, 18200 Praha 8, Czech Republic
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44
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Michalska-Smith M, Enns EA, White LA, Gilbertson MLJ, Craft ME. The illusion of personal health decisions for infectious disease management: disease spread in social contact networks. ROYAL SOCIETY OPEN SCIENCE 2023; 10:221122. [PMID: 36998767 PMCID: PMC10049757 DOI: 10.1098/rsos.221122] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Accepted: 03/06/2023] [Indexed: 06/19/2023]
Abstract
Close contacts between individuals provide opportunities for the transmission of diseases, including COVID-19. While individuals take part in many different types of interactions, including those with classmates, co-workers and household members, it is the conglomeration of all of these interactions that produces the complex social contact network interconnecting individuals across the population. Thus, while an individual might decide their own risk tolerance in response to a threat of infection, the consequences of such decisions are rarely so confined, propagating far beyond any one person. We assess the effect of different population-level risk-tolerance regimes, population structure in the form of age and household-size distributions, and different interaction types on epidemic spread in plausible human contact networks to gain insight into how contact network structure affects pathogen spread through a population. In particular, we find that behavioural changes by vulnerable individuals in isolation are insufficient to reduce those individuals' infection risk and that population structure can have varied and counteracting effects on epidemic outcomes. The relative impact of each interaction type was contingent on assumptions underlying contact network construction, stressing the importance of empirical validation. Taken together, these results promote a nuanced understanding of disease spread on contact networks, with implications for public health strategies.
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Affiliation(s)
- Matthew Michalska-Smith
- Department of Ecology, Evolution and behavior, University of Minnesota, Minneapolis, MN, USA
- Department of Plant Pathology, University of Minnesota, Minneapolis, MN, USA
| | - Eva A. Enns
- School of Public Health, University of Minnesota, Minneapolis, MN, USA
| | - Lauren A. White
- National Socio-Environmental Synthesis Center, University of Maryland, Annapolis, MD, USA
| | - Marie L. J. Gilbertson
- Department of Veterinary Population Medicine, University of Minnesota, Minneapolis, MN, USA
| | - Meggan E. Craft
- Department of Ecology, Evolution and behavior, University of Minnesota, Minneapolis, MN, USA
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45
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Bosman M, Esteve A, Gabbanelli L, Jordan X, López-Gay A, Manera M, Martínez M, Masjuan P, Mir L, Paradells J, Pignatelli A, Riu I, Vitagliano V. Stochastic simulation of successive waves of COVID-19 in the province of Barcelona. Infect Dis Model 2023; 8:145-158. [PMID: 36589597 PMCID: PMC9792425 DOI: 10.1016/j.idm.2022.12.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Revised: 12/21/2022] [Accepted: 12/22/2022] [Indexed: 12/29/2022] Open
Abstract
Analytic compartmental models are currently used in mathematical epidemiology to forecast the COVID-19 pandemic evolution and explore the impact of mitigation strategies. In general, such models treat the population as a single entity, losing the social, cultural and economical specificities. We present a network model that uses socio-demographic datasets with the highest available granularity to predict the spread of COVID-19 in the province of Barcelona. The model is flexible enough to incorporate the effect of containment policies, such as lockdowns or the use of protective masks, and can be easily adapted to future epidemics. We follow a stochastic approach that combines a compartmental model with detailed individual microdata from the population census, including social determinants and age-dependent strata, and time-dependent mobility information. We show that our model reproduces the dynamical features of the disease across two waves and demonstrates its capability to become a powerful tool for simulating epidemic events.
