Building a COVID-19 Vulnerability Index
This work addresses the need for targeted outreach campaigns to mitigate COVID-19 effects, but it is incremental as it adapts existing methods to a new context without novel methodological breakthroughs.
The paper tackled the problem of identifying individuals at high risk for severe COVID-19 complications by developing three predictive models using data from other upper respiratory infections as a proxy, with each model improving predictive effectiveness at the cost of implementation ease.
COVID-19 is an acute respiratory disease that has been classified as a pandemic by the World Health Organization. Characterization of this disease is still in its early stages. However, it is known to have high mortality rates, particularly among individuals with preexisting medical conditions. Creating models to identify individuals who are at the greatest risk for severe complications due to COVID-19 will be useful for outreach campaigns to help mitigate the disease's worst effects. While information specific to COVID-19 is limited, a model using complications due to other upper respiratory infections can be used as a proxy to help identify those individuals who are at the greatest risk. We present the results for three models predicting such complications, with each model increasing predictive effectiveness at the expense of ease of implementation.