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Begga A, Garibo-i-Orts Ò, de María-García S, Escolano F, Lozano MA, Oliver N, Conejero JA. Predicting COVID-19 pandemic waves including vaccination data with deep learning. Front Public Health 2023; 11:1279364. [PMID: 38162619 PMCID: PMC10757845 DOI: 10.3389/fpubh.2023.1279364] [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] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Accepted: 11/13/2023] [Indexed: 01/03/2024] Open
Abstract
Introduction During the recent COVID-19 pandemics, many models were developed to predict the number of new infections. After almost a year, models had also the challenge to include information about the waning effect of vaccines and by infection, and also how this effect start to disappear. Methods We present a deep learning-based approach to predict the number of daily COVID-19 cases in 30 countries, considering the non-pharmaceutical interventions (NPIs) applied in those countries and including vaccination data of the most used vaccines. Results We empirically validate the proposed approach for 4 months between January and April 2021, once vaccination was available and applied to the population and the COVID-19 variants were closer to the one considered for developing the vaccines. With the predictions of new cases, we can prescribe NPIs plans that present the best trade-off between the expected number of COVID-19 cases and the social and economic cost of applying such interventions. Discussion Whereas, mathematical models which include the effect of vaccines in the spread of the SARS-COV-2 pandemic are available, to the best of our knowledge we are the first to propose a data driven method based on recurrent neural networks that considers the waning effect of the immunization acquired either by vaccine administration or by recovering from the illness. This work contributes with an accurate, scalable, data-driven approach to modeling the pandemic curves of cases when vaccination data is available.
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Affiliation(s)
- Ahmed Begga
- Instituto Universitario de Matemática Pura y Aplicada, Universitat Politécnica de València, València, Spain
| | - Òscar Garibo-i-Orts
- Instituto Universitario de Matemática Pura y Aplicada, Universitat Politécnica de València, València, Spain
| | - Sergi de María-García
- Instituto Universitario de Matemática Pura y Aplicada, Universitat Politécnica de València, València, Spain
| | - Francisco Escolano
- Departamento de Ciencia de la Computación e I.A., Universidad de Alicante, Alicante, Spain
| | - Miguel A. Lozano
- Departamento de Ciencia de la Computación e I.A., Universidad de Alicante, Alicante, Spain
| | | | - J. Alberto Conejero
- Instituto Universitario de Matemática Pura y Aplicada, Universitat Politécnica de València, València, Spain
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Libkind S, Baas A, Halter M, Patterson E, Fairbanks JP. An algebraic framework for structured epidemic modelling. Philos Trans A Math Phys Eng Sci 2022; 380:20210309. [PMID: 35965465 PMCID: PMC9376710 DOI: 10.1098/rsta.2021.0309] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Accepted: 06/07/2022] [Indexed: 05/07/2023]
Abstract
Pandemic management requires that scientists rapidly formulate and analyse epidemiological models in order to forecast the spread of disease and the effects of mitigation strategies. Scientists must modify existing models and create novel ones in light of new biological data and policy changes such as social distancing and vaccination. Traditional scientific modelling workflows detach the structure of a model-its submodels and their interactions-from its implementation in software. Consequently, incorporating local changes to model components may require global edits to the code base through a manual, time-intensive and error-prone process. We propose a compositional modelling framework that uses high-level algebraic structures to capture domain-specific scientific knowledge and bridge the gap between how scientists think about models and the code that implements them. These algebraic structures, grounded in applied category theory, simplify and expedite modelling tasks such as model specification, stratification, analysis and calibration. With their structure made explicit, models also become easier to communicate, criticize and refine in light of stakeholder feedback. This article is part of the theme issue 'Technical challenges of modelling real-life epidemics and examples of overcoming these'.
