Using hawkes processes to model imported and local malaria cases in near-elimination settings

Juliette T.H. Unwin; Isobel Routledge; Seth Flaxman; Marian Andrei Rizoiu; Shengjie Lai; Justin Cohen; Daniel J. Weiss; Swapnil Mishra; Samir Bhatt

doi:10.1371/JOURNAL.PCBI.1008830

Using hawkes processes to model imported and local malaria cases in near-elimination settings

Juliette T.H. Unwin^*, Isobel Routledge, Seth Flaxman, Marian Andrei Rizoiu, Shengjie Lai, Justin Cohen, Daniel J. Weiss, Swapnil Mishra, Samir Bhatt

^*Corresponding author af dette arbejde

Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › peer review

16 Citationer (Scopus)

Abstract

Developing new methods for modelling infectious diseases outbreaks is important for monitoring transmission and developing policy. In this paper we propose using semi-mechanistic Hawkes Processes for modelling malaria transmission in near-elimination settings. Hawkes Processes are well founded mathematical methods that enable us to combine the benefits of both statistical and mechanistic models to recreate and forecast disease transmission beyond just malaria outbreak scenarios. These methods have been successfully used in numerous applications such as social media and earthquake modelling, but are not yet widespread in epidemiology. By using domain-specific knowledge, we can both recreate transmission curves for malaria in China and Eswatini and disentangle the proportion of cases which are imported from those that are community based.

Originalsprog	Engelsk
Artikelnummer	e1008830
Tidsskrift	PLOS Computational Biology
Vol/bind	17
Udgave nummer	4
ISSN	1553-734X
DOI	https://doi.org/10.1371/JOURNAL.PCBI.1008830
Status	Udgivet - 2021
Udgivet eksternt	Ja

Bibliografisk note

Publisher Copyright:
© 2021 Unwin et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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10.1371/JOURNAL.PCBI.1008830Licens: CC BY

Citationsformater

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title = "Using hawkes processes to model imported and local malaria cases in near-elimination settings",

abstract = "Developing new methods for modelling infectious diseases outbreaks is important for monitoring transmission and developing policy. In this paper we propose using semi-mechanistic Hawkes Processes for modelling malaria transmission in near-elimination settings. Hawkes Processes are well founded mathematical methods that enable us to combine the benefits of both statistical and mechanistic models to recreate and forecast disease transmission beyond just malaria outbreak scenarios. These methods have been successfully used in numerous applications such as social media and earthquake modelling, but are not yet widespread in epidemiology. By using domain-specific knowledge, we can both recreate transmission curves for malaria in China and Eswatini and disentangle the proportion of cases which are imported from those that are community based.",

author = "Unwin, {Juliette T.H.} and Isobel Routledge and Seth Flaxman and Rizoiu, {Marian Andrei} and Shengjie Lai and Justin Cohen and Weiss, {Daniel J.} and Swapnil Mishra and Samir Bhatt",

note = "Funding Information: HJTU is funded by Imperial College London through an Imperial College Research Fellowship grant. SB acknowledges funding from the NIHR BRC Imperial College NHS Trust Infection themes (RDA02), the Academy of Medical Sciences Springboard award (SBF004/1080) and the Bill and Melinda Gates Foundation (CRR00280). HJTU, SM, IR and SB acknowledge joint centre funding (reference MR/R015600/1) by the UK Medical Research Council (MRC) and the UK Department for International Development (DFID) under the MRC/DFID Concordat agreement and is also part of the EDCTP2 programme supported by the European Union. MAR acknowledges funding from Facebook Research under the Content Policy Research Initiative grants, and the Defence Science and Technology Group of the Australian Department of Defence. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Publisher Copyright: {\textcopyright} 2021 Unwin et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.",

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doi = "10.1371/JOURNAL.PCBI.1008830",

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AU - Unwin, Juliette T.H.

AU - Routledge, Isobel

AU - Flaxman, Seth

AU - Rizoiu, Marian Andrei

AU - Lai, Shengjie

AU - Cohen, Justin

AU - Weiss, Daniel J.

AU - Mishra, Swapnil

AU - Bhatt, Samir

N1 - Funding Information: HJTU is funded by Imperial College London through an Imperial College Research Fellowship grant. SB acknowledges funding from the NIHR BRC Imperial College NHS Trust Infection themes (RDA02), the Academy of Medical Sciences Springboard award (SBF004/1080) and the Bill and Melinda Gates Foundation (CRR00280). HJTU, SM, IR and SB acknowledge joint centre funding (reference MR/R015600/1) by the UK Medical Research Council (MRC) and the UK Department for International Development (DFID) under the MRC/DFID Concordat agreement and is also part of the EDCTP2 programme supported by the European Union. MAR acknowledges funding from Facebook Research under the Content Policy Research Initiative grants, and the Defence Science and Technology Group of the Australian Department of Defence. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Publisher Copyright: © 2021 Unwin et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

PY - 2021

Y1 - 2021

N2 - Developing new methods for modelling infectious diseases outbreaks is important for monitoring transmission and developing policy. In this paper we propose using semi-mechanistic Hawkes Processes for modelling malaria transmission in near-elimination settings. Hawkes Processes are well founded mathematical methods that enable us to combine the benefits of both statistical and mechanistic models to recreate and forecast disease transmission beyond just malaria outbreak scenarios. These methods have been successfully used in numerous applications such as social media and earthquake modelling, but are not yet widespread in epidemiology. By using domain-specific knowledge, we can both recreate transmission curves for malaria in China and Eswatini and disentangle the proportion of cases which are imported from those that are community based.

AB - Developing new methods for modelling infectious diseases outbreaks is important for monitoring transmission and developing policy. In this paper we propose using semi-mechanistic Hawkes Processes for modelling malaria transmission in near-elimination settings. Hawkes Processes are well founded mathematical methods that enable us to combine the benefits of both statistical and mechanistic models to recreate and forecast disease transmission beyond just malaria outbreak scenarios. These methods have been successfully used in numerous applications such as social media and earthquake modelling, but are not yet widespread in epidemiology. By using domain-specific knowledge, we can both recreate transmission curves for malaria in China and Eswatini and disentangle the proportion of cases which are imported from those that are community based.

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DO - 10.1371/JOURNAL.PCBI.1008830

M3 - Journal article

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AN - SCOPUS:85104276046

SN - 1553-734X

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JO - PLOS Computational Biology

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