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Using Hawkes Processes to model imported and local malaria cases in near-elimination settings

Using Hawkes Processes to model imported and local malaria cases in near-elimination settings
Using Hawkes Processes to model imported and local malaria cases in near-elimination settings

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.

1553-734X
e1008830
Unwin, H Juliette T
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Routledge, Isobel
b4aa9e56-7ebe-4ca6-8112-567cf6bcd0ca
Flaxman, Seth
cc7c16fe-2d0e-4c03-877a-78b99562117c
Rizoiu, Marian-Andrei
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Lai, Shengjie
b57a5fe8-cfb6-4fa7-b414-a98bb891b001
Cohen, Justin
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Weiss, Daniel J
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Mishra, Swapnil
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Bhatt, Samir
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Unwin, H Juliette T
fd5295e7-5ac1-4ee2-886b-18dcef5e4aae
Routledge, Isobel
b4aa9e56-7ebe-4ca6-8112-567cf6bcd0ca
Flaxman, Seth
cc7c16fe-2d0e-4c03-877a-78b99562117c
Rizoiu, Marian-Andrei
590e71f3-6dc8-48ed-80dc-c204e18a7301
Lai, Shengjie
b57a5fe8-cfb6-4fa7-b414-a98bb891b001
Cohen, Justin
7de99049-a4c3-4fa1-8ff8-cc1bc5dcdfc9
Weiss, Daniel J
a5b8e0dc-a451-496d-ad27-35944684cdcf
Mishra, Swapnil
46a21337-415e-415e-a572-82b633e9231a
Bhatt, Samir
b29447e1-3caa-4c11-8e5c-5daa0011b9fa

Unwin, H Juliette T, Routledge, Isobel, Flaxman, Seth, Rizoiu, Marian-Andrei, Lai, Shengjie, Cohen, Justin, Weiss, Daniel J, Mishra, Swapnil and Bhatt, Samir (2021) Using Hawkes Processes to model imported and local malaria cases in near-elimination settings. PLoS Computational Biology, 17 (4), e1008830, [e1008830]. (doi:10.1371/journal.pcbi.1008830).

Record type: Article

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.

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Submitted date: 31 July 2020
Accepted/In Press date: 23 February 2021
Published date: 1 April 2021
Additional Information: Copyright: This record is sourced from MEDLINE/PubMed, a database of the U.S. National Library of Medicine Funding: 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.

Identifiers

Local EPrints ID: 448634
URI: http://eprints.soton.ac.uk/id/eprint/448634
ISSN: 1553-734X
PURE UUID: b6b1e992-df3f-4869-b572-701d3299c029
ORCID for Shengjie Lai: ORCID iD orcid.org/0000-0001-9781-8148

Catalogue record

Date deposited: 28 Apr 2021 16:34
Last modified: 26 Nov 2021 03:16

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Contributors

Author: H Juliette T Unwin
Author: Isobel Routledge
Author: Seth Flaxman
Author: Marian-Andrei Rizoiu
Author: Shengjie Lai ORCID iD
Author: Justin Cohen
Author: Daniel J Weiss
Author: Swapnil Mishra
Author: Samir Bhatt

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