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Participatory syndromic surveillance as a tool for tracking COVID-19 in Bangladesh
Ayesha S Mahmud1, Shayan Chowdhury2, Kawsar Hossain Sojib2
1Department of Demography, University of California, Berkeley, USA.
Insights
Participatory surveillance using self-reported symptoms effectively tracked the COVID-19 outbreak in Bangladesh. This approach revealed earlier disease spread than confirmed cases alone, aiding public health response.
Area of Science:
- Epidemiology
- Public Health Surveillance
Background:
- Laboratory diagnostic limitations and reporting delays hindered global COVID-19 control.
- Traditional surveillance systems faced challenges in real-time outbreak monitoring.
Purpose of the Study:
- To evaluate a participatory surveillance system for tracking the COVID-19 outbreak in Bangladesh.
- To assess the utility of self-reported syndromic data in augmenting traditional surveillance.
Main Methods:
- Established a participatory surveillance system enabling public self-reporting of COVID-19 symptoms via multiple channels.
- Collected over 3 million responses within two months.
- Analyzed self-reported syndromic data against lab-confirmed cases and population mobility data.
Main Results:
- The system received substantial public engagement with over 3 million responses in two months.
- Self-reported syndromic data showed a strong correlation with lab-confirmed COVID-19 cases at a local level.
- Syndromic data indicated a potentially earlier and wider spread of the outbreak than official case counts suggested.
Conclusions:
- Participatory syndromic surveillance is a valuable tool for mapping disease burden, especially during emerging outbreaks.
- This approach can provide timely insights to complement traditional surveillance methods.
- The system demonstrated effectiveness in tracking the COVID-19 pandemic in Bangladesh.
Abstract:
Limitations in laboratory diagnostic capacity and reporting delays have hampered efforts to mitigate and control the ongoing coronavirus disease 2019 (COVID-19) pandemic globally. To augment traditional lab and hospital-based surveillance, Bangladesh established a participatory surveillance system for the public to self-report symptoms consistent with COVID-19 through multiple channels. Here, we report on the use of this system, which received over 3 million responses within two months, for tracking the COVID-19 outbreak in Bangladesh. Although we observe considerable noise in the data and initial volatility in the use of the different reporting mechanisms, the self-reported syndromic data exhibits a strong association with lab-confirmed cases at a local scale. Moreover, the syndromic data also suggests an earlier spread of the outbreak across Bangladesh than is evident from the confirmed case counts, consistent with predicted spread of the outbreak based on population mobility data. Our results highlight the usefulness of participatory syndromic surveillance for mapping disease burden generally, and particularly during the initial phases of an emerging outbreak.
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