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Zhang R, Tai J, Yao Q, Yang W, Ruggeri K, Shaman J, Pei S. Behavior-driven forecasts of neighborhood-level COVID-19 spread in New York City. PLoS Comput Biol 2025; 21:e1012979. [PMID: 40300036 PMCID: PMC12101855 DOI: 10.1371/journal.pcbi.1012979] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2024] [Revised: 05/23/2025] [Accepted: 03/18/2025] [Indexed: 05/01/2025] Open
Abstract
The COVID-19 pandemic in New York City (NYC) was characterized by marked disparities in disease burdens across neighborhoods. Accurate neighborhood-level forecasts are critical for planning more equitable resource allocation to reduce health inequalities; however, such spatially high-resolution forecasts remain scarce in operational use. In this study, we analyze aggregated foot traffic data derived from mobile devices to measure the connectivity among 42 NYC neighborhoods driven by various human activities such as dining, shopping, and entertainment. Using real-world time-varying contact patterns in different place categories, we develop a parsimonious behavior-driven epidemic model that incorporates population mixing, indoor crowdedness, dwell time, and seasonality of virus transmissibility. We fit this model to neighborhood-level COVID-19 case data in NYC and further couple this model with a data assimilation algorithm to generate short-term forecasts of neighborhood-level COVID-19 cases in 2020. We find differential contact patterns and connectivity between neighborhoods driven by different human activities. The behavior-driven model supports accurate modeling of neighborhood-level SARS-CoV-2 transmission throughout 2020. In the best-fitting model, we estimate that the force of infection (FOI) in indoor settings increases sublinearly with crowdedness and dwell time. Retrospective forecasting demonstrates that this behavior-driven model generates improved short-term forecasts in NYC neighborhoods compared to several baseline models. Our findings indicate that aggregated foot-traffic data for routine human activities can support neighborhood-level COVID-19 forecasts in NYC. This behavior-driven model may be adapted for use with other respiratory pathogens sharing similar transmission routes.
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Affiliation(s)
- Renquan Zhang
- School of Mathematical Sciences, Dalian University of Technology, Dalian, China
| | - Jilei Tai
- School of Mathematical Sciences, Dalian University of Technology, Dalian, China
| | - Qing Yao
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, New York, United States of America
| | - Wan Yang
- Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, New York, United States of America
- Herbert Irving Comprehensive Cancer Center, Columbia University Medical Center, New York, New York, United States of America
| | - Kai Ruggeri
- Department of Health Policy and Management, Mailman School of Public Health, Columbia University, New York, New York, United States of America
| | - Jeffrey Shaman
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, New York, United States of America
- Columbia Climate School, Columbia University, New York, New York, United States of America
| | - Sen Pei
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, New York, United States of America
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2
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Loyens DB, Caetano C, Matias-Dias C. Impact of non-pharmacological interventions on the first wave of COVID-19 in Portugal 2020. Heliyon 2025; 11:e41569. [PMID: 40028538 PMCID: PMC11868933 DOI: 10.1016/j.heliyon.2024.e41569] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2024] [Revised: 12/23/2024] [Accepted: 12/28/2024] [Indexed: 03/05/2025] Open
Abstract
Introduction The COVID-19 pandemic caused over 7 million global deaths. Without vaccines during the first wave, governments implemented nonpharmacological interventions (NPIs) such as lockdowns, school closures, and travel restrictions. This study quantifies the impact of NPIs on COVID-19 transmission in Portugal between 24th February and 1st May. Methods A compartmental SEIR (Susceptible, Exposed, Infectious, Removed) model was employed to simulate the first COVID-19 wave in Portugal, using a Bayesian approach and symptom-onset incidence data. The effect of the lockdown, which began on March 22, 2020, on the effective reproductive number, R t was measured. A counterfactual scenario was created to ascertain the number of cases prevented by the NPIs during the first 15 days after the implementation of NPI. Results The lockdown reduced overall transmission by 68·6 % (95%Credible Interval (95%CrI): 59·2 %; 77·5 %), almost immediately. This corresponds to a reduction in the effective reproductive number from 2·56 (95%CrI: 2·08; 3·40) to 0·80 (95%CrI: 0·76; 0·84). The counterfactual scenario estimated that the lockdown prevented 118052 (95%CrI: 99464; 145605) cases between 24th February and 6th April. Discussion The lockdown significantly reduced COVID-19 transmission in Portugal, bringing Rt below 1, meaning each person infected fewer than one individual. While costly, lockdowns effectively control disease spread in the absence of vaccines. Conclusion Our findings suggest NPIs curbed epidemic transmission, reducing Rt below 1 and easing hospital loads and deaths. This research will help inform future pandemic decision-making and infectious disease modeling worldwide.
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Affiliation(s)
- Dinis B. Loyens
- Unidade de Saúde Pública da Amadora, Unidade Local de Saúde Amadora/Sintra, Portugal
- Departamento de Epidemiologia, Instituto Nacional de Saúde Doutor Ricardo Jorge, Portugal
| | - Constantino Caetano
- Departamento de Epidemiologia, Instituto Nacional de Saúde Doutor Ricardo Jorge, Portugal
| | - Carlos Matias-Dias
- Departamento de Epidemiologia, Instituto Nacional de Saúde Doutor Ricardo Jorge, Portugal
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3
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Avilov KK, Li Q, Lin L, Demirhan H, Stone L, He D. The 1978 English boarding school influenza outbreak: where the classic SEIR model fails. J R Soc Interface 2024; 21:20240394. [PMID: 39563495 PMCID: PMC11576841 DOI: 10.1098/rsif.2024.0394] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2023] [Revised: 08/05/2024] [Accepted: 09/17/2024] [Indexed: 11/21/2024] Open
Abstract
Previous work has failed to fit classic SEIR epidemic models satisfactorily to the prevalence data of the famous English boarding school 1978 influenza A/H1N1 outbreak during the children's pandemic. It is still an open question whether a biologically plausible model can fit the prevalence time series and the attack rate correctly. To construct the final model, we first used an intentionally very flexible and overfitted discrete-time epidemiologic model to learn the epidemiological features from the data. The final model was a susceptible (S) - exposed (E) - infectious (I) - confined-to-bed (B) - convalescent (C) - recovered (R) model with time delay (constant residence time) in E and I compartments and multi-stage (Erlang-distributed residence time) in B and C compartments. We simultaneously fitted the reported B and C prevalence curves as well as the attack rate (proportion of children infected during the outbreak). The non-exponential residence times were crucial for good fits. The estimates of the generation time and the basic reproductive number ([Formula: see text]) were biologically reasonable. A simplified discrete-time model was built and fitted using the Bayesian procedure. Our work not only provided an answer to the open question, but also demonstrated an approach to constructive model generation.
