Tracking progress towards malaria elimination in China: Individual-level estimates of transmission and its spatiotemporal variation using a diffusion network approach.


Journal

PLoS computational biology
ISSN: 1553-7358
Titre abrégé: PLoS Comput Biol
Pays: United States
ID NLM: 101238922

Informations de publication

Date de publication:
03 2020
Historique:
received: 24 09 2019
accepted: 03 02 2020
revised: 02 04 2020
pubmed: 24 3 2020
medline: 1 7 2020
entrez: 24 3 2020
Statut: epublish

Résumé

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.

Identifiants

pubmed: 32203520
doi: 10.1371/journal.pcbi.1007707
pii: PCOMPBIOL-D-19-01618
pmc: PMC7117777
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

e1007707

Subventions

Organisme : Medical Research Council
ID : MR/R015600/1
Pays : United Kingdom
Organisme : Wellcome Trust
ID : 109310/Z/15/Z
Pays : United Kingdom

Déclaration de conflit d'intérêts

The authors have declared that no competing interests exist.

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Auteurs

Isobel Routledge (I)

Imperial College London, London, United Kingom.

Shengjie Lai (S)

University of Southampton, Southampton, United Kingdom.

Katherine E Battle (KE)

University of Oxford, Oxford, United Kingdom.

Azra C Ghani (AC)

Imperial College London, London, United Kingom.

Manuel Gomez-Rodriguez (M)

Max Planck Institute for Software Systems, Saarbrücken, Germany.

Kyle B Gustafson (KB)

Institute for Disease Modelling, Bellevue, Washington, United States of America.

Swapnil Mishra (S)

Imperial College London, London, United Kingom.

Juliette Unwin (J)

Imperial College London, London, United Kingom.

Joshua L Proctor (JL)

Institute for Disease Modelling, Bellevue, Washington, United States of America.

Andrew J Tatem (AJ)

University of Southampton, Southampton, United Kingdom.

Zhongjie Li (Z)

Chinese Centers for Disease Control and Prevention, Beijing, China.

Samir Bhatt (S)

Imperial College London, London, United Kingom.

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Classifications MeSH