A syndromic surveillance tool to detect anomalous clusters of COVID-19 symptoms in the United States

Amparo Güemes, Soumyajit Ray, Khaled Aboumerhi, Michael R. Desjardins, Anton Kvit, Anne E. Corrigan, Brendan Fries, Timothy Shields, Robert D. Stevens, Frank C. Curriero, Ralph Etienne-Cummings

Research output: Contribution to journalArticlepeer-review

Abstract

Coronavirus SARS-COV-2 infections continue to spread across the world, yet effective large-scale disease detection and prediction remain limited. COVID Control: A Johns Hopkins University Study, is a novel syndromic surveillance approach, which collects body temperature and COVID-like illness (CLI) symptoms across the US using a smartphone app and applies spatio-temporal clustering techniques and cross-correlation analysis to create maps of abnormal symptomatology incidence that are made publicly available. The results of the cross-correlation analysis identify optimal temporal lags between symptoms and a range of COVID-19 outcomes, with new taste/smell loss showing the highest correlations. We also identified temporal clusters of change in taste/smell entries and confirmed COVID-19 incidence in Baltimore City and County. Further, we utilized an extended simulated dataset to showcase our analytics in Maryland. The resulting clusters can serve as indicators of emerging COVID-19 outbreaks, and support syndromic surveillance as an early warning system for disease prevention and control.

Original languageEnglish (US)
Article number4660
JournalScientific reports
Volume11
Issue number1
DOIs
StatePublished - Dec 2021

ASJC Scopus subject areas

  • General

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