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Affiliation(s)
- Sophie Libkind
- Department of Mathematics, Stanford University, Stanford, CA, USA
| | - Andrew Baas
- Georgia Tech Research Institute, Atlanta, GA, USA
| | - Micah Halter
- Georgia Tech Research Institute, Atlanta, GA, USA
| | | | - James P. Fairbanks
- Computer and Information Science and Engineering, University of Florida, Gainesville, FL, USA
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Chang SL, Cliff OM, Zachreson C, Prokopenko M. Simulating Transmission Scenarios of the Delta Variant of SARS-CoV-2 in Australia. Front Public Health 2022; 10:823043. [PMID: 35284395 PMCID: PMC8907620 DOI: 10.3389/fpubh.2022.823043] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [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] [Received: 11/26/2021] [Accepted: 01/20/2022] [Indexed: 11/13/2022] Open
Abstract
An outbreak of the Delta (B.1.617.2) variant of SARS-CoV-2 that began around mid-June 2021 in Sydney, Australia, quickly developed into a nation-wide epidemic. The ongoing epidemic is of major concern as the Delta variant is more infectious than previous variants that circulated in Australia in 2020. Using a re-calibrated agent-based model, we explored a feasible range of non-pharmaceutical interventions, including case isolation, home quarantine, school closures, and stay-at-home restrictions (i.e., "social distancing.") Our modelling indicated that the levels of reduced interactions in workplaces and across communities attained in Sydney and other parts of the nation were inadequate for controlling the outbreak. A counter-factual analysis suggested that if 70% of the population followed tight stay-at-home restrictions, then at least 45 days would have been needed for new daily cases to fall from their peak to below ten per day. Our model predicted that, under a progressive vaccination rollout, if 40-50% of the Australian population follow stay-at-home restrictions, the incidence will peak by mid-October 2021: the peak in incidence across the nation was indeed observed in mid-October. We also quantified an expected burden on the healthcare system and potential fatalities across Australia.
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Affiliation(s)
- Sheryl L. Chang
- Centre for Complex Systems, Faculty of Engineering, The University of Sydney, Sydney, NSW, Australia
| | - Oliver M. Cliff
- Centre for Complex Systems, Faculty of Engineering, The University of Sydney, Sydney, NSW, Australia
- School of Physics, The University of Sydney, Sydney, NSW, Australia
| | - Cameron Zachreson
- Centre for Complex Systems, Faculty of Engineering, The University of Sydney, Sydney, NSW, Australia
- School of Computing and Information Systems, The University of Melbourne, Parkville, VIC, Australia
| | - Mikhail Prokopenko
- Centre for Complex Systems, Faculty of Engineering, The University of Sydney, Sydney, NSW, Australia
- Sydney Institute for Infectious Diseases, The University of Sydney, Westmead, NSW, Australia
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Zachreson C, Chang SL, Cliff OM, Prokopenko M. How will mass-vaccination change COVID-19 lockdown requirements in Australia? Lancet Reg Health West Pac 2021; 14:100224. [PMID: 34345875 PMCID: PMC8323620 DOI: 10.1016/j.lanwpc.2021.100224] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/19/2021] [Revised: 07/05/2021] [Accepted: 07/09/2021] [Indexed: 01/04/2023]
Abstract
Background To prevent future outbreaks of COVID-19, Australia is pursuing a mass-vaccination approach in which a targeted group of the population comprising healthcare workers, aged-care residents and other individuals at increased risk of exposure will receive a highly effective priority vaccine. The rest of the population will instead have access to a less effective vaccine. Methods We apply a large-scale agent-based model of COVID-19 in Australia to investigate the possible implications of this hybrid approach to mass-vaccination. The model is calibrated to recent epidemiological and demographic data available in Australia, and accounts for several components of vaccine efficacy. Findings Within a feasible range of vaccine efficacy values, our model supports the assertion that complete herd immunity due to vaccination is not likely in the Australian context. For realistic scenarios in which herd immunity is not achieved, we simulate the effects of mass-vaccination on epidemic growth rate, and investigate the requirements of lockdown measures applied to curb subsequent outbreaks. In our simulations, Australia's vaccination strategy can feasibly reduce required lockdown intensity and initial epidemic growth rate by 43% and 52%, respectively. The severity of epidemics, as measured by the peak number of daily new cases, decreases by up to two orders of magnitude under plausible mass-vaccination and lockdown strategies. Interpretation The study presents a strong argument for a large-scale vaccination campaign in Australia, which would substantially reduce both the intensity of future outbreaks and the stringency of non-pharmaceutical interventions required for their suppression. Funding Australian Research Council; National Health and Medical Research Council.
