Assessment and prediction of the impact of climate change on maternal and perinatal health using data science and AI methodologies.

• Grant number: 25/10862-7
• Support Opportunities: Scholarships in Brazil - Post-Doctoral
• Start date: August 01, 2025
• End date: July 31, 2027
• Field of knowledge: Health Sciences - Medicine - Maternal and Child Health
• Principal Investigator: João Marcos Travassos Romano
• Grantee: Charles M'Poca Charles
• Host Institution: Faculdade de Ciências Médicas (FCM). Universidade Estadual de Campinas (UNICAMP). Campinas , SP, Brazil
• Company: Universidade Estadual de Campinas (UNICAMP). Faculdade de Engenharia Elétrica e de Computação (FEEC)
• Associated research grant: 20/09838-0 - BI0S - Brazilian Institute of Data Science, AP.PCPE

Abstract

Introduction: Climate change presents significant challenges to maternal and perinatal health, leading to an increased incidence of adverse outcomes such as preterm birth, low birth weight, and stillbirth. In this context, the application of Data Science and Artificial Intelligence (AI) provides a promising approach for analyzing and forecasting these implications. Objective: This study employs AI methodologies to investigate the correlation between extreme climatic events and maternal and perinatal health outcomes. The primary aim is to identify relevant patterns and risk factors while developing predictive models and risk calculators for adverse outcomes within the Brazilian population. Methodology: The research follows a structured framework centered on two main axes, integrating a diverse array of research methods and data analysis techniques to support the formulation of public health policies. The methodological process includes the analysis of historical time series data, the implementation of predictive and response tools, and the use of both quantitative and qualitative approaches. Additionally, specialized tools will be developed to assess and predict maternal and perinatal outcomes in relation to extreme climatic events. This will involve applying machine learning (ML) models using data sourced from the Climaterna project repository. Expected Results: This study aims to develop interactive dashboards, early warning systems, and contingency plans designed to mitigate the impacts of extreme climatic events on maternal and neonatal health. It also seeks to identify key climatic variables associated with outcomes such as prematurity, low birth weight, fetal death, and neonatal mortality. Ultimately, we aspire to create predictive models that support decision-making and contribute to the development of preventive and intervention strategies in public health. (AU)