Integrating indigenous and scientific knowledge in community-based early warning system development for climate-related malaria risk reduction in Mopani District of South Africa

• Main Author: Ramutsa, Brenda Nyeverwai
• Other Authors: Nethengwe, N. S.
• Format: Others
• Language: en
• Published: 2020
• Subjects: Malaria; Indigenous Knowledge System (IKS); Scientific knowledge; Climate; Climate change; Disaster risk reduction; Early warning system; 614.5320968259; Insects as carriers disease > South Africa > Limpopo; Mosquitoes as carrier of disease > South Africa > Limpopo; Communicable diseases > Prevention; Malaria > South Africa > Limpopo; Plasmodium > South Africa > Limpopo; Fever > South Africa > Limpopo; Protozoan diseases > South Africa > Limpopo; Plasmodium falciparum > South Africa > Limpopo; Malaria > Prevention
• Online Access: Ramutsa, Brenda Nyeverwai (2020) Integrating indigenous and scientific knowledge in community-based early warning system development for climate-related malaria risk reduction in Mopani District of South Africa. University of Venda, South Africa.<http://hdl.handle.net/11602/1520>.; http://hdl.handle.net/11602/1520

PhD (Geography) === Department of Geography and Geo-Information Sciences === Malaria is a climate-change concatenated biological hazard that may, like any other natural hazard, can lead to a disaster if there is a failure in handling emergencies or risks. A holistic solution for malaria mitigation can be provided when indigenous knowledge is complemented with scientific knowledge. Malaria remains a challenge in South Africa and Limpopo province is the highest burdened malaria-endemic region. Specifically, Vhembe District is the highest burdened followed by Mopani District (Raman et al., 2016). This research sought to mitigate malaria transmissions in Mopani District through the integration of indigenous and scientific knowledge. The study was carried out in Mopani District of South Africa and 4 municipalities were involved. These are Ba-Phalaborwa, Greater Tzaneen, Greater Letaba, and Maruleng. A pragmatism philosophy was adopted hence the study took a mixed approach (sequential multiphase design). Data was collected from 381 selected participants through in-depth interviews, a survey and a focus group discussion. Participants for the in-depth interviews were obtained through snowballing and selected randomly for the survey, while for the focus group discussion purposive sampling was used. The study applied constructivist grounded theory to analyze qualitative data and to generate theory. Statistical Package for Social Sciences version 23.0 was used for quantitative data. Based on empirical findings, it was concluded that temperature and rainfall among other various factors exacerbate malaria transmission in the study area. Results of the study also show that people in Mopani District predict the malaria season onset by forecasting rainfall using various indigenous knowledge based indicators. The rainfall indicators mentioned by participants in the study were used in the developed early warning system. An Early warning system is an essential tool that builds the capacities of communities so that they can reduce their vulnerability to hazards or disasters. In the design of the system, Apache Cordova, JDK 1.8, Node JS, and XAMPP software were used. The study recommends malaria management and control key stakeholders to adopt the developed early warning system as a further mitigation strategy to the problem of malaria transmission in Mopani District. === NRF