Lifemesh Population Risk Profiles provide meaningful insights to municipalities, businesses, and individuals that may be used to ascertain the risk of spread to COVID-19, and the risk of mortality should COVID-19 impact a community.
The primary goal of Lifemesh Population Risk Profiles is to allow for mitigation protocols to be put into place in terms of testing, social distancing, and contact tracing based on the likelihood of impact of the virus. Lifemesh Population Risk Profiles may also be used to map against hospital beds and ICU capabilities for planning and contingency planning purposes.
Note, the Lifemesh Care Index analysis and Lifemesh HPSA scoring may also be used to determine the healthcare and care profiles by county to provide further insights. These will be made available to the public over the coming weeks.
The Population Risk Profile is designed to quantify the risk of spread as the virus is introduced to the community. The Population Risk Profile has been developed at the County level, and is based on ranges in risk scale from A-E, with A being the lowest risk, and E being the highest risk.
The following Map visualization shows the risk scores by county across the US. The darker the county, the higher the risk.
Zooming into a particular area shows a greater level of detail. For example, on the large map the counties representing the NY metropolitan areas do not appear at higher risk, but this is a function of the size of the county(s), and not the relative risk.
In this case the corresponding counties representing NYC are combined into a single metric showing the high-risk aspect of the New York City area, and risk profiles of nearby NY and NJ counties.
In order to view and track accuracy of the model, Lifemesh then superimposed the number of cases, and subsequent deaths by adding a layer that highlights the number of cases per county (darker red represents higher rate per 100K). Further, the size of the “bubble” correlates to the number of subsequent deaths per 100K members of the population (Larger the bubble the greater deaths/100K).
Zooming in on Kentucky, as an example, we see a large swath of counties that are at high risk. We also see varying degrees of infection (pink to red bubbles), and varying degrees of current mortality (large pink/red bubbles)
“COVID-19 Population Risk Profile, Contagion and Deaths (KY)”
Similarly, focusing on Arizona, we see a single county at high risk of infection spread, and a dark red bubble relating to that county. This county (Coconino) has recently begun to see viral spread, so is worthy of tracking based on our County Risk Profile.
In order to derive the Population Risk Profiles we combined current COVID-19 data with US Census, Bureau of Labor Statistics, and CDC data to create risk models for both contagion and deaths. Based on the combined risk models, we derived overall Population Risk Profiles and created specific segments defining these underlying profiles.
Below is a breakdown of the Population Risk Profiles, and corresponding contagions and deaths. You will note a consistent predictive nature of the slope from Profile to Profile.
Each of the overall Population Risk Profiles were then assigned to each US county to provide a county-level risk score. The range of determinant values derived were weighted by both positive and negative correlating features of the underlying population. The following shows the breakdown of the top 10 positive and negative correlations.
“Top Ten Positive and Negative Correlating Population Features”
While our County Risk Profile is fairly new (created in April 2020), we have seen some very interesting trends that reinforce the current model. For example, only Risk Profile C and D demonstrate a positive correlation between populations living in high-density dwellings to infection rates. While the model may adjust over time, due to new county outbreaks, the Risk Profile has been highly accurate over the last 30 days.
We welcome your input and offer the ability to request an overall breakdown of the County Risk Profile for all States and Counties in our study. Should you wish to obtain this information, please fill out the following request.