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Affiliation(s)
- Konstantin K. Avilov
- Department of Applied Mathematics, The Hong Kong Polytechnic University, Hong Kong SAR, People’s Republic of China
| | - Qiong Li
- Guangdong Provincial Key Laboratory of Interdisciplinary Research and Application for Data Science, BNU-HKBU United International College, Zhuhai, People’s Republic of China
| | - Lixin Lin
- Mathematical Sciences, School of Science, Royal Melbourne Institute of Technology (RMIT) University, Melbourne, Victoria, Australia
| | - Haydar Demirhan
- Mathematical Sciences, School of Science, Royal Melbourne Institute of Technology (RMIT) University, Melbourne, Victoria, Australia
| | - Lewi Stone
- Mathematical Sciences, School of Science, Royal Melbourne Institute of Technology (RMIT) University, Melbourne, Victoria, Australia
- Biomathematics Unit, Faculty of Life Sciences, Tel Aviv University, Ramat Aviv, Israel
| | - Daihai He
- Department of Applied Mathematics, The Hong Kong Polytechnic University, Hong Kong SAR, People’s Republic of China
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4
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Jdid T, Benbrahim M, Kabbaj MN, Naji M. A vaccination-based COVID-19 model: Analysis and prediction using Hamiltonian Monte Carlo. Heliyon 2024; 10:e38204. [PMID: 39391520 PMCID: PMC11466577 DOI: 10.1016/j.heliyon.2024.e38204] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Revised: 09/18/2024] [Accepted: 09/19/2024] [Indexed: 10/12/2024] Open
Abstract
Compartmental models have emerged as robust computational frameworks and have yielded remarkable success in the fight against COVID-19. This study proposes a vaccination-based compartmental model for COVID-19 transmission dynamics. The model reflects the specific stages of COVID-19 infection and integrates a vaccination strategy, allowing for a comprehensive analysis of how vaccination rates influence the disease spread. We fit this model to daily confirmed COVID-19 cases in Tennessee, United States of America (USA), from June 4 to November 26, 2021, in a Bayesian inference approach using the Hamiltonian Monte Carlo (HMC) algorithm. First, excluding vaccination dynamics from the model, we estimated key epidemiological parameters like infection, recovery, and disease-induced death rates. This analysis yielded a basic reproduction number (R 0 ) of 1.5. Second, we incorporated vaccination dynamics and estimated the vaccination rate for three vaccines: 0.0051 per day for both Pfizer and Moderna and 0.0059 per day for Janssen. The fitted curves show reductions in the epidemic peak for all three vaccines. Pfizer and Moderna vaccines bring the peak down from 8,029 infected cases to 5,616 infected cases, while the Janssen vaccine reduces it, to 6,493 infected cases. Simulations of the model by varying the vaccination rate and vaccine efficacy were performed. A highly effective vaccine (95% efficacy) with a daily vaccination rate of 0.006 halved COVID-19 infections, reducing cases from 8,029 to around 4,000. The results also show that the model's prediction accuracy for new observations improves with the number of observed data used to train the model.
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Affiliation(s)
- Touria Jdid
- Laboratory of Engineering, Modeling and Systems Analysis (LIMAS), Faculty of Sciences, Sidi Mohamed Ben Abdellah University, Fez, Morocco
| | - Mohammed Benbrahim
- Laboratory of Engineering, Modeling and Systems Analysis (LIMAS), Faculty of Sciences, Sidi Mohamed Ben Abdellah University, Fez, Morocco
| | - Mohammed Nabil Kabbaj
- Laboratory of Engineering, Modeling and Systems Analysis (LIMAS), Faculty of Sciences, Sidi Mohamed Ben Abdellah University, Fez, Morocco
| | - Mohamed Naji
- Laboratory of Applied Physics Informatics and Statistics (LPAIS), Faculty of Sciences, Sidi Mohamed Ben Abdellah University, Fez, Morocco
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5
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Humphreys JM, Pelzel-McCluskey AM, Shults PT, Velazquez-Salinas L, Bertram MR, McGregor BL, Cohnstaedt LW, Swanson DA, Scroggs SLP, Fautt C, Mooney A, Peters DPC, Rodriguez LL. Modeling the 2014-2015 Vesicular Stomatitis Outbreak in the United States Using an SEIR-SEI Approach. Viruses 2024; 16:1315. [PMID: 39205289 PMCID: PMC11359999 DOI: 10.3390/v16081315] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2024] [Revised: 08/02/2024] [Accepted: 08/15/2024] [Indexed: 09/04/2024] Open
Abstract
Vesicular stomatitis (VS) is a vector-borne livestock disease caused by the vesicular stomatitis New Jersey virus (VSNJV). This study presents the first application of an SEIR-SEI compartmental model to analyze VSNJV transmission dynamics. Focusing on the 2014-2015 outbreak in the United States, the model integrates vertebrate hosts and insect vector demographics while accounting for heterogeneous competency within the populations and observation bias in documented disease cases. Key epidemiological parameters were estimated using Bayesian inference and Markov chain Monte Carlo (MCMC) methods, including the force of infection, effective reproduction number (Rt), and incubation periods. The model revealed significant underreporting, with only 10-24% of infections documented, 23% of which presented with clinical symptoms. These findings underscore the importance of including competence and imperfect detection in disease models to depict outbreak dynamics and inform effective control strategies accurately. As a baseline model, this SEIR-SEI implementation is intended to serve as a foundation for future refinements and expansions to improve our understanding of VS dynamics. Enhanced surveillance and targeted interventions are recommended to manage future VS outbreaks.
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Affiliation(s)
- John M. Humphreys
- Foreign Animal Disease Research Unit, Agricultural Research Service, U.S. Department of Agriculture, Plum Island Animal Disease Center (PIADC) and National Bio Agro Defense Facility (NBAF), Manhattan, KS 66502, USA; (L.V.-S.); (M.R.B.); (C.F.); (A.M.); (L.L.R.)
| | - Angela M. Pelzel-McCluskey
- Veterinary Services, Animal and Plant Health Inspection Service (APHIS), U.S. Department of Agriculture, Fort Collins, CO 80526, USA;
| | - Phillip T. Shults
- Arthropod-Borne Animal Disease Research Unit, Agricultural Research Service, U.S. Department of Agriculture, Manhattan, KS 66502, USA; (P.T.S.); (B.L.M.); (S.L.P.S.)
| | - Lauro Velazquez-Salinas
- Foreign Animal Disease Research Unit, Agricultural Research Service, U.S. Department of Agriculture, Plum Island Animal Disease Center (PIADC) and National Bio Agro Defense Facility (NBAF), Manhattan, KS 66502, USA; (L.V.-S.); (M.R.B.); (C.F.); (A.M.); (L.L.R.)
| | - Miranda R. Bertram
- Foreign Animal Disease Research Unit, Agricultural Research Service, U.S. Department of Agriculture, Plum Island Animal Disease Center (PIADC) and National Bio Agro Defense Facility (NBAF), Manhattan, KS 66502, USA; (L.V.-S.); (M.R.B.); (C.F.); (A.M.); (L.L.R.)
| | - Bethany L. McGregor
- Arthropod-Borne Animal Disease Research Unit, Agricultural Research Service, U.S. Department of Agriculture, Manhattan, KS 66502, USA; (P.T.S.); (B.L.M.); (S.L.P.S.)
| | - Lee W. Cohnstaedt
- Foreign Arthropod-Borne Animal Diseases Research Unit National Bio- and Agro-Defense Facility, Agricultural Research Service, U.S. Department of Agriculture, Manhattan, KS 66502, USA;
| | - Dustin A. Swanson
- Center for Grain and Animal Health Research, Agricultural Research Service, U.S. Department of Agriculture, Manhattan, KS 66502, USA;
| | - Stacey L. P. Scroggs
- Arthropod-Borne Animal Disease Research Unit, Agricultural Research Service, U.S. Department of Agriculture, Manhattan, KS 66502, USA; (P.T.S.); (B.L.M.); (S.L.P.S.)
| | - Chad Fautt
- Foreign Animal Disease Research Unit, Agricultural Research Service, U.S. Department of Agriculture, Plum Island Animal Disease Center (PIADC) and National Bio Agro Defense Facility (NBAF), Manhattan, KS 66502, USA; (L.V.-S.); (M.R.B.); (C.F.); (A.M.); (L.L.R.)
