Gbenga J. Abiodun
Scientist
South Africa
Biography:
Gbenga J Abiodun is a young Scientist whose research interest focuses on biomathematics, epidemiology and mathematical