Digital Biosurveillance for Zoonotic Disease Detection in Kenya

Abstract

Infectious disease surveillance is crucial for early detection and situational awareness of disease outbreaks. Digital biosurveillance monitors large volumes of open-source data to flag potential health threats. This study investigates the potential of digital surveillance in the detection of the top five priority zoonotic diseases in Kenya: Rift Valley fever (RVF), anthrax, rabies, brucellosis, and trypanosomiasis. Open-source disease events reported between August 2016 and October 2020 were collected and key event-specific information was extracted using a newly developed disease event taxonomy. A total of 424 disease reports encompassing 55 unique events belonging to anthrax (43.6%), RVF (34.6%), and rabies (21.8%) were identified. Most events were first reported by news media (78.2%) followed by international health organizations (16.4%). News media reported the events 4.1 (±4.7) days faster than the official reports. There was a positive association between official reporting and RVF events (odds ratio (OR) 195.5, 95% confidence interval (CI); 24.01–4756.43, p < 0.001) and a negative association between official reporting and local media coverage of events (OR 0.03, 95% CI; 0.00–0.17, p = 0.030). This study highlights the usefulness of local news in the detection of potentially neglected zoonotic disease events and the importance of digital biosurveillance in resource-limited settings.

Predicting Kyasanur forest disease in resource-limited settings using event-based surveillance and transfer learning

“…Hence, EBS data, such as online news articles and Google trends, could be an important source of information to improve nowcasting and short-term (1 or 2 months) forecasting performances of KFD models. With rapidly growing internet usage in remote locations and rising health literacy among populations, these non-traditional means of disease intelligence have become more readily available and a vital source of data for zoonotic disease prediction under resource and data-limited settings 23 . This current study clearly demonstrates how the inclusion of internet data along with traditional climate data can improve the performance of forecasting models.…”

“…News media acts as an important means of information for EBS in most developed and developing countries. This data source has been successfully used for the early epidemiologic assessment of disease outbreaks, such as Rift Valley Fever 23 , Cholera 24 , Anthrax 23 , Ebola 25 , and COVID-19 26 . In addition, the internet has become an important source of health-related information for millions of people worldwide.…”

Predicting emerging zoonotic disease under resource-limited settings: Case study of Kyasanur Forest Disease using event-based surveillance data and transfer learning

“…Our results showed that EBS data, such as online news articles and google trends, could be an important source of information to improve the nowcasting and short-term forecasting performances of KFD models. With rapidly growing internet usage in remote locations and rising health literacy among populations, these non-traditional means of disease intelligence have become more readily available and a vital source of data for zoonotic disease prediction under resource and data-limited settings 23 . This current study clearly demonstrates how the inclusion of internet data along with traditional climate data can improve the performance of forecasting models.…”

Are integrated crop-livestock clinics an option for Kenyan smallholder farmers to address One Health issues?

“…More than 80% of the respondents had heard about zoonotic diseases, and mentioned brucellosis, anthrax and rabies as the main zoonoses they were aware of. The top five zoonotic diseases in Kenya are anthrax, trypanosomiasis, rabies, brucellosis and Rift Valley Fever (Munyua et al, 2019, Keshavamurthy et al, 2021. The surveyed farmers may not have heard about trypanosomiasis and Rift Valley Fever, or do not consider them as zoonoses of importance to them.…”