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Affiliation(s)
- M. Bosman
- Institut de Física d’Altes Energies (IFAE), The Barcelona Institute of Science and Technology, Barcelona, Spain
- Corresponding author.
| | - A. Esteve
- Centre d’Estudis Demogràfics (CED-CERCA), Barcelona, Spain
- Serra Húnter Fellow, Departament de Ciències Polítiques i Socials, Universitat Pompeu Fabra, Barcelona, Spain
| | - L. Gabbanelli
- Institut de Física d’Altes Energies (IFAE), The Barcelona Institute of Science and Technology, Barcelona, Spain
| | - X. Jordan
- i2CAT Foundation, Edifici Nexus (Campus Nord UPC), Barcelona, Spain
| | - A. López-Gay
- Centre d’Estudis Demogràfics (CED-CERCA), Barcelona, Spain
- Departament de Geografia, Universitat Autònoma de Barcelona, Bellaterra, Spain
| | - M. Manera
- Institut de Física d’Altes Energies (IFAE), The Barcelona Institute of Science and Technology, Barcelona, Spain
- Serra Húnter Fellow, Departament de Física, Universitat Autònoma de Barcelona, Bellaterra, Spain
| | - M. Martínez
- Institut de Física d’Altes Energies (IFAE), The Barcelona Institute of Science and Technology, Barcelona, Spain
- Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Spain
| | - P. Masjuan
- Institut de Física d’Altes Energies (IFAE), The Barcelona Institute of Science and Technology, Barcelona, Spain
- Departament de Física, Universitat Autònoma de Barcelona, Bellaterra, Spain
| | - Ll.M. Mir
- Institut de Física d’Altes Energies (IFAE), The Barcelona Institute of Science and Technology, Barcelona, Spain
| | - J. Paradells
- i2CAT Foundation, Edifici Nexus (Campus Nord UPC), Barcelona, Spain
- Departament d’Enginyeria Telemàtica, Universitat Politècnica de Catalunya, Barcelona, Spain
| | - A. Pignatelli
- Institut de Física d’Altes Energies (IFAE), The Barcelona Institute of Science and Technology, Barcelona, Spain
| | - I. Riu
- Institut de Física d’Altes Energies (IFAE), The Barcelona Institute of Science and Technology, Barcelona, Spain
| | - V. Vitagliano
- Institut de Física d’Altes Energies (IFAE), The Barcelona Institute of Science and Technology, Barcelona, Spain
- DIME, University of Genova, Via all’Opera Pia 15, 16145, Genova, Italy
- INFN, Sezione di Genova, via Dodecaneso 33, 16146, Genoa, Italy
- Department of Mathematics and Physics, University of Hull, Kingston upon Hull, HU6 7RX, UK
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46
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Pozo-Martin F, Beltran Sanchez MA, Müller SA, Diaconu V, Weil K, El Bcheraoui C. Comparative effectiveness of contact tracing interventions in the context of the COVID-19 pandemic: a systematic review. Eur J Epidemiol 2023; 38:243-266. [PMID: 36795349 PMCID: PMC9932408 DOI: 10.1007/s10654-023-00963-z] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Accepted: 12/31/2022] [Indexed: 02/17/2023]
Abstract
Contact tracing is a non-pharmaceutical intervention (NPI) widely used in the control of the COVID-19 pandemic. Its effectiveness may depend on a number of factors including the proportion of contacts traced, delays in tracing, the mode of contact tracing (e.g. forward, backward or bidirectional contact training), the types of contacts who are traced (e.g. contacts of index cases or contacts of contacts of index cases), or the setting where contacts are traced (e.g. the household or the workplace). We performed a systematic review of the evidence regarding the comparative effectiveness of contact tracing interventions. 78 studies were included in the review, 12 observational (ten ecological studies, one retrospective cohort study and one pre-post study with two patient cohorts) and 66 mathematical modelling studies. Based on the results from six of the 12 observational studies, contact tracing can be effective at controlling COVID-19. Two high quality ecological studies showed the incremental effectiveness of adding digital contact tracing to manual contact tracing. One ecological study of intermediate quality showed that increases in contact tracing were associated with a drop in COVID-19 mortality, and a pre-post study of acceptable quality showed that prompt contact tracing of contacts of COVID-19 case clusters / symptomatic individuals led to a reduction in the reproduction number R. Within the seven observational studies exploring the effectiveness of contact tracing in the context of the implementation of other non-pharmaceutical interventions, contact tracing was found to have an effect on COVID-19 epidemic control in two studies and not in the remaining five studies. However, a limitation in many of these studies is the lack of description of the extent of implementation of contact tracing interventions. Based on the results from the mathematical modelling studies, we identified the following highly effective policies: (1) manual contact tracing with high tracing coverage and either medium-term immunity, highly efficacious isolation/quarantine and/ or physical distancing (2) hybrid manual and digital contact tracing with high app adoption with highly effective isolation/ quarantine and social distancing, (3) secondary contact tracing, (4) eliminating contact tracing delays, (5) bidirectional contact tracing, (6) contact tracing with high coverage in reopening educational institutions. We also highlighted the role of social distancing to enhance the effectiveness of some of these interventions in the context of 2020 lockdown reopening. While limited, the evidence from observational studies shows a role for manual and digital contact tracing in controlling the COVID-19 epidemic. More empirical studies accounting for the extent of contact tracing implementation are required.
