1
|
Tang M, Dudas G, Bedford T, Minin VN. Fitting stochastic epidemic models to gene genealogies using linear noise approximation. Ann Appl Stat 2023. [DOI: 10.1214/21-aoas1583] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Affiliation(s)
- Mingwei Tang
- Department of Statistics, University of Washington, Seattle
| | - Gytis Dudas
- Gothenburg Global Biodiversity Centre (GGBC)
| | - Trevor Bedford
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center
| | | |
Collapse
|
2
|
Giffin A, Gong W, Majumder S, Rappold AG, Reich BJ, Yang S. Estimating intervention effects on infectious disease control: The effect of community mobility reduction on Coronavirus spread. SPATIAL STATISTICS 2022; 52:100711. [PMID: 36284923 PMCID: PMC9584839 DOI: 10.1016/j.spasta.2022.100711] [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: 06/07/2021] [Revised: 01/29/2022] [Accepted: 10/12/2022] [Indexed: 06/16/2023]
Abstract
Understanding the effects of interventions, such as restrictions on community and large group gatherings, is critical to controlling the spread of COVID-19. Susceptible-Infectious-Recovered (SIR) models are traditionally used to forecast the infection rates but do not provide insights into the causal effects of interventions. We propose a spatiotemporal model that estimates the causal effect of changes in community mobility (intervention) on infection rates. Using an approximation to the SIR model and incorporating spatiotemporal dependence, the proposed model estimates a direct and indirect (spillover) effect of intervention. Under an interference and treatment ignorability assumption, this model is able to estimate causal intervention effects, and additionally allows for spatial interference between locations. Reductions in community mobility were measured by cell phone movement data. The results suggest that the reductions in mobility decrease Coronavirus cases 4 to 7 weeks after the intervention.
Collapse
Affiliation(s)
- Andrew Giffin
- North Carolina State University, Department of Statistics, 2311 Stinson Drive, Raleigh, NC 27607, United States of America
| | - Wenlong Gong
- North Carolina State University, Department of Statistics, 2311 Stinson Drive, Raleigh, NC 27607, United States of America
| | - Suman Majumder
- Harvard T.H. Chan School of Public Health, 677 Huntington Ave, Boston, MA 02115, United States of America
| | - Ana G Rappold
- Environmental Protection Agency, 104 Mason Farm Road, Chapel Hill, NC 27514, United States of America
| | - Brian J Reich
- North Carolina State University, Department of Statistics, 2311 Stinson Drive, Raleigh, NC 27607, United States of America
| | - Shu Yang
- North Carolina State University, Department of Statistics, 2311 Stinson Drive, Raleigh, NC 27607, United States of America
| |
Collapse
|
3
|
Modelling Holling type II functional response in deterministic and stochastic food chain models with mass conservation. ECOLOGICAL COMPLEXITY 2022. [DOI: 10.1016/j.ecocom.2022.100982] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
|
5
|
Li YI, Turk G, Rohrbach PB, Pietzonka P, Kappler J, Singh R, Dolezal J, Ekeh T, Kikuchi L, Peterson JD, Bolitho A, Kobayashi H, Cates ME, Adhikari R, Jack RL. Efficient Bayesian inference of fully stochastic epidemiological models with applications to COVID-19. ROYAL SOCIETY OPEN SCIENCE 2021; 8:211065. [PMID: 34430050 PMCID: PMC8355677 DOI: 10.1098/rsos.211065] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/21/2021] [Accepted: 07/23/2021] [Indexed: 06/13/2023]
Abstract
Epidemiological forecasts are beset by uncertainties about the underlying epidemiological processes, and the surveillance process through which data are acquired. We present a Bayesian inference methodology that quantifies these uncertainties, for epidemics that are modelled by (possibly) non-stationary, continuous-time, Markov population processes. The efficiency of the method derives from a functional central limit theorem approximation of the likelihood, valid for large populations. We demonstrate the methodology by analysing the early stages of the COVID-19 pandemic in the UK, based on age-structured data for the number of deaths. This includes maximum a posteriori estimates, Markov chain Monte Carlo sampling of the posterior, computation of the model evidence, and the determination of parameter sensitivities via the Fisher information matrix. Our methodology is implemented in PyRoss, an open-source platform for analysis of epidemiological compartment models.