- Oak Ridge Institute for Science and Education (ORISE)-NBAF, Oak Ridge, TN 37831, USA
| | - Amber Mooney
- Foreign Animal Disease Research Unit, Agricultural Research Service, U.S. Department of Agriculture, Plum Island Animal Disease Center (PIADC) and National Bio Agro Defense Facility (NBAF), Manhattan, KS 66502, USA; (L.V.-S.); (M.R.B.); (C.F.); (A.M.); (L.L.R.)
- Oak Ridge Institute for Science and Education (ORISE)-NBAF, Oak Ridge, TN 37831, USA
| | - Debra P. C. Peters
- Office of National Programs, Agricultural Research Service, U.S. Department of Agriculture, Beltsville, MD 20705, USA;
| | - Luis L. Rodriguez
- Foreign Animal Disease Research Unit, Agricultural Research Service, U.S. Department of Agriculture, Plum Island Animal Disease Center (PIADC) and National Bio Agro Defense Facility (NBAF), Manhattan, KS 66502, USA; (L.V.-S.); (M.R.B.); (C.F.); (A.M.); (L.L.R.)
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6
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Earn DJD, Park SW, Bolker BM. Fitting Epidemic Models to Data: A Tutorial in Memory of Fred Brauer. Bull Math Biol 2024; 86:109. [PMID: 39052140 DOI: 10.1007/s11538-024-01326-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2024] [Accepted: 06/04/2024] [Indexed: 07/27/2024]
Abstract
Fred Brauer was an eminent mathematician who studied dynamical systems, especially differential equations. He made many contributions to mathematical epidemiology, a field that is strongly connected to data, but he always chose to avoid data analysis. Nevertheless, he recognized that fitting models to data is usually necessary when attempting to apply infectious disease transmission models to real public health problems. He was curious to know how one goes about fitting dynamical models to data, and why it can be hard. Initially in response to Fred's questions, we developed a user-friendly R package, fitode, that facilitates fitting ordinary differential equations to observed time series. Here, we use this package to provide a brief tutorial introduction to fitting compartmental epidemic models to a single observed time series. We assume that, like Fred, the reader is familiar with dynamical systems from a mathematical perspective, but has limited experience with statistical methodology or optimization techniques.
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Affiliation(s)
- David J D Earn
- Department of Mathematics and Statistics, McMaster University, Hamilton, ON, L8S 4K1, Canada.
| | - Sang Woo Park
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, 08544, USA
| | - Benjamin M Bolker
- Department of Mathematics and Statistics, McMaster University, Hamilton, ON, L8S 4K1, Canada
- Department of Biology, McMaster University, Hamilton, ON, L8S 4K1, Canada
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7
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Schmidt PW. Inference under superspreading: Determinants of SARS-CoV-2 transmission in Germany. Stat Med 2024; 43:1933-1954. [PMID: 38422989 DOI: 10.1002/sim.10046] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2022] [Revised: 01/11/2024] [Accepted: 02/10/2024] [Indexed: 03/02/2024]
Abstract
Superspreading, under-reporting, reporting delay, and confounding complicate statistical inference on determinants of disease transmission. A model that accounts for these factors within a Bayesian framework is estimated using German Covid-19 surveillance data. Compartments based on date of symptom onset, location, and age group allow to identify age-specific changes in transmission, adjusting for weather, reported prevalence, and testing and tracing. Several factors were associated with a reduction in transmission: public awareness rising, information on local prevalence, testing and tracing, high temperature, stay-at-home orders, and restaurant closures. However, substantial uncertainty remains for other interventions including school closures and mandatory face coverings. The challenge of disentangling the effects of different determinants is discussed and examined through a simulation study. On a broader perspective, the study illustrates the potential of surveillance data with demographic information and date of symptom onset to improve inference in the presence of under-reporting and reporting delay.
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8
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Chowell G, Bleichrodt A, Luo R. Parameter estimation and forecasting with quantified uncertainty for ordinary differential equation models using QuantDiffForecast: A MATLAB toolbox and tutorial. Stat Med 2024; 43:1826-1848. [PMID: 38378161 PMCID: PMC11031352 DOI: 10.1002/sim.10036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2023] [Revised: 01/17/2024] [Accepted: 01/31/2024] [Indexed: 02/22/2024]
Abstract
Mathematical models based on systems of ordinary differential equations (ODEs) are frequently applied in various scientific fields to assess hypotheses, estimate key model parameters, and generate predictions about the system's state. To support their application, we present a comprehensive, easy-to-use, and flexible MATLAB toolbox, QuantDiffForecast, and associated tutorial to estimate parameters and generate short-term forecasts with quantified uncertainty from dynamical models based on systems of ODEs. We provide software ( https://github.com/gchowell/paramEstimation_forecasting_ODEmodels/) and detailed guidance on estimating parameters and forecasting time-series trajectories that are characterized using ODEs with quantified uncertainty through a parametric bootstrapping approach. It includes functions that allow the user to infer model parameters and assess forecasting performance for different ODE models specified by the user, using different estimation methods and error structures in the data. The tutorial is intended for a diverse audience, including students training in dynamic systems, and will be broadly applicable to estimate parameters and generate forecasts from models based on ODEs. The functions included in the toolbox are illustrated using epidemic models with varying levels of complexity applied to data from the 1918 influenza pandemic in San Francisco. A tutorial video that demonstrates the functionality of the toolbox is included.