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Affiliation(s)
- Cameron Zachreson
- Centre for Complex Systems, Faculty of Engineering, University of Sydney, Sydney, NSW 2006, Australia
- School of Computing and Information Systems, The University of Melbourne, Parkville, VIC 3052, Australia
| | - Sheryl L. Chang
- Centre for Complex Systems, Faculty of Engineering, University of Sydney, Sydney, NSW 2006, Australia
| | - Oliver M. Cliff
- Centre for Complex Systems, Faculty of Engineering, University of Sydney, Sydney, NSW 2006, Australia
| | - Mikhail Prokopenko
- Centre for Complex Systems, Faculty of Engineering, University of Sydney, Sydney, NSW 2006, Australia
- Sydney Institute for Infectious Diseases, University of Sydney, Westmead, NSW 2145, Australia
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Abstract
Background To prevent future outbreaks of COVID-19, Australia is pursuing a mass-vaccination approach in which a targeted group of the population comprising healthcare workers, aged-care residents and other individuals at increased risk of exposure will receive a highly effective priority vaccine. The rest of the population will instead have access to a less effective vaccine. Methods We apply a large-scale agent-based model of COVID-19 in Australia to investigate the possible implications of this hybrid approach to mass-vaccination. The model is calibrated to recent epidemiological and demographic data available in Australia, and accounts for several components of vaccine efficacy. Findings Within a feasible range of vaccine efficacy values, our model supports the assertion that complete herd immunity due to vaccination is not likely in the Australian context. For realistic scenarios in which herd immunity is not achieved, we simulate the effects of mass-vaccination on epidemic growth rate, and investigate the requirements of lockdown measures applied to curb subsequent outbreaks. In our simulations, Australia's vaccination strategy can feasibly reduce required lockdown intensity and initial epidemic growth rate by 43% and 52%, respectively. The severity of epidemics, as measured by the peak number of daily new cases, decreases by up to two orders of magnitude under plausible mass-vaccination and lockdown strategies. Interpretation The study presents a strong argument for a large-scale vaccination campaign in Australia, which would substantially reduce both the intensity of future outbreaks and the stringency of non-pharmaceutical interventions required for their suppression. Funding Australian Research Council; National Health and Medical Research Council.
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Affiliation(s)
- Cameron Zachreson
- Centre for Complex Systems, Faculty of Engineering, University of Sydney, Sydney, NSW 2006, Australia
- School of Computing and Information Systems, The University of Melbourne, Parkville, VIC 3052, Australia
| | - Sheryl L Chang
- Centre for Complex Systems, Faculty of Engineering, University of Sydney, Sydney, NSW 2006, Australia
| | - Oliver M Cliff
- Centre for Complex Systems, Faculty of Engineering, University of Sydney, Sydney, NSW 2006, Australia
| | - Mikhail Prokopenko
- Centre for Complex Systems, Faculty of Engineering, University of Sydney, Sydney, NSW 2006, Australia
- Sydney Institute for Infectious Diseases, University of Sydney, Westmead, NSW 2145, Australia
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Staffini A, Svensson AK, Chung UI, Svensson T. An Agent-Based Model of the Local Spread of SARS-CoV-2: Modeling Study. JMIR Med Inform 2021; 9:e24192. [PMID: 33750735 PMCID: PMC8025915 DOI: 10.2196/24192] [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] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2020] [Revised: 11/06/2020] [Accepted: 03/21/2021] [Indexed: 01/29/2023] Open
Abstract
BACKGROUND The spread of SARS-CoV-2, originating in Wuhan, China, was classified as a pandemic by the World Health Organization on March 11, 2020. The governments of affected countries have implemented various measures to limit the spread of the virus. The starting point of this paper is the different government approaches, in terms of promulgating new legislative regulations to limit the virus diffusion and to contain negative effects on the populations. OBJECTIVE This paper aims to study how the spread of SARS-CoV-2 is linked to government policies and to analyze how different policies have produced different results on public health. METHODS Considering the official data provided by 4 countries (Italy, Germany, Sweden, and Brazil) and from the measures implemented