Mobile phone-based surveillance for animal disease in rural communities: implications for detection of zoonoses spillover

Abstract

Improving the speed of outbreak detection and reporting at the community level are critical in managing the threat of emerging infectious diseases, many of which are zoonotic. The widespread use of mobile phones, including in rural areas, constitutes a potentially effective tool for real-time surveillance of infectious diseases. Using longitudinal data from a disease surveillance system implemented in 1500 households in rural Kenya, we test the effectiveness of mobile phone animal syndromic surveillance by comparing it with routine household animal health surveys, determine the individual and household correlates of its use and examine the broader implications for surveillance of zoonotic diseases. A total of 20 340 animal and death events were reported from the community through the two surveillance systems, half of which were confirmed as valid disease events. The probability of an event being valid was 2.1 times greater for the phone-based system, compared with the household visits. Illness events were 15 times (95% CI 12.8, 17.1) more likely to be reported through the phone system compared to routine household visits, but not death events (OR 0.1 (95% CI 0.09, 0.11)). Disease syndromes with severe presentations were more likely to be reported through the phone system. While controlling for herd and flock sizes owned, phone ownership was not a determinant of using the phone-based surveillance system, but the lack of a formal education, and having additional sources of income besides farming were associated with decreased likelihood of reporting through the phone system. Our study suggests that a phone-based surveillance system will be effective at detecting outbreaks of diseases such as Rift Valley fever that present with severe clinical signs in animal populations, but in the absence of additional reporting incentives, it may miss early outbreaks of diseases such as avian influenza that present primarily with mortality. This article is part of the theme issue ‘Dynamic and integrative approaches to understanding pathogen spillover’.

Long-term wildlife mortality surveillance in northern Congo: a model for the detection of Ebola virus disease epizootics

“…Conversely, several communities did not report carcasses to us during this period. The threshold to reporting must be looked at in the context of the difficulties of doing so in the rural Congo, and reporting could be affected by factors such as educational status, level of economic dependence on bushmeat [28] or availability of a mobile network. However, if some carcasses were missed, we may have built critical baseline awareness for the rural communities to alert us in case of a larger die-off.…”

High Real-time Reporting of Domestic and Wild Animal Diseases Following Rollout of Mobile Phone Reporting System in Kenya

“…Reports were submitted immediately, eliminating the lag time experienced with other systems that require an additional step of data compilation, and thus enhancing Kenya’s ability to submit data to the OIE in a timely manner. [3,22] -24 Further, the application worked well in remote areas with no internet connectivity, allowing login and data collection while offline and automatic transmission of results to the KDVS once connectivity was restored. The automated collection of geographic coordinates of reported disease events enhanced follow-up by the county and national animal health officers.…”

“…Similar trends have been reported in other studies, associated with farmers attaching greater social-economic value to these animals. [24] Among wildlife, the variable number of reports across species is likely due to several factors including the size of animal populations, higher number of surveillance officers in some conservation areas, and the ease of sighting and observing some species. Thus, it was easier to sight, observe and report disease conditions in larger terrestrial wild mammals (elephants, buffalos, zebras, giraffes, lions) than in birds and aquatic animals.…”

High real-time reporting of domestic and wild animal diseases following rollout of mobile phone reporting system in Kenya

“…Third, the application was user friendly, in part because the data collection tools were familiar, and syndromes selected for reporting covered important and notifiable transboundary animal diseases that the government was required to report. Reports were submitted immediately, eliminating the lag time experienced with other systems that require an additional step of data compilation, and thus enhancing Kenya's ability to submit data to the OIE in a timely manner [3,[22][23][24]. Further, the application worked well in remote areas with no internet connectivity, allowing login and data collection while offline and automatic transmission of results to the KDVS once connectivity was restored.…”