Towards an integrated animal health surveillance system in Tanzania: making better use of existing and potential data sources for early warning surveillance

Janeth George,Barbara Häsler,James Mlangwa,Calvin Sindato,Erick Komba,Mark Rweyemamu

doi:10.1186/s12917-021-02789-x

Abstract

BackgroundEffective 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.ResultsA 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.ConclusionThe 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.

Highlights

Effective animal health surveillance systems require reliable, high-quality, and timely data for decision making
Identification and assessment of surveillance data sources Data sources were identified from the review of animal health surveillance documents obtained through personal communication with officials of the relevant institutions, websites, grey literature search as well as interviews with experts on the subject matter and individuals from institutions working on animal health surveillance
The identified data sources were grouped into two categories: Primary sources which included data collected or generated directly for the national animal health surveillance and secondary sources, which included information collected for other purposes, with potential benefit to the national surveillance system

Summary

Introduction

Effective animal health surveillance systems require reliable, high-quality, and timely data for decision making. There have been increased concerns on the spread of animal infectious diseases due to their overwhelming impact on animal welfare [1], international trade [2], public health, ecosystem health and economic well-being of people who depend on animals as a source of livelihood [3, 4]. These overwhelming challenges highlight the need for more effective and efficient animal health surveillance systems. Surveillance data can be used to estimate the magnitude of specific problems, determine the distribution of illness, portray the natural history of a disease, generate hypotheses, stimulate research, evaluate control measures, monitor changes, and facilitate planning [8]

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Conclusion