by each government, we built an agent-based model to study the effects that these measures will have over time on different variables such as the total number of COVID-19 cases, intensive care unit (ICU) bed occupancy rates, and recovery and case-fatality rates. The model we implemented provides the possibility of modifying some starting variables, and it was thus possible to study the effects that some policies (eg, keeping the national borders closed or increasing the ICU beds) would have had on the spread of the infection. RESULTS The 4 considered countries have adopted different containment measures for COVID-19, and the forecasts provided by the model for the considered variables have given different results. Italy and Germany seem to be able to limit the spread of the infection and any eventual second wave, while Sweden and Brazil do not seem to have the situation under control. This situation is also reflected in the forecasts of pressure on the National Health Services, which see Sweden and Brazil with a high occupancy rate of ICU beds in the coming months, with a consequent high number of deaths. CONCLUSIONS In line with what we expected, the obtained results showed that the countries that have taken restrictive measures in terms of limiting the population mobility have managed more successfully than others to contain the spread of COVID-19. Moreover, the model demonstrated that herd immunity cannot be reached even in countries that have relied on a strategy without strict containment measures.
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Affiliation(s)
- Alessio Staffini
- Department of Economics and Finance, Catholic University of Milan, Milan, Italy
- Project Promotion Department, ALBERT Inc, Tokyo, Japan
- Precision Health, Department of Bioengineering, Graduate School of Engineering, The University of Tokyo, Tokyo, Japan
| | - Akiko Kishi Svensson
- Precision Health, Department of Bioengineering, Graduate School of Engineering, The University of Tokyo, Tokyo, Japan
- Department of Clinical Sciences, Lund University, Malmö, Sweden
- Department of Diabetes and Metabolic Diseases, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Ung-Il Chung
- Precision Health, Department of Bioengineering, Graduate School of Engineering, The University of Tokyo, Tokyo, Japan
- School of Health Innovation, Kanagawa University of Human Services, Tonomachi, Japan
- Clinical Biotechnology, Center for Disease Biology and Integrative Medicine, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Thomas Svensson
- Precision Health, Department of Bioengineering, Graduate School of Engineering, The University of Tokyo, Tokyo, Japan
- Department of Clinical Sciences, Lund University, Malmö, Sweden
- School of Health Innovation, Kanagawa University of Human Services, Tonomachi, Japan
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Nobles AL, Dredze M, Ayers JW. “Repeal and replace”: increased demand for intrauterine devices following the 2016 presidential election. Contraception 2019; 99:293-295. [PMID: 30878137 DOI: 10.1016/j.contraception.2018.10.012] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2018] [Revised: 10/29/2018] [Accepted: 10/29/2018] [Indexed: 10/27/2022]
Abstract
OBJECTIVE To evaluate public's interest in contraceptive options following heightened focus on a repeal of the Affordable Care Act (ACA) since the 2016 United States presidential election. STUDY DESIGN We monitored the fraction of Google searches emerging from the United States for the three most popular reversible contraceptive methods - oral contraceptives, intrauterine devices (IUDs) and condoms - from January 1, 2004, through October 31, 2017 (1 year after the presidential election). RESULTS IUD searches were cumulatively 15% (95% CI: 10 to 20) higher than expected the year following the 2016 election, reflecting 10 to 21 million excess searches. IUD searches were statistically significantly higher in all states, except NV, and were consistent across states won by Trump or Clinton (Welch t test=0.60, p=.548). Conversely, searches for oral contraceptives and condoms remained stable (0%; 95% CI: -2 to 1) or declined (-4%; 95% CI: -5 to -2), respectively, following the election. CONCLUSIONS The etiology of increased searches for IUDs is likely multifaceted. However, it may largely be because IUDs will confer continued protection even after an ACA repeal, thereby providing a medical hedge against a possible repeal. Regardless, these data suggest the heightened focus on an ACA repeal is a concern to the record number of Americans seeking out information about IUDs.