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Affiliation(s)
- Francisco Pozo-Martin
- Evidence-based Public Health Unit, Centre for International Health Protection, Robert Koch Institute, Nordufer 20, 13353, Berlin, Germany.
| | | | - Sophie Alice Müller
- Centre for International Health Protection, Robert Koch Institute, Nordufer 20, 13353, Berlin, Germany
| | - Viorela Diaconu
- Evidence-based Public Health Unit, Centre for International Health Protection, Robert Koch Institute, Nordufer 20, 13353, Berlin, Germany
| | - Kilian Weil
- Evidence-based Public Health Unit, Centre for International Health Protection, Robert Koch Institute, Nordufer 20, 13353, Berlin, Germany
| | - Charbel El Bcheraoui
- Evidence-based Public Health Unit, Centre for International Health Protection, Robert Koch Institute, Nordufer 20, 13353, Berlin, Germany
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47
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Gibson LA, Dixon EL, Sharif MA, Rodriguez AC, Cappella JN. Impact of Privacy Messaging on COVID-19 Exposure Notification App Downloads: Evidence From a Randomized Experiment. AJPM FOCUS 2023; 2:100059. [PMID: 36573173 PMCID: PMC9771837 DOI: 10.1016/j.focus.2022.100059] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Introduction Digital contact-tracing smartphone apps have the potential to slow the spread of disease but are not widely used. We tested whether messages describing how a COVID-19 digital contact-tracing app protects users' privacy led to increased or decreased intentions to download the app by either calming privacy concerns or increasing their saliency. Design Randomized controlled trial. Setting/participants We recruited adult smartphone owners in the U.S. (oversampled for younger adults aged 18-34 years) in November 2020 through an online panel. Intervention Survey software randomly assigned 860 participants to 1 of 2 parallel messaging conditions (n=430 privacy assured, n=430 no privacy described). Main outcome measures 4-point scale of intention to use the app "if public health officials released a COVID Exposure Notification app in their state" that averaged likelihood to (1) download and install the app on their phone; (2) keep the app active on their phone; and (3) keep Bluetooth active on their phone (needed for the app to work). Results After removing incompletes, those who failed the manipulation checks, or those who had already downloaded a COVID-19 digital contact-tracing app, we analyzed 671 participants (n=330 privacy, n=341 no privacy) in 2021. There was no relationship between privacy condition and download intention (meanprivacy=2.69, meannoprivacy=2.69, b=0.01, 95% CI= -0.13, 0.15, p=0.922) but also no evidence that describing the app's security increased context-dependent privacy concerns (measured 3 ways). Instead, we found increased endorsement of data security in the privacy condition using a scale of beliefs about the app keeping privacy secure (meanprivacy=2.74, meannoprivacy=2.58, b=0.16, 95% CI=0.04, 0.28, p=0.009, small effect ω2=0.009). Conclusions This study provides some evidence that people developing contact-tracing messaging campaigns do not need to worry that describing a digital contact-tracing app's privacy protections will backfire. Future mixed methods testing of messages about who has access to information-and for how long-may uncover new communication strategies to increase public trust in contact-tracing apps. Trial registration This study is registered with AsPredicted#51826.
