A new online calculator for estimating individual and community-level risk of dying from COVID-19 has been developed by researchers at the Johns Hopkins Bloomberg School of Public Health. The researchers who developed the calculator expect it to be useful to public health authorities for assessing mortality risks in different communities, and for prioritizing certain groups for vaccination as COVID-19 vaccines become available.
The algorithm underlying the calculator uses information from existing large studies to estimate risk of COVID-19 mortality for individuals based on age, gender, sociodemographic factors and a variety of different health conditions. The risk estimates apply to individuals in the general population who are currently uninfected, and captures factors associated with both risk of future infection and complications after infection.
“Our calculator represents a more quantitative approach and should complement other proposed qualitative guidelines, such as those by the National Academy of Sciences and Medicine, for determining individual and community risks and allocating vaccines,” says study senior author Nilanjan Chatterjee, PhD, Bloomberg Distinguished Professor in the departments of Biostatistics and Epidemiology at the Bloomberg School.
The new risk calculator is presented in a paper that appears in the journal Nature Medicine.
The researchers also collaborated with PolicyMap, Inc. to develop interactive maps for viewing numbers and the proportion of individuals at various levels of risks across U.S. cities, counties and states. These maps will allow local policymakers to plan for vaccination, shielding high-risk individuals, and other targeted intervention efforts.
COVID-19, the pandemic infectious disease that has swept the world over the past ten months, afflicting nearly 70 million people and killing more than 1.5 million worldwide, can affect different people in starkly different ways. Children and young adults may suffer very mild disease or no symptoms at all, whereas the elderly have infection mortality rates of at least several percent. There are also clear ethnic and racial differences—Black and Latinx patients in the U.S., for example, have died of COVID-19 infections at much higher rates than white patients—as well as differences linked to preexisting medical conditions such as diabetes.
“Although we have long known about factors associated with greater mortality, there has been limited effort to incorporate these factors into prevention strategies and forecasting models,” Chatterjee says.
He and his team developed their risk model using several COVID-19-related datasets, including from a large U.K.-based study and state-level death rates published by the Centers for Disease Control and Prevention, and then validated the model for predicting community-level mortality rates using recent deaths across U.S. cities and counties.