Towards a spatio-temporal deep learning approach to predict malaria outbreaks using earth observation measurements in South Asia

Environment plays an important role in health in/equity and it holds potential for community-level intervention. However, there are important gaps in the literature about the impact of changing environment on an individual's health across countries. The overarching objective of this paper is to examine how changes in the environment (green spaces, temperature, night-time lights, built environment, etc.) influence health equity by applying Multi-dimensional LSTM (M-LSTM) to routine collected data for people living in diverse environments. We developed and validated a data fusion approach to predict malaria incidence rate for the year 2017 using spatio-temporal data from 2000 - 2016 across three South Asian countries: Pakistan, India and Bangladesh. The proposed M-LSTM model improves prediction by 1.75% compared to Conv-LSTM model on all South Asian countries. Additionally, M-LSTM also outperforms Random Forest. The data and code will be made available.