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

Conference item

Environmental indicators can play a crucial role in forecasting infectious disease outbreaks, holding promise for community-level interventions. Yet, significant gaps exist in the literature regarding the influence of changes in environmental conditions on disease spread over time and across different regions and climates making it challenging to obtain reliable forecasts. This paper aims to propose an approach to predict malaria incidence over time and space by employing a multi-dimensional long short-term memory model (M-LSTM) to simultaneously analyse environmental indicators such as vegetation, temperature, night-time lights, urban/rural settings, and precipitation. We developed and validated a spatio-temporal data fusion approach to predict district-level malaria incidence rates for the year 2017 using spatio-temporal data from 2000 to 2016 across three South Asian countries: Pakistan, India, and Bangladesh. In terms of predictive performance the proposed M-LSTM model results in lower country-specific error rates compared to existing spatio-temporal deep learning models. The data and code have been made publicly available at the study GitHub repository.

Authors: Nazir, U; Ejaz, A; Quddoos, MT; Uppal, M; Khalid, S

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Climate Change AI
• Host title: Proceedings of the NeurIPS 2023 Workshop on Tackling Climate Change with Machine Learning
• Series: Climate Change AI Workshop Papers
• Publication date: 2023-12-16
• Acceptance date: 2023-11-01
• Event title: NeurIPS 2023 Workshop: Tackling Climate Change with Machine Learning
• Event location: New Orleans, USA
• Event website: https://www.climatechange.ai/events/neurips2023
• Event start date: 2023-12-16
• Event end date: 2023-12-16
• Language: English