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Mining geographic variations of Plasmodium vivax for active surveillance: a case study in China

Overview of attention for article published in Malaria Journal, May 2015
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Title
Mining geographic variations of Plasmodium vivax for active surveillance: a case study in China
Published in
Malaria Journal, May 2015
DOI 10.1186/s12936-015-0719-y
Pubmed ID
Authors

Benyun Shi, Qi Tan, Xiao-Nong Zhou, Jiming Liu

Abstract

Geographic 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. A 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. Since 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. Although 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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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 23 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Ecuador 1 4%
Brazil 1 4%
Unknown 21 91%

Demographic breakdown

Readers by professional status Count As %
Student > Master 7 30%
Researcher 6 26%
Student > Ph. D. Student 3 13%
Student > Bachelor 1 4%
Lecturer > Senior Lecturer 1 4%
Other 1 4%
Unknown 4 17%
Readers by discipline Count As %
Agricultural and Biological Sciences 6 26%
Medicine and Dentistry 5 22%
Computer Science 2 9%
Nursing and Health Professions 1 4%
Economics, Econometrics and Finance 1 4%
Other 1 4%
Unknown 7 30%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 30 May 2015.
All research outputs
#13,436,543
of 22,807,037 outputs
Outputs from Malaria Journal
#3,518
of 5,563 outputs
Outputs of similar age
#126,743
of 266,724 outputs
Outputs of similar age from Malaria Journal
#62
of 106 outputs
Altmetric has tracked 22,807,037 research outputs across all sources so far. This one is in the 39th percentile – i.e., 39% of other outputs scored the same or lower than it.
So far Altmetric has tracked 5,563 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.8. This one is in the 33rd percentile – i.e., 33% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 266,724 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 50% of its contemporaries.
We're also able to compare this research output to 106 others from the same source and published within six weeks on either side of this one. This one is in the 39th percentile – i.e., 39% of its contemporaries scored the same or lower than it.