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Koichubekov B, Takuadina A, Korshukov I, Sorokina M, Turmukhambetova A. The Epidemiological and Economic Impact of COVID-19 in Kazakhstan: An Agent-Based Modeling. Healthcare (Basel) 2023; 11:2968. [PMID: 37998460 PMCID: PMC10671669 DOI: 10.3390/healthcare11222968] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2023] [Revised: 11/09/2023] [Accepted: 11/09/2023] [Indexed: 11/25/2023] Open
Abstract
BACKGROUND Our study aimed to assess how effective the preventative measures taken by the state authorities during the pandemic were in terms of public health protection and the rational use of material and human resources. MATERIALS AND METHODS We utilized a stochastic agent-based model for COVID-19's spread combined with the WHO-recommended COVID-ESFT version 2.0 tool for material and labor cost estimation. RESULTS Our long-term forecasts (up to 50 days) showed satisfactory results with a steady trend in the total cases. However, the short-term forecasts (up to 10 days) were more accurate during periods of relative stability interrupted by sudden outbreaks. The simulations indicated that the infection's spread was highest within families, with most COVID-19 cases occurring in the 26-59 age group. Government interventions resulted in 3.2 times fewer cases in Karaganda than predicted under a "no intervention" scenario, yielding an estimated economic benefit of 40%. CONCLUSION The combined tool we propose can accurately forecast the progression of the infection, enabling health organizations to allocate specialists and material resources in a timely manner.
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Affiliation(s)
- Berik Koichubekov
- Department of Informatics and Biostatistics, Karaganda Medical University, Gogol St. 40, Karaganda 100008, Kazakhstan; (A.T.); (I.K.); (M.S.)
| | - Aliya Takuadina
- Department of Informatics and Biostatistics, Karaganda Medical University, Gogol St. 40, Karaganda 100008, Kazakhstan; (A.T.); (I.K.); (M.S.)
| | - Ilya Korshukov
- Department of Informatics and Biostatistics, Karaganda Medical University, Gogol St. 40, Karaganda 100008, Kazakhstan; (A.T.); (I.K.); (M.S.)
| | - Marina Sorokina
- Department of Informatics and Biostatistics, Karaganda Medical University, Gogol St. 40, Karaganda 100008, Kazakhstan; (A.T.); (I.K.); (M.S.)
| | - Anar Turmukhambetova
- Institute of Life Sciences, Karaganda Medical University, Gogol St. 40, Karaganda 100008, Kazakhstan;
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Musa SS, Abdullahi ZU, Zhao S, Bello UM, Hussaini N, Habib AG, He D. Transmission Dynamics of Monkeypox Virus in Nigeria during the Current COVID-19 Pandemic and Estimation of Effective Reproduction Number. Vaccines (Basel) 2022; 10. [PMID: 36560564 DOI: 10.3390/vaccines10122153] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Revised: 12/05/2022] [Accepted: 12/10/2022] [Indexed: 12/23/2022] Open
Abstract
Monkeypox virus (MPXV) continues to pose severe threats to global public health, especially in non-endemic areas. Like all other regions, Africa faces potential public health crises due to the ongoing COVID-19 pandemic and other infectious disease outbreaks (such as Lassa fever and malaria) that have devastated the region and overwhelmed the healthcare systems. Owing to the recent surge in the MPXV and other infections, the COVID-19-control efforts could deteriorate and further worsen. This study discusses the potential emergencies of MPXV transmission during the current COVID-19 pandemic. We hypothesize some of the underlying drivers that possibly resulted in an increase in rodent-to-human interaction, such as the COVID-19 pandemic's impact and other human behavioral or environmental factors. Furthermore, we estimate the MPXV time-varying effective reproduction number (Rt) based on case notification in Nigeria. We find that Rt reached a peak in 2022 with a mean of 1.924 (95% CrI: 1.455, 2.485) and a median of 1.921 (95% CrI: 1.450, 2.482). We argue that the real-time monitoring of Rt is practical and can give public health authorities crucial data for circumstantial awareness and strategy recalibration. We also emphasize the need to improve awareness programs and the provision of adequate health care resources to suppress the outbreaks. These could also help to increase the reporting rate and, in turn, prevent large community transmission of the MPXV in Nigeria and beyond.
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Ryu S, Chun JY, Lee S, Yoo D, Kim Y, Ali ST, Chun BC. Epidemiology and Transmission Dynamics of Infectious Diseases and Control Measures. Viruses 2022; 14. [PMID: 36423119 DOI: 10.3390/v14112510] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2022] [Revised: 11/10/2022] [Accepted: 11/10/2022] [Indexed: 11/16/2022] Open
Abstract
The epidemiology and transmission dynamics of infectious diseases must be understood at the individual and community levels to improve public health decision-making for real-time and integrated community-based control strategies. Herein, we explore the epidemiological characteristics for assessing the impact of public health interventions in the community setting and their applications. Computational statistical methods could advance research on infectious disease epidemiology and accumulate scientific evidence of the potential impacts of pharmaceutical/nonpharmaceutical measures to mitigate or control infectious diseases in the community. Novel public health threats from emerging zoonotic infectious diseases are urgent issues. Given these direct and indirect mitigating impacts at various levels to different infectious diseases and their burdens, we must consider an integrated assessment approach, 'One Health', to understand the dynamics and control of infectious diseases.
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Challen R, Brooks-Pollock E, Tsaneva-Atanasova K, Danon L. Meta-analysis of the severe acute respiratory syndrome coronavirus 2 serial intervals and the impact of parameter uncertainty on the coronavirus disease 2019 reproduction number. Stat Methods Med Res 2022; 31:1686-1703. [PMID: 34931917 PMCID: PMC9465543 DOI: 10.1177/09622802211065159] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
The serial interval of an infectious disease, commonly interpreted as the time between the onset of symptoms in sequentially infected individuals within a chain of transmission, is a key epidemiological quantity involved in estimating the reproduction number. The serial interval is closely related to other key quantities, including the incubation period, the generation interval (the time between sequential infections), and time delays between infection and the observations associated with monitoring an outbreak such as confirmed cases, hospital admissions, and deaths. Estimates of these quantities are often based on small data sets from early contact tracing and are subject to considerable uncertainty, which is especially true for early coronavirus disease 2019 data. In this paper, we estimate these key quantities in the context of coronavirus disease 2019 for the UK, including a meta-analysis of early estimates of the serial interval. We estimate distributions for the serial interval with a mean of 5.9 (95% CI 5.2; 6.7) and SD 4.1 (95% CI 3.8; 4.7) days (empirical distribution), the generation interval with a mean of 4.9 (95% CI 4.2; 5.5) and SD 2.0 (95% CI 0.5; 3.2) days (fitted gamma distribution), and the incubation period with a mean 5.2 (95% CI 4.9; 5.5) and SD 5.5 (95% CI 5.1; 5.9) days (fitted log-normal distribution). We quantify the impact of the uncertainty surrounding the serial interval, generation interval, incubation period, and time delays, on the subsequent estimation of the reproduction number, when pragmatic and more formal approaches are taken. These estimates place empirical bounds on the estimates of most relevant model parameters and are expected to contribute to modeling coronavirus disease 2019 transmission.
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Affiliation(s)
- Robert Challen
- EPSRC Centre for Predictive Modelling in Healthcare, 3286University of Exeter, UK
- 7852Somerset NHS Foundation Trust, UK
- Joint Universities Pandemic and Epidemiological Research (JUNIPER) consortium, UK
| | - Ellen Brooks-Pollock
- Joint Universities Pandemic and Epidemiological Research (JUNIPER) consortium, UK
- 152331Bristol Medical School, Population Health Sciences, 1980University of Bristol, UK
| | - Krasimira Tsaneva-Atanasova
- EPSRC Centre for Predictive Modelling in Healthcare, 3286University of Exeter, UK
- 522468The Alan Turing Institute, British Library, UK
- Data Science Institute, 151756College of Engineering, Mathematics and Physical Sciences, 3286University of Exeter, UK
| | - Leon Danon
- 152331Bristol Medical School, Population Health Sciences, 1980University of Bristol, UK
- 522468The Alan Turing Institute, British Library, UK
- Data Science Institute, 151756College of Engineering, Mathematics and Physical Sciences, 3286University of Exeter, UK
- Department of Engineering Mathematics, 1980University of Bristol, UK
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5
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Cascante-Vega J, Cordovez JM, Santos-Vega M. Estimating and forecasting the burden and spread of Colombia's SARS-CoV2 first wave. Sci Rep 2022; 12:13568. [PMID: 35945249 PMCID: PMC9427755 DOI: 10.1038/s41598-022-15514-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2021] [Accepted: 06/24/2022] [Indexed: 12/03/2022] Open
Abstract
Following the rapid dissemination of COVID-19 cases in Colombia in 2020, large-scale non-pharmaceutical interventions (NPIs) were implemented as national emergencies in most of the country’s municipalities, starting with a lockdown on March 20th, 2020. Recently, approaches that combine movement data (measured as the number of commuters between units), metapopulation models to describe disease dynamics subdividing the population into Susceptible-Exposed-Asymptomatic-Infected-Recovered-Diseased and statistical inference algorithms have been pointed as a practical approach to both nowcast and forecast the number of cases and deaths. We used an iterated filtering (IF) framework to estimate the model transmission parameters using the reported data across 281 municipalities from March to late October in locations with more than 50 reported deaths and cases in Colombia. Since the model is high dimensional (6 state variables in every municipality), inference on those parameters is highly non-trivial, so we used an Ensemble-Adjustment-Kalman-Filter (EAKF) to estimate time variable system states and parameters. Our results show the model’s ability to capture the characteristics of the outbreak in the country and provide estimates of the epidemiological parameters in time at the national level. Importantly, these estimates could become the base for planning future interventions as well as evaluating the impact of NPIs on the effective reproduction number (\documentclass[12pt]{minimal}
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\begin{document}$$\mathscr {R}_{eff}$$\end{document}Reff) and the critical epidemiological parameters, such as the contact rate or the reporting rate. However, our forecast presents some inconsistency as it overestimates the deaths for some locations as Medellín. Nevertheless, our approach demonstrates that real-time, publicly available ensemble forecasts can provide short-term predictions of reported COVID-19 deaths in Colombia. Therefore, this model can be used as a forecasting tool to evaluate disease dynamics and aid policymakers in infectious outbreak management and control.
