Tracking progress towards malaria elimination in China: Individual-level estimates of transmission and its spatiotemporal variation using a diffusion network approach
Tracking progress towards malaria elimination in China: Individual-level estimates of transmission and its spatiotemporal variation using a diffusion network approach
In order to monitor progress towards malaria elimination, it is crucial to be able to measure changes in spatio-temporal transmission. However, common metrics of malaria transmission such as parasite prevalence are under powered in elimination contexts. China has achieved major reductions in malaria incidence and is on track to eliminate, having reporting zero locally-acquired malaria cases in 2017 and 2018. Understanding the spatio-temporal pattern underlying this decline, especially the relationship between locally-acquired and imported cases, can inform efforts to maintain elimination and prevent re-emergence. This is particularly pertinent in Yunnan province, where the potential for local transmission is highest. Using a geo-located individual-level dataset of cases recorded in Yunnan province between 2011 and 2016, we introduce a novel Bayesian framework to model a latent diffusion process and estimate the joint likelihood of transmission between cases and the number of cases with unobserved sources of infection. This is used to estimate the case reproduction number, Rc. We use these estimates within spatio-temporal geostatistical models to map how transmission varied over time and space, estimate the timeline to elimination and the risk of resurgence. We estimate the mean Rc between 2011 and 2016 to be 0.171 (95% CI = 0.165, 0.178) for P. vivax cases and 0.089 (95% CI = 0.076, 0.103) for P. falciparum cases. From 2014 onwards, no cases were estimated to have a Rc value above one. An unobserved source of infection was estimated to be moderately likely (p>0.5) for 19/ 611 cases and high (p>0.8) for 2 cases, suggesting very high levels of case ascertainment. Our estimates suggest that, maintaining current intervention efforts, Yunnan is unlikely to experience sustained local transmission up to 2020. However, even with a mean of 0.005 projected up to 2020, locally-acquired cases are possible due to high levels of importation.
1-20
Routledge, Isobel
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Lai, Shengjie
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Battle, Katherine E
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Ghani, Azra C.
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Gomez-Rodriguez, Manuel
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Gustafson, Kyle B.
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Mishra, Swapnil
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Unwin, Juliette
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Proctor, Joshua L.
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Tatem, Andrew J.
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Li, Zhongjie
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Bhatt, Samir
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23 March 2020
Routledge, Isobel
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Lai, Shengjie
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Battle, Katherine E
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Ghani, Azra C.
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Gomez-Rodriguez, Manuel
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Gustafson, Kyle B.
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Mishra, Swapnil
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Unwin, Juliette
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Proctor, Joshua L.
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Tatem, Andrew J.
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Li, Zhongjie
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Bhatt, Samir
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Routledge, Isobel, Lai, Shengjie, Battle, Katherine E, Ghani, Azra C., Gomez-Rodriguez, Manuel, Gustafson, Kyle B., Mishra, Swapnil, Unwin, Juliette, Proctor, Joshua L., Tatem, Andrew J., Li, Zhongjie and Bhatt, Samir
(2020)
Tracking progress towards malaria elimination in China: Individual-level estimates of transmission and its spatiotemporal variation using a diffusion network approach.
PLoS Computational Biology, 16 (3), , [e1007707].
(doi:10.1371/journal.pcbi.1007707).
Abstract
In order to monitor progress towards malaria elimination, it is crucial to be able to measure changes in spatio-temporal transmission. However, common metrics of malaria transmission such as parasite prevalence are under powered in elimination contexts. China has achieved major reductions in malaria incidence and is on track to eliminate, having reporting zero locally-acquired malaria cases in 2017 and 2018. Understanding the spatio-temporal pattern underlying this decline, especially the relationship between locally-acquired and imported cases, can inform efforts to maintain elimination and prevent re-emergence. This is particularly pertinent in Yunnan province, where the potential for local transmission is highest. Using a geo-located individual-level dataset of cases recorded in Yunnan province between 2011 and 2016, we introduce a novel Bayesian framework to model a latent diffusion process and estimate the joint likelihood of transmission between cases and the number of cases with unobserved sources of infection. This is used to estimate the case reproduction number, Rc. We use these estimates within spatio-temporal geostatistical models to map how transmission varied over time and space, estimate the timeline to elimination and the risk of resurgence. We estimate the mean Rc between 2011 and 2016 to be 0.171 (95% CI = 0.165, 0.178) for P. vivax cases and 0.089 (95% CI = 0.076, 0.103) for P. falciparum cases. From 2014 onwards, no cases were estimated to have a Rc value above one. An unobserved source of infection was estimated to be moderately likely (p>0.5) for 19/ 611 cases and high (p>0.8) for 2 cases, suggesting very high levels of case ascertainment. Our estimates suggest that, maintaining current intervention efforts, Yunnan is unlikely to experience sustained local transmission up to 2020. However, even with a mean of 0.005 projected up to 2020, locally-acquired cases are possible due to high levels of importation.
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journal.pcbi.1007707
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Submitted date: 24 September 2019
Accepted/In Press date: 3 February 2020
Published date: 23 March 2020
Additional Information:
Funding Information:
This work was funded by a studentship to IR from the Wellcome Trust (109310/Z/15/Z). IR, SB, AG and JU additionally acknowledge Centre support (MR/R015600/1; MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London) jointly funded 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. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Publisher Copyright:
© 2020 Routledge 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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Local EPrints ID: 440937
URI: http://eprints.soton.ac.uk/id/eprint/440937
ISSN: 1553-734X
PURE UUID: 3900e2cc-6223-4054-9fba-f63bf82a187e
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Date deposited: 22 May 2020 16:40
Last modified: 17 Mar 2024 03:52
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Contributors
Author:
Isobel Routledge
Author:
Katherine E Battle
Author:
Azra C. Ghani
Author:
Manuel Gomez-Rodriguez
Author:
Kyle B. Gustafson
Author:
Swapnil Mishra
Author:
Juliette Unwin
Author:
Joshua L. Proctor
Author:
Zhongjie Li
Author:
Samir Bhatt
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