Four challenges associated with current mathematical modeling paradigm of infectious diseases and call for a shift

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Janies, D., Chen, S., Robinson, P., & Dulin, M. (2020). Four challenges associated with current mathematical modeling paradigm of infectious diseases and call for a shift. Open Forum Infectious Diseases. https://doi.org/doi:10.1093/ofid/ofaa333
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Faculty, Mathematical models are critical tools to characterize COVID-19 dynamics and take action accordingly. We identified 4 major challenges associated with the current modeling paradigm (SEIR) that hinder the efforts to accurately characterize the emerging COVID-19 and future epidemics. These challenges included (1) lack of consistent definition of case; (2) discrepancy between patient-level clinical insights and population-level modeling efforts; (3) lack of adequate inclusion of individual behavioral and social influence; and (4) allowing little flexibility of including new evidence and insights when our knowledge evolved rapidly during the pandemic. Therefore, these challenges made the current SEIR modeling paradigm less practical to handle the complex COVID-19 and future pandemics. Novel and more reliable data sources and alternative modeling paradigms are needed to address these issues.

Details
Title

Four challenges associated with current mathematical modeling paradigm of infectious diseases and call for a shift

Author(s)

Janies, Daniel
Chen, Shi
Robinson, Patrick
Dulin, Michael

UNC Charlotte Affiliate Type

Faculty

Extent

1 online resource

Date Created

2020

Genre

articles

Resource Type

text

Abstract

Mathematical models are critical tools to characterize COVID-19 dynamics and take action accordingly. We identified 4 major challenges associated with the current modeling paradigm (SEIR) that hinder the efforts to accurately characterize the emerging COVID-19 and future epidemics. These challenges included (1) lack of consistent definition of case; (2) discrepancy between patient-level clinical insights and population-level modeling efforts; (3) lack of adequate inclusion of individual behavioral and social influence; and (4) allowing little flexibility of including new evidence and insights when our knowledge evolved rapidly during the pandemic. Therefore, these challenges made the current SEIR modeling paradigm less practical to handle the complex COVID-19 and future pandemics. Novel and more reliable data sources and alternative modeling paradigms are needed to address these issues.

Subjects--Topics

Communicable diseases
Biology

Is Part Of

Open forum infectious diseases

Language

English

Published Version (Please cite this version)

doi:10.1093/ofid/ofaa333

Publisher's Version

https://doi.org/10.1093/ofid/ofaa333

Permalink

http://hdl.handle.net/20.500.13093/work:1147

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