Novel Machine-Learned Approach for COVID-19 Resource Allocation: A Tool for EvaluatingCommunity Susceptibility

Despite worldwide efforts to develop an effective COVID vaccine, it is quite evident that initial supplies will be limited. Therefore, it is important to develop methods that will ensure that the COVID vaccine is allocated to the people who are at major risk until there is a sufficient global supply. Herein, I developed a machine-learning tool that could be applied to assess the risk in communities based on social, medical, and lifestyle risk factors. As a "proof of concept," I modeled COVID risk in the Massachusetts communities using 29 risk factors, including the prevalence of preexisting comorbid conditions like COPD and social factors such as racial composition. Of the 29 factors, 14 were found to be significant (p < 0.1) indicators: poverty, food insecurity, lack of high school education, lack of health insurance coverage, premature mortality, population, population density, recent population growth, Asian percentage, high-occupancy housing, and preexisting prevalence of cancer, COPD, overweightness, and heart attacks. The machine-learning approach finds the 9 highest risk communities in the state of Massachusetts: Lynn, Brockton, Revere, Randolph, Lowell, New Bedford, Everett, Waltham, and Fitchburg. The 5 most at-risk counties are Suffolk, Middlesex, Bristol, Norfolk, and Plymouth. With appropriate data, the tool could evaluate risk in other communities, or even enumerate individual patient susceptibility. A ranking of communities by risk may help policymakers ensure equitable allocation of limited doses of the COVID vaccine.