Community health surveillance via digital collection of syndromic and behavior data by community healthcare workers in rural Kenya: a pilot study

Disease surveillance is essential to enable adequate detection and response to disease outbreaks. Syndromic surveillance is used to augment traditional approaches, especially in community-based surveillance. Here we demonstrate that Community Healthcare Workers (CHWs) supported by a mobile phone application can provide community-based syndromic disease surveillance in low-resource settings, and that they are able to generate relevant symptom-based and behavior data such as cough symptoms, use of mosquito nets and availability of household handwashing facilities. We analyzed 1.6 million data points collected by CHWs during home visits in rural Kenya as a proof of principle that the symptoms and behavior they observe can be used as a community-based health surveillance tool. To demonstrate the relevance of the data, we show that national covid-19 case numbers did not align with reported cough symptoms in remote populations, which implies that rural populations did not experience covid-19 outbreaks in tandem with urban populations. We also found that the behavior of using long-lasting insecticidal nets could be tracked by the CHWs, and it followed the seasonality of the mosquito burden. Strengthening community-based syndromic and behavior surveillance through CHWs is therefore a great opportunity to strengthen national public health surveillance and response in Africa and should be included in the Integrated Disease Surveillance and Response (IDSR) strategy.