Inferring Plasmodium vivax transmission networks from tempo-spatial surveillance data.
Background 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 f...
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ftdoajarticles:oai:doaj.org/article:e0729005b51d4560b5fc2b731552a79c 2023-05-15T15:15:52+02:00 Inferring Plasmodium vivax transmission networks from tempo-spatial surveillance data. Benyun Shi Jiming Liu Xiao-Nong Zhou Guo-Jing Yang 2014-02-01T00:00:00Z https://doi.org/10.1371/journal.pntd.0002682 https://doaj.org/article/e0729005b51d4560b5fc2b731552a79c EN eng Public Library of Science (PLoS) https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/24516684/?tool=EBI https://doaj.org/toc/1935-2727 https://doaj.org/toc/1935-2735 1935-2727 1935-2735 doi:10.1371/journal.pntd.0002682 https://doaj.org/article/e0729005b51d4560b5fc2b731552a79c PLoS Neglected Tropical Diseases, Vol 8, Iss 2, p e2682 (2014) Arctic medicine. Tropical medicine RC955-962 Public aspects of medicine RA1-1270 article 2014 ftdoajarticles https://doi.org/10.1371/journal.pntd.0002682 2022-12-31T15:38:05Z Background 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. Methodology 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. Principal findings 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. Conclusion 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. Article in Journal/Newspaper Arctic Directory of Open Access Journals: DOAJ Articles Arctic PLoS Neglected Tropical Diseases 8 2 e2682 |
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Directory of Open Access Journals: DOAJ Articles |
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English |
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Arctic medicine. Tropical medicine RC955-962 Public aspects of medicine RA1-1270 |
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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. |
topic_facet |
Arctic medicine. Tropical medicine RC955-962 Public aspects of medicine RA1-1270 |
description |
Background 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. Methodology 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. Principal findings 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. Conclusion 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 in Journal/Newspaper |
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://doi.org/10.1371/journal.pntd.0002682 https://doaj.org/article/e0729005b51d4560b5fc2b731552a79c |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic |
genre_facet |
Arctic |
op_source |
PLoS Neglected Tropical Diseases, Vol 8, Iss 2, p e2682 (2014) |
op_relation |
https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/24516684/?tool=EBI https://doaj.org/toc/1935-2727 https://doaj.org/toc/1935-2735 1935-2727 1935-2735 doi:10.1371/journal.pntd.0002682 https://doaj.org/article/e0729005b51d4560b5fc2b731552a79c |
op_doi |
https://doi.org/10.1371/journal.pntd.0002682 |
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PLoS Neglected Tropical Diseases |
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2 |
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e2682 |
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