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Enabling Hotspot Detection and Public Health Response to the COVID-19 Pandemic.
Foraker, Randi; Landman, Joshua; Lackey, Ian; Haslam, Matthew D; Antes, Alison L; Goldfarb, Dennis.
  • Foraker R; Institute for Informatics, Washington University School of Medicine in St. Louis, St. Louis, Missouri.
  • Landman J; Division of General Medical Sciences, Department of Medicine, Washington University School of Medicine in St. Louis, 600 S Taylor Ave, St. Louis, MO 63110. Email: randi.foraker@wustl.edu.
  • Lackey I; Division of Computational and Data Sciences, Washington University in St. Louis, St. Louis, Missouri.
  • Haslam MD; Institute for Informatics, Washington University School of Medicine in St. Louis, St. Louis, Missouri.
  • Antes AL; Department of Health, City of St. Louis, St. Louis, Missouri.
  • Goldfarb D; Institute for Informatics, Washington University School of Medicine in St. Louis, St. Louis, Missouri.
Prev Chronic Dis ; 19: E35, 2022 06 30.
Artículo en Inglés | MEDLINE | ID: covidwho-1912044
ABSTRACT

INTRODUCTION:

Public-facing maps of COVID-19 cases, hospital admissions, and deaths are commonly displayed at the state, county, and zip code levels, and low case counts are suppressed to protect confidentiality. Public health authorities are tasked with case identification, contact tracing, and canvasing for educational purposes during a pandemic. Given limited resources, authorities would benefit from the ability to tailor their efforts to a particular neighborhood or congregate living facility.

METHODS:

We describe the methods of building a real-time visualization of patients with COVID-19-positive tests, which facilitates timely public health response to the pandemic. We developed an interactive street-level visualization that shows new cases developing over time and resolving after 14 days of infection. Our source data included patient demographics (ie, age, race and ethnicity, and sex), street address of residence, respiratory test results, and date of test.

RESULTS:

We used colored dots to represent infections. The resulting animation shows where new cases developed in the region and how patterns changed over the course of the pandemic. Users can enlarge specific areas of the map and see street-level detail on residential location of each case and can select from demographic overlays and contour mapping options to see high-level patterns and associations with demographics and chronic disease prevalence as they emerge.

CONCLUSIONS:

Before the development of this tool, local public health departments in our region did not have a means to map cases of disease to the street level and gain real-time insights into the underlying population where hotspots had developed. For privacy reasons, this tool is password-protected and not available to the public. We expect this tool to prove useful to public health departments as they navigate not only COVID-19 pandemic outcomes but also other public health threats, including chronic diseases and communicable disease outbreaks.
Asunto(s)

Texto completo: Disponible Colección: Bases de datos internacionales Base de datos: MEDLINE Asunto principal: Salud Pública / Pandemias / COVID-19 Tipo de estudio: Estudios diagnósticos / Estudio observacional / Estudio pronóstico Límite: Humanos Idioma: Inglés Revista: Prev Chronic Dis Asunto de la revista: Salud Pública Año: 2022 Tipo del documento: Artículo

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Texto completo: Disponible Colección: Bases de datos internacionales Base de datos: MEDLINE Asunto principal: Salud Pública / Pandemias / COVID-19 Tipo de estudio: Estudios diagnósticos / Estudio observacional / Estudio pronóstico Límite: Humanos Idioma: Inglés Revista: Prev Chronic Dis Asunto de la revista: Salud Pública Año: 2022 Tipo del documento: Artículo