Accepted for/Published in: JMIR Public Health and Surveillance
Date Submitted: Jun 2, 2022
Date Accepted: Dec 27, 2022
Syndromic surveillance using structured telehealth data: a case study of the first wave of COVID-19 in Brazil
ABSTRACT
Background:
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.
Objective:
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).
Methods:
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.
Results:
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.
Conclusions:
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.
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