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Affiliation(s)
- Laura A. Gibson
- Department of Medical Ethics and Health Policy, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania,Address correspondence to: Laura A. Gibson, PhD, Department of Medical Ethics and Health Policy, Perelman School of Medicine, University of Pennsylvania, 1105B Blockley Hall, 423 Guardian Drive, Philadelphia PA 19104
| | - Erica L. Dixon
- Department of Medical Ethics and Health Policy, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Marissa A. Sharif
- Marketing Department, Wharton School, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Anyara C. Rodriguez
- Department of Medical Ethics and Health Policy, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Joseph N. Cappella
- Annenberg School for Communication, University of Pennsylvania, Philadelphia, Pennsylvania
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48
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Agusto FB, Numfor E, Srinivasan K, Iboi EA, Fulk A, Saint Onge JM, Peterson AT. Impact of public sentiments on the transmission of COVID-19 across a geographical gradient. PeerJ 2023; 11:e14736. [PMID: 36819996 PMCID: PMC9938658 DOI: 10.7717/peerj.14736] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2021] [Accepted: 12/21/2022] [Indexed: 02/17/2023] Open
Abstract
COVID-19 is a respiratory disease caused by a recently discovered, novel coronavirus, SARS-COV-2. The disease has led to over 81 million confirmed cases of COVID-19, with close to two million deaths. In the current social climate, the risk of COVID-19 infection is driven by individual and public perception of risk and sentiments. A number of factors influences public perception, including an individual's belief system, prior knowledge about a disease and information about a disease. In this article, we develop a model for COVID-19 using a system of ordinary differential equations following the natural history of the infection. The model uniquely incorporates social behavioral aspects such as quarantine and quarantine violation. The model is further driven by people's sentiments (positive and negative) which accounts for the influence of disinformation. People's sentiments were obtained by parsing through and analyzing COVID-19 related tweets from Twitter, a social media platform across six countries. Our results show that our model incorporating public sentiments is able to capture the trend in the trajectory of the epidemic curve of the reported cases. Furthermore, our results show that positive public sentiments reduce disease burden in the community. Our results also show that quarantine violation and early discharge of the infected population amplifies the disease burden on the community. Hence, it is important to account for public sentiment and individual social behavior in epidemic models developed to study diseases like COVID-19.
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Affiliation(s)
| | - Eric Numfor
- Augusta University, Augusta, Georgia, United States
| | | | | | | | - Jarron M. Saint Onge
- University of Kansas, Lawrence, Kansas, United States,University of Kansas Medical Center, Kansas City, Kansas, United States
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49
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Forecasting hospital-level COVID-19 admissions using real-time mobility data. COMMUNICATIONS MEDICINE 2023; 3:25. [PMID: 36788347 PMCID: PMC9927044 DOI: 10.1038/s43856-023-00253-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Accepted: 01/31/2023] [Indexed: 02/16/2023] Open
Abstract
BACKGROUND For each of the COVID-19 pandemic waves, hospitals have had to plan for deploying surge capacity and resources to manage large but transient increases in COVID-19 admissions. While a lot of effort has gone into predicting regional trends in COVID-19 cases and hospitalizations, there are far fewer successful tools for creating accurate hospital-level forecasts. METHODS Large-scale, anonymized mobile phone data has been shown to correlate with regional case counts during the first two waves of the pandemic (spring 2020, and fall/winter 2021). Building off this success, we developed a multi-step, recursive forecasting model to predict individual hospital admissions; this model incorporates the following data: (i) hospital-level COVID-19 admissions, (ii) statewide test positivity data, and (iii) aggregate measures of large-scale human mobility, contact patterns, and commuting volume. RESULTS Incorporating large-scale, aggregate mobility data as exogenous variables in prediction models allows us to make hospital-specific COVID-19 admission forecasts 21 days ahead. We show this through highly accurate predictions of hospital admissions for five hospitals in Massachusetts during the first year of the COVID-19 pandemic. CONCLUSIONS The high predictive capability of the model was achieved by combining anonymized, aggregated mobile device data about users' contact patterns, commuting volume, and mobility range with COVID hospitalizations and test-positivity data. Mobility-informed forecasting models can increase the lead-time of accurate predictions for individual hospitals, giving managers valuable time to strategize how best to allocate resources to manage forthcoming surges.
