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Weather integrated malaria prediction system using Bayesian structural time series model for northeast states of India

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Abstract

Malaria is an endemic disease in India and targeted to eliminate by the year 2030. The present study is aimed at understanding the epidemiological patterns of malaria transmission dynamics in Assam and Arunachal Pradesh followed by the development of a malaria prediction model using monthly climate factors. A total of 144,055 cases in Assam during 2011–2018 and 42,970 cases in Arunachal Pradesh were reported during the 2011–2019 period observed, and Plasmodium falciparum (74.5%) was the most predominant parasite in Assam, whereas Plasmodium vivax (66%) in Arunachal Pradesh. Malaria transmission showed a strong seasonal variation where most of the cases were reported during the monsoon period (Assam, 51.9%, and Arunachal Pradesh, 53.6%). Similarly, the malaria incidence was highest in the male population in both states (Asam, 55.75%, and Arunachal Pradesh, 51.43%), and the disease risk is also higher among the > 15 years age group (Assam, 61.7%, and Arunachal Pradesh, 67.9%). To predict the malaria incidence, Bayesian structural time series (BSTS) and Seasonal Auto-Regressive Integrated Moving Average with eXogenous factors (SARIMAX) models were implemented. A statistically significant association between malaria cases and climate variables was observed. The most influencing climate factors are found to be maximum and mean temperature with a 6-month lag, and it showed a negative association with malaria incidence. The BSTS model has shown superior performance on the optimal auto-correlated dataset (OAD) which contains auto-correlated malaria cases, cross-correlated climate variables besides malaria cases in both Assam (RMSE, 0.106; MAE, 0.089; and SMAPE, 19.2%) and Arunachal Pradesh (RMSE, 0.128; MAE, 0.122; and SMAPE, 22.6%) than the SARIMAX model. The findings suggest that the predictive performance of the BSTS model is outperformed, and it may be helpful for ongoing intervention strategies by governmental and nongovernmental agencies in the northeast region to combat the disease effectively.

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The data and materials are available with the corresponding author and will be provided upon request.

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Acknowledgements

The authors are grateful to the directors of the Council of Scientific and Industrial Research-Indian Institute of Chemical Technology, Hyderabad, and 4PI, Bangalore, for their encouragement and support. Suryanarayana Murty Upadhyayula acknowledges the support from the Ministry of Chemicals and Fertilizers, Govt. of India. Srinivasa Rao Mutheneni is grateful to the MoEF & CC (Ministry of Environment Forest & Climate Change), Govt. India, for an environmental information system (ENVIS) and Center for Climate Change and Public Health. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. CSIR-IICT communication number of the article is IICT/pubs./2022/057.

Funding

The present work is supported by the DST (Department of Science and Technology) under the Epidemiology Data Analytics (EDA) of Interdisciplinary cyber-physical systems (ICPS) program (Grant number: DST/ICPS/EDA/2018), Govt. of India.

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Authors and Affiliations

  1. ENVIS Resource Partner On Climate Change and Public Health, Applied Biology Division, CSIR-Indian Institute of Chemical Technology (CSIR-IICT), Tarnaka, Hyderabad, 500007, Telangana, India

    Hariprasad Vavilala, Nikhila Yaladanda, Phani Krishna Kondeti,  Rafiq Unissa, Rajasekhar Mopuri, Madhusudhan Rao Kadiri & Srinivasa Rao Mutheneni

  2. Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, 201002, India

    Hariprasad Vavilala, Nikhila Yaladanda, Phani Krishna Kondeti,  Rafiq Unissa, Rajasekhar Mopuri & Srinivasa Rao Mutheneni

  3. CSIR-Fourth Paradigm Institute, NAL Belur Campus, Bangalore, 560037, Karnataka, India

    Krushna Chandra Gouda & Kantha Rao Bhimala

  4. National Institute of Pharmaceutical Education and Research (NIPER), Sila Katamur, Halugurisuk, Changsari, Kamrup, 781101, Assam, India

    Suryanaryana Murty Upadhyayula

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  1. Hariprasad Vavilala
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  2. Nikhila Yaladanda
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  3. Phani Krishna Kondeti
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  4. Rafiq Unissa
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  5. Rajasekhar Mopuri
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  7. Kantha Rao Bhimala
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  8. Madhusudhan Rao Kadiri
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Contributions

All authors contributed to the study design, data analysis, and manuscript preparation. HV, NY, PKK, RU, and MRK are involved in data compilation and pre-processing, RM is involved for spatial mapping. HV and SRM are involved in data analysis and draft manuscript preparation. KCG, KRB, and SMU are involved in draft manuscript correction. Overall work was supervised by SRM.

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Correspondence to Srinivasa Rao Mutheneni.

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Vavilala, H., Yaladanda, N., Krishna Kondeti, P. et al. Weather integrated malaria prediction system using Bayesian structural time series model for northeast states of India. Environ Sci Pollut Res 29, 68232–68246 (2022). https://doi.org/10.1007/s11356-022-20642-y

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