Assessing community vulnerability to initial COVID-19 spread in Florida ZIP Codes using Shapley additive explanations with random forest modeling

Background The identification of the population attributes that play important roles in the early-phase community spread of an epidemic is critical to improving our ability to prepare and develop the resilience of societies to future pandemic-potential pathogens. Our study aimed to assess the vulnerability of communities at the ZIP code-level in the state of Florida to the growth in the case incidence of the COVID-19 epidemic during its initial outbreak phase using local case and CDC/ATDSR SVI data and the application of a novel explainable machine learning model. Methods The COVID-19 growth rates were estimated from a log-linear regression fitted to the daily number of cases reported for the initial wave of the pandemic in each ZIP code (n = 935). A random forest model was trained to predict COVID-19 growth rates using 22 social vulnerability indicators. The trained model was interpreted with Shapley additive explanations (SHAP) to investigate the contribution of social vulnerability features to early COVID-19 spread across all ZIP codes in Florida. SHAP feature ranking and results were used to calculate a Social Vulnerability Index (SVI) for each ZIP code. Results Estimated COVID-19 growth rates ranged from 1 to 1.247 (mean = 1.054). The percent of single-parent households was the most important feature in predicting growth rates, followed by (in order) population density and the percentages of the population facing language barriers, living in group quarters, burdened by housing costs, and diagnosed with coronary heart disease in a ZIP code. High values of the five highest ranking features were shown to contribute positively to predicted growth rates, whereas high values of the sixth feature contributed negatively. The constructed SVI had a significant positive association (p-value < 0.0001) with the ZIP code-level epidemic growth rates. Conclusions The constructed ML-SHAP modeling approach and SVI can help assess the social vulnerability of communities to the early COVID-19 spread that was observed in Florida ZIP codes. They can also serve to identify high risk sub-populations and localities, which will be important for advancing development of mitigation strategies to prevent, enhance community resilience, and respond to future novel pathogens of pandemic potential.