George J(1)(2), Häsler B(3), Komba E(4), Sindato C(5)(6), Rweyemamu M(5), Mlangwa J(4). Author information:
(1)Department of Veterinary Medicine and Public Health, Sokoine University of
Agriculture, P.O. Box 3021, Morogoro, Tanzania. [Email]
(2)SACIDS Foundation for One Health, Sokoine University of Agriculture, P.O. Box
3297, Morogoro, Tanzania. [Email]
(3)Department of Pathobiology and Population Sciences, Veterinary Epidemiology,
Economics, and Public Health Group, Royal Veterinary College, Hawkshead Lane,
North Mymms, Hatfield, Hertfordshire, AL9 7TA, UK.
(4)Department of Veterinary Medicine and Public Health, Sokoine University of
Agriculture, P.O. Box 3021, Morogoro, Tanzania.
(5)SACIDS Foundation for One Health, Sokoine University of Agriculture, P.O. Box
3297, Morogoro, Tanzania.
(6)National Institute for Medical Research, Tabora Research Centre, Tabora,
BACKGROUND: Effective animal health surveillance systems require reliable, high-quality, and timely data for decision making. In Tanzania, the animal health surveillance system has been relying on a few data sources, which suffer from delays in reporting, underreporting, and high cost of data collection and transmission. The integration of data from multiple sources can enhance early detection and response to animal diseases and facilitate the early control of outbreaks. This study aimed to identify and assess existing and potential data sources for the animal health surveillance system in Tanzania and how they can be better used for early warning surveillance. The study used a mixed-method design to identify and assess data sources. Data were collected through document reviews, internet search, cross-sectional survey, key informant interviews, site visits, and non-participant observation. The assessment was done using pre-defined criteria. RESULTS: A total of 13 data sources were identified and assessed. Most surveillance data came from livestock farmers, slaughter facilities, and livestock markets; while animal dip sites were the least used sources. Commercial farms and veterinary shops, electronic surveillance tools like AfyaData and Event Mobile Application (EMA-i) and information systems such as the Tanzania National Livestock Identification and Traceability System (TANLITS) and Agricultural Routine Data System (ARDS) show potential to generate relevant data for the national animal health surveillance system. The common variables found across most sources were: the name of the place (12/13), animal type/species (12/13), syndromes (10/13) and number of affected animals (8/13). The majority of the sources had good surveillance data contents and were accessible with medium to maximum spatial coverage. However, there was significant variation in terms of data frequency, accuracy and cost. There were limited integration and coordination of data flow from the identified sources with minimum to non-existing automated data entry and transmission. CONCLUSION: The study demonstrated how the available data sources have great potential for early warning surveillance in Tanzania. Both existing and potential data sources had complementary strengths and weaknesses; a multi-source surveillance system would be best placed to harness these different strengths.
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