Reviewed by Lexie CornerOct 28 2024
In a recent study published in the journal Environmental Research Letters, researchers from the University of Maryland introduced an AI-based method to predict the likelihood of deadly diarrheal disease outbreaks, offering public health systems crucial lead time—weeks or even months—to prepare and potentially save lives.
Extreme weather events associated with climate change, such as severe flooding and prolonged droughts, often lead to deadly outbreaks of diarrheal diseases. These epidemics are particularly devastating in developing countries, where diarrheal illness is the third leading cause of death among young children.
Increases in extreme weather events related to climate change will only continue in the foreseeable future. We must adapt as a society. The early warning systems outlined in this research are a step in that direction to enhance community resilience to the health threats posed by climate change.
Amir Sapkota, Professor and Chair, Department of Epidemiology and Biostatistics, University of Maryland
The multidisciplinary team collaborated with multiple institutions and utilized data from El Niño climate patterns, temperature, precipitation, historical disease rates, and various environmental and geographic factors in Nepal, Taiwan, and Vietnam from 2000 to 2019.
Using this data, the researchers developed AI-based models capable of forecasting local disease burden weeks to months in advance.
Knowing expected disease burden weeks to months ahead of time provides public health practitioners crucial time to prepare. This way they are better prepared to respond, when the time comes.
Amir Sapkota, Professor and Chair, Department of Epidemiology and Biostatistics, University of Maryland
Although Taiwan, Vietnam, and Nepal were the primary focus of the study, "our findings are quite applicable to other parts of the world as well, particularly areas where communities lack access to municipal drinking water and functioning sanitation systems," said Raul Curz-Cano, Associate Professor and Study Lead Author at the School of Public Health, Indiana University.
Sapkota noted that this study is only the beginning, as AI's capacity to handle large data sets will enable the development of increasingly accurate predictive models for early warning systems. He hopes these advancements will allow public health systems to help communities better protect themselves against rising risks of diarrheal epidemics.
The research team included experts from diverse fields, such as water resources engineering, community health research, and atmospheric and oceanic sciences. Authors represented the University of Maryland's Department of Epidemiology and Biostatistics and Department of Atmospheric and Oceanic Science, Indiana University School of Public Health in Bloomington, the Nepal Health Research Council, Hue University of Medicine and Pharmacy in Vietnam, Lund University in Sweden, and Chung Yuan Christian University in Taiwan.
The study was supported by grants from the National Science Foundation through Belmont Forum, the Swedish Research Council for Health, Working Life and Welfare, Taiwan’s Ministry of Science and Technology, and the National Science Foundation National Research Traineeship Program.
Journal Reference:
Cruz-Cano, R., et al. (2024) A prototype early warning system for diarrhoeal disease to combat health threats of climate change in the Asia-Pacific region. Environmental Research Letters. doi.org/10.1088/1748-9326/ad8366.