ArticlePloS one2014
Relative roles of weather variables and change in human population in malaria: comparison over different states of India.
Article in PloS one, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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6 citing papers in PubMed.
- Modelling the impact of climate variability on malaria morbidity in the Tamale Metropolitan Area: a time series analysis.BMC infectious diseases · 2026Article
- Descriptive, Hospital-Based, 10-Year Study of Malaria Transmission in Goa, a Southwest Indian State in the Malaria Elimination Phase.The American journal of tropical medicine and hygiene · 2024Article
- Statistical Modelling of the Effects of Weather Factors on Malaria Occurrence in Abuja, Nigeria.International journal of environmental research and public health · 2020Article
- [Climate-sensitive diseases in Brazil and the world: systematic reviewEnfermedades sensibles al clima en Brasil y el mundo: revisión sistemática].Revista panamericana de salud publica = Pan American journal of public health · 2018Review
- Biology, distribution and control of Anopheles (Cellia) minimus in the context of malaria transmission in northeastern India.Parasites & vectors · 2016Review
- Towards seasonal forecasting of malaria in India.Malaria journal · 2014Article
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4 authors.
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Abstract
backgroundPro-active and effective control as well as quantitative assessment of impact of climate change on malaria requires identification of the major drivers of the epidemic. Malaria depends on vector abundance which, in turn, depends on a combination of weather variables. However, there remain several gaps in our understanding and assessment of malaria in a changing climate. Most of the studies have considered weekly or even monthly mean values of weather variables, while the malaria vector is sensitive to daily variations. Secondly, rarely all the relevant meteorological variables have been considered together. An important question is the relative roles of weather variables (vector abundance) and change in host (human) population, in the change in disease load.
methodWe consider the 28 states of India, characterized by diverse climatic zones and changing population as well as complex variability in malaria, as a natural test bed. An annual vector load for each of the 28 states is defined based on the number of vector genesis days computed using daily values of temperature, rainfall and humidity from NCEP daily Reanalysis; a prediction of potential malaria load is defined by taking into consideration changes in the human population and compared with the reported number of malaria cases.
resultsFor most states, the number of malaria cases is very well correlated with the vector load calculated with the combined conditions of daily values of temperature, rainfall and humidity; no single weather variable has any significant association with the observed disease prevalence.
conclusionThe association between vector-load and daily values of weather variables is robust and holds for different climatic regions (states of India). Thus use of all the three weather variables provides a reliable means of pro-active and efficient vector sanitation and control as well as assessment of impact of climate change on malaria.
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