A case for ongoing structural support to maximise infectious disease modelling efficiency for future public health emergencies: A modelling perspective

Epke A. Le Rutte; Andrew J. Shattock; Cheng Zhao; Soushieta Jagadesh; Miloš Balać; Sebastian A. Müller; Kai Nagel; Alexander L. Erath; Kay W. Axhausen; Thomas P. Van Boeckel; Melissa A. Penny

doi:10.1016/j.epidem.2023.100734

A case for ongoing structural support to maximise infectious disease modelling efficiency for future public health emergencies: A modelling perspective

Epke A. Le Rutte, Andrew J. Shattock, Cheng Zhao, Soushieta Jagadesh, Miloš Balać, Sebastian A. Müller, Kai Nagel, Alexander L. Erath, Kay W. Axhausen, Thomas P. Van Boeckel, Melissa A. Penny

Research output: Contribution to journal › Article › peer-review

Abstract

This short communication reflects upon the challenges and recommendations of multiple COVID-19 modelling and data analytic groups that provided quantitative evidence to support health policy discussions in Switzerland and Germany during the SARS-CoV-2 pandemic. Capacity strengthening outside infectious disease emergencies will be required to enable an environment for a timely, efficient, and data-driven response to support decisions during any future infectious disease emergency. This will require 1) a critical mass of trained experts who continuously advance state-of-the-art methodological tools, 2) the establishment of structural liaisons amongst scientists and decision-makers, and 3) the foundation and management of data-sharing frameworks.

Original language	English
Article number	100734
Number of pages	5
Journal	Epidemics
Volume	46
Early online date	13 Dec 2023
DOIs	https://doi.org/10.1016/j.epidem.2023.100734
Publication status	Published - Mar 2024
Externally published	Yes

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10.1016/j.epidem.2023.100734Licence: CC BY

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Le Rutte, E. A., Shattock, A. J., Zhao, C., Jagadesh, S., Balać, M., Müller, S. A., Nagel, K., Erath, A. L., Axhausen, K. W., Van Boeckel, T. P., & Penny, M. A. (2024). A case for ongoing structural support to maximise infectious disease modelling efficiency for future public health emergencies: A modelling perspective. Epidemics, 46, Article 100734. https://doi.org/10.1016/j.epidem.2023.100734

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title = "A case for ongoing structural support to maximise infectious disease modelling efficiency for future public health emergencies: A modelling perspective",

abstract = "This short communication reflects upon the challenges and recommendations of multiple COVID-19 modelling and data analytic groups that provided quantitative evidence to support health policy discussions in Switzerland and Germany during the SARS-CoV-2 pandemic. Capacity strengthening outside infectious disease emergencies will be required to enable an environment for a timely, efficient, and data-driven response to support decisions during any future infectious disease emergency. This will require 1) a critical mass of trained experts who continuously advance state-of-the-art methodological tools, 2) the establishment of structural liaisons amongst scientists and decision-makers, and 3) the foundation and management of data-sharing frameworks.",

keywords = "COVID-19, Modelling, Pandemic, Policy, Public health emergency, SARS-CoV-2",

author = "{Le Rutte}, {Epke A.} and Shattock, {Andrew J.} and Cheng Zhao and Soushieta Jagadesh and Milo{\v s} Bala{\'c} and M{\"u}ller, {Sebastian A.} and Kai Nagel and Erath, {Alexander L.} and Axhausen, {Kay W.} and {Van Boeckel}, {Thomas P.} and Penny, {Melissa A.}",

year = "2024",

month = mar,

doi = "10.1016/j.epidem.2023.100734",

language = "English",

volume = "46",

journal = "Epidemics",

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Le Rutte, EA, Shattock, AJ, Zhao, C, Jagadesh, S, Balać, M, Müller, SA, Nagel, K, Erath, AL, Axhausen, KW, Van Boeckel, TP & Penny, MA 2024, 'A case for ongoing structural support to maximise infectious disease modelling efficiency for future public health emergencies: A modelling perspective', Epidemics, vol. 46, 100734. https://doi.org/10.1016/j.epidem.2023.100734

TY - JOUR

T1 - A case for ongoing structural support to maximise infectious disease modelling efficiency for future public health emergencies

T2 - A modelling perspective

AU - Le Rutte, Epke A.

AU - Shattock, Andrew J.

AU - Zhao, Cheng

AU - Jagadesh, Soushieta

AU - Balać, Miloš

AU - Müller, Sebastian A.

AU - Nagel, Kai

AU - Erath, Alexander L.

AU - Axhausen, Kay W.

AU - Van Boeckel, Thomas P.

AU - Penny, Melissa A.

PY - 2024/3

Y1 - 2024/3

N2 - This short communication reflects upon the challenges and recommendations of multiple COVID-19 modelling and data analytic groups that provided quantitative evidence to support health policy discussions in Switzerland and Germany during the SARS-CoV-2 pandemic. Capacity strengthening outside infectious disease emergencies will be required to enable an environment for a timely, efficient, and data-driven response to support decisions during any future infectious disease emergency. This will require 1) a critical mass of trained experts who continuously advance state-of-the-art methodological tools, 2) the establishment of structural liaisons amongst scientists and decision-makers, and 3) the foundation and management of data-sharing frameworks.

AB - This short communication reflects upon the challenges and recommendations of multiple COVID-19 modelling and data analytic groups that provided quantitative evidence to support health policy discussions in Switzerland and Germany during the SARS-CoV-2 pandemic. Capacity strengthening outside infectious disease emergencies will be required to enable an environment for a timely, efficient, and data-driven response to support decisions during any future infectious disease emergency. This will require 1) a critical mass of trained experts who continuously advance state-of-the-art methodological tools, 2) the establishment of structural liaisons amongst scientists and decision-makers, and 3) the foundation and management of data-sharing frameworks.

KW - COVID-19

KW - Modelling

KW - Pandemic

KW - Policy

KW - Public health emergency

KW - SARS-CoV-2

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DO - 10.1016/j.epidem.2023.100734

M3 - Article

C2 - 38118273

AN - SCOPUS:85180610649

SN - 1755-4365

VL - 46

JO - Epidemics

JF - Epidemics

M1 - 100734

ER -

A case for ongoing structural support to maximise infectious disease modelling efficiency for future public health emergencies: A modelling perspective

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