ArticleMalaria journal2015
Mining geographic variations of Plasmodium vivax for active surveillance: a case study in China.
Article in Malaria journal, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Bayesian inference for the onset time and epidemiological characteristics of emerging infectious diseases.Frontiers in public health · 2024Article
- Accessing the syndemic of COVID-19 and malaria intervention in Africa.Infectious diseases of poverty · 2021Article
- Inference and prediction of malaria transmission dynamics using time series data.Infectious diseases of poverty · 2020Article
- Modeling the transmission and control of Zika in Brazil.Scientific reports · 2017Article
- Early rigorous control interventions can largely reduce dengue outbreak magnitude: experience from Chaozhou, China.BMC public health · 2017Article
- Risk assessment of malaria transmission at the border area of China and Myanmar.Infectious diseases of poverty · 2017Article
- Inferring the Spatio-temporal Patterns of Dengue Transmission from Surveillance Data in Guangzhou, China.PLoS neglected tropical diseases · 2016Article
- IASM: A System for the Intelligent Active Surveillance of Malaria.Computational and mathematical methods in medicine · 2016Article
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4 authors.
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Abstract
backgroundGeographic variations of an infectious disease characterize the spatial differentiation of disease incidences caused by various impact factors, such as environmental, demographic, and socioeconomic factors. Some factors may directly determine the force of infection of the disease (namely, explicit factors), while many other factors may indirectly affect the number of disease incidences via certain unmeasurable processes (namely, implicit factors). In this study, the impact of heterogeneous factors on geographic variations of Plasmodium vivax incidences is systematically investigate in Tengchong, Yunnan province, China.
methodsA space-time model that resembles a P. vivax transmission model and a hidden time-dependent process, is presented by taking into consideration both explicit and implicit factors. Specifically, the transmission model is built upon relevant demographic, environmental, and biophysical factors to describe the local infections of P. vivax. While the hidden time-dependent process is assessed by several socioeconomic factors to account for the imported cases of P. vivax. To quantitatively assess the impact of heterogeneous factors on geographic variations of P. vivax infections, a Markov chain Monte Carlo (MCMC) simulation method is developed to estimate the model parameters by fitting the space-time model to the reported spatial-temporal disease incidences.
resultsSince there is no ground-truth information available, the performance of the MCMC method is first evaluated against a synthetic dataset. The results show that the model parameters can be well estimated using the proposed MCMC method. Then, the proposed model is applied to investigate the geographic variations of P. vivax incidences among all 18 towns in Tengchong, Yunnan province, China. Based on the geographic variations, the 18 towns can be further classify into five groups with similar socioeconomic causality for P. vivax incidences.
conclusionsAlthough this study focuses mainly on the transmission of P. vivax, the proposed space-time model is general and can readily be extended to investigate geographic variations of other diseases. Practically, such a computational model will offer new insights into active surveillance and strategic planning for disease surveillance and control.
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