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Affiliation(s)
- Gerardo Chowell
- Department of Population Health Sciences, School of Public Health, Georgia State University, Atlanta, GA, USA
| | - Amanda Bleichrodt
- Department of Population Health Sciences, School of Public Health, Georgia State University, Atlanta, GA, USA
| | - Ruiyan Luo
- Department of Population Health Sciences, School of Public Health, Georgia State University, Atlanta, GA, USA
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9
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Bouman JA, Hauser A, Grimm SL, Wohlfender M, Bhatt S, Semenova E, Gelman A, Althaus CL, Riou J. Bayesian workflow for time-varying transmission in stratified compartmental infectious disease transmission models. PLoS Comput Biol 2024; 20:e1011575. [PMID: 38683878 PMCID: PMC11081492 DOI: 10.1371/journal.pcbi.1011575] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2023] [Revised: 05/09/2024] [Accepted: 04/12/2024] [Indexed: 05/02/2024] Open
Abstract
Compartmental models that describe infectious disease transmission across subpopulations are central for assessing the impact of non-pharmaceutical interventions, behavioral changes and seasonal effects on the spread of respiratory infections. We present a Bayesian workflow for such models, including four features: (1) an adjustment for incomplete case ascertainment, (2) an adequate sampling distribution of laboratory-confirmed cases, (3) a flexible, time-varying transmission rate, and (4) a stratification by age group. Within the workflow, we benchmarked the performance of various implementations of two of these features (2 and 3). For the second feature, we used SARS-CoV-2 data from the canton of Geneva (Switzerland) and found that a quasi-Poisson distribution is the most suitable sampling distribution for describing the overdispersion in the observed laboratory-confirmed cases. For the third feature, we implemented three methods: Brownian motion, B-splines, and approximate Gaussian processes (aGP). We compared their performance in terms of the number of effective samples per second, and the error and sharpness in estimating the time-varying transmission rate over a selection of ordinary differential equation solvers and tuning parameters, using simulated seroprevalence and laboratory-confirmed case data. Even though all methods could recover the time-varying dynamics in the transmission rate accurately, we found that B-splines perform up to four and ten times faster than Brownian motion and aGPs, respectively. We validated the B-spline model with simulated age-stratified data. We applied this model to 2020 laboratory-confirmed SARS-CoV-2 cases and two seroprevalence studies from the canton of Geneva. This resulted in detailed estimates of the transmission rate over time and the case ascertainment. Our results illustrate the potential of the presented workflow including stratified transmission to estimate age-specific epidemiological parameters. The workflow is freely available in the R package HETTMO, and can be easily adapted and applied to other infectious diseases.
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Affiliation(s)
- Judith A. Bouman
- Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland
- Multidisciplinary Center for Infectious Diseases, University of Bern, Bern, Switzerland
| | - Anthony Hauser
- Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland
- Institut national de la santé et de la recherche médicale Sorbonne Université (INSERM), Sorbonne Université, Paris, France
| | - Simon L. Grimm
- Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland
- Center for Space and Habitability, University of Bern, Bern, Switzerland
| | - Martin Wohlfender
- Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland
- Multidisciplinary Center for Infectious Diseases, University of Bern, Bern, Switzerland
| | - Samir Bhatt
- MRC Centre for Global Infectious Disease Analysis, Jameel Institute, School of Public Health, Imperial College London, London, United Kingdom
- Section of Epidemiology, Department of Public Health, University of Copenhagen, Copenhagen, Denmark
| | - Elizaveta Semenova
- Department of Epidemiology and Biostatistics, Imperial College London, London, United Kingdom
| | - Andrew Gelman
- Department of Statistics, Columbia University, New York, New York, United States of America
- Department of Political Science, Columbia University, New York, New York, United States of America
| | - Christian L. Althaus
- Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland
- Multidisciplinary Center for Infectious Diseases, University of Bern, Bern, Switzerland
| | - Julien Riou
- Department of Epidemiology and Health Systems, Unisanté, Center for Primary Care and Public Health & University of Lausanne, Lausanne, Switzerland
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10
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Gibson GC, Reich NG, Sheldon D. REAL-TIME MECHANISTIC BAYESIAN FORECASTS OF COVID-19 MORTALITY. Ann Appl Stat 2023; 17:1801-1819. [PMID: 38983109 PMCID: PMC11229176 DOI: 10.1214/22-aoas1671] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/11/2024]
Abstract
The COVID-19 pandemic emerged in late December 2019. In the first six months of the global outbreak, the U.S. reported more cases and deaths than any other country in the world. Effective modeling of the course of the pandemic can help assist with public health resource planning, intervention efforts, and vaccine clinical trials. However, building applied forecasting models presents unique challenges during a pandemic. First, case data available to models in real time represent a nonstationary fraction of the true case incidence due to changes in available diagnostic tests and test-seeking behavior. Second, interventions varied across time and geography leading to large changes in transmissibility over the course of the pandemic. We propose a mechanistic Bayesian model that builds upon the classic compartmental susceptible-exposed-infected-recovered (SEIR) model to operationalize COVID-19 forecasting in real time. This framework includes nonparametric modeling of varying transmission rates, nonparametric modeling of case and death discrepancies due to testing and reporting issues, and a joint observation likelihood on new case counts and new deaths; it is implemented in a probabilistic programming language to automate the use of Bayesian reasoning for quantifying uncertainty in probabilistic forecasts. The model has been used to submit forecasts to the U.S. Centers for Disease Control through the COVID-19 Forecast Hub under the name MechBayes. We examine the performance relative to a baseline model as well as alternate models submitted to the forecast hub. Additionally, we include an ablation test of our extensions to the classic SEIR model. We demonstrate a significant gain in both point and probabilistic forecast scoring measures using MechBayes, when compared to a baseline model, and show that MechBayes ranks as one of the top two models out of nine which regularly submitted to the COVID-19 Forecast Hub for the duration of the pandemic, trailing only the COVID-19 Forecast Hub ensemble model of which which MechBayes is a part.
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Affiliation(s)
- Graham C. Gibson
- School of Public Health and Health Sciences, University of Massachusetts Amherst
| | - Nicholas G. Reich
- School of Public Health and Health Sciences, University of Massachusetts Amherst
| | - Daniel Sheldon
- College of Information and Computer Sciences, University of Massachusetts Amherst
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11
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Simpson MJ, Maclaren OJ. Profile-Wise Analysis: A profile likelihood-based workflow for identifiability analysis, estimation, and prediction with mechanistic mathematical models. PLoS Comput Biol 2023; 19:e1011515. [PMID: 37773942 PMCID: PMC10566698 DOI: 10.1371/journal.pcbi.1011515] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Revised: 10/11/2023] [Accepted: 09/14/2023] [Indexed: 10/01/2023] Open
Abstract
Interpreting data using mechanistic mathematical models provides a foundation for discovery and decision-making in all areas of science and engineering. Developing mechanistic insight by combining mathematical models and experimental data is especially critical in mathematical biology as new data and new types of data are collected and reported. Key steps in using mechanistic mathematical models to interpret data include: (i) identifiability analysis; (ii) parameter estimation; and (iii) model prediction. Here we present a systematic, computationally-efficient workflow we call Profile-Wise Analysis (PWA) that addresses all three steps in a unified way. Recently-developed methods for constructing 'profile-wise' prediction intervals enable this workflow and provide the central linkage between different workflow components. These methods propagate profile-likelihood-based confidence sets for model parameters to predictions in a way that isolates how different parameter combinations affect model predictions. We show how to extend these profile-wise prediction intervals to two-dimensional interest parameters. We then demonstrate how to combine profile-wise prediction confidence sets to give an overall prediction confidence set that approximates the full likelihood-based prediction confidence set well. Our three case studies illustrate practical aspects of the workflow, focusing on ordinary differential equation (ODE) mechanistic models with both Gaussian and non-Gaussian noise models. While the case studies focus on ODE-based models, the workflow applies to other classes of mathematical models, including partial differential equations and simulation-based stochastic models. Open-source software on GitHub can be used to replicate the case studies.
