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.
Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, 201002, India.
Environ Sci Pollut Res Int. 2022 Sep;29(45):68232-68246. doi: 10.1007/s11356-022-20642-y. Epub 2022 May 10.
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.
疟疾是印度的地方病，目标是在 2030 年消除。本研究旨在了解阿萨姆邦和阿鲁纳恰尔邦疟疾传播动态的流行病学模式，然后使用每月气候因素开发疟疾预测模型。在 2011-2018 年期间，阿萨姆邦共报告了 144055 例病例，在 2011-2019 年期间，阿鲁纳恰尔邦报告了 42970 例病例，其中恶性疟原虫（74.5%）是阿萨姆邦最主要的寄生虫，而在阿鲁纳恰尔邦则是间日疟原虫（66%）。疟疾传播具有很强的季节性变化，大多数病例发生在季风期（阿萨姆邦为 51.9%，阿鲁纳恰尔邦为 53.6%）。同样，在这两个邦，男性的疟疾发病率最高（阿萨姆邦为 55.75%，阿鲁纳恰尔邦为 51.43%），而年龄在 15 岁以上的人群的疾病风险也更高（阿萨姆邦为 61.7%，阿鲁纳恰尔邦为 67.9%）。为了预测疟疾发病率，实施了贝叶斯结构时间序列（BSTS）和季节性自回归综合移动平均与外生因素（SARIMAX）模型。观察到疟疾病例与气候变量之间存在统计学显著关联。发现最具影响力的气候因素是最大和平均温度，滞后 6 个月，与疟疾发病率呈负相关。BSTS 模型在包含自相关疟疾病例、交叉相关气候变量以及阿萨姆邦和阿鲁纳恰尔邦疟疾病例的最优自相关数据集（OAD）上表现出优越的性能（阿萨姆邦 RMSE，0.106；MAE，0.089；SMAPE，19.2%；阿鲁纳恰尔邦 RMSE，0.128；MAE，0.122；SMAPE，22.6%）优于 SARIMAX 模型。研究结果表明，BSTS 模型的预测性能更好，这可能有助于东北部地区的政府和非政府机构实施正在进行的干预策略，以有效防治这种疾病。
