Urban Footprint has combined datasets to help governments and frontline organisations better understand where spikes may happen next and plan ahead to deliver relief and essential resources.
Missouri, Washington, Illinois, Michigan and Texas are the top five US states likely to see coronavirus case spikes in the coming weeks, according to the Covid-19 Watch List from location intelligence and planning software specialist UrbanFootprint.
The company has combined datasets covering disease, movement, economic activity and socio-demographics to dynamically measure Covid-19 susceptibility.
UrbanFootprint has released the Watch List to help governments and frontline organisations better understand where spikes may happen next and plan ahead to deliver relief and essential resources to those who need it most.
Writing in a blog, UrbanFootprint CEO Joe DiStefano said that, in the sixth month of the pandemic, many communities are experiencing a confluence of crises and are facing high levels of unemployment, high risk of eviction and homelessness, critically low access to healthy food, and poor access to medical and testing services.
“Better data can help governments and frontline organisations understand where spikes may happen next and plan ahead”
He writes: “As federal and state assistance and protection expires or is reduced, these communities are also at very high risk of disease transmission and, in many cases, residents are also more likely to have underlying health conditions and socio-economic factors that place them at higher risk of complications and death.”
The Covid-19 Watch List covers three elements:
Fever and case data: using fever, symptom, and illness transmission data from smart thermometer maker Kinsa, it can see both where the rate of illness transmission has been highest over the past few weeks and the states that are most at risk of case load increases in the next two to three weeks.
“Looking ahead, Kinsa’s data indicates that public health measures are likely reducing risk in many hot spots, while Missouri, Washington, Illinois, Michigan, and parts of Texas are more likely to see case load increases,” states DiStefano. “These states can prepare by deploying public health measures and evaluating medical and testing facilities to ensure there is enough capacity to handle demand.”
Movement data: this can be used to identify areas that are at even higher risk of accelerated Covid-19 transmission over the next several weeks. Missouri and Texas both stand out as states that have high Covid rates and increasing levels of movement, suggesting they are particularly at-risk for continued community transmission.
DiStefano explains that further breaking down the most populated counties in at-risk states presents a more refined view and can serve to further assess transmission risk and plan interventions.
He writes: “In counties where movement and per capita Covid-19 rates are trending upwards, interventions can be targeted to reduce movement, increase public health measures, increase testing and assess medical and ICU capacity.”
Social vulnerability, economic stress, accessibility and health risk data: using a variety of risk factors, the Watch List also seeks to help locate the most vulnerable populations in emerging hot spot, areas where underlying health and socio-demographic factors increase risk to residents, and where increased unemployment, food insecurity, and eviction risk are highest.
DiStefano reckons that geotargeting public health and social and economic interventions into such areas will serve to protect those most at risk and could prevent the crisis from digging even deeper into the most vulnerable communities.
He concludes: “As the race for a vaccine continues, we need better tools to fight the compounding impacts of the coronavirus pandemic for the long term. Better data can help governments and frontline organisations understand where spikes may happen next and plan ahead to deliver relief and essential resources to those that need it most.”
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