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Affiliation(s)
- Matthew J. Simpson
- School of Mathematical Sciences, Queensland University of Technology, Brisbane, Australia
| | - Oliver J. Maclaren
- Department of Engineering Science, University of Auckland, Auckland, New Zealand
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12
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Andrade J, Duggan J. Anchoring the mean generation time in the SEIR to mitigate biases in ℜ 0 estimates due to uncertainty in the distribution of the epidemiological delays. ROYAL SOCIETY OPEN SCIENCE 2023; 10:230515. [PMID: 37538746 PMCID: PMC10394422 DOI: 10.1098/rsos.230515] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Accepted: 07/13/2023] [Indexed: 08/05/2023]
Abstract
The basic reproduction number, ℜ 0 , is of paramount importance in the study of infectious disease dynamics. Primarily, ℜ 0 serves as an indicator of the transmission potential of an emerging infectious disease and the effort required to control the invading pathogen. However, its estimates from compartmental models are strongly conditioned by assumptions in the model structure, such as the distributions of the latent and infectious periods (epidemiological delays). To further complicate matters, models with dissimilar delay structures produce equivalent incidence dynamics. Following a simulation study, we reveal that the nature of such equivalency stems from a linear relationship between ℜ 0 and the mean generation time, along with adjustments to other parameters in the model. Leveraging this knowledge, we propose and successfully test an alternative parametrization of the SEIR model that produces accurate ℜ 0 estimates regardless of the distribution of the epidemiological delays, at the expense of biases in other quantities deemed of lesser importance. We further explore this approach's robustness by testing various transmissibility levels, generation times and data fidelity (overdispersion). Finally, we apply the proposed approach to data from the 1918 influenza pandemic. We anticipate that this work will mitigate biases in estimating ℜ 0 .
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Affiliation(s)
- Jair Andrade
- Data Science Institute and School of Computer Science, University of Galway, Galway, Republic of Ireland
| | - Jim Duggan
- Insight Centre for Data Analytics and School of Computer Science, University of Galway, Galway, Republic of Ireland
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13
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Akuno AO, Ramírez-Ramírez LL, Espinoza JF. Inference on a Multi-Patch Epidemic Model with Partial Mobility, Residency, and Demography: Case of the 2020 COVID-19 Outbreak in Hermosillo, Mexico. ENTROPY (BASEL, SWITZERLAND) 2023; 25:968. [PMID: 37509915 PMCID: PMC10378648 DOI: 10.3390/e25070968] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Revised: 06/02/2023] [Accepted: 06/14/2023] [Indexed: 07/30/2023]
Abstract
Most studies modeling population mobility and the spread of infectious diseases, particularly those using meta-population multi-patch models, tend to focus on the theoretical properties and numerical simulation of such models. As such, there is relatively scant literature focused on numerical fit, inference, and uncertainty quantification of epidemic models with population mobility. In this research, we use three estimation techniques to solve an inverse problem and quantify its uncertainty for a human-mobility-based multi-patch epidemic model using mobile phone sensing data and confirmed COVID-19-positive cases in Hermosillo, Mexico. First, we utilize a Brownian bridge model using mobile phone GPS data to estimate the residence and mobility parameters of the epidemic model. In the second step, we estimate the optimal model epidemiological parameters by deterministically inverting the model using a Darwinian-inspired evolutionary algorithm (EA)-that is, a genetic algorithm (GA). The third part of the analysis involves performing inference and uncertainty quantification in the epidemic model using two Bayesian Monte Carlo sampling methods: t-walk and Hamiltonian Monte Carlo (HMC). The results demonstrate that the estimated model parameters and incidence adequately fit the observed daily COVID-19 incidence in Hermosillo. Moreover, the estimated parameters from the HMC method yield large credible intervals, improving their coverage for the observed and predicted daily incidences. Furthermore, we observe that the use of a multi-patch model with mobility yields improved predictions when compared to a single-patch model.
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Affiliation(s)
- Albert Orwa Akuno
- Departamento de Probabilidad y Estadística, Centro de Investigación en Matemáticas A.C., Jalisco s/n, Colonia Valenciana, Guanajuato C.P. 36023, Gto, Mexico
| | - L Leticia Ramírez-Ramírez
- Departamento de Probabilidad y Estadística, Centro de Investigación en Matemáticas A.C., Jalisco s/n, Colonia Valenciana, Guanajuato C.P. 36023, Gto, Mexico
| | - Jesús F Espinoza
- Departamento de Matemáticas, Universidad de Sonora, Rosales y Boulevard Luis Encinas, Hermosillo C.P. 83000, Sonora, Mexico
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14
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Lison A, Banholzer N, Sharma M, Mindermann S, Unwin HJT, Mishra S, Stadler T, Bhatt S, Ferguson NM, Brauner J, Vach W. Effectiveness assessment of non-pharmaceutical interventions: lessons learned from the COVID-19 pandemic. Lancet Public Health 2023; 8:e311-e317. [PMID: 36965985 PMCID: PMC10036127 DOI: 10.1016/s2468-2667(23)00046-4] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Revised: 02/07/2023] [Accepted: 02/08/2023] [Indexed: 03/27/2023]
Abstract
Effectiveness of non-pharmaceutical interventions (NPIs), such as school closures and stay-at-home orders, during the COVID-19 pandemic has been assessed in many studies. Such assessments can inform public health policies and contribute to evidence-based choices of NPIs during subsequent waves or future epidemics. However, methodological issues and no standardised assessment practices have restricted the practical value of the existing evidence. Here, we present and discuss lessons learned from the COVID-19 pandemic and make recommendations for standardising and improving assessment, data collection, and modelling. These recommendations could contribute to reliable and policy-relevant assessments of the effectiveness of NPIs during future epidemics.
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Affiliation(s)
- Adrian Lison
- Department of Biosystems Science and Engineering, ETH Zurich, Zurich, Switzerland
| | - Nicolas Banholzer
- Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland
| | - Mrinank Sharma
- Department of Statistics, University of Oxford, Oxford, UK; Future of Humanity Institute, University of Oxford, Oxford, UK
| | - Sören Mindermann
- Department of Computer Science, University of Oxford, Oxford, UK
| | - H Juliette T Unwin
- Medical Research Council Centre for Global Infectious Disease Analysis, Jameel Institute, Imperial College London, London, UK
| | - Swapnil Mishra
- Department of Public Health, University of Copenhagen, Copenhagen, Denmark; Saw Swee Hock School of Public Health, National University of Singapore, Singapore
| | - Tanja Stadler
- Department of Biosystems Science and Engineering, ETH Zurich, Zurich, Switzerland
| | - Samir Bhatt
- Medical Research Council Centre for Global Infectious Disease Analysis, Jameel Institute, Imperial College London, London, UK; Department of Public Health, University of Copenhagen, Copenhagen, Denmark.
