Author: Fusillo, T.
Title: Predicting Health Disparities in Regions at Risk of Severe Illness to inform Healthcare Resource Allocations during Pandemics Cord-id: ffr8gcky Document date: 2020_7_8
ID: ffr8gcky
Snippet: Pandemics including COVID-19 have disproportionately affected socioeconomically vulnerable populations. To create a repeatable modelling process to identify regional population centers with pandemic vulnerability, readily available COVID-19 and socioeconomic variable datasets were compiled, and linear regression models were built during the early days of the COVID-19 pandemic. The models were validated later in the pandemic timeline using actual COVID-19 mortality rates in states with high popul
Document: Pandemics including COVID-19 have disproportionately affected socioeconomically vulnerable populations. To create a repeatable modelling process to identify regional population centers with pandemic vulnerability, readily available COVID-19 and socioeconomic variable datasets were compiled, and linear regression models were built during the early days of the COVID-19 pandemic. The models were validated later in the pandemic timeline using actual COVID-19 mortality rates in states with high population densities, with New York, New Jersey, Connecticut, Massachusetts, Louisiana, Michigan and Pennsylvania showing the strongest predictive results. Our models have been shared with the Department of Health Commissioners of each of these states as input into a much needed pandemic playbook for local healthcare agencies in allocating medical testing and treatment resources.
Search related documents:
Co phrase search for related documents, hyperlinks ordered by date