Abstract
Malaria is a major public health problem in tropical and subtropical countries of the World. During the year 1999, Visakhapatnam district of Andhra Pradesh, India experienced a major epidemic of malaria, and nearly 41,805 cases were reported. Hence, a retrospective malaria surveillance study was conducted from 2001 to 2016 and reported nearly a total of 149,317 malaria cases during the study period. Of which, Plasmodium vivax contributes 32%, and Plasmodium falciparum contributes 68% of the total cases. Malaria cases follow a strong seasonal variation and 70% of cases are reported during the monsoon periods. In the present study, we exploited multi step polynomial regression and seasonal autoregressive integrated moving average (SARIMA) models to forecast the malaria cases in the study area. The polynomial model predicted malaria cases with high predictive power and found that malaria cases at lag one, and population played a vital role in malaria transmission. Similarly, mean temperature, rainfall and Normalized Difference Vegetation Index build a significant impact on malaria cases. The best fit model was SARIMA (1, 1, 2) (2, 1, 1)12 which was used for forecasting monthly malaria incidence for the period of January 2015 to December 2016. The performance accuracy of both models are similar, however lowest Akaike information criterion score was observed by the polynomial model, and this approach can be helpful further for forecasting malaria incidence to implement effective control measures in advance for combating malaria in India.
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Acknowledgements
The authors Rajasekhar Mopuri, Srinivasa Rao Mutheneni, Sriram Kumaraswamy, Madhusudhan Rao Kadiri are grateful to the Director of the Council of Scientific and Industrial Research-Indian Institute of Chemical Technology, Hyderabad for his encouragement and support. Rajasekhar Mopuri acknowledge the DST-INSPIRE for providing the fellowship. The authors also acknowledged the district malaria officer, Govt. of Andhra Pradesh for providing the Visakhapatnam malaria data. Srinivasa Rao Mutheneni acknowledges Ministry of Environment, Forest & Climate Change (MoEF& CC), Government of India for funding the project environmental information system (ENVIS: Resource Partner on Climate Change and Public Health). CSIR-IICT communication number of the article is IICT/Pubs./2019/267.
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Mopuri, R., Kakarla, S.G., Mutheneni, S.R. et al. Climate based malaria forecasting system for Andhra Pradesh, India. J Parasit Dis 44, 497–510 (2020). https://doi.org/10.1007/s12639-020-01216-6
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DOI: https://doi.org/10.1007/s12639-020-01216-6