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Affiliation(s)
- Jaime Cascante-Vega
- Universidad de los Andes, Grupo de Biología y Matemática Computacional (BIOMAC), Bogotá D.C., 111711, Colombia.,Facultad de Medicina, Universidad de los Andes, Bogotá D.C., Colombia
| | - Juan Manuel Cordovez
- Universidad de los Andes, Grupo de Biología y Matemática Computacional (BIOMAC), Bogotá D.C., 111711, Colombia
| | - Mauricio Santos-Vega
- Universidad de los Andes, Grupo de Biología y Matemática Computacional (BIOMAC), Bogotá D.C., 111711, Colombia. .,Facultad de Medicina, Universidad de los Andes, Bogotá D.C., Colombia.
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Li W, Bulekova K, Gregor B, White LF, Kolaczyk ED. Estimation of local time-varying reproduction numbers in noisy surveillance data. medRxiv 2022:2021.04.23.21255958. [PMID: 33948612 PMCID: PMC8095231 DOI: 10.1101/2021.04.23.21255958] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
A valuable metric in understanding local infectious disease dynamics is the local time-varying reproduction number, i.e. the expected number of secondary local cases caused by each infected individual. Accurate estimation of this quantity requires distinguishing cases arising from local transmission from those imported from elsewhere. Realistically, we can expect identification of cases as local or imported to be imperfect. We study the propagation of such errors in estimation of the local time-varying reproduction number. In addition, we propose a Bayesian framework for estimation of the true local time-varying reproduction number when identification errors exist. And we illustrate the practical performance of our estimator through simulation studies and with outbreaks of COVID-19 in Hong Kong and Victoria, Australia.
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Affiliation(s)
- Wenrui Li
- Department of Mathematics and Statistics, Boston University, Boston MA, USA
| | - Katia Bulekova
- Research Computing Services, Information Services and Technology, Boston University, Boston MA, USA
| | - Brian Gregor
- Research Computing Services, Information Services and Technology, Boston University, Boston MA, USA
| | - Laura F. White
- Department of Biostatistics, Boston University, Boston MA, USA
| | - Eric D. Kolaczyk
- Department of Mathematics and Statistics, Boston University, Boston MA, USA
- Hariri Institute for Computing, Boston University, Boston MA, USA
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7
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Ram SK, Sornette D. Impact of Governmental interventions on epidemic progression and workplace activity during the COVID-19 outbreak. Sci Rep 2021; 11:21939. [PMID: 34753988 PMCID: PMC8578600 DOI: 10.1038/s41598-021-01276-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2020] [Accepted: 09/09/2021] [Indexed: 01/09/2023] Open
Abstract
In the first quarter of 2020, the COVID-19 pandemic brought the world to a state of paralysis. During this period, humanity saw by far the largest organized travel restrictions and unprecedented efforts and global coordination to contain the spread of the SARS-CoV-2 virus. Using large scale human mobility and fine grained epidemic incidence data, we develop a framework to understand and quantify the effectiveness of the interventions implemented by various countries to control epidemic growth. Our analysis reveals the importance of timing and implementation of strategic policy in controlling the epidemic. We also unearth significant spatial diffusion of the epidemic before and during the lockdown measures in several countries, casting doubt on the effectiveness or on the implementation quality of the proposed Governmental policies.
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Affiliation(s)
- Sumit Kumar Ram
- grid.5801.c0000 0001 2156 2780Department of Management Technology and Economics, ETH Zurich, Zurich, Switzerland ,grid.116068.80000 0001 2341 2786Connection Science, Massachusetts Institute of Technology, Cambridge, MA USA
| | - Didier Sornette
- grid.5801.c0000 0001 2156 2780Department of Management Technology and Economics, ETH Zurich, Zurich, Switzerland ,grid.5801.c0000 0001 2156 2780Department of Earth Sciences, ETH Zurich, Zurich, Switzerland ,grid.5801.c0000 0001 2156 2780Department of Physics, ETH Zurich, Zurich, Switzerland ,grid.8591.50000 0001 2322 4988Swiss Finance Institute c/o University of Geneva, Geneva, Switzerland ,grid.263817.90000 0004 1773 1790Institute of Risk Analysis, Prediction and Management (Risks-X), Academy for Advanced Interdisciplinary Studies, Southern University of Science and Technology (SUSTech), Shenzhen, 518055 China ,grid.32197.3e0000 0001 2179 2105Tokyo Tech World Research Hub Initiative, Institute of Innovative Research, Tokyo Institute of Technology, Tokyo, Japan
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8
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Abstract
The time-dependent reproduction number, Rt, is a key metric used by epidemiologists to assess the current state of an outbreak of an infectious disease. This quantity is usually estimated using time-series observations on new infections combined with assumptions about the distribution of the serial interval of transmissions. Bayesian methods are often used with the new cases data smoothed using a simple, but to some extent arbitrary, moving average. This paper describes a new class of time-series models, estimated by classical statistical methods, for tracking and forecasting the growth rate of new cases and deaths. Very few assumptions are needed and those that are made can be tested. Estimates of Rt, together with their standard deviations, are obtained as a by-product.
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Affiliation(s)
- Andrew Harvey
- Faculty of Economics, University of Cambridge, Cambridge, UK
| | - Paul Kattuman
- Cambridge Judge Business School, University of Cambridge, Cambridge, UK
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9
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Sherratt K, Abbott S, Meakin SR, Hellewell J, Munday JD, Bosse N, Jit M, Funk S. Exploring surveillance data biases when estimating the reproduction number: with insights into subpopulation transmission of COVID-19 in England. Philos Trans R Soc Lond B Biol Sci 2021; 376:20200283. [PMID: 34053260 PMCID: PMC8165604 DOI: 10.1098/rstb.2020.0283] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/31/2021] [Indexed: 12/22/2022] Open
Abstract
The time-varying reproduction number (Rt: the average number of secondary infections caused by each infected person) may be used to assess changes in transmission potential during an epidemic. While new infections are not usually observed directly, they can be estimated from data. However, data may be delayed and potentially biased. We investigated the sensitivity of Rt estimates to different data sources representing COVID-19 in England, and we explored how this sensitivity could track epidemic dynamics in population sub-groups. We sourced public data on test-positive cases, hospital admissions and deaths with confirmed COVID-19 in seven regions of England over March through August 2020. We estimated Rt using a model that mapped unobserved infections to each data source. We then compared differences in Rt with the demographic and social context of surveillance data over time. Our estimates of transmission potential varied for each data source, with the relative inconsistency of estimates varying across regions and over time. Rt estimates based on hospital admissions and deaths were more spatio-temporally synchronous than when compared to estimates from all test positives. We found these differences may be linked to biased representations of subpopulations in each data source. These included spatially clustered testing, and where outbreaks in hospitals, care homes, and young age groups reflected the link between age and severity of the disease. We highlight that policy makers could better target interventions by considering the source populations of Rt estimates. Further work should clarify the best way to combine and interpret Rt estimates from different data sources based on the desired use. This article is part of the theme issue 'Modelling that shaped the early COVID-19 pandemic response in the UK'.
