Journal Article

A prototype early warning system for diarrhoeal disease to combat health threats of climate change in the asia-pacific region

Wang, Yu-Chun, Sharma, Ayushi, Dhimal, Megnath, Cruz Cano, Raul, Andhikaputra, Gerry, Thu, Dang Thi Anh, Liang, Xin-Zhong, Gao, Chuansi, Sapkota, Amir, He, Hao, Zhang, Linus, Ma, Tianzhou, Murtugudde, Raghu, Aryal, Samyam

2024

Overview

Ongoing climate variability and change are increasing the burden of diarrhoeal disease worldwide. Meaningful early warning systems with adequate lead times (weeks to months) are needed to guide public health decision–making and enhance community resilience against health threats posed by climate change. Toward this goal, we trained various machine-learning models to predict diarrhoeal disease rates in Nepal (2002–2014), Taiwan (2008–2019), and Vietnam (2000–2015) using temperature, precipitation, previous disease rates, and El Niño Southern Oscillation phases. We also compared the performance of shallow time-series neural network (NN), Random Forest Regressor, artificial nn, gradient boosting regressor, and long short-term memory–based methods for their effectiveness in predicting diarrhoeal disease burden across multiple countries. We evaluated model performance using a test dataset and assessed the accuracy of predicted diarrhoeal disease incidence rates for the last year of available data in each district. Our results suggest that even in the absence of the most recent disease surveillance data, a likely scenario in most low- and middle-income countries, our NN-based early warning system using historical data performs reasonably well. However, future studies are needed to perform prospective evaluations of such early warning systems in real-world settings.

• Publisher: IOP Publishing
• Subject: asia–pacific region / Climate change / Climate models / Climate prediction / Climate Science / Climate variability / Decision making / Decision trees / diarrhoea / diarrhoea early warning system / Early warning systems / Earth and Related Environmental Sciences / El Nino / Geovetenskap och relaterad miljövetenskap / Health risks / Health Sciences / Hälsovetenskap / Klimatvetenskap / Long short-term memory / Machine learning / Medical and Health Sciences / Medicin och hälsovetenskap / Natural Sciences / Naturvetenskap / Neural networks / Oceanografi, hydrologi och vattenresurser / Oceanography, Hydrology and Water Resources / Performance evaluation / Performance prediction / Public health / Southern Oscillation / time-series neural networks / Warning systems