Literature DB >> 32007792

Choices and trade-offs in inference with infectious disease models.

Sebastian Funk1, Aaron A King2.   

Abstract

Inference using mathematical models of infectious disease dynamics can be an invaluable tool for the interpretation and analysis of epidemiological data. However, researchers wishing to use this tool are faced with a choice of models and model types, simulation methods, inference methods and software packages. Given the multitude of options, it can be challenging to decide on the best approach. Here, we delineate the choices and trade-offs involved in deciding on an approach for inference, and discuss aspects that might inform this decision. We provide examples of inference with a dataset of influenza cases using the R packages pomp and rbi.
Copyright © 2019. Published by Elsevier B.V.

Entities:  

Keywords:  Bayesian; Frequentist; Infectious disease model; Inference; Model fitting

Year:  2019        PMID: 32007792     DOI: 10.1016/j.epidem.2019.100383

Source DB:  PubMed          Journal:  Epidemics        ISSN: 1878-0067            Impact factor:   4.396


  8 in total

Review 1.  Modeling transmission of pathogens in healthcare settings.

Authors:  Anna Stachel; Lindsay T Keegan; Seth Blumberg
Journal:  Curr Opin Infect Dis       Date:  2021-08-01       Impact factor: 4.968

Review 2.  Tooling-up for infectious disease transmission modelling.

Authors:  Marc Baguelin; Graham F Medley; Emily S Nightingale; Kathleen M O'Reilly; Eleanor M Rees; Naomi R Waterlow; Moritz Wagner
Journal:  Epidemics       Date:  2020-05-13       Impact factor: 4.396

Review 3.  Development and dissemination of infectious disease dynamic transmission models during the COVID-19 pandemic: what can we learn from other pathogens and how can we move forward?

Authors:  Alexander D Becker; Kyra H Grantz; Sonia T Hegde; Sophie Bérubé; Derek A T Cummings; Amy Wesolowski
Journal:  Lancet Digit Health       Date:  2020-12-07

4.  A runtime alterable epidemic model with genetic drift, waning immunity and vaccinations.

Authors:  Wayne M Getz; Richard Salter; Ludovica Luisa Vissat; James S Koopman; Carl P Simon
Journal:  J R Soc Interface       Date:  2021-11-24       Impact factor: 4.118

5.  Possible future waves of SARS-CoV-2 infection generated by variants of concern with a range of characteristics.

Authors:  Louise Dyson; Edward M Hill; Sam Moore; Jacob Curran-Sebastian; Michael J Tildesley; Katrina A Lythgoe; Thomas House; Lorenzo Pellis; Matt J Keeling
Journal:  Nat Commun       Date:  2021-09-30       Impact factor: 14.919

6.  Planning as Inference in Epidemiological Dynamics Models.

Authors:  Frank Wood; Andrew Warrington; Saeid Naderiparizi; Christian Weilbach; Vaden Masrani; William Harvey; Adam Ścibior; Boyan Beronov; John Grefenstette; Duncan Campbell; S Ali Nasseri
Journal:  Front Artif Intell       Date:  2022-03-31

7.  Inferring the effective reproductive number from deterministic and semi-deterministic compartmental models using incidence and mobility data.

Authors:  Jair Andrade; Jim Duggan
Journal:  PLoS Comput Biol       Date:  2022-06-27       Impact factor: 4.779

8.  Examining the impact of ICU population interaction structure on modeled colonization dynamics of Staphylococcus aureus.

Authors:  Matthew S Mietchen; Christopher T Short; Matthew Samore; Eric T Lofgren
Journal:  PLoS Comput Biol       Date:  2022-07-25       Impact factor: 4.779

  8 in total

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