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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
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.

1553-734X
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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Routledge, Isobel
b4aa9e56-7ebe-4ca6-8112-567cf6bcd0ca
Lai, Shengjie
b57a5fe8-cfb6-4fa7-b414-a98bb891b001
Battle, Katherine E
a3915800-1889-4832-9a1e-b1d85e6713f7
Ghani, Azra C.
a2d49285-efef-4f6c-881e-acdf1c75ff20
Gomez-Rodriguez, Manuel
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Gustafson, Kyle B.
08d72674-03a3-4b91-98c9-61ee38a8ba77
Mishra, Swapnil
46a21337-415e-415e-a572-82b633e9231a
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), 1-20, [e1007707]. (doi:10.1371/journal.pcbi.1007707).

Record type: Article

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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Submitted date: 24 September 2019
Accepted/In Press date: 3 February 2020
Published date: 23 March 2020

Identifiers

Local EPrints ID: 440937
URI: http://eprints.soton.ac.uk/id/eprint/440937
ISSN: 1553-734X
PURE UUID: 3900e2cc-6223-4054-9fba-f63bf82a187e
ORCID for Shengjie Lai: ORCID iD orcid.org/0000-0001-9781-8148
ORCID for Andrew J. Tatem: ORCID iD orcid.org/0000-0002-7270-941X

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Date deposited: 22 May 2020 16:40
Last modified: 07 Oct 2020 02:21

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Contributors

Author: Isobel Routledge
Author: Shengjie Lai ORCID iD
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: Andrew J. Tatem ORCID iD
Author: Zhongjie Li
Author: Samir Bhatt

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