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• W4286286251 abstract "<sec> <title>BACKGROUND</title> Telehealth has been widely used for new case detection and telemonitoring during the COVID-19 pandemic. However, the use of this approach for syndromic surveillance has been little explored. This data can provide qualified information to feed computational models to study the disease spread. </sec> <sec> <title>OBJECTIVE</title> Herein we report using a high-quality dataset obtained from a state-based telehealth service for forecasting the geographical spread of new cases of COVID-19 in Salvador (Bahia, Brazil). </sec> <sec> <title>METHODS</title> A wide-state toll-free telehealth service collected clinical-demographic structured data four months following the first notification of COVID-19 in the Bahia State, Brazil. Calls that reported COVID-19- like symptoms were selected for temporal-spatial analysis compared to notification of COVID-19 cases. </sec> <sec> <title>RESULTS</title> For 181 out of 417 (43%) municipalities of Bahia, the first call to the telehealth service reporting COVID-like symptoms preceded the first notification of the disease. The calls reporting COVID-19-like symptoms preceded, on average, 30 days of the first notification of COVID-19 in the municipalities of the State of Bahia, Brazil. Additionally, data obtained by the telehealth service were used to effectively reproduce the disease spread in Salvador using a Susceptible (S) - Exposed (E) - Infected (I) - Recovered (R) - Deceased (D) (SEIRD) mathematical model to simulate the spatio- temporal spread of the disease. </sec> <sec> <title>CONCLUSIONS</title> In conclusion, data from telehealth services may confer high effectiveness in anticipating new waves of COVID-19 and may be helpful to study the epidemic dynamics. </sec>" @default.
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• W4286286251 date "2022-06-02" @default.
• W4286286251 modified "2023-09-24" @default.
• W4286286251 title "Syndromic surveillance using structured telehealth data in the first wave of COVID-19 in Brazil (Preprint)" @default.
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• W4286286251 doi "https://doi.org/10.2196/preprints.40036" @default.
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