| | - Neil M Ferguson
- Medical Research Council Centre for Global Infectious Disease Analysis, Jameel Institute, Imperial College London, London, UK
| | - Jan Brauner
- Department of Computer Science, University of Oxford, Oxford, UK; Future of Humanity Institute, University of Oxford, Oxford, UK
| | - Werner Vach
- Basel Academy for Quality and Research in Medicine, Basel, Switzerland; Department of Environmental Sciences, University of Basel, Basel, Switzerland
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15
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Emmenegger M, De Cecco E, Lamparter D, Jacquat RP, Riou J, Menges D, Ballouz T, Ebner D, Schneider MM, Morales IC, Doğançay B, Guo J, Wiedmer A, Domange J, Imeri M, Moos R, Zografou C, Batkitar L, Madrigal L, Schneider D, Trevisan C, Gonzalez-Guerra A, Carrella A, Dubach IL, Xu CK, Meisl G, Kosmoliaptsis V, Malinauskas T, Burgess-Brown N, Owens R, Hatch S, Mongkolsapaya J, Screaton GR, Schubert K, Huck JD, Liu F, Pojer F, Lau K, Hacker D, Probst-Müller E, Cervia C, Nilsson J, Boyman O, Saleh L, Spanaus K, von Eckardstein A, Schaer DJ, Ban N, Tsai CJ, Marino J, Schertler GF, Ebert N, Thiel V, Gottschalk J, Frey BM, Reimann RR, Hornemann S, Ring AM, Knowles TP, Puhan MA, Althaus CL, Xenarios I, Stuart DI, Aguzzi A. Continuous population-level monitoring of SARS-CoV-2 seroprevalence in a large European metropolitan region. iScience 2023; 26:105928. [PMID: 36619367 PMCID: PMC9811913 DOI: 10.1016/j.isci.2023.105928] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2022] [Revised: 12/18/2022] [Accepted: 01/03/2023] [Indexed: 01/06/2023] Open
Abstract
Effective public health measures against SARS-CoV-2 require granular knowledge of population-level immune responses. We developed a Tripartite Automated Blood Immunoassay (TRABI) to assess the IgG response against three SARS-CoV-2 proteins. We used TRABI for continuous seromonitoring of hospital patients and blood donors (n = 72'250) in the canton of Zurich from December 2019 to December 2020 (pre-vaccine period). We found that antibodies waned with a half-life of 75 days, whereas the cumulative incidence rose from 2.3% in June 2020 to 12.2% in mid-December 2020. A follow-up health survey indicated that about 10% of patients infected with wildtype SARS-CoV-2 sustained some symptoms at least twelve months post COVID-19. Crucially, we found no evidence of a difference in long-term complications between those whose infection was symptomatic and those with asymptomatic acute infection. The cohort of asymptomatic SARS-CoV-2-infected subjects represents a resource for the study of chronic and possibly unexpected sequelae.
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Affiliation(s)
- Marc Emmenegger
- Institute of Neuropathology, University of Zurich, 8091 Zurich, Switzerland
| | - Elena De Cecco
- Institute of Neuropathology, University of Zurich, 8091 Zurich, Switzerland
| | - David Lamparter
- Health2030 Genome Center, 9 Chemin des Mines, 1202 Geneva, Switzerland
| | - Raphaël P.B. Jacquat
- Centre for Misfolding Diseases, Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, UK
- Cavendish Laboratory, Department of Physics, University of Cambridge, JJ Thomson Avenue, Cambridge CB3 0HE, UK
| | - Julien Riou
- Institute of Social and Preventive Medicine, University of Bern, 3012 Bern, Switzerland
| | - Dominik Menges
- Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Zürich, Switzerland
| | - Tala Ballouz
- Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Zürich, Switzerland
| | - Daniel Ebner
- Target Discovery Institute, University of Oxford, Oxford OX3 7FZ, England
| | - Matthias M. Schneider
- Centre for Misfolding Diseases, Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, UK
| | | | - Berre Doğançay
- Institute of Neuropathology, University of Zurich, 8091 Zurich, Switzerland
| | - Jingjing Guo
- Institute of Neuropathology, University of Zurich, 8091 Zurich, Switzerland
| | - Anne Wiedmer
- Institute of Neuropathology, University of Zurich, 8091 Zurich, Switzerland
| | - Julie Domange
- Institute of Neuropathology, University of Zurich, 8091 Zurich, Switzerland
| | - Marigona Imeri
- Institute of Neuropathology, University of Zurich, 8091 Zurich, Switzerland
| | - Rita Moos
- Institute of Neuropathology, University of Zurich, 8091 Zurich, Switzerland
| | - Chryssa Zografou
- Institute of Neuropathology, University of Zurich, 8091 Zurich, Switzerland
| | - Leyla Batkitar
- Institute of Neuropathology, University of Zurich, 8091 Zurich, Switzerland
| | - Lidia Madrigal
- Institute of Neuropathology, University of Zurich, 8091 Zurich, Switzerland
| | - Dezirae Schneider
- Institute of Neuropathology, University of Zurich, 8091 Zurich, Switzerland
| | - Chiara Trevisan
- Institute of Neuropathology, University of Zurich, 8091 Zurich, Switzerland
| | | | | | - Irina L. Dubach
- Division of Internal Medicine, University Hospital Zurich, 8091 Zurich, Switzerland
| | - Catherine K. Xu
- Centre for Misfolding Diseases, Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, UK
| | - Georg Meisl
- Centre for Misfolding Diseases, Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, UK
| | - Vasilis Kosmoliaptsis
- Department of Surgery, Addenbrooke’s Hospital, University of Cambridge, Hills Road, Cambridge CB2 0QQ, UK
- NIHR Blood and Transplant Research Unit in Organ Donation and Transplantation, University of Cambridge, Hills Road, Cambridge CB2 0QQ, UK
| | - Tomas Malinauskas
- Division of Structural Biology, The Wellcome Centre for Human Genetics, University of Oxford, Headington, Oxford OX3 7BN, UK
| | | | - Ray Owens
- Division of Structural Biology, The Wellcome Centre for Human Genetics, University of Oxford, Headington, Oxford OX3 7BN, UK
- The Rosalind Franklin Institute, Harwell Campus, Oxford OX11 0FA, UK
| | - Stephanie Hatch
- Target Discovery Institute, University of Oxford, Oxford OX3 7FZ, England
| | - Juthathip Mongkolsapaya
- Nuffield Department of Medicine, Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK
| | - Gavin R. Screaton
- Nuffield Department of Medicine, Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK
| | - Katharina Schubert
- Department of Biology, Institute of Molecular Biology and Biophysics, ETH Zurich, Zurich, Switzerland
| | - John D. Huck
- Department of Immunobiology, Yale School of Medicine, New Haven, CT, USA
| | - Feimei Liu
- Department of Immunobiology, Yale School of Medicine, New Haven, CT, USA
| | - Florence Pojer
- Protein Production and Structure Core Facility, EPFL SV PTECH PTPSP, 1015 Lausanne, Switzerland
| | - Kelvin Lau
- Protein Production and Structure Core Facility, EPFL SV PTECH PTPSP, 1015 Lausanne, Switzerland
| | - David Hacker
- Protein Production and Structure Core Facility, EPFL SV PTECH PTPSP, 1015 Lausanne, Switzerland
| | | | - Carlo Cervia
- Department of Immunology, University Hospital Zurich, 8091 Zurich, Switzerland
| | - Jakob Nilsson
- Department of Immunology, University Hospital Zurich, 8091 Zurich, Switzerland
| | - Onur Boyman
- Department of Immunology, University Hospital Zurich, 8091 Zurich, Switzerland
- Faculty of Medicine, University of Zurich, 8006 Zurich, Switzerland
| | - Lanja Saleh
- Institute of Clinical Chemistry, University Hospital Zurich, 8091 Zurich, Switzerland
| | - Katharina Spanaus
- Institute of Clinical Chemistry, University Hospital Zurich, 8091 Zurich, Switzerland
| | | | - Dominik J. Schaer
- Division of Internal Medicine, University Hospital Zurich, 8091 Zurich, Switzerland
| | - Nenad Ban
- Department of Biology, Institute of Molecular Biology and Biophysics, ETH Zurich, Zurich, Switzerland
| | - Ching-Ju Tsai
- Department of Biology and Chemistry, Laboratory of Biomolecular Research, Paul Scherrer Institute, 5303 Villigen-PSI, Switzerland
| | - Jacopo Marino
- Department of Biology and Chemistry, Laboratory of Biomolecular Research, Paul Scherrer Institute, 5303 Villigen-PSI, Switzerland
| | - Gebhard F.X. Schertler
