  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.