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Affiliation(s)
- Katharine Sherratt
- Department of Infectious Disease Epidemiology, Faculty of Epidemiology and Population Health, Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, UK
| | - Sam Abbott
- Department of Infectious Disease Epidemiology, Faculty of Epidemiology and Population Health, Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, UK
| | - Sophie R. Meakin
- Department of Infectious Disease Epidemiology, Faculty of Epidemiology and Population Health, Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, UK
| | - Joel Hellewell
- Department of Infectious Disease Epidemiology, Faculty of Epidemiology and Population Health, Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, UK
| | - James D. Munday
- Department of Infectious Disease Epidemiology, Faculty of Epidemiology and Population Health, Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, UK
| | - Nikos Bosse
- Department of Infectious Disease Epidemiology, Faculty of Epidemiology and Population Health, Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, UK
| | - CMMID COVID-19 Working Group
- Department of Infectious Disease Epidemiology, Faculty of Epidemiology and Population Health, Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, UK
| | - Mark Jit
- Department of Infectious Disease Epidemiology, Faculty of Epidemiology and Population Health, Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, UK
| | - Sebastian Funk
- Department of Infectious Disease Epidemiology, Faculty of Epidemiology and Population Health, Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, UK
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Challen R, Tsaneva-Atanasova K, Pitt M, Edwards T, Gompels L, Lacasa L, Brooks-Pollock E, Danon L. Estimates of regional infectivity of COVID-19 in the United Kingdom following imposition of social distancing measures. Philos Trans R Soc Lond B Biol Sci 2021; 376:20200280. [PMID: 34053251 PMCID: PMC8165582 DOI: 10.1098/rstb.2020.0280] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/02/2020] [Indexed: 01/10/2023] Open
Abstract
The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) reproduction number has become an essential parameter for monitoring disease transmission across settings and guiding interventions. The UK published weekly estimates of the reproduction number in the UK starting in May 2020 which are formed from multiple independent estimates. In this paper, we describe methods used to estimate the time-varying SARS-CoV-2 reproduction number for the UK. We used multiple data sources and estimated a serial interval distribution from published studies. We describe regional variability and how estimates evolved during the early phases of the outbreak, until the relaxing of social distancing measures began to be introduced in early July. Our analysis is able to guide localized control and provides a longitudinal example of applying these methods over long timescales. This article is part of the theme issue 'Modelling that shaped the early COVID-19 pandemic response in the UK'.
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Affiliation(s)
- Robert Challen
- EPSRC Centre for Predictive Modelling in Healthcare, University of Exeter, Exeter EX4 4SB, UK
- Taunton and Somerset NHS Foundation Trust, Musgrove Park Hospital, Taunton TA1 5DA, UK
| | - Krasimira Tsaneva-Atanasova
- EPSRC Centre for Predictive Modelling in Healthcare, University of Exeter, Exeter EX4 4SB, UK
- The Alan Turing Institute, British Library, 96 Euston Road, London NW1 2DB, UK
| | - Martin Pitt
- NIHR CLAHRC for the South West Peninsula, University of Exeter Medical School, St Luke's Campus, Exeter, UK
| | - Tom Edwards
- Taunton and Somerset NHS Foundation Trust, Musgrove Park Hospital, Taunton TA1 5DA, UK
| | - Luke Gompels
- Taunton and Somerset NHS Foundation Trust, Musgrove Park Hospital, Taunton TA1 5DA, UK
| | - Lucas Lacasa
- School of Mathematical Sciences, Queen Mary University of London, London E1 4NS, UK
| | - Ellen Brooks-Pollock
- Bristol Medical School, Population Health Sciences, University of Bristol, Bristol, UK
| | - Leon Danon
- Data Science Institute, College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter EX4 4SB, UK
- The Alan Turing Institute, British Library, 96 Euston Road, London NW1 2DB, UK
- Bristol Medical School, Population Health Sciences, University of Bristol, Bristol, UK
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11
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Mokhtari A, Mineo C, Kriseman J, Kremer P, Neal L, Larson J. A multi-method approach to modeling COVID-19 disease dynamics in the United States. Sci Rep 2021; 11:12426. [PMID: 34127757 PMCID: PMC8203660 DOI: 10.1038/s41598-021-92000-w] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2021] [Accepted: 06/01/2021] [Indexed: 12/12/2022] Open
Abstract
In this paper, we proposed a multi-method modeling approach to community-level spreading of COVID-19 disease. Our methodology was composed of interconnected age-stratified system dynamics models in an agent-based modeling framework that allowed for a granular examination of the scale and severity of disease spread, including metrics such as infection cases, deaths, hospitalizations, and ICU usage. Model parameters were calibrated using an optimization technique with an objective function to minimize error associated with the cumulative cases of COVID-19 during a training period between March 15 and October 31, 2020. We outlined several case studies to demonstrate the model's state- and local-level projection capabilities. We further demonstrated how model outcomes could be used to evaluate perceived levels of COVID-19 risk across different localities using a multi-criteria decision analysis framework. The model's two, three, and four week out-of-sample projection errors varied on a state-by-state basis, and generally increased as the out-of-sample projection period was extended. Additionally, the prediction error in the state-level projections was generally due to an underestimation of cases and an overestimation of deaths. The proposed modeling approach can be used as a virtual laboratory to investigate a wide range of what-if scenarios and easily adapted to future high-consequence public health threats.
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Affiliation(s)
- Amir Mokhtari
- Booz Allen Hamilton, 4747 Bethesda Ave., Bethesda, MD, 20814, USA.
| | - Cameron Mineo
- Booz Allen Hamilton, 4747 Bethesda Ave., Bethesda, MD, 20814, USA
| | - Jeffrey Kriseman
- Booz Allen Hamilton, 4747 Bethesda Ave., Bethesda, MD, 20814, USA
| | - Pedro Kremer
- Booz Allen Hamilton, 4747 Bethesda Ave., Bethesda, MD, 20814, USA
| | - Lauren Neal
- Booz Allen Hamilton, 4747 Bethesda Ave., Bethesda, MD, 20814, USA
| | - John Larson
- Booz Allen Hamilton, 4747 Bethesda Ave., Bethesda, MD, 20814, USA
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12
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Giovanetti M, Cella E, Benedetti F, Rife Magalis B, Fonseca V, Fabris S, Campisi G, Ciccozzi A, Angeletti S, Borsetti A, Tambone V, Sagnelli C, Pascarella S, Riva A, Ceccarelli G, Marcello A, Azarian T, Wilkinson E, de Oliveira T, Alcantara LCJ, Cauda R, Caruso A, Dean NE, Browne C, Lourenco J, Salemi M, Zella D, Ciccozzi M. SARS-CoV-2 shifting transmission dynamics and hidden reservoirs potentially limit efficacy of public health interventions in Italy. Commun Biol 2021; 4:489. [PMID: 33883675 PMCID: PMC8060392 DOI: 10.1038/s42003-021-02025-0] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2020] [Accepted: 03/03/2021] [Indexed: 01/08/2023] Open
Abstract
We investigated SARS-CoV-2 transmission dynamics in Italy, one of the countries hit hardest by the pandemic, using phylodynamic analysis of viral genetic and epidemiological data. We observed the co-circulation of multiple SARS-CoV-2 lineages over time, which were linked to multiple importations and characterized by large transmission clusters concomitant with a high number of infections. Subsequent implementation of a three-phase nationwide lockdown strategy greatly reduced infection numbers and hospitalizations. Yet we present evidence of sustained viral spread among sporadic clusters acting as "hidden reservoirs" during summer 2020. Mathematical modelling shows that increased mobility among residents eventually catalyzed the coalescence of such clusters, thus driving up the number of infections and initiating a new epidemic wave. Our results suggest that the efficacy of public health interventions is, ultimately, limited by the size and structure of epidemic reservoirs, which may warrant prioritization during vaccine deployment.