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Affiliation(s)
- Alicia L Nobles
- Division of Infectious Disease and Global Public Health, UC San Diego, La Jolla, CA.
| | - Mark Dredze
- Department of Computer Science, Johns Hopkins University, Baltimore.
| | - John W Ayers
- Division of Infectious Disease and Global Public Health, UC San Diego, La Jolla, CA.
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Fairchild G, Tasseff B, Khalsa H, Generous N, Daughton AR, Velappan N, Priedhorsky R, Deshpande A. Epidemiological Data Challenges: Planning for a More Robust Future Through Data Standards. Front Public Health 2018; 6:336. [PMID: 30533407 PMCID: PMC6265573 DOI: 10.3389/fpubh.2018.00336] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [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: 07/17/2018] [Accepted: 11/01/2018] [Indexed: 12/23/2022] Open
Abstract
Accessible epidemiological data are of great value for emergency preparedness and response, understanding disease progression through a population, and building statistical and mechanistic disease models that enable forecasting. The status quo, however, renders acquiring and using such data difficult in practice. In many cases, a primary way of obtaining epidemiological data is through the internet, but the methods by which the data are presented to the public often differ drastically among institutions. As a result, there is a strong need for better data sharing practices. This paper identifies, in detail and with examples, the three key challenges one encounters when attempting to acquire and use epidemiological data: (1) interfaces, (2) data formatting, and (3) reporting. These challenges are used to provide suggestions and guidance for improvement as these systems evolve in the future. If these suggested data and interface recommendations were adhered to, epidemiological and public health analysis, modeling, and informatics work would be significantly streamlined, which can in turn yield better public health decision-making capabilities.
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Affiliation(s)
- Geoffrey Fairchild
- Analytics, Intelligence, and Technology Division, Los Alamos National Laboratory, Los Alamos, NM, United States
| | - Byron Tasseff
- Analytics, Intelligence, and Technology Division, Los Alamos National Laboratory, Los Alamos, NM, United States
| | - Hari Khalsa
- Analytics, Intelligence, and Technology Division, Los Alamos National Laboratory, Los Alamos, NM, United States
| | - Nicholas Generous
- Analytics, Intelligence, and Technology Division, Los Alamos National Laboratory, Los Alamos, NM, United States
| | - Ashlynn R Daughton
- Analytics, Intelligence, and Technology Division, Los Alamos National Laboratory, Los Alamos, NM, United States
| | - Nileena Velappan
- Bioscience Division, Los Alamos National Laboratory, Los Alamos, NM, United States
| | - Reid Priedhorsky
- High Performance Computing Division, Los Alamos National Laboratory, Los Alamos, NM, United States
| | - Alina Deshpande
- Bioscience Division, Los Alamos National Laboratory, Los Alamos, NM, United States
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Abstract
Precision public health is an emerging practice to more granularly predict and understand public health risks and customize treatments for more specific and homogeneous subpopulations, often using new data, technologies, and methods. Big data is one element that has consistently helped to achieve these goals, through its ability to deliver to practitioners a volume and variety of structured or unstructured data not previously possible. Big data has enabled more widespread and specific research and trials of stratifying and segmenting populations at risk for a variety of health problems. Examples of success using big data are surveyed in surveillance and signal detection, predicting future risk, targeted interventions, and understanding disease. Using novel big data or big data approaches has risks that remain to be resolved. The continued growth in volume and variety of available data, decreased costs of data capture, and emerging computational methods mean big data success will likely be a required pillar of precision public health into the future. This review article aims to identify the precision public health use cases where big data has added value, identify classes of value that big data may bring, and outline the risks inherent in using big data in precision public health efforts.
