Integrating socioeconomic and geographic data to enhance infectious disease prediction in Brazilian cities

TL;DR

This study enhances infectious disease prediction in Brazilian cities by integrating socioeconomic and geographic data into machine learning models, improving forecasts for COVID-19 and Zika by considering related cities' information.

Contribution

The paper introduces a novel approach that incorporates geographic and socioeconomic data from related cities into disease forecasting models, addressing a gap in existing models.

Findings

Geographic proximity improves COVID-19 and Zika predictions.

Socioeconomic data like GDP per capita influences model accuracy.

The approach has limitations during anomalous contagion patterns.

Abstract

Supervised machine learning models and public surveillance data has been employed for infectious disease forecasting in many settings. These models leverage various data sources capturing drivers of disease spread, such as climate conditions or human behavior. However, few models have incorporated the organizational structure of different geographic locations for forecasting. Traveling waves of seasonal outbreaks have been reported for dengue, influenza, and other infectious diseases, and many of the drivers of infectious disease dynamics may be shared across different cities, either due to their geographic or socioeconomic proximity. In this study, we developed a machine learning model to predict case counts of four infectious diseases across Brazilian cities one week ahead by incorporating information from related cities. We compared selecting related cities using both geographic distance and GDP per capita. Incorporating information from geographically proximate cities improved predictive performance for two of the four diseases, specifically COVID-19 and Zika. We also discuss the impact on forecasts in the presence of anomalous contagion patterns and the limitations of the proposed methodology.