ArticleJournal of parasitic diseases : official organ of the Indian Society for Parasitology2020
Modeling an association between malaria cases and climate variables for Keonjhar district of Odisha, India: a Bayesian approach.
Article in Journal of parasitic diseases : official organ of the Indian Society for Parasitology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
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7 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Association between temperature and malaria (1959-2019): a systematic review and meta-analysis.Archives of public health = Archives belges de sante publique · 2025Pooled it
- Climatic drivers of the 2024 malaria outbreak in two neighbouring districts in northeastern India and its corridor: The indirect role of El Niño.One health (Amsterdam, Netherlands) · 2026Article
- Seasonal Distribution and Trend Analysis of Urban Malaria Prevalence in a Malaria Clinic, South Delhi, India, between 2012 and 2019.The American journal of tropical medicine and hygiene · 2022Article
- Evaluation of prediction models for the malaria incidence in Marodijeh Region, Somaliland.Journal of parasitic diseases : official organ of the Indian Society for Parasitology · 2022Article
- Active Pharmacovigilance for Primaquine Radical Cure of Plasmodium vivax Malaria in Odisha, India.The American journal of tropical medicine and hygiene · 2022Article
- Bayesian spatio-temporal distributed lag modeling for delayed climatic effects on sparse malaria incidence data.BMC medical research methodology · 2021Article
- Association between meteorological factors and the prevalence dynamics of Japanese encephalitis.PloS one · 2021Article
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5 authors.
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Abstract
Malaria, a vector-borne disease, is a significant public health problem in Keonjhar district of Odisha (the malaria capital of India). Prediction of malaria, in advance, is an urgent need for reporting rolling cases of disease throughout the year. The climate condition do play an essential role in the transmission of malaria. Hence, the current study aims to develop and assess a simple and straightforward statistical model of an association between malaria cases and climate variates. It may help in accurate predictions of malaria cases given future climate conditions. For this purpose, a Bayesian Gaussian time series regression model is adopted to fit a relationship of the square root of malaria cases with climate variables with practical lag effects. The model fitting is assessed using a Bayesian version of R
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