Characterizing Persistence and Disparity of Covid-19 Infection Rates with City Level Demographic and Regional Features

TL;DR

This study analyzes Covid-19 infection disparities across regions and demographic groups in Wisconsin, using statistical metrics to identify persistent disparities and proposing a data dashboard for community engagement.

Contribution

It introduces a novel persistence index and rank difference metric to quantify and visualize Covid-19 disparities over time at the municipality level.

Findings

Disparities persist in low-population, rural municipalities for Black and Hispanic populations.

Regions away from urban centers show continued infection rate disparities.

A dashboard prototype visualizes temporal and regional infection patterns.

Abstract

The design of data-driven dashboards that inform municipalities on ongoing changes in infections within their community is addressed in this research. Daily reports of Covid-19 infections published by the state of Wisconsin as the initial surge in the pandemic ensued during the October 2020 to September 2021 time frame is considered as a case study. Of particular interest is the identification of regions and population groups distinguished by race and ethnicity that may be experiencing a disproportional rate of infections over time. This study integrates the municipality-level daily positive cases that are disaggregated by race and ethnicity and population size data derived from the US Census Bureau. The goal is to present timely data-driven information in a manner that is accessible to the general population, is relatable to the constituents and promotes community engagement in managing and mitigating the infections. A statistical metric referred to as the rank difference and its persistence over time is used to capture the disproportional incidence of Covid-19 positive cases on particular race and ethnic groups in relation to their population size. A persistence index is derived to identify regions that continually exhibit positive rank differences on a daily time scale and indicate disparity in disease incidence. The analysis leads to the identification that several municipalities in Wisconsin that are located in regions of low population and away from the denser urban centers are those that continue to exhibit disparity in the infection rates for Black/African American and Hispanic/Latino population groups. Examples of a dashboard that can be utilized to capture both aggregate level and temporal patterns of Covid-19 infections are presented.