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Affiliation(s)
- Marta Giovanetti
- Laboratório de Flavivírus, Instituto Oswaldo Cruz, Fundação Oswaldo Cruz, Rio de Janeiro, Brazil
- Laboratório de Genética Celular e Molecular, ICB, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil
- Medical Statistic and Molecular Epidemiology Unit, University of Biomedical Campus, Rome, Italy
| | - Eleonora Cella
- Burnett School of Biomedical Sciences, University of Central Florida, Orlando, FL, USA
| | - Francesca Benedetti
- Institute of Human Virology and Global Virus Network Center, Department of Biochemistry and Molecular Biology, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Brittany Rife Magalis
- Emerging Pathogens Institute & Department of Pathology, College of Medicine, University of Florida, Gainesville, FL, USA
| | - Vagner Fonseca
- Laboratório de Genética Celular e Molecular, ICB, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil
- KwaZulu-Natal Research Innovation and Sequencing Platform (KRISP), School of Laboratory Medicine and Medical Sciences, College of Health Sciences, University of KwaZulu-Natal, Durban, South Africa
- Coordenação Geral dos Laboratórios de Saúde Pública/Secretaria de Vigilância em Saúde, Ministério da Saúde, (CGLAB/SVS-MS) Brasília, Distrito Federal, Brazil
| | - Silvia Fabris
- Medical Statistic and Molecular Epidemiology Unit, University of Biomedical Campus, Rome, Italy
| | - Giovanni Campisi
- Department of Molecular and Translational Medicine, Section of Microbiology, University of Brescia, Brescia, Italy
| | - Alessandra Ciccozzi
- Medical Statistic and Molecular Epidemiology Unit, University of Biomedical Campus, Rome, Italy
| | - Silvia Angeletti
- Unit of Clinical Laboratory Science, University Campus Bio-Medico of Rome, Rome, Italy
| | | | | | - Caterina Sagnelli
- Department of Mental Health and Public Medicine, University of Campania "Luigi Vanvitelli", Naples, Italy
| | - Stefano Pascarella
- Department of Biochemical Sciences "A. Rossi Fanelli", University of Rome "La Sapienza", Rome, Italy
| | - Alberto Riva
- ICBR, University of Florida, Gainesville, FL, USA
| | - Giancarlo Ceccarelli
- Department of Public Health and Infectious Diseases, Policlinico Umberto I Università 'Sapienza', Rome, Italy
| | - Alessandro Marcello
- Laboratory of Molecular Virology, International Centre for Genetic Engineering and Biotechnology (ICGEB), Trieste, Italy
| | - Taj Azarian
- Burnett School of Biomedical Sciences, University of Central Florida, Orlando, FL, USA
| | - Eduan Wilkinson
- KwaZulu-Natal Research Innovation and Sequencing Platform (KRISP), School of Laboratory Medicine and Medical Sciences, College of Health Sciences, University of KwaZulu-Natal, Durban, South Africa
| | - Tulio de Oliveira
- KwaZulu-Natal Research Innovation and Sequencing Platform (KRISP), School of Laboratory Medicine and Medical Sciences, College of Health Sciences, University of KwaZulu-Natal, Durban, South Africa
| | - Luiz Carlos Junior Alcantara
- Laboratório de Flavivírus, Instituto Oswaldo Cruz, Fundação Oswaldo Cruz, Rio de Janeiro, Brazil
- Laboratório de Genética Celular e Molecular, ICB, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil
| | - Roberto Cauda
- Department Infectious Diseases, - Fondazione Policlinico Universitario "A. Gemelli" IRCCS, Rome, Italy
| | - Arnaldo Caruso
- Department of Molecular and Translational Medicine, Section of Microbiology, University of Brescia, Brescia, Italy
| | - Natalie E Dean
- Department of Epidemiology, College of Public Health and Health Professions, University of Florida, Gainesville, FL, USA
| | - Cameron Browne
- Department of Mathematics, University of Lafayette, Lafayette, LA, USA
| | - Jose Lourenco
- Department of Zoology, University of Oxford, Oxford, UK
| | - Marco Salemi
- Emerging Pathogens Institute & Department of Pathology, College of Medicine, University of Florida, Gainesville, FL, USA.
| | - Davide Zella
- Institute of Human Virology and Global Virus Network Center, Department of Biochemistry and Molecular Biology, University of Maryland School of Medicine, Baltimore, MD, USA.
| | - Massimo Ciccozzi
- Medical Statistic and Molecular Epidemiology Unit, University of Biomedical Campus, Rome, Italy.
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13
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Wang H, Ghosh A, Ding J, Sarkar R, Gao J. Heterogeneous interventions reduce the spread of COVID-19 in simulations on real mobility data. Sci Rep 2021; 11:7809. [PMID: 33833298 PMCID: PMC8034422 DOI: 10.1038/s41598-021-87034-z] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2020] [Accepted: 03/16/2021] [Indexed: 01/10/2023] Open
Abstract
Major interventions have been introduced worldwide to slow down the spread of the SARS-CoV-2 virus. Large scale lockdown of human movements are effective in reducing the spread, but they come at a cost of significantly limited societal functions. We show that natural human movements are statistically diverse, and the spread of the disease is significantly influenced by a small group of active individuals and gathering venues. We find that interventions focused on these most mobile individuals and popular venues reduce both the peak infection rate and the total infected population while retaining high social activity levels. These trends are seen consistently in simulations with real human mobility data of different scales, resolutions, and modalities from multiple cities across the world. The observation implies that compared to broad sweeping interventions, more heterogeneous strategies that are targeted based on the network effects in human mobility provide a better balance between pandemic control and regular social activities.
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Affiliation(s)
- Haotian Wang
- Department of Computer Science, Rutgers University, Piscataway, USA
| | - Abhirup Ghosh
- Department of Computer Science and Technology, University of Cambridge, Cambridge, UK
| | - Jiaxin Ding
- John Hopcroft Center for Computer Science, Shanghai Jiao Tong University, Minhang, China
| | - Rik Sarkar
- School of Informatics, University of Edinburgh, Edinburgh, UK.
| | - Jie Gao
- Department of Computer Science, Rutgers University, Piscataway, USA
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14
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Bushman M, Worby C, Chang HH, Kraemer MUG, Hanage WP. Transmission of SARS-CoV-2 before and after symptom onset: impact of nonpharmaceutical interventions in China. Eur J Epidemiol 2021; 36:429-439. [PMID: 33881667 PMCID: PMC8058147 DOI: 10.1007/s10654-021-00746-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2020] [Accepted: 03/27/2021] [Indexed: 02/08/2023]
Abstract
Nonpharmaceutical interventions, such as contact tracing and quarantine, have been the primary means of controlling the spread of SARS-CoV-2; however, it remains uncertain which interventions are most effective at reducing transmission at the population level. Using serial interval data from before and after the rollout of nonpharmaceutical interventions in China, we estimate that the relative frequency of presymptomatic transmission increased from 34% before the rollout to 71% afterward. The shift toward earlier transmission indicates a disproportionate reduction in transmission post-symptom onset. We estimate that, following the rollout of nonpharmaceutical interventions, transmission post-symptom onset was reduced by 82% whereas presymptomatic transmission decreased by only 16%. The observation that only one-third of transmission was presymptomatic at baseline, combined with the finding that NPIs reduced presymptomatic transmission by less than 20%, suggests that the overall impact of NPIs was driven in large part by reductions in transmission following symptom onset. This implies that interventions which limit opportunities for transmission in the later stages of infection, such as contact tracing and isolation, are particularly important for control of SARS-CoV-2. Interventions which specifically reduce opportunities for presymptomatic transmission, such as quarantine of asymptomatic contacts, are likely to have smaller, but non-negligible, effects on overall transmission.
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Affiliation(s)
- Mary Bushman
- Harvard T.H. Chan School of Public Health, Boston, MA, USA.
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15
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Nakajo K, Nishiura H. Assessing Interventions against Coronavirus Disease 2019 (COVID-19) in Osaka, Japan: A Modeling Study. J Clin Med 2021; 10:jcm10061256. [PMID: 33803634 PMCID: PMC8003080 DOI: 10.3390/jcm10061256] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2021] [Revised: 03/16/2021] [Accepted: 03/17/2021] [Indexed: 12/13/2022] Open
Abstract
Estimation of the effective reproduction number, R(t), of coronavirus disease (COVID-19) in real-time is a continuing challenge. R(t) reflects the epidemic dynamics based on readily available illness onset data, and is useful for the planning and implementation of public health and social measures. In the present study, we proposed a method for computing the R(t) of COVID-19, and applied this method to the epidemic in Osaka prefecture from February to September 2020. We estimated R(t) as a function of the time of infection using the date of illness onset. The epidemic in Osaka came under control around 2 April during the first wave, and 26 July during the second wave. R(t) did not decline drastically following any single intervention. However, when multiple interventions were combined, the relative reductions in R(t) during the first and second waves were 70% and 51%, respectively. Although the second wave was brought under control without declaring a state of emergency, our model comparison indicated that relying on a single intervention would not be sufficient to reduce R(t) < 1. The outcome of the COVID-19 pandemic continues to rely on political leadership to swiftly design and implement combined interventions capable of broadly and appropriately reducing contacts.
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Affiliation(s)
- Ko Nakajo
- Kyoto University School of Public Health, Yoshida-Konoe-cho, Sakyo-ku, Kyoto 606-8501, Japan;
- Graduate School of Medicine, Hokkaido University, Kita 15 Jo Nishi 7 Chome, Kita-ku, Sapporo-shi, Hokkaido 060-8638, Japan
- Sanofi K.K. Tokyo Opera City Tower, 3-20-2, Nishi Shinjuku, Shinjuku-ku, Tokyo 163-1488, Japan
| | - Hiroshi Nishiura
- Kyoto University School of Public Health, Yoshida-Konoe-cho, Sakyo-ku, Kyoto 606-8501, Japan;
- Correspondence: ; Tel.: +81-75-753-4490
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16
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Pedersen MG, Meneghini M. Data-driven estimation of change points reveals correlation between face mask use and accelerated curtailing of the first wave of the COVID-19 epidemic in Italy. Infect Dis (Lond) 2021; 53:243-251. [PMID: 33631075 DOI: 10.1080/23744235.2021.1877810] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/12/2023] Open
Abstract
BACKGROUND Italy was the first Western country to be seriously affected by COVID-19, and the first to implement drastic measures, which successfully curtailed the first wave of the epidemic. METHODS To understand which containment measures altered disease dynamics, we estimated change points in COVID-19 dynamics from official Italian data. RESULTS We found an excellent correlation between nationwide lockdown and the epidemic peak in late March 2020. Surprisingly, we found a change point in mid-April, which did not correspond to national measures, but may be explained by regional interventions. Change points in regional COVID-19 dynamics correlated well with local distribution of free face masks and regional orders requiring their mandatory use. Regions with no specific interventions showed no change point during April. CONCLUSIONS Our findings of the observed correlation between face mask use and disease dynamics lend further support to the importance of face masks in addition to lockdowns and other restrictions for the control of COVID-19.
