Prioritizing COVID-19 tests based on participatory surveillance and spatial scanning

Area of Science:

• Epidemiology
• Public Health
• Infectious Disease Surveillance

Background:

• Traditional surveillance systems face challenges in real-time data acquisition for emerging infectious diseases.
• The COVID-19 pandemic highlighted the need for innovative and agile surveillance methods.

Purpose of the Study:

• To identify and analyze priority areas for COVID-19 testing.
• To evaluate the integration of participatory surveillance with traditional surveillance methods.

Main Methods:

• A descriptive transversal study was conducted in Caruaru, Brazil (20/02/2020–05/05/2020).
• Data combined official influenza-like illness reports with self-reports from the Brasil Sem Corona participatory surveillance platform.
• Statistical analyses included linear regression, loess regression, and spatial scanning to identify risk clusters.

Main Results:

• The participatory surveillance platform had 861 active users, contributing an average of 1.2 reports per user weekly.
• Spatial scanning identified 18 clusters, with 6 statistically significant clusters (p < 0.1).
• An overlapping area of significant clusters led to targeted COVID-19 serology testing, revealing a 32% positivity rate for antibodies.

Conclusions:

• Participatory surveillance is an effective epidemiological tool for complementing traditional systems.
• Integrating real-time spatial data from participatory surveillance enhances the detection of priority areas for COVID-19 testing.
• This combined approach improves the timeliness and spatial accuracy of public health responses to pandemics.