African swine fever in Uganda: qualitative evaluation of three surveillance methods with implications for other resource-poor settings

Animal diseases impact negatively on households and on national economies. In low-income countries this pertains especially to socio-economic effects on household level. To control animal diseases and mitigate their impact, it is necessary to understand the epidemiology of the disease in its local context. Such understanding, gained through disease surveillance, is often lacking in resource-poor settings. Alternative surveillance methods have been developed to overcome some of the hurdles obstructing surveillance. The objective of this study was to evaluate and qualitatively compare three methods for surveillance of acute infectious diseases using African swine fever (ASF) in northern Uganda as an example. Report-driven outbreak investigations, participatory rural appraisals (PRA), and a household survey using a smartphone application were evaluated. All three methods had good disease-detecting capacity, each of them detected many more outbreaks compared to those reported to the World Organization for Animal Health (OIE) during the same time period. Apparent mortality rates were similar for the three methods although highest for the report-driven outbreak investigations, followed by the PRAs, and then the household survey. The three methods have different characteristics and the method of choice will depend on the surveillance objective. The optimal situation might be achieved by a combination of the methods: outbreak detection via smartphone-based real-time surveillance, outbreak investigation for collection of biological samples, and a PRA for a better understanding of the epidemiology of the specific outbreak. All three methods require initial investments and continuous efforts. The sustainability of the surveillance system should therefore be carefully evaluated before making such investments.

Abstract licence: CC BY

65 more references, whose wording the paper’s licence keeps private.

A person counts once a day per paper. We keep no cookie and no address: a reader is a hash that changes every day. Any month under five reads shows no number.