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Zhang Q, Sun K, Chinazzi M, Pastore Y Piontti A, Dean NE, Rojas DP, Merler S, Mistry D, Poletti P, Rossi L, Bray M, Halloran ME, Longini IM, Vespignani A. Spread of Zika virus in the Americas. Proc Natl Acad Sci U S A 2017; 114:E4334-E4343. [PMID: 28442561 PMCID: PMC5465916 DOI: 10.1073/pnas.1620161114] [Citation(s) in RCA: 171] [Impact Index Per Article: 24.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022] Open
Abstract
We use a data-driven global stochastic epidemic model to analyze the spread of the Zika virus (ZIKV) in the Americas. The model has high spatial and temporal resolution and integrates real-world demographic, human mobility, socioeconomic, temperature, and vector density data. We estimate that the first introduction of ZIKV to Brazil likely occurred between August 2013 and April 2014 (90% credible interval). We provide simulated epidemic profiles of incident ZIKV infections for several countries in the Americas through February 2017. The ZIKV epidemic is characterized by slow growth and high spatial and seasonal heterogeneity, attributable to the dynamics of the mosquito vector and to the characteristics and mobility of the human populations. We project the expected timing and number of pregnancies infected with ZIKV during the first trimester and provide estimates of microcephaly cases assuming different levels of risk as reported in empirical retrospective studies. Our approach represents a modeling effort aimed at understanding the potential magnitude and timing of the ZIKV epidemic and it can be potentially used as a template for the analysis of future mosquito-borne epidemics.
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Affiliation(s)
- Qian Zhang
- Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, MA 02115
| | - Kaiyuan Sun
- Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, MA 02115
| | - Matteo Chinazzi
- Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, MA 02115
| | - Ana Pastore Y Piontti
- Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, MA 02115
| | - Natalie E Dean
- Department of Biostatistics, College of Public Health and Health Professions, University of Florida, Gainesville, FL 32611
| | - Diana Patricia Rojas
- Department of Epidemiology, College of Public Health and Health Professions, University of Florida, Gainesville, FL 32611
| | | | - Dina Mistry
- Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, MA 02115
| | - Piero Poletti
- Dondena Centre for Research on Social Dynamics and Public Policy, Universitá Commerciale L. Bocconi, 20136 Milan, Italy
| | - Luca Rossi
- Institute for Scientific Interchange Foundation, 10126 Turin, Italy
| | - Margaret Bray
- Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, MA 02115
| | - M Elizabeth Halloran
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109
- Department of Biostatistics, University of Washington, Seattle, WA 98195
| | - Ira M Longini
- Department of Biostatistics, College of Public Health and Health Professions, University of Florida, Gainesville, FL 32611
| | - Alessandro Vespignani
- Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, MA 02115;
- Institute for Scientific Interchange Foundation, 10126 Turin, Italy
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Tizzoni M, Bajardi P, Poletto C, Ramasco JJ, Balcan D, Gonçalves B, Perra N, Colizza V, Vespignani A. Real-time numerical forecast of global epidemic spreading: case study of 2009 A/H1N1pdm. BMC Med 2012; 10:165. [PMID: 23237460 PMCID: PMC3585792 DOI: 10.1186/1741-7015-10-165] [Citation(s) in RCA: 136] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/12/2012] [Accepted: 12/13/2012] [Indexed: 12/20/2022] Open
Abstract
