Malaria prevails in subtropical countries where health monitoring facilities are minimal. Time series prediction models are required to forecast malaria and minimize the effect of this disease on the population. This study proposes a novel scalable framework to predict the instances of malaria in selected geographical locations. Satellite data and clinical data, along with a long short-term memory (LSTM) classifier, were used to predict malaria abundances in the state of Telangana, India. The proposed model provided a 12 months seasonal pattern for selected regions in the state. Each region had different responses based on environmental factors. Analysis indicated that both environmental and clinical variables play an important role in malaria transmission. In conclusion, the Apache Spark-based LSTM presents an effective strategy to identify locations of endemic malaria.

疟疾在健康监测设施很少的亚热带国家盛行。需要时间序列预测模型来预测疟疾，并最大程度地减少这种疾病对人群的影响。这项研究提出了一种新颖的可扩展框架，用于预测选定地理位置的疟疾病例。卫星数据和临床数据以及长期短期记忆（LSTM）分类器被用于预测印度Telangana州的疟疾数量。建议的模型为该州的选定区域提供了12个月的季节性模式。每个地区根据环境因素都有不同的反应。分析表明，环境和临床变量均在疟疾传播中起重要作用。结论，