Abstract
BackgroundThe 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.MethodologyWe 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 FindingsWe 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.ConclusionThe 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.
Highlights
As one of the malaria parasites that can infect and be transmitted by human beings, Plasmodium vivax has induced enormous challenges to the public health of human population
By focusing on the malaria transmission in Yunnan province, People’s Republic of China, we pay special attention to the problem of how to infer the underlying transmission networks of P. vivax based on tempo-spatial patterns of observed/reported cases
To infer the underlying transmission networks of P. vivax, it would be desirable to address the following two computational issues: N How can we model the dynamics of P. vivax transmission by taking into consideration the heterogeneous transmission potential caused by various factors at or across different scales?
Summary
As one of the malaria parasites that can infect and be transmitted by human beings, Plasmodium vivax has induced enormous challenges to the public health of human population. Eliminate or even eradicate malaria, WHO has suggested that the most important measure is a timely response with the implementation of strategic intervention [2] This requires the establishment of effective and efficient monitoring or surveillance systems [3]. 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. 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. 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
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