- Department of Biology and Chemistry, Laboratory of Biomolecular Research, Paul Scherrer Institute, 5303 Villigen-PSI, Switzerland
- Department of Biology, ETH Zürich, 8093 Zürich, Switzerland
| | - Nadine Ebert
- Institute of Virology and Immunology, 3012 Bern, Switzerland
- Department of Infectious Diseases and Pathobiology, Vetsuisse Faculty, University of Bern, 3012 Bern, Switzerland
| | - Volker Thiel
- Institute of Virology and Immunology, 3012 Bern, Switzerland
- Department of Infectious Diseases and Pathobiology, Vetsuisse Faculty, University of Bern, 3012 Bern, Switzerland
| | - Jochen Gottschalk
- Regional Blood Transfusion Service Zurich, Swiss Red Cross, 8952 Schlieren, Switzerland
| | - Beat M. Frey
- Regional Blood Transfusion Service Zurich, Swiss Red Cross, 8952 Schlieren, Switzerland
| | - Regina R. Reimann
- Institute of Neuropathology, University of Zurich, 8091 Zurich, Switzerland
| | - Simone Hornemann
- Institute of Neuropathology, University of Zurich, 8091 Zurich, Switzerland
| | - Aaron M. Ring
- Department of Immunobiology, Yale School of Medicine, New Haven, CT, USA
| | - Tuomas P.J. Knowles
- Centre for Misfolding Diseases, Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, UK
- Cavendish Laboratory, Department of Physics, University of Cambridge, JJ Thomson Avenue, Cambridge CB3 0HE, UK
| | - Milo A. Puhan
- Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Zürich, Switzerland
| | - Christian L. Althaus
- Institute of Social and Preventive Medicine, University of Bern, 3012 Bern, Switzerland
| | - Ioannis Xenarios
- Health2030 Genome Center, 9 Chemin des Mines, 1202 Geneva, Switzerland
- Agora Center, University of Lausanne, 25 Avenue du Bugnon, 1005 Lausanne, Switzerland
| | - David I. Stuart
- Division of Structural Biology, The Wellcome Centre for Human Genetics, University of Oxford, Headington, Oxford OX3 7BN, UK
| | - Adriano Aguzzi
- Institute of Neuropathology, University of Zurich, 8091 Zurich, Switzerland
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16
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Dai C, Heng J, Jacob PE, Whiteley N. An Invitation to Sequential Monte Carlo Samplers. J Am Stat Assoc 2022. [DOI: 10.1080/01621459.2022.2087659] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Affiliation(s)
| | | | | | - Nick Whiteley
- School of Mathematics, University of Bristol, Bristol, UK
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17
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Andrade J, Duggan J. Inferring the effective reproductive number from deterministic and semi-deterministic compartmental models using incidence and mobility data. PLoS Comput Biol 2022; 18:e1010206. [PMID: 35759506 PMCID: PMC9269962 DOI: 10.1371/journal.pcbi.1010206] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2021] [Revised: 07/08/2022] [Accepted: 05/11/2022] [Indexed: 11/19/2022] Open
Abstract
The effective reproduction number (ℜt) is a theoretical indicator of the course of an infectious disease that allows policymakers to evaluate whether current or previous control efforts have been successful or whether additional interventions are necessary. This metric, however, cannot be directly observed and must be inferred from available data. One approach to obtaining such estimates is fitting compartmental models to incidence data. We can envision these dynamic models as the ensemble of structures that describe the disease's natural history and individuals' behavioural patterns. In the context of the response to the COVID-19 pandemic, the assumption of a constant transmission rate is rendered unrealistic, and it is critical to identify a mathematical formulation that accounts for changes in contact patterns. In this work, we leverage existing approaches to propose three complementary formulations that yield similar estimates for ℜt based on data from Ireland's first COVID-19 wave. We describe these Data Generating Processes (DGP) in terms of State-Space models. Two (DGP1 and DGP2) correspond to stochastic process models whose transmission rate is modelled as Brownian motion processes (Geometric and Cox-Ingersoll-Ross). These DGPs share a measurement model that accounts for incidence and transmission rates, where mobility data is assumed as a proxy of the transmission rate. We perform inference on these structures using Iterated Filtering and the Particle Filter. The final DGP (DGP3) is built from a pool of deterministic models that describe the transmission rate as information delays. We calibrate this pool of models to incidence reports using Hamiltonian Monte Carlo. By following this complementary approach, we assess the tradeoffs associated with each formulation and reflect on the benefits/risks of incorporating proxy data into the inference process. We anticipate this work will help evaluate the implications of choosing a particular formulation for the dynamics and observation of the time-varying transmission rate.
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Affiliation(s)
- Jair Andrade
- Data Science Institute and School of Computer Science, National University of Ireland Galway, Ireland
| | - Jim Duggan
- School of Computer Science, Ryan Institute and Data Science Institute, National University of Ireland Galway, Ireland
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18
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Grolimund CM, Bärenbold O, Hatz CF, Vennervald BJ, Mayombana C, Mshinda H, Utzinger J, Vounatsou P. Infection intensity-dependent accuracy of reagent strip for the diagnosis of Schistosoma haematobium and estimation of treatment prevalence thresholds. PLoS Negl Trop Dis 2022; 16:e0010332. [PMID: 35468129 PMCID: PMC9071146 DOI: 10.1371/journal.pntd.0010332] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2020] [Revised: 05/05/2022] [Accepted: 03/15/2022] [Indexed: 11/26/2022] Open
Abstract
BACKGROUND Reagent strip to detect microhematuria as a proxy for Schistosoma haematobium infections has been considered an alternative to urine filtration for individual diagnosis and community-based estimates of treatment needs for preventive chemotherapy. However, the diagnostic accuracy of reagent strip needs further investigation, particularly at low infection intensity levels. METHODS We used existing data from a study conducted in Tanzania that employed urine filtration and reagent strip testing for S. haematobium in two villages, including a baseline and six follow-up surveys after praziquantel treatment representing a wide range of infection prevalence. We developed a Bayesian model linking individual S. haematobium egg count data based on urine filtration to reagent strip binary test results available on multiple days and estimated the relation between infection intensity and sensitivity of reagent strip. Furthermore, we simulated data from 3,000 hypothetical populations with varying mean infection intensity to infer on the relation between prevalence observed by urine filtration and the interpretation of reagent strip readings. PRINCIPAL FINDINGS Reagent strip showed excellent sensitivity even for single measurement reaching 100% at around 15 eggs of S. haematobium per 10 ml of urine when traces on reagent strip were considered positive. The corresponding specificity was 97%. When traces were considered negative, the diagnostic accuracy of the reagent strip was equivalent to urine filtration data obtained on a single day. A 10% and 50% urine filtration prevalence based on a single day sampling corresponds to 11.2% and 48.6% prevalence by reagent strip, respectively, when traces were considered negative, and 17.6% and 57.7%, respectively, when traces were considered positive. CONCLUSIONS/SIGNIFICANCE Trace results should be included in reagent strip readings when high sensitivity is required, but excluded when high specificity is needed. The observed prevalence of reagent strip results, when traces are considered negative, is a good proxy for prevalence estimates of S. haematobium infection by urine filtration on a single day.