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Affiliation(s)
- Morten Gram Pedersen
- Department of Information Engineering, University of Padova, Padova, Italy.,Department of Mathematics "Tullio Levi-Civita", University of Padova, Padova, Italy
| | - Matteo Meneghini
- Department of Information Engineering, University of Padova, Padova, Italy
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17
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Korobelnik JF, Loewenstein A, Eldem B, Joussen AM, Koh A, Lambrou GN, Lanzetta P, Li X, Lövestam-Adrian M, Navarro R, Okada AA, Pearce I, Rodríguez FJ, Wong DT, Wu L. Anti-VEGF intravitreal injections in the era of COVID-19: responding to different levels of epidemic pressure. Graefes Arch Clin Exp Ophthalmol 2021; 259:567-574. [PMID: 33528647 PMCID: PMC7852054 DOI: 10.1007/s00417-021-05097-0] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2020] [Revised: 01/15/2021] [Accepted: 01/25/2021] [Indexed: 12/12/2022] Open
Abstract
Purpose Following the first wave of the COVID-19 pandemic in early 2020, the easing of strict measures to reduce its spread has led to a resurgence of cases in many countries at both the national and local level. This article addresses how guidance for ophthalmologists on managing patients with retinal disease receiving intravitreal injections of anti-vascular endothelial growth factor (VEGF) during the pandemic should be adapted to the local epidemic pressure, with more or less stringent measures implemented according to the ebb and flow of the pandemic. Methods The Vision Academy’s membership of international retinal disease experts analyzed guidance for anti-VEGF intravitreal injections during the COVID-19 pandemic and graded the recommendations according to three levels of increasing epidemic pressure. The revised recommendations were discussed, refined, and voted on by the 14-member Vision Academy Steering Committee for consensus. Results Protocols to minimize the exposure of patients and healthcare staff to COVID-19, including use of personal protective equipment, physical distancing, and hygiene measures, should be routinely implemented and intensified according to local infection rates and pressure on the hospital/clinic or healthcare system. In areas with many COVID-19-positive clusters, additional measures including pre-screening of patients, postponement of non-urgent appointments, and simplification of complex intravitreal anti-VEGF regimens should be considered. Treatment prioritization for those at greatest risk of irreversible vision loss should be implemented in areas where COVID-19 cases are increasing exponentially and healthcare resources are strained. Conclusion Consistency in monitoring of local infection rates and adjustment of clinical practice accordingly will be required as we move forward through the COVID-19 era. Ophthalmologists must continue to carefully weigh the risk–benefits to minimize the exposure of patients and healthcare staff to COVID-19, ensure that patients receive sight-saving treatment, and avoid the potential long-term impact of prolonged treatment postponement.
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Affiliation(s)
- Jean-François Korobelnik
- Service d'ophtalmologie, CHU Bordeaux, Bordeaux, France. .,Inserm, Bordeaux Population Health Research Center, team LEHA, Université de Bordeaux, UMR 1219, F-33000, Bordeaux, France.
| | - Anat Loewenstein
- Division of Ophthalmology, Tel Aviv Medical Center, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Bora Eldem
- Department of Ophthalmology, Hacettepe University, Ankara, Turkey
| | | | - Adrian Koh
- Eye and Retina Surgeons, Camden Medical Centre, Singapore, Singapore
| | | | - Paolo Lanzetta
- Department of Medicine-Ophthalmology, University of Udine, Udine, Italy.,Department of Ophthalmology, Ospedale Santa Maria della Misericordia, Azienda Sanitaria Universitaria Friuli Centrale (ASUFC), Udine, Italy.,Istituto Europeo di Microchirurgia Oculare, IEMO, Udine, Italy
| | - Xiaoxin Li
- Eye Center and Eye Institute, Peking University People's Hospital, Beijing, China
| | | | | | - Annabelle A Okada
- Department of Ophthalmology, Kyorin University School of Medicine, Tokyo, Japan
| | - Ian Pearce
- Royal Liverpool University Hospital, Liverpool, UK
| | - Francisco J Rodríguez
- Fundación Oftalmologia Nacional, Escuela de Medicina y Ciencias de la Salud, Universidad del Rosario, Bogotá, Colombia
| | - David T Wong
- Unity Health Toronto-St. Michael's Hospital, University of Toronto, Toronto, Ontario, Canada
| | - Lihteh Wu
- Macula, Vitreous and Retina Associates of Costa Rica, San José, Costa Rica
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18
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Pasetto D, Lemaitre JC, Bertuzzo E, Gatto M, Rinaldo A. Range of reproduction number estimates for COVID-19 spread. Biochem Biophys Res Commun 2021; 538:253-258. [PMID: 33342517 PMCID: PMC7723757 DOI: 10.1016/j.bbrc.2020.12.003] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2020] [Accepted: 12/01/2020] [Indexed: 12/15/2022]
Abstract
To monitor local and global COVID-19 outbreaks, and to plan containment measures, accessible and comprehensible decision-making tools need to be based on the growth rates of new confirmed infections, hospitalization or case fatality rates. Growth rates of new cases form the empirical basis for estimates of a variety of reproduction numbers, dimensionless numbers whose value, when larger than unity, describes surging infections and generally worsening epidemiological conditions. Typically, these determinations rely on noisy or incomplete data gained over limited periods of time, and on many parameters to estimate. This paper examines how estimates from data and models of time-evolving reproduction numbers of national COVID-19 infection spread change by using different techniques and assumptions. Given the importance acquired by reproduction numbers as diagnostic tools, assessing their range of possible variations obtainable from the same epidemiological data is relevant. We compute control reproduction numbers from Swiss and Italian COVID-19 time series adopting both data convolution (renewal equation) and a SEIR-type model. Within these two paradigms we run a comparative analysis of the possible inferences obtained through approximations of the distributions typically used to describe serial intervals, generation, latency and incubation times, and the delays between onset of symptoms and notification. Our results suggest that estimates of reproduction numbers under these different assumptions may show significant temporal differences, while the actual variability range of computed values is rather small.
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Affiliation(s)
- Damiano Pasetto
- Dipartimento di Scienze Ambientali, Informatica e Statistica, Università Ca’ Foscari Venezia, 30172, Venezia-Mestre, (IT), Italy,Corresponding author
| | - Joseph C. Lemaitre
- Laboratory of Ecohydrology, School of Architecture, Civil and Environmental Engineering, École Polytechnique Fédérale de Lausanne, CH-1015, Lausanne, (CH), Switzerland
| | - Enrico Bertuzzo
- Dipartimento di Scienze Ambientali, Informatica e Statistica, Università Ca’ Foscari Venezia, 30172, Venezia-Mestre, (IT), Italy
| | - Marino Gatto
- Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, 20133, Milan, (IT), Italy
| | - Andrea Rinaldo
- Laboratory of Ecohydrology, School of Architecture, Civil and Environmental Engineering, École Polytechnique Fédérale de Lausanne, CH-1015, Lausanne, (CH), Switzerland,Dipartimento di Ingegneria Civile Edile ed Ambientale, Università di Padova, I-35131, Padua, (IT), Italy
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19
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De Salazar PM, Lu F, Hay JA, Gómez-Barroso D, Fernández-Navarro P, Martínez E, Astray-Mochales J, Amillategui R, García-Fulgueiras A, Chirlaque MD, Sánchez-Migallón A, Larrauri A, Sierra MJ, Lipsitch M, Simón F, Santillana M, Hernán MA. Near real-time surveillance of the SARS-CoV-2 epidemic with incomplete data. medRxiv 2021:2021.01.25.20230094. [PMID: 33532788 PMCID: PMC7852239 DOI: 10.1101/2021.01.25.20230094] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
Designing public health responses to outbreaks requires close monitoring of population-level health indicators in real-time. Thus, an accurate estimation of the epidemic curve is critical. We propose an approach to reconstruct epidemic curves in near real time. We apply this approach to characterize the early SARS-CoV-2 outbreak in two Spanish regions between March and April 2020. We address two data collection problems that affected the reliability of the available real-time epidemiological data, namely, the frequent missing information documenting when a patient first experienced symptoms, and the frequent retrospective revision of historical information (including right censoring). This is done by using a novel back-calculating procedure based on imputing patients' dates of symptom onset from reported cases, according to a dynamically-estimated "backward" reporting delay conditional distribution, and adjusting for right censoring using an existing package, NobBS , to estimate in real time (nowcast) cases by date of symptom onset. This process allows us to obtain an approximation of the time-varying reproduction number ( R t ) in real-time. At each step, we evaluate how different assumptions affect the recovered epidemiological events and compare the proposed approach to the alternative procedure of merely using curves of case counts, by report day, to characterize the time-evolution of the outbreak. Finally, we assess how these real-time estimates compare with subsequently documented epidemiological information that is considered more reliable and complete that became available later in time. Our approach may help improve accuracy, quantify uncertainty, and evaluate frequently unstated assumptions when recovering the epidemic curves from limited data obtained from public health surveillance systems in other locations.
