Evidence map›Paper›PMID 31206514›Full record

ArticlePLoS neglected tropical diseases2019

Identification of high-risk habitats of Oncomelania hupensis, the intermediate host of schistosoma japonium in the Poyang Lake region, China: A spatial and ecological analysis.

Congcong Xia, Yi Hu, Michael P Ward, Henry Lynn, Si Li, Jun Zhang, Jian Hu, Shuang Xiao, Chengfang Lu, Shizhu Li and 2 more

Abstract read
In one paragraph

Article in PLoS neglected tropical diseases, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

16 citing papers in PubMed.

  1. Article
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  4. Simulation ofFrontiers in plant science · 2024
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  5. Proliferation of MDSCs may indicate a lowerParasite (Paris, France) · 2024
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  8. International journal for parasitology. Parasites and wildlife · 2023
    Article
  9. Global Distribution ofInternational journal of environmental research and public health · 2023
    Article
  10. Article
  11. Article
  12. Article
  13. Identifying the Determinants of Distribution ofPathogens (Basel, Switzerland) · 2022
    Article
  14. Article
  15. Article
  16. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Congcong XiaDepartment of Epidemiology and Biostatistics, School of Public Health, Fudan University, Shanghai, P. R. China.
Yi HuDepartment of Epidemiology and Biostatistics, School of Public Health, Fudan University, Shanghai, P. R. China.
Michael P WardSydney School of Veterinary Science, The University of Sydney, Sydney, Australia.
Henry LynnDepartment of Epidemiology and Biostatistics, School of Public Health, Fudan University, Shanghai, P. R. China.
Si LiDepartment of Epidemiology and Biostatistics, School of Public Health, Fudan University, Shanghai, P. R. China.
Jun ZhangDepartment of Epidemiology and Biostatistics, School of Public Health, Fudan University, Shanghai, P. R. China.
Jian HuDepartment of Epidemiology and Biostatistics, School of Public Health, Fudan University, Shanghai, P. R. China.
Shuang XiaoDepartment of Epidemiology and Biostatistics, School of Public Health, Fudan University, Shanghai, P. R. China.
Chengfang LuKey Laboratory of Poyang Lake Wetland and Watershed Research, Ministry of Education, Jiangxi Normal University, Nanchang, P. R. China.
Shizhu LiNational Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention, Key Laboratory of Parasite and Vector Biology, Ministry of Health, Shanghai, P. R. China.
Ying LiuKey Laboratory of Poyang Lake Wetland and Watershed Research, Ministry of Education, Jiangxi Normal University, Nanchang, P. R. China.
Zhijie ZhangDepartment of Epidemiology and Biostatistics, School of Public Health, Fudan University, Shanghai, P. R. China.ORCID 0000-0002-1276-787X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundIdentifying and eliminating snail habitats is the key measure for schistosomiasis control, critical for the nationwide strategy of eliminating schistosomiasis in China. Here, our aim was to construct a new analytical framework to predict high-risk snail habitats based on a large sample field survey for Oncomelania hupensis, providing guidance for schistosomiasis control and prevention. METHODOLOGY/PRINCIPAL

findingsTen ecological models were constructed based on the occurrence data of Oncomelania hupensis and a range of variables in the Poyang Lake region of China, including four presence-only models (Maximum Entropy Models, Genetic Algorithm for rule-set Production, Bioclim and Domain) and six presence-absence models (Generalized Linear Models, Multivariate Adaptive Regression Splines, Flexible Discriminant Analysis, as well as machine algorithmic models-Random Forest, Classification Tree Analysis, Generalized Boosted Model), to predict high-risk snail habitats. Based on overall predictive performance, we found Presence-absence models outperformed the presence-only models and the models based on machine learning algorithms of classification trees showed the highest accuracy. The highest risk was located in the watershed of the River Fu in Yugan County, as well as the watershed of the River Gan and the River Xiu in Xingzi County, covering an area of 52.3 km2. The other high-risk areas for both snail habitats and schistosomiasis were mainly concentrated at the confluence of Poyang Lake and its five main tributaries. CONCLUSIONS/SIGNIFICANCE: This study developed a new distribution map of snail habitats in the Poyang Lake region, and demonstrated the critical role of ecological models in risk assessment to directing local field investigation of Oncomelania hupensis. Moreover, this study could also contribute to the development of effective strategies to prevent further spread of schistosomiasis from endemic areas to non-endemic areas.

Indexed as

Disease VectorsEcosystemLakesAnimalsChinaGastropodaSchistosomiasisSpatial Analysis

Identifiers

PMID31206514
PMCPMC6597197

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.