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Research Project

Submitted by Prof F. Nchu

Cape Peninsula University of Technology

South Africa

Title: Implementing an inclusive tick surveillance programme in Africa: a case of South Africa

Investigators: Prof F. Nchu and Prof N Nyangiwe

 Tick infestations can have devastating effects on livestock, human health, and the livelihoods of livestock farmers. Indisputably, the burden of ticks and tick-borne diseases (T&TBDs) on the economies and livelihoods of livestock keepers and rural communities in African countries is significant. While monitoring and surveillance programmes are among the most cost-effective tools for sustainable management of T&TBDs, unfortunately, Africa is grossly lagging far behind other regions. Hence, the development of collaborative tick surveillance programmes involving scientists, citizens, and communities for effective implementation of active and passive country-wide surveillance of T&TBDs will facilitate the development of management of these pests in the continent. This proposal is inspired by the outcome of a tick and tick-borne diseases workshop held in Tanzania in June of 2019 by some African Scientists. 

The aim of this study is to implement collaborative tick surveillance programmes in South Africa and some African countries as well as collect baseline data on T&TBD occurrences over three years with the view of laying the foundation for long-term, comprehensive monitoring. Furthermore, the project is part of AfriCoTT’s research agenda of fostering collaboration and partnership between researchers and communities in Africa.

Objectives

-Evaluate community knowledge of ticks and tick-borne diseases in selected localities in South Africa before and after training and access to tick posters...
-Establish how community members perceive an inclusive tick surveillance approach.

-Develop standardize protocols for monitoring of T&TBDs.
-Monitor changes in the geographical distribution of T&TBDs in the study locations.
-Determine the suitable locations for the different tick species using predictive models.
-Creation of an online open-access database on T&TBDs.

-Develop an app for reporting, recording, and sharing T&TBD data.

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