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Affiliation(s)
- PM De Salazar
- Center for Communicable Disease Dynamics, Department of Epidemiology, Harvard TH Chan School of Public Health, Boston, United States
| | - F Lu
- Machine Intelligence Lab, Boston Children’s Hospital, Boston, United States
- Computational Health Informatics Program, Boston Children’s Hospital, Boston, United States
| | - JA Hay
- Center for Communicable Disease Dynamics, Department of Epidemiology, Harvard TH Chan School of Public Health, Boston, United States
| | - D Gómez-Barroso
- Centro Nacional de Epidemiología, Carlos III Health Institute, Madrid, Spain
- Consorcio de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP)
| | - P Fernández-Navarro
- Centro Nacional de Epidemiología, Carlos III Health Institute, Madrid, Spain
- Consorcio de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP)
| | - E Martínez
- Consorcio de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP)
- Centro de Coordinación de Alertas y Emergencias Sanitarias, Ministry of Health, Madrid, Spain
| | - J Astray-Mochales
- Directorate-General for Public Health, Madrid General Health Authority, Spain
| | - R Amillategui
- Centro Nacional de Epidemiología, Carlos III Health Institute, Madrid, Spain
| | - A García-Fulgueiras
- Department of Epidemiology, Regional Health Council, IMIB-Arrixaca, Murcia, Spain CIBER in Epidemiology and Public Health (CIBERESP), Madrid, Spain
| | - MD Chirlaque
- Department of Epidemiology, Regional Health Council, IMIB-Arrixaca, Murcia, Spain CIBER in Epidemiology and Public Health (CIBERESP), Madrid, Spain
| | - A Sánchez-Migallón
- Directorate-General for Public Health, Madrid General Health Authority, Spain
| | - A Larrauri
- Computational Health Informatics Program, Boston Children’s Hospital, Boston, United States
- Centro Nacional de Epidemiología, Carlos III Health Institute, Madrid, Spain
| | - MJ Sierra
- Centro de Coordinación de Alertas y Emergencias Sanitarias, Ministry of Health, Madrid, Spain
| | - M Lipsitch
- Center for Communicable Disease Dynamics, Department of Epidemiology, Harvard TH Chan School of Public Health, Boston, United States
| | - F Simón
- Consorcio de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP)
- Centro de Coordinación de Alertas y Emergencias Sanitarias, Ministry of Health, Madrid, Spain
| | - M Santillana
- Center for Communicable Disease Dynamics, Department of Epidemiology, Harvard TH Chan School of Public Health, Boston, United States
- Machine Intelligence Lab, Boston Children’s Hospital, Boston, United States
- Computational Health Informatics Program, Boston Children’s Hospital, Boston, United States
- Department of Pediatrics, Harvard Medical School, Harvard University, Boston, United States
| | - MA Hernán
- Department of Epidemiology and Department of Biostatistics, Harvard T.H. Chan School of Public Health; Harvard-MIT Division of Health Sciences and Technology, Boston, United States
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20
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Turbé H, Bjelogrlic M, Robert A, Gaudet-Blavignac C, Goldman JP, Lovis C. Adaptive Time-Dependent Priors and Bayesian Inference to Evaluate SARS-CoV-2 Public Health Measures Validated on 31 Countries. Front Public Health 2021; 8:583401. [PMID: 33553088 PMCID: PMC7862946 DOI: 10.3389/fpubh.2020.583401] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2020] [Accepted: 12/03/2020] [Indexed: 01/19/2023] Open
Abstract
With the rapid spread of the SARS-CoV-2 virus since the end of 2019, public health confinement measures to contain the propagation of the pandemic have been implemented. Our method to estimate the reproduction number using Bayesian inference with time-dependent priors enhances previous approaches by considering a dynamic prior continuously updated as restrictive measures and comportments within the society evolve. In addition, to allow direct comparison between reproduction number and introduction of public health measures in a specific country, the infection dates are inferred from daily confirmed cases and confirmed death. The evolution of this reproduction number in combination with the stringency index is analyzed on 31 European countries. We show that most countries required tough state interventions with a stringency index equal to 79.6 out of 100 to reduce their reproduction number below one and control the progression of the pandemic. In addition, we show a direct correlation between the time taken to introduce restrictive measures and the time required to contain the spread of the pandemic with a median time of 8 days. This analysis is validated by comparing the excess deaths and the time taken to implement restrictive measures. Our analysis reinforces the importance of having a fast response with a coherent and comprehensive set of confinement measures to control the pandemic. Only restrictions or combinations of those have shown to effectively control the pandemic.
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Affiliation(s)
- Hugues Turbé
- Medical Information Sciences Division, Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland
- Medical Information Sciences Division, Diagnostic Department, University Hospitals of Geneva, Geneva, Switzerland
| | - Mina Bjelogrlic
- Medical Information Sciences Division, Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland
- Medical Information Sciences Division, Diagnostic Department, University Hospitals of Geneva, Geneva, Switzerland
| | - Arnaud Robert
- Medical Information Sciences Division, Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland
- Medical Information Sciences Division, Diagnostic Department, University Hospitals of Geneva, Geneva, Switzerland
| | - Christophe Gaudet-Blavignac
- Medical Information Sciences Division, Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland
- Medical Information Sciences Division, Diagnostic Department, University Hospitals of Geneva, Geneva, Switzerland
| | - Jean-Philippe Goldman
- Medical Information Sciences Division, Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland
- Medical Information Sciences Division, Diagnostic Department, University Hospitals of Geneva, Geneva, Switzerland
| | - Christian Lovis
- Medical Information Sciences Division, Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland
- Medical Information Sciences Division, Diagnostic Department, University Hospitals of Geneva, Geneva, Switzerland
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21
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Abstract
The coronavirus disease of 2019 (COVID-19) is a pandemic. To characterize its disease transmissibility, we propose a Bayesian change point detection model using daily actively infectious cases. Our model builds on a Bayesian Poisson segmented regression model that can 1) capture the epidemiological dynamics under the changing conditions caused by external or internal factors; 2) provide uncertainty estimates of both the number and locations of change points; and 3) adjust any explanatory time-varying covariates. Our model can be used to evaluate public health interventions, identify latent events associated with spreading rates, and yield better short-term forecasts.
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Affiliation(s)
- Shuang Jiang
- Department of Statistical Science, Southern Methodist University, Dallas, TX 75205, USA
- Quantitative Biomedical Research Center, Department of Population and Data Sciences, The University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
| | - Quan Zhou
- Department of Statistics, Texas A&M University, College Station, TX 77843, USA
| | - Xiaowei Zhan
- Quantitative Biomedical Research Center, Department of Population and Data Sciences, The University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
| | - Qiwei Li
- Department of Mathematical Sciences, The University of Texas at Dallas, Richardson, TX 75080, USA
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22
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Arroyo-Marioli F, Bullano F, Kucinskas S, Rondón-Moreno C. Tracking [Formula: see text] of COVID-19: A new real-time estimation using the Kalman filter. PLoS One 2021; 16:e0244474. [PMID: 33439880 PMCID: PMC7806155 DOI: 10.1371/journal.pone.0244474] [Citation(s) in RCA: 96] [Impact Index Per Article: 32.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2020] [Accepted: 12/11/2020] [Indexed: 01/08/2023] Open
Abstract
We develop a new method for estimating the effective reproduction number of an infectious disease ([Formula: see text]) and apply it to track the dynamics of COVID-19. The method is based on the fact that in the SIR model, [Formula: see text] is linearly related to the growth rate of the number of infected individuals. This time-varying growth rate is estimated using the Kalman filter from data on new cases. The method is easy to implement in standard statistical software, and it performs well even when the number of infected individuals is imperfectly measured, or the infection does not follow the SIR model. Our estimates of [Formula: see text] for COVID-19 for 124 countries across the world are provided in an interactive online dashboard, and they are used to assess the effectiveness of non-pharmaceutical interventions in a sample of 14 European countries.
