Improving cattle disease reporting and surveillance in Ethiopia using smartphone-based application

Beyene, TJ and Asfaw, F and Getachew, Y and Beyene, T and Collins, I and Beyi, FA and Revie, CW; (2017) Improving cattle disease reporting and surveillance in Ethiopia using smartphone-based application. In: Proceeding of the 3rd International Conference of Animal Health Surveillance. New Zealand Veterinary Association, Wellington, New Zealand, pp. 77-80.

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    This study explores the use of a smartphone-based application to increase the accuracy and completeness of cattle disease reporting and surveillance in three regions of Ethiopia. We compared the performance of a smartphone-based application with traditional (paper-based) cattle diagnosis and reporting, in terms of demographics and disease information, level of detail and delay in time to transmit information to higher levels. A total of 547 and 678 clinical cattle cases were diagnosed in veterinary clinics visited by two groups of final-year veterinary students using the VetAfrica-Ethiopia (VAE) smartphone app and manual approach respectively. The group using the VAE application diagnosed over 90% cases as diseases of a specific name, while in reports from the manual system almost 50% of cases were diagnosed as non-specific diseases or ‘syndromes’. Furthermore, the mean duration of time required for smartphone data to be received by zonal- and federal- level veterinary services through a Cloud-based server were estimated to be two days (95% CI: 1.6–2.3), five days (95% CI: 3.8–5.4), and 13 days (95% CI: 12–14.9) in the Central, Eastern and Southern regions. The traditional reporting system adopted a batch reporting approach and only around two thirds of all cases reach the federal veterinary service by the end of a month. Despite the fact that such smartphone technology-assisted reporting and surveillance involves considerable start-up challenges and may be affected by intermittent mobile internet network coverage, they offer significant benefits in terms of improving data integrity, timeliness and reduced costs in the long run.