Forecasting Malaria Cases Using Climatic Factors in Delhi, India: A Time Series Analysis

Background. Malaria still remains a public health problem in developing countries and changing environmental and climatic factors pose the biggest challenge in fighting against the scourge of malaria. Therefore, the study was designed to forecast malaria cases using climatic factors as predictors in...

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Bibliographic Details
Published in:Malaria Research and Treatment 2014, Vol.2014 (2014), p.101-106
Main Authors: Kumar, Varun, Mangal, Abha, Panesar, Sanjeet, Yadav, Geeta, Talwar, Richa, Raut, Deepak, Singh, Saudan
Format: Article
Language:English
Subjects:
Analysis
Climatic changes
Demographic aspects
Environmental aspects
Health aspects
Humidity
Infections
Malaria
Mortality
Mosquitoes
Risk factors
Seasons
Time series
Vectors (Biology)
Wind
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Summary:Background. Malaria still remains a public health problem in developing countries and changing environmental and climatic factors pose the biggest challenge in fighting against the scourge of malaria. Therefore, the study was designed to forecast malaria cases using climatic factors as predictors in Delhi, India. Methods. The total number of monthly cases of malaria slide positives occurring from January 2006 to December 2013 was taken from the register maintained at the malaria clinic at Rural Health Training Centre (RHTC), Najafgarh, Delhi. Climatic data of monthly mean rainfall, relative humidity, and mean maximum temperature were taken from Regional Meteorological Centre, Delhi. Expert modeler of SPSS ver. 21 was used for analyzing the time series data. Results. Autoregressive integrated moving average, ARIMA (0,1,1) (0,1,0)12, was the best fit model and it could explain 72.5% variability in the time series data. Rainfall (P value = 0.004) and relative humidity (P value = 0.001) were found to be significant predictors for malaria transmission in the study area. Seasonal adjusted factor (SAF) for malaria cases shows peak during the months of August and September. Conclusion. ARIMA models of time series analysis is a simple and reliable tool for producing reliable forecasts for malaria in Delhi, India.
ISSN:2044-4362
2090-8075
2044-4362
DOI:10.1155/2014/482851