Crowdsourced surveillance for neglected tropical diseases, Nigeria

To validate a crowdsourced, image-based morbidity hotspot method for surveillance of neglected tropical diseases.

We conducted our crowdsourced surveillance pilot implementation study between November 2022 and October 2024 in 45 communities across three Nigerian states, covering a population of 477 138 people. Three additional states, where the project was not implemented but surveillance data obtained, served as control. Residents self-reported suspected symptoms by using smartphones to capture and transmit images of skin and eye manifestations via digital communication platforms. An expert panel then examined the images to confirm signs of neglected tropical diseases. We used frequency and percentages to present data; we also compared incidence data from both pilot and control locations.

In total, 512 subjects submitted images, either themselves or via a community focal point. Their mean age was 53  years (standard deviation: 20.7). Forty-six percent (234/512) were women and 55% (281/512) were farmers. Notably, 43% (218/512) had experienced symptoms of neglected tropical diseases for 1-5 years before our study and 85% (437/512) had not received any intervention. Of all photos submitted, 75% (386/512) showed signs of neglected tropical diseases. In Ondo state crowdsourced surveillance led to an average of 54.3 monthly reports, versus traditional surveillance which averaged 6.8 (P < 0.01). Cost analysis showed that crowdsourced surveillance cost 72.4 United States dollars per case detected.

Our surveillance method outperformed traditional surveillance, showing its promise for enhancing neglected tropical disease surveillance. The method's ability to detect emerging conditions and support post-elimination surveillance reinforces its value.