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Affiliation(s)
- Carla M. Grolimund
- Swiss Tropical and Public Health Institute, Allschwil, Switzerland
- University of Basel, Basel, Switzerland
| | - Oliver Bärenbold
- Swiss Tropical and Public Health Institute, Allschwil, Switzerland
- University of Basel, Basel, Switzerland
| | - Christoph F. Hatz
- Swiss Tropical and Public Health Institute, Allschwil, Switzerland
- University of Basel, Basel, Switzerland
| | - Birgitte J. Vennervald
- Department of Veterinary and Animal Science, University of Copenhagen, Copenhagen, Denmark
| | | | | | - Jürg Utzinger
- Swiss Tropical and Public Health Institute, Allschwil, Switzerland
- University of Basel, Basel, Switzerland
| | - Penelope Vounatsou
- Swiss Tropical and Public Health Institute, Allschwil, Switzerland
- University of Basel, Basel, Switzerland
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19
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Prieto K, Chávez–Hernández MV, Romero–Leiton JP. On mobility trends analysis of COVID-19 dissemination in Mexico City. PLoS One 2022; 17:e0263367. [PMID: 35143548 PMCID: PMC8830699 DOI: 10.1371/journal.pone.0263367] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2021] [Accepted: 01/18/2022] [Indexed: 01/04/2023] Open
Abstract
This work presents a tool for forecasting the spread of the new coronavirus in Mexico City, which is based on a mathematical model with a metapopulation structure that uses Bayesian statistics and is inspired by a data-driven approach. The daily mobility of people in Mexico City is mathematically represented by an origin-destination matrix using the open mobility data from Google and the Transportation Mexican Survey. This matrix is incorporated in a compartmental model. We calibrate the model against borough-level incidence data collected between 27 February 2020 and 27 October 2020, while using Bayesian inference to estimate critical epidemiological characteristics associated with the coronavirus spread. Given that working with metapopulation models leads to rather high computational time consumption, and parameter estimation of these models may lead to high memory RAM consumption, we do a clustering analysis that is based on mobility trends to work on these clusters of borough separately instead of taken all of the boroughs together at once. This clustering analysis can be implemented in smaller or larger scales in different parts of the world. In addition, this clustering analysis is divided into the phases that the government of Mexico City has set up to restrict individual movement in the city. We also calculate the reproductive number in Mexico City using the next generation operator method and the inferred model parameters obtaining that this threshold is in the interval (1.2713, 1.3054). Our analysis of mobility trends can be helpful when making public health decisions.
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Affiliation(s)
- Kernel Prieto
- Instituto de Matemáticas, Universidad Nacional Autónoma de México, Mexico, México
| | - M. Victoria Chávez–Hernández
- Facultad de Ingeniería Mecánica y Eléctrica, Universidad Autónoma de Nuevo León, San Nicolás de los Garza, Mexico, México
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20
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Prieto K. Current forecast of COVID-19 in Mexico: A Bayesian and machine learning approaches. PLoS One 2022; 17:e0259958. [PMID: 35061688 PMCID: PMC8782335 DOI: 10.1371/journal.pone.0259958] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2021] [Accepted: 10/29/2021] [Indexed: 12/24/2022] Open
Abstract
The COVID-19 pandemic has been widely spread and affected millions of people and caused hundreds of deaths worldwide, especially in patients with comorbilities and COVID-19. This manuscript aims to present models to predict, firstly, the number of coronavirus cases and secondly, the hospital care demand and mortality based on COVID-19 patients who have been diagnosed with other diseases. For the first part, I present a projection of the spread of coronavirus in Mexico, which is based on a contact tracing model using Bayesian inference. I investigate the health profile of individuals diagnosed with coronavirus to predict their type of patient care (inpatient or outpatient) and survival. Specifically, I analyze the comorbidity associated with coronavirus using Machine Learning. I have implemented two classifiers: I use the first classifier to predict the type of care procedure that a person diagnosed with coronavirus presenting chronic diseases will obtain (i.e. outpatient or hospitalised), in this way I estimate the hospital care demand; I use the second classifier to predict the survival or mortality of the patient (i.e. survived or deceased). I present two techniques to deal with these kinds of unbalanced datasets related to outpatient/hospitalised and survived/deceased cases (which occur in general for these types of coronavirus datasets) to obtain a better performance for the classification.
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Affiliation(s)
- Kernel Prieto
- Instituto de Matemáticas, Universidad Nacional Autónoma de México, Mexico City, México
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Grinsztajn L, Semenova E, Margossian CC, Riou J. Bayesian workflow for disease transmission modeling in Stan. Stat Med 2021; 40:6209-6234. [PMID: 34494686 PMCID: PMC8661657 DOI: 10.1002/sim.9164] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2021] [Revised: 07/06/2021] [Accepted: 07/29/2021] [Indexed: 12/18/2022]
Abstract
This tutorial shows how to build, fit, and criticize disease transmission models in Stan, and should be useful to researchers interested in modeling the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic and other infectious diseases in a Bayesian framework. Bayesian modeling provides a principled way to quantify uncertainty and incorporate both data and prior knowledge into the model estimates. Stan is an expressive probabilistic programming language that abstracts the inference and allows users to focus on the modeling. As a result, Stan code is readable and easily extensible, which makes the modeler's work more transparent. Furthermore, Stan's main inference engine, Hamiltonian Monte Carlo sampling, is amiable to diagnostics, which means the user can verify whether the obtained inference is reliable. In this tutorial, we demonstrate how to formulate, fit, and diagnose a compartmental transmission model in Stan, first with a simple susceptible-infected-recovered model, then with a more elaborate transmission model used during the SARS-CoV-2 pandemic. We also cover advanced topics which can further help practitioners fit sophisticated models; notably, how to use simulations to probe the model and priors, and computational techniques to scale-up models based on ordinary differential equations.
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Affiliation(s)
| | - Elizaveta Semenova
- Data Sciences and Quantitative BiologyDiscovery Sciences, R&D, AstraZenecaCambridgeUK
| | | | - Julien Riou
- Institute of Social and Preventive MedicineUniversity of BernBernSwitzerland
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