Using hawkes processes to model imported and local malaria cases in near-elimination settings
Research output: Contribution to journal › Journal article › Research › peer-review
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.
| Original language | English |
|---|---|
| Article number | e1008830 |
| Journal | PLOS Computational Biology |
| Volume | 17 |
| Issue number | 4 |
| ISSN | 1553-734X |
| DOIs | |
| Publication status | Published - 2021 |
| Externally published | Yes |
Bibliographical 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:
© 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.
ID: 355234409