BACKGROUND Mathematical and computational models for infectious diseases are increasingly used to support public-health decisions; however, their reliability is currently under debate. Real-time forecasts of epidemic spread using data-driven models have been hindered by the technical challenges posed by parameter estimation and validation. Data gathered for the 2009 H1N1 influenza crisis represent an unprecedented opportunity to validate real-time model predictions and define the main success criteria for different approaches. METHODS We used the Global Epidemic and Mobility Model to generate stochastic simulations of epidemic spread worldwide, yielding (among other measures) the incidence and seeding events at a daily resolution for 3,362 subpopulations in 220 countries. Using a Monte Carlo Maximum Likelihood analysis, the model provided an estimate of the seasonal transmission potential during the early phase of the H1N1 pandemic and generated ensemble forecasts for the activity peaks in the northern hemisphere in the fall/winter wave. These results were validated against the real-life surveillance data collected in 48 countries, and their robustness assessed by focusing on 1) the peak timing of the pandemic; 2) the level of spatial resolution allowed by the model; and 3) the clinical attack rate and the effectiveness of the vaccine. In addition, we studied the effect of data incompleteness on the prediction reliability. RESULTS Real-time predictions of the peak timing are found to be in good agreement with the empirical data, showing strong robustness to data that may not be accessible in real time (such as pre-exposure immunity and adherence to vaccination campaigns), but that affect the predictions for the attack rates. The timing and spatial unfolding of the pandemic are critically sensitive to the level of mobility data integrated into the model. CONCLUSIONS Our results show that large-scale models can be used to provide valuable real-time forecasts of influenza spreading, but they require high-performance computing. The quality of the forecast depends on the level of data integration, thus stressing the need for high-quality data in population-based models, and of progressive updates of validated available empirical knowledge to inform these models.
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Affiliation(s)
- Michele Tizzoni
- Computational Epidemiology Laboratory, Institute for Scientific Interchange, ISI, Torino, Italy
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Balcan D, Gonçalves B, Hu H, Ramasco JJ, Colizza V, Vespignani A. Modeling the spatial spread of infectious diseases: the GLobal Epidemic and Mobility computational model. J Comput Sci 2010; 1:132-145. [PMID: 21415939 PMCID: PMC3056392 DOI: 10.1016/j.jocs.2010.07.002] [Citation(s) in RCA: 205] [Impact Index Per Article: 14.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
Here we present the Global Epidemic and Mobility (GLEaM) model that integrates sociodemographic and population mobility data in a spatially structured stochastic disease approach to simulate the spread of epidemics at the worldwide scale. We discuss the flexible structure of the model that is open to the inclusion of different disease structures and local intervention policies. This makes GLEaM suitable for the computational modeling and anticipation of the spatio-temporal patterns of global epidemic spreading, the understanding of historical epidemics, the assessment of the role of human mobility in shaping global epidemics, and the analysis of mitigation and containment scenarios.
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Affiliation(s)
- Duygu Balcan
- Center for Complex Networks and Systems Research (CNetS), School of Informatics and Computing, Indiana University, Bloomington, IN 47408, USA
- Pervasive Technology Institute, Indiana University, Bloomington, IN 47406, USA
| | - Bruno Gonçalves
- Center for Complex Networks and Systems Research (CNetS), School of Informatics and Computing, Indiana University, Bloomington, IN 47408, USA
- Pervasive Technology Institute, Indiana University, Bloomington, IN 47406, USA
| | - Hao Hu
- Department of Physics, Indiana University, Bloomington, IN 47406, USA
| | - José J. Ramasco
- Computational Epidemiology Laboratory, Institute for Scientific Interchange (ISI), Torino, Italy
| | - Vittoria Colizza
- Computational Epidemiology Laboratory, Institute for Scientific Interchange (ISI), Torino, Italy
| | - Alessandro Vespignani
- Center for Complex Networks and Systems Research (CNetS), School of Informatics and Computing, Indiana University, Bloomington, IN 47408, USA
- Pervasive Technology Institute, Indiana University, Bloomington, IN 47406, USA
- Computational Epidemiology Laboratory, Institute for Scientific Interchange (ISI), Torino, Italy
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