LSTM based prediction of malaria abundances using big data

Abstract

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.