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Challenges in modeling the emergence of novel pathogens

Emma E. Glennon, Marjolein Bruijning, Justin Lessler, Ian F. Miller, Benjamin L. Rice, Robin N. Thompson, Konstans Wells Orcid Logo, C. Jessica E. Metcalf

Epidemics, Volume: 37, Start page: 100516

Swansea University Author: Konstans Wells Orcid Logo

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DOI (Published version): 10.1016/j.epidem.2021.100516

Abstract

The emergence of infectious agents with pandemic potential present scientific challenges from detection to data interpretation to understanding determinants of risk and forecasts. Mathematical models could play an essential role in how we prepare for future emergent pathogens. Here, we describe core...

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Published in: Epidemics
ISSN: 1755-4365
Published: Elsevier BV 2021
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URI: https://cronfa.swan.ac.uk/Record/cronfa58490
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first_indexed 2021-10-28T12:18:18Z
last_indexed 2021-11-19T04:25:52Z
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spelling 2021-11-18T17:05:47.1039558 v2 58490 2021-10-28 Challenges in modeling the emergence of novel pathogens d18166c31e89833c55ef0f2cbb551243 0000-0003-0377-2463 Konstans Wells Konstans Wells true false 2021-10-28 SBI The emergence of infectious agents with pandemic potential present scientific challenges from detection to data interpretation to understanding determinants of risk and forecasts. Mathematical models could play an essential role in how we prepare for future emergent pathogens. Here, we describe core directions for expansion of the existing tools and knowledge base, including: using mathematical models to identify critical directions and paths for strengthening data collection to detect and respond to outbreaks of novel pathogens; expanding basic theory to identify infectious agents and contexts that present the greatest risks, over both the short and longer term; by strengthening estimation tools that make the most use of the likely range and uncertainties in existing data; and by ensuring modelling applications are carefully communicated and developed within diverse and equitable collaborations for increased public health benefit. Journal Article Epidemics 37 100516 Elsevier BV 1755-4365 Immune landscape; Genotype to phenotype map; Big data; Data integration; Fundamental theory; Health system functioning 1 12 2021 2021-12-01 10.1016/j.epidem.2021.100516 COLLEGE NANME Biosciences COLLEGE CODE SBI Swansea University Another institution paid the OA fee EPSRC EP/R014604/1 2021-11-18T17:05:47.1039558 2021-10-28T13:15:56.6770314 Faculty of Science and Engineering School of Biosciences, Geography and Physics - Biosciences Emma E. Glennon 1 Marjolein Bruijning 2 Justin Lessler 3 Ian F. Miller 4 Benjamin L. Rice 5 Robin N. Thompson 6 Konstans Wells 0000-0003-0377-2463 7 C. Jessica E. Metcalf 8 58490__21585__6c0d66f0568445829362c9beef7f5691.pdf 58490.pdf 2021-11-18T17:04:12.0103664 Output 499064 application/pdf Version of Record true © 2021 The Authors. This is an open access article under the CC BY license true eng http://creativecommons.org/licenses/by/4.0/
title Challenges in modeling the emergence of novel pathogens
spellingShingle Challenges in modeling the emergence of novel pathogens
Konstans Wells
title_short Challenges in modeling the emergence of novel pathogens
title_full Challenges in modeling the emergence of novel pathogens
title_fullStr Challenges in modeling the emergence of novel pathogens
title_full_unstemmed Challenges in modeling the emergence of novel pathogens
title_sort Challenges in modeling the emergence of novel pathogens
author_id_str_mv d18166c31e89833c55ef0f2cbb551243
author_id_fullname_str_mv d18166c31e89833c55ef0f2cbb551243_***_Konstans Wells
author Konstans Wells
author2 Emma E. Glennon
Marjolein Bruijning
Justin Lessler
Ian F. Miller
Benjamin L. Rice
Robin N. Thompson
Konstans Wells
C. Jessica E. Metcalf
format Journal article
container_title Epidemics
container_volume 37
container_start_page 100516
publishDate 2021
institution Swansea University
issn 1755-4365
doi_str_mv 10.1016/j.epidem.2021.100516
publisher Elsevier BV
college_str Faculty of Science and Engineering
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hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Biosciences, Geography and Physics - Biosciences{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Biosciences, Geography and Physics - Biosciences
document_store_str 1
active_str 0
description The emergence of infectious agents with pandemic potential present scientific challenges from detection to data interpretation to understanding determinants of risk and forecasts. Mathematical models could play an essential role in how we prepare for future emergent pathogens. Here, we describe core directions for expansion of the existing tools and knowledge base, including: using mathematical models to identify critical directions and paths for strengthening data collection to detect and respond to outbreaks of novel pathogens; expanding basic theory to identify infectious agents and contexts that present the greatest risks, over both the short and longer term; by strengthening estimation tools that make the most use of the likely range and uncertainties in existing data; and by ensuring modelling applications are carefully communicated and developed within diverse and equitable collaborations for increased public health benefit.
published_date 2021-12-01T04:15:03Z
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score 10.998093

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