Inferring Plasmodium vivax transmission networks from tempo-spatial surveillance data.

<h4>Background</h4>The transmission networks of Plasmodium vivax characterize how the parasite transmits from one location to another, which are informative and insightful for public health policy makers to accurately predict the patterns of its geographical spread. However, such network...

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Autores principales:	Benyun Shi, Jiming Liu, Xiao-Nong Zhou, Guo-Jing Yang
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2014
Materias:	Arctic medicine. Tropical medicine RC955-962 Public aspects of medicine RA1-1270
Acceso en línea:	https://doaj.org/article/e0729005b51d4560b5fc2b731552a79c
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spelling	oai:doaj.org-article:e0729005b51d4560b5fc2b731552a79c2021-11-18T09:16:25ZInferring Plasmodium vivax transmission networks from tempo-spatial surveillance data.1935-27271935-273510.1371/journal.pntd.0002682https://doaj.org/article/e0729005b51d4560b5fc2b731552a79c2014-02-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/24516684/?tool=EBIhttps://doaj.org/toc/1935-2727https://doaj.org/toc/1935-2735<h4>Background</h4>The transmission networks of Plasmodium vivax characterize how the parasite transmits from one location to another, which are informative and insightful for public health policy makers to accurately predict the patterns of its geographical spread. However, such networks are not apparent from surveillance data because P. vivax transmission can be affected by many factors, such as the biological characteristics of mosquitoes and the mobility of human beings. Here, we pay special attention to the problem of how to infer the underlying transmission networks of P. vivax based on available tempo-spatial patterns of reported cases.<h4>Methodology</h4>We first define a spatial transmission model, which involves representing both the heterogeneous transmission potential of P. vivax at individual locations and the mobility of infected populations among different locations. Based on the proposed transmission model, we further introduce a recurrent neural network model to infer the transmission networks from surveillance data. Specifically, in this model, we take into account multiple real-world factors, including the length of P. vivax incubation period, the impact of malaria control at different locations, and the total number of imported cases.<h4>Principal findings</h4>We implement our proposed models by focusing on the P. vivax transmission among 62 towns in Yunnan province, People's Republic China, which have been experiencing high malaria transmission in the past years. By conducting scenario analysis with respect to different numbers of imported cases, we can (i) infer the underlying P. vivax transmission networks, (ii) estimate the number of imported cases for each individual town, and (iii) quantify the roles of individual towns in the geographical spread of P. vivax.<h4>Conclusion</h4>The demonstrated models have presented a general means for inferring the underlying transmission networks from surveillance data. The inferred networks will offer new insights into how to improve the predictability of P. vivax transmission.Benyun ShiJiming LiuXiao-Nong ZhouGuo-Jing YangPublic Library of Science (PLoS)articleArctic medicine. Tropical medicineRC955-962Public aspects of medicineRA1-1270ENPLoS Neglected Tropical Diseases, Vol 8, Iss 2, p e2682 (2014)
institution	DOAJ
collection	DOAJ
language	EN
topic	Arctic medicine. Tropical medicine RC955-962 Public aspects of medicine RA1-1270
spellingShingle	Arctic medicine. Tropical medicine RC955-962 Public aspects of medicine RA1-1270 Benyun Shi Jiming Liu Xiao-Nong Zhou Guo-Jing Yang Inferring Plasmodium vivax transmission networks from tempo-spatial surveillance data.
description	<h4>Background</h4>The transmission networks of Plasmodium vivax characterize how the parasite transmits from one location to another, which are informative and insightful for public health policy makers to accurately predict the patterns of its geographical spread. However, such networks are not apparent from surveillance data because P. vivax transmission can be affected by many factors, such as the biological characteristics of mosquitoes and the mobility of human beings. Here, we pay special attention to the problem of how to infer the underlying transmission networks of P. vivax based on available tempo-spatial patterns of reported cases.<h4>Methodology</h4>We first define a spatial transmission model, which involves representing both the heterogeneous transmission potential of P. vivax at individual locations and the mobility of infected populations among different locations. Based on the proposed transmission model, we further introduce a recurrent neural network model to infer the transmission networks from surveillance data. Specifically, in this model, we take into account multiple real-world factors, including the length of P. vivax incubation period, the impact of malaria control at different locations, and the total number of imported cases.<h4>Principal findings</h4>We implement our proposed models by focusing on the P. vivax transmission among 62 towns in Yunnan province, People's Republic China, which have been experiencing high malaria transmission in the past years. By conducting scenario analysis with respect to different numbers of imported cases, we can (i) infer the underlying P. vivax transmission networks, (ii) estimate the number of imported cases for each individual town, and (iii) quantify the roles of individual towns in the geographical spread of P. vivax.<h4>Conclusion</h4>The demonstrated models have presented a general means for inferring the underlying transmission networks from surveillance data. The inferred networks will offer new insights into how to improve the predictability of P. vivax transmission.
format	article
author	Benyun Shi Jiming Liu Xiao-Nong Zhou Guo-Jing Yang
author_facet	Benyun Shi Jiming Liu Xiao-Nong Zhou Guo-Jing Yang
author_sort	Benyun Shi
title	Inferring Plasmodium vivax transmission networks from tempo-spatial surveillance data.
title_short	Inferring Plasmodium vivax transmission networks from tempo-spatial surveillance data.
title_full	Inferring Plasmodium vivax transmission networks from tempo-spatial surveillance data.
title_fullStr	Inferring Plasmodium vivax transmission networks from tempo-spatial surveillance data.
title_full_unstemmed	Inferring Plasmodium vivax transmission networks from tempo-spatial surveillance data.
title_sort	inferring plasmodium vivax transmission networks from tempo-spatial surveillance data.
publisher	Public Library of Science (PLoS)
publishDate	2014
url	https://doaj.org/article/e0729005b51d4560b5fc2b731552a79c
work_keys_str_mv	AT benyunshi inferringplasmodiumvivaxtransmissionnetworksfromtempospatialsurveillancedata AT jimingliu inferringplasmodiumvivaxtransmissionnetworksfromtempospatialsurveillancedata AT xiaonongzhou inferringplasmodiumvivaxtransmissionnetworksfromtempospatialsurveillancedata AT guojingyang inferringplasmodiumvivaxtransmissionnetworksfromtempospatialsurveillancedata
_version_	1718420934443925504

Inferring Plasmodium vivax transmission networks from tempo-spatial surveillance data.

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