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

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.

Syndromic surveillance of COVID-19 using crowdsourced data

“…The authors' syndromic surveillance system is an example of volunteered geographic information (VGI) [9] , without the public dissemination component. A comprehensive VGI-based syndromic surveillance system will provide direct feedback to its users, public, and decision-makers via a dashboard, for example [10] . Furthermore, to fully maximize the public health response capabilities of syndromic surveillance systems, developers should both analyze and disseminate results daily [ 4 , 10 ].…”

Assessing the capacity of symptom scores to predict COVID-19 positivity in Nigeria: a national derivation and validation cohort study

“…However, Nigeria has limited SARS-CoV-2 testing capacity with an average turnaround of 2 days, making syndromic surveillance (symptomatic monitoring) a viable complementary surveillance system. As such, our findings would be relevant in informing the design of such a surveillance system, which has been demonstrated in Japan 42 43 and in the USA, 44 to be useful in improving the understanding of COVID-19 epidemiology (often in real time), assessing the effectiveness of public health interventions and enhancing preparedness for the emergence of COVID-19 wave or a surge. For instance, an evaluation of a syndromic surveillance system in the USA found new taste/smell loss to be highly correlated with a range of COVID-19 outcomes, highlighting their usefulness in supporting the surveillance system as an early warning system for COVID-19 prevention and control.…”

Critical timing and extent of public health interventions to control outbreaks dominated by SARS-CoV-2 variants in Australia: a mathematical modelling study

“…Several studies have explored surveillance indicators for the early detection of COVID-19 outbreaks, such as COVID-19-related digital data streams and SARS-CoV-2 viral fragments detection in wastewater (Güemes et al, 2021;Hasan et al, 2021;Kogan et al, 2021). In addition, modelling studies provide an early assessment of the severity of COVID-19 epidemics to help stakeholders act swiftly and decisively.…”