Literature DB >> 36266704

The role of models in the covid-19 pandemic.

David M Steinberg1, Ran D Balicer2,3, Yoav Benjamini4, Hilla De-Leon5, Doron Gazit6, Hagai Rossman7, Eli Sprecher8,9.   

Abstract

Mathematical and statistical models have played an important role in the analysis of data from COVID-19. They are important for tracking the progress of the pandemic, for understanding its spread in the population, and perhaps most significantly for forecasting the future course of the pandemic and evaluating potential policy options. This article describes the types of models that were used by research teams in Israel, presents their assumptions and basic elements, and illustrates how they were used, and how they influenced decisions. The article grew out of a "modelists' dialog" organized by the Israel National Institute for Health Policy Research with participation from some of the leaders in the local modeling effort.
© 2022. The Author(s).

Entities:  

Keywords:  Agent-Based models; Data Analysis; Forecasting; Nowcasting; SIR Model; Statistics

Mesh:

Year:  2022        PMID: 36266704      PMCID: PMC9584247          DOI: 10.1186/s13584-022-00546-5

Source DB:  PubMed          Journal:  Isr J Health Policy Res        ISSN: 2045-4015


  17 in total

Review 1.  Mathematical modelling and prediction in infectious disease epidemiology.

Authors:  A Huppert; G Katriel
Journal:  Clin Microbiol Infect       Date:  2013-11       Impact factor: 8.067

2.  COVID-19 dynamics after a national immunization program in Israel.

Authors:  Malka Gorfine; Uri Shalit; Eran Segal; Hagai Rossman; Smadar Shilo; Tomer Meir
Journal:  Nat Med       Date:  2021-04-19       Impact factor: 53.440

3.  Modern statistical tools for inference and prediction of infectious diseases using mathematical models.

Authors:  Itai Dattner; Amit Huppert
Journal:  Stat Methods Med Res       Date:  2018-07       Impact factor: 3.021

4.  Seasonality and period-doubling bifurcations in an epidemic model.

Authors:  J L Aron; I B Schwartz
Journal:  J Theor Biol       Date:  1984-10-21       Impact factor: 2.691

5.  Nowcasting the spread of SARS-CoV-2.

Authors:  Hagai Rossman; Eran Segal
Journal:  Nat Microbiol       Date:  2022-01       Impact factor: 17.745

Review 6.  Influenza forecasting in human populations: a scoping review.

Authors:  Jean-Paul Chretien; Dylan George; Jeffrey Shaman; Rohit A Chitale; F Ellis McKenzie
Journal:  PLoS One       Date:  2014-04-08       Impact factor: 3.240

Review 7.  The COVID-19 epidemic, its mortality, and the role of non-pharmaceutical interventions.

Authors:  Niel Hens; Pascal Vranck; Geert Molenberghs
Journal:  Eur Heart J Acute Cardiovasc Care       Date:  2020-04-30

8.  Epidemic tracking and forecasting: Lessons learned from a tumultuous year.

Authors:  Roni Rosenfeld; Ryan J Tibshirani
Journal:  Proc Natl Acad Sci U S A       Date:  2021-12-21       Impact factor: 12.779

9.  A framework for identifying regional outbreak and spread of COVID-19 from one-minute population-wide surveys.

Authors:  Hagai Rossman; Ayya Keshet; Smadar Shilo; Amir Gavrieli; Tal Bauman; Ori Cohen; Esti Shelly; Ran Balicer; Benjamin Geiger; Yuval Dor; Eran Segal
Journal:  Nat Med       Date:  2020-05       Impact factor: 87.241

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