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Updated: Dec 11, 2025
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A Multi-detection Assay for Malaria Transmitting Mosquitoes
Published on: February 28, 2015
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Climate based malaria forecasting system for Andhra Pradesh, India
Rajasekhar Mopuri1,2, Satya Ganesh Kakarla1,2, Srinivasa Rao Mutheneni1,2
1ENVIS RP on Climate Change & Public Health, Department of Applied Biology, CSIR-Indian Institute of Chemical Technology, Tarnaka, Hyderabad, 500 007 Telangana India.
Summary
This study forecasts malaria cases in India using polynomial regression and SARIMA models. The polynomial model showed high predictive power, identifying population and environmental factors impacting malaria transmission for better control.
Area of Science:
- Epidemiology
- Environmental Science
- Biostatistics
Background:
- Malaria poses a significant global health threat, particularly in tropical regions.
- Visakhapatnam district, India, reported 149,317 malaria cases between 2001-2016, with Plasmodium falciparum (68%) predominant.
- Malaria incidence exhibits strong seasonality, with 70% of cases occurring during monsoon periods.
Purpose of the Study:
- To forecast malaria incidence in Visakhapatnam district, India.
- To identify key factors influencing malaria transmission using statistical models.
- To inform advance malaria control strategies.
Main Methods:
- Retrospective analysis of malaria surveillance data (2001-2016).
- Application of multi-step polynomial regression and Seasonal Autoregressive Integrated Moving Average (SARIMA) models.
- Evaluation of model performance using Akaike information criterion (AIC).
Main Results:
- Polynomial regression demonstrated high predictive power, with lag one malaria cases and population being significant predictors.
- Mean temperature, rainfall, and Normalized Difference Vegetation Index (NDVI) significantly impacted malaria cases.
- The best-fit SARIMA model (1,1,2)(2,1,1)12 was used for forecasting. Both models showed similar accuracy, but the polynomial model had a lower AIC score.
Conclusions:
- Polynomial regression is a valuable tool for forecasting malaria incidence, considering epidemiological and environmental factors.
- Accurate malaria forecasting enables proactive implementation of control measures.
- This approach can aid in combating malaria in India and similar endemic regions.
Keywords:
Forecasting modelMalariaPolynomial regression and seasonal autoregressive integrated moving averageVisakhapatnamMore Related Videos
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