Collapse
Affiliation(s)
- Yuting I. Li
- Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Wilberforce Road, Cambridge CB3 0WA, UK
| | - Günther Turk
- Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Wilberforce Road, Cambridge CB3 0WA, UK
| | - Paul B. Rohrbach
- Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Wilberforce Road, Cambridge CB3 0WA, UK
| | - Patrick Pietzonka
- Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Wilberforce Road, Cambridge CB3 0WA, UK
| | - Julian Kappler
- Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Wilberforce Road, Cambridge CB3 0WA, UK
| | - Rajesh Singh
- Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Wilberforce Road, Cambridge CB3 0WA, UK
| | - Jakub Dolezal
- Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Wilberforce Road, Cambridge CB3 0WA, UK
| | - Timothy Ekeh
- Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Wilberforce Road, Cambridge CB3 0WA, UK
| | - Lukas Kikuchi
- Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Wilberforce Road, Cambridge CB3 0WA, UK
| | - Joseph D. Peterson
- Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Wilberforce Road, Cambridge CB3 0WA, UK
| | - Austen Bolitho
- Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Wilberforce Road, Cambridge CB3 0WA, UK
| | - Hideki Kobayashi
- Yusuf Hamied Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, UK
| | - Michael E. Cates
- Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Wilberforce Road, Cambridge CB3 0WA, UK
| | - R. Adhikari
- Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Wilberforce Road, Cambridge CB3 0WA, UK
| | - Robert L. Jack
- Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Wilberforce Road, Cambridge CB3 0WA, UK
- Yusuf Hamied Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, UK
| |
Collapse
|
6
|
Locey KJ, Webb TA, Khan J, Antony AK, Hota B. An interactive tool to forecast US hospital needs in the coronavirus 2019 pandemic. JAMIA Open 2020; 3:506-512. [PMID: 33619466 PMCID: PMC7543612 DOI: 10.1093/jamiaopen/ooaa045] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2020] [Revised: 08/31/2020] [Accepted: 09/14/2020] [Indexed: 12/20/2022] Open
Abstract
OBJECTIVE We developed an application (https://rush-covid19.herokuapp.com/) to aid US hospitals in planning their response to the ongoing Coronavirus Disease 2019 (COVID-19) pandemic. MATERIALS AND METHODS Our application forecasts hospital visits, admits, discharges, and needs for hospital beds, ventilators, and personal protective equipment by coupling COVID-19 predictions to models of time lags, patient carry-over, and length-of-stay. Users can choose from 7 COVID-19 models, customize 23 parameters, examine trends in testing and hospitalization, and download forecast data. RESULTS Our application accurately predicts the spread of COVID-19 across states and territories. Its hospital-level forecasts are in continuous use by our home institution and others. DISCUSSION Our application is versatile, easy-to-use, and can help hospitals plan their response to the changing dynamics of COVID-19, while providing a platform for deeper study. CONCLUSION Empowering healthcare responses to COVID-19 is as crucial as understanding the epidemiology of the disease. Our application will continue to evolve to meet this need.
Collapse
Affiliation(s)
- Kenneth J Locey
- Center for Quality, Safety and Value Analytics, Rush University Medical Center, Chicago, Illinois, USA
| | - Thomas A Webb
- Center for Quality, Safety and Value Analytics, Rush University Medical Center, Chicago, Illinois, USA
| | - Jawad Khan
- Knowledge Management Services, Rush University Medical Center, Chicago, Illinois, USA
| | - Anuja K Antony
- Division of Plastic and Reconstructive Surgery, Department of Surgery, Rush University Medical Center, Chicago, Illinois, USA
| | - Bala Hota
- Center for Quality, Safety and Value Analytics, Rush University Medical Center, Chicago, Illinois, USA
- Knowledge Management Services, Rush University Medical Center, Chicago, Illinois, USA
- Division of Infectious Diseases, Department of Internal Medicine, Rush Medical College, Chicago, Illinois, USA
| |
Collapse
|