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Wang B, Liang B, Chen Q, Wang S, Wang S, Huang Z, Long Y, Wu Q, Xu S, Jinna P, Yang F, Ming WK, Liu Q. COVID-19 Related Early Google Search Behavior and Health Communication in the United States: Panel Data Analysis on Health Measures. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:3007. [PMID: 36833701 PMCID: PMC9958808 DOI: 10.3390/ijerph20043007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/26/2022] [Revised: 01/20/2023] [Accepted: 02/04/2023] [Indexed: 06/18/2023]
Abstract
The COVID-19 outbreak at the end of December 2019 spread rapidly all around the world. The objective of this study is to investigate and understand the relationship between public health measures and the development of the pandemic through Google search behaviors in the United States. Our collected data includes Google search queries related to COVID-19 from 1 January to 4 April 2020. After using unit root tests (ADF test and PP test) to examine the stationary and a Hausman test to choose a random effect model, a panel data analysis is conducted to investigate the key query terms with the newly added cases. In addition, a full sample regression and two sub-sample regressions are proposed to explain: (1) The changes in COVID-19 cases number are partly related to search variables related to treatments and medical resources, such as ventilators, hospitals, and masks, which correlate positively with the number of new cases. In contrast, regarding public health measures, social distancing, lockdown, stay-at-home, and self-isolation measures were negatively associated with the number of new cases in the US. (2) In mild states, which ranked one to twenty by the average daily new cases from least to most in 50 states, the query terms about public health measures (quarantine, lockdown, and self-isolation) have a significant negative correlation with the number of new cases. However, only the query terms about lockdown and self-isolation are also negatively associated with the number of new cases in serious states (states ranking 31 to 50). Furthermore, public health measures taken by the government during the COVID-19 outbreak are closely related to the situation of controlling the pandemic.
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Affiliation(s)
- Binhui Wang
- School of Management, Jinan University, Guangzhou 510632, China
| | - Beiting Liang
- College of Economics, Jinan University, Guangzhou 510632, China
| | - Qiuyi Chen
- School of Journalism, Fudan University, Shanghai 200433, China
| | - Shu Wang
- Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China
- Laboratory of Biomass and Green Technologies, Gembloux Agro-Bio Tech, University of Liège, 5030 Gembloux, Belgium
| | - Siyi Wang
- Department of Public Health and Preventive Medicine, School of Medicine, Jinan University, Guangzhou 510632, China
| | - Zhongguo Huang
- Department of Public Health and Preventive Medicine, School of Medicine, Jinan University, Guangzhou 510632, China
| | - Yi Long
- Law School of Artificial Intelligence, Shanghai University of Political Science and Law, Shanghai 201701, China
| | - Qili Wu
- School of Journalism and Communication, Jinan University National Media Experimental Teaching Demonstration Center, Jinan University, Guangzhou 510632, China
| | - Shulin Xu
- School of Economic, Guangzhou College of Commerce, Guangzhou 511363, China
| | - Pranay Jinna
- School of Business, University at Albany, State University of New York, Albany, NY 12222, USA
| | - Fan Yang
- Communication Department, University at Albany, State University of New York, Albany, NY 12222, USA
| | - Wai-Kit Ming
- Department of Public Health and Preventive Medicine, School of Medicine, Jinan University, Guangzhou 510632, China
- Department of Infectious Diseases and Public Health, Jockey Club College of Veterinary Medicine and Life Science, City University of Hong Kong, Hong Kong SAR, China
| | - Qian Liu
- School of Journalism and Communication, Jinan University National Media Experimental Teaching Demonstration Center, Jinan University, Guangzhou 510632, China
- School of Business, University at Albany, State University of New York, Albany, NY 12222, USA
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