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Arroyo-Marioli F, Bullano F, Kucinskas S, Rondón-Moreno C. Tracking [Formula: see text] of COVID-19: A new real-time estimation using the Kalman filter. PLoS One 2021; 16:e0244474. [PMID: 33439880 DOI: 10.2139/ssrn.3581633] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2020] [Accepted: 12/11/2020] [Indexed: 05/21/2023] Open
Abstract
We develop a new method for estimating the effective reproduction number of an infectious disease ([Formula: see text]) and apply it to track the dynamics of COVID-19. The method is based on the fact that in the SIR model, [Formula: see text] is linearly related to the growth rate of the number of infected individuals. This time-varying growth rate is estimated using the Kalman filter from data on new cases. The method is easy to implement in standard statistical software, and it performs well even when the number of infected individuals is imperfectly measured, or the infection does not follow the SIR model. Our estimates of [Formula: see text] for COVID-19 for 124 countries across the world are provided in an interactive online dashboard, and they are used to assess the effectiveness of non-pharmaceutical interventions in a sample of 14 European countries.
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Arroyo-Marioli F, Bullano F, Kucinskas S, Rondón-Moreno C. Tracking [Formula: see text] of COVID-19: A new real-time estimation using the Kalman filter. PLoS One 2021; 16:e0244474. [PMID: 33439880 DOI: 10.1101/2020.04.19.20071886] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2020] [Accepted: 12/11/2020] [Indexed: 05/20/2023] Open
Abstract
We develop a new method for estimating the effective reproduction number of an infectious disease ([Formula: see text]) and apply it to track the dynamics of COVID-19. The method is based on the fact that in the SIR model, [Formula: see text] is linearly related to the growth rate of the number of infected individuals. This time-varying growth rate is estimated using the Kalman filter from data on new cases. The method is easy to implement in standard statistical software, and it performs well even when the number of infected individuals is imperfectly measured, or the infection does not follow the SIR model. Our estimates of [Formula: see text] for COVID-19 for 124 countries across the world are provided in an interactive online dashboard, and they are used to assess the effectiveness of non-pharmaceutical interventions in a sample of 14 European countries.
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Abstract
Improved understanding of the effects of meteorological conditions on the transmission of SARS-CoV-2, the causative agent for COVID-19 disease, is urgently needed to inform mitigation efforts. Here, we estimated the relationship between air temperature or specific humidity (SH) and SARS-CoV-2 transmission in 913 U.S. counties with abundant reported infections from March 15 to August 31, 2020. Specifically, we quantified the associations of daily mean temperature and SH with daily estimates of the SARS-CoV-2 reproduction number ( Rt ) and calculated the fraction of Rt attributable to these meteorological conditions. Both lower temperature and lower SH were significantly associated with increased Rt . The fraction of Rt attributable to temperature was 5.10% (95% eCI: 5.00 - 5.18%), and the fraction of Rt attributable to SH was 14.47% (95% eCI: 14.37 - 14.54%). These fractions generally were higher in northern counties than in southern counties. Our findings indicate that cold and dry weather are moderately associated with increased SARS-CoV-2 transmissibility, with humidity playing a larger role than temperature.
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Affiliation(s)
- Yiqun Ma
- Department of Environmental Health Sciences, Yale School of Public Health, 60 College Street, New Haven, CT, 06520-8034, USA
- Yale Center on Climate Change and Health, Yale School of Public Health, 60 College Street, New Haven, CT, 06520-8034, USA
| | - Sen Pei
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, NY 10032, USA
| | - Jeffrey Shaman
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, NY 10032, USA
| | - Robert Dubrow
- Department of Environmental Health Sciences, Yale School of Public Health, 60 College Street, New Haven, CT, 06520-8034, USA
- Yale Center on Climate Change and Health, Yale School of Public Health, 60 College Street, New Haven, CT, 06520-8034, USA
| | - Kai Chen
- Department of Environmental Health Sciences, Yale School of Public Health, 60 College Street, New Haven, CT, 06520-8034, USA
- Yale Center on Climate Change and Health, Yale School of Public Health, 60 College Street, New Haven, CT, 06520-8034, USA
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Kwon S, Joshi AD, Lo CH, Drew DA, Nguyen LH, Guo CG, Ma W, Mehta RS, Warner ET, Astley CM, Merino J, Murray B, Wolf J, Ourselin S, Steves CJ, Spector TD, Hart JE, Song M, VoPham T, Chan AT. Association of social distancing and masking with risk of COVID-19. medRxiv 2020:2020.11.11.20229500. [PMID: 33200150 PMCID: PMC7668763 DOI: 10.1101/2020.11.11.20229500] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Given the continued burden of severe acute respiratory syndrome coronavirus 2 (SARS CoV-2) disease (COVID-19) across the U.S., there is a high unmet need for data to inform decision-making regarding social distancing and universal masking. We examined the association of community-level social distancing measures and individual masking with risk of predicted COVID-19 in a large prospective U.S. cohort study of 198,077 participants. Individuals living in communities with the greatest social distancing had a 31% lower risk of predicted COVID-19 compared with those living in communities with poor social distancing. Self-reported masking was associated with a 63% reduced risk of predicted COVID-19 even among individuals living in a community with poor social distancing. These findings provide support for the efficacy of mask-wearing even in settings of poor social distancing in reducing COVID-19 transmission. In the current environment of relaxed social distancing mandates and practices, universal masking may be particularly important in mitigating risk of infection.
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Affiliation(s)
- Sohee Kwon
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Amit D. Joshi
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Chun-Han Lo
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - David A. Drew
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Long H. Nguyen
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Chuan-Guo Guo
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Department of Medicine, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong, China
| | - Wenjie Ma
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Raaj S. Mehta
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Erica T. Warner
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Harvard/MGH Center on Genomics, Vulnerable Populations, and Health Disparities, Massachusetts General Hospital, Boston, MA, USA
| | - Christina M. Astley
- Division of Endocrinology and Computational Epidemiology, Boston Children’s Hospital and Harvard Medical School, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Jordi Merino
- Diabetes Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Benjamin Murray
- School of Biomedical Engineering & Imaging Sciences, King’s College London, London, U.K
| | | | - Sebastien Ourselin
- School of Biomedical Engineering & Imaging Sciences, King’s College London, London, U.K
| | - Claire J. Steves
- Department of Twin Research and Genetic Epidemiology, King’s College London, London, U.K
| | - Tim D. Spector
- Department of Twin Research and Genetic Epidemiology, King’s College London, London, U.K
| | - Jaime E. Hart
- Channing Division of Network Medicine, Department of Medicine, Brigham and Hospital and Harvard Medical School, Boston, MA, USA
- Exposure, Epidemiology and Risk Program, Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Mingyang Song
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Trang VoPham
- Epidemiology Program, Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, 15 Seattle, WA, USA
- Department of Epidemiology, University of Washington School of Public Health, Seattle, WA, USA
| | - Andrew T. Chan
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Diabetes Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Immunology and Infectious Disease, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Massachusetts Consortium on Pathogen Readiness
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Chan YWD, Flasche S, Lam TLT, Leung MHJ, Wong ML, Lam HY, Chuang SK. Transmission dynamics, serial interval and epidemiology of COVID-19 diseases in Hong Kong under different control measures. Wellcome Open Res 2020. [DOI: 10.12688/wellcomeopenres.15896.2] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023] Open
Abstract
Background: The outbreak of coronavirus disease 2019 (COVID-19) started in Wuhan, China in late December 2019, and subsequently became a pandemic. Hong Kong had implemented a series of control measures since January 2020, including enhanced surveillance, isolation and quarantine, border control and social distancing. Hong Kong recorded its first case on 23 January 2020, who was a visitor from Wuhan. We analysed the surveillance data of COVID-19 to understand the transmission dynamics and epidemiology in Hong Kong. Methods: We constructed the epidemic curve of daily COVID-19 incidence from 23 January to 6 April 2020 and estimated the time-varying reproduction number (Rt) with the R package EpiEstim, with serial interval computed from local data. We described the demographic and epidemiological characteristics of reported cases. We computed weekly incidence by age and residential district to understand the spatial and temporal transmission of the disease. Results: COVID-19 disease in Hong Kong was characterised with local cases and clusters detected after two waves of importations, first in late January (week 4 to 6) and the second one in early March (week 9 to 10). The Rt increased to approximately 2 95% credible interval (CI): 0.3-3.3) and approximately 1 (95%CI: 0.2-1.7), respectively, following these importations; it decreased to below 1 afterwards from weeks 11 to 13, which coincided with the implementation, modification and intensification of different control measures. Compared to local cases, imported cases were younger (mean age: 52 years among local cases vs 35 years among imported cases), had a lower proportion of underlying disease (9% vs 5%) and severe outcome (13% vs 5%). Cases were recorded in all districts but the incidence was highest in those in the Hong Kong Island region. Conclusions: Stringent and sustained public health measures at population level could contain the COVID-19 disease at a relatively low level.
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Rader B, White LF, Burns MR, Chen J, Brilliant J, Cohen J, Shaman J, Brilliant L, Kraemer MU, Hawkins JB, Scarpino SV, Astley CM, Brownstein JS. Mask Wearing and Control of SARS-CoV-2 Transmission in the United States. medRxiv 2020:2020.08.23.20078964. [PMID: 32869039 PMCID: PMC7457618 DOI: 10.1101/2020.08.23.20078964] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
INTRODUCTION Cloth face coverings and surgical masks have become commonplace across the United States in response to the SARS-CoV-2 epidemic. While evidence suggests masks help curb the spread of respiratory pathogens, population level, empirical research remains limited. Face masks have quickly become a topic of public debate as government mandates have started requiring their use. Here we investigate the association between self-reported mask wearing, social distancing and community SARS-CoV-2 transmission in the United States, as well as the effect of statewide mandates on mask uptake. METHODS Serial cross-sectional surveys were administered June 3 through July 27, 2020 via a web platform. Surveys queried individuals' likelihood to wear a face mask to the grocery store or with family and friends. Responses (N = 378,207) were aggregated by week and state and combined with measures of the instantaneous reproductive number (R t ), social distancing proxies, respondent demographics and other potential sources of confounding. We fit multivariate logistic regression models to estimate the association between mask wearing and community transmission control (R t <1) for each state and week. Multiple sensitivity analyses were considered to corroborate findings across mask wearing definitions, R t estimators and data sources. Additionally, mask wearing in 12 states was evaluated two weeks before and after statewide mandates. RESULTS We find an increasing trend in mask usage across the U.S., although uptake varies by geography and demographic groups. A multivariate logistic model controlling for social distancing and other variables found a 10% increase in mask wearing was associated with a 3.53 (95% CI: 2.03, 6.43) odds of transmission control (R t <1). We also find that communities with high mask wearing and social distancing have the highest predicted probability of a controlled epidemic. These positive associations were maintained across sensitivity analyses. Following state mandates, mask wearing did not show significant statistical changes in uptake, however the positive trend of increased mask wearing over time was preserved. CONCLUSION Widespread utilization of face masks combined with social distancing increases the odds of SARS-CoV-2 transmission control. Mask wearing rose separately from government mask mandates, suggesting supplemental public health interventions are needed to maximize mask adoption and disrupt the spread of SARS-CoV-2, especially as social distancing measures are relaxed.
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Affiliation(s)
- Benjamin Rader
- Computational Epidemiology Lab, Boston Children’s Hospital, Boston, USA
- Department of Epidemiology, Boston University School of Public Health, Boston, USA
| | - Laura F. White
- Department of Biostatistics, Boston University School of Public Health, Boston, USA
| | - Michael R. Burns
- Computational Epidemiology Lab, Boston Children’s Hospital, Boston, USA
| | | | | | | | - Jeffrey Shaman
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, USA
| | | | - Moritz U.G. Kraemer
- Computational Epidemiology Lab, Boston Children’s Hospital, Boston, USA
- Department of Zoology, University of Oxford, Oxford, UK
- Harvard Medical School, Harvard University, Boston, USA
| | - Jared B. Hawkins
- Computational Epidemiology Lab, Boston Children’s Hospital, Boston, USA
- Harvard Medical School, Harvard University, Boston, USA
| | - Samuel V. Scarpino
- Network Science Institute, Northeastern University, Boston, USA
- Santa Fe Institute, Santa Fe, USA
| | - Christina M. Astley
- Computational Epidemiology Lab, Boston Children’s Hospital, Boston, USA
- Harvard Medical School, Harvard University, Boston, USA
- Division of Endocrinology, Boston Children’s Hospital, Boston, USA
- Broad Institute of Harvard and MIT, Cambridge, USA
| | - John S. Brownstein
- Computational Epidemiology Lab, Boston Children’s Hospital, Boston, USA
- Harvard Medical School, Harvard University, Boston, USA
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29
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Ali ST, Wang L, Lau EHY, Xu XK, Du Z, Wu Y, Leung GM, Cowling BJ. Serial interval of SARS-CoV-2 was shortened over time by nonpharmaceutical interventions. Science 2020; 369:1106-1109. [PMID: 32694200 PMCID: PMC7402628 DOI: 10.1126/science.abc9004] [Citation(s) in RCA: 210] [Impact Index Per Article: 52.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2020] [Accepted: 07/13/2020] [Indexed: 12/13/2022]
Abstract
In epidemiology, serial intervals are measured from when one infected person starts to show symptoms to when the next person infected becomes symptomatic. For any specific infection, the serial interval is assumed to be a fixed characteristic. Using valuable transmission pair data for coronavirus disease (COVID-19) in mainland China, Ali et al. noticed that the average serial interval changed as nonpharmaceutical interventions were introduced. In mid-January 2020, serial intervals were on average 7.8 days, whereas in early February 2020, they decreased to an average of 2.2 days. The more quickly infected persons were identified and isolated, the shorter the serial interval became and the fewer the opportunities for virus transmission. The change in serial interval may not only measure the effectiveness of infection control interventions but may also indicate rising population immunity. Science, this issue p. 1106 Studies of novel coronavirus disease 2019 (COVID-19), which is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), have reported varying estimates of epidemiological parameters, including serial interval distributions—i.e., the time between illness onset in successive cases in a transmission chain—and reproduction numbers. By compiling a line-list database of transmission pairs in mainland China, we show that mean serial intervals of COVID-19 shortened substantially from 7.8 to 2.6 days within a month (9 January to 13 February 2020). This change was driven by enhanced nonpharmaceutical interventions, particularly case isolation. We also show that using real-time estimation of serial intervals allowing for variation over time provides more accurate estimates of reproduction numbers than using conventionally fixed serial interval distributions. These findings could improve our ability to assess transmission dynamics, forecast future incidence, and estimate the impact of control measures.
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Affiliation(s)
- Sheikh Taslim Ali
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Lin Wang
- Department of Genetics, University of Cambridge, Cambridge CB2 3EH, UK.,Mathematical Modelling of Infectious Diseases Unit, Institut Pasteur, UMR2000, CNRS, Paris 75015, France
| | - Eric H Y Lau
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Xiao-Ke Xu
- College of Information and Communication Engineering, Dalian Minzu University, Dalian 116600, China
| | - Zhanwei Du
- Department of Integrative Biology, University of Texas at Austin, Austin, TX 78705, USA
| | - Ye Wu
- School of Journalism and Communication, Beijing Normal University, Beijing 100875, China.,Computational Communication Research Center, Beijing Normal University, Zhuhai 519087, China
| | - Gabriel M Leung
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Benjamin J Cowling
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China.
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Loeffler-Wirth H, Schmidt M, Binder H. Covid-19 Transmission Trajectories-Monitoring the Pandemic in the Worldwide Context. Viruses 2020; 12:E777. [PMID: 32698418 PMCID: PMC7412525 DOI: 10.3390/v12070777] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2020] [Revised: 07/11/2020] [Accepted: 07/14/2020] [Indexed: 01/20/2023] Open
Abstract
The Covid-19 pandemic is developing worldwide with common dynamics but also with marked differences between regions and countries. These are not completely understood, but presumably, provide a clue to find ways to mitigate epidemics until strategies leading to its eradication become available. We describe an iteractive monitoring tool available in the internet. It enables inspection of the dynamic state of the epidemic in 187 countries using trajectories that visualize the transmission and removal rates of the epidemic and in this way bridge epi-curve tracking with modelling approaches. Examples were provided which characterize state of epidemic in different regions of the world in terms of fast and slow growing and decaying regimes and estimate associated rate factors. The basic spread of the disease is associated with transmission between two individuals every two-three days on the average. Non-pharmaceutical interventions decrease this value to up to ten days, whereas 'complete lock down' measures are required to stop the epidemic. Comparison of trajectories revealed marked differences between the countries regarding efficiency of measures taken against the epidemic. Trajectories also reveal marked country-specific recovery and death rate dynamics. The results presented refer to the pandemic state in May to July 2020 and can serve as 'working instruction' for timely monitoring using the interactive monitoring tool as a sort of 'seismometer' for the evaluation of the state of epidemic, e.g., the possible effect of measures taken in both, lock-down and lock-up directions. Comparison of trajectories between countries and regions will support developing hypotheses and models to better understand regional differences of dynamics of Covid-19.
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Affiliation(s)
- Henry Loeffler-Wirth
- IZBI, Interdisciplinary Centre for Bioinformatics, Universität Leipzig, Härtelstr. 16–18, 04107 Leipzig, Germany;
| | | | - Hans Binder
- IZBI, Interdisciplinary Centre for Bioinformatics, Universität Leipzig, Härtelstr. 16–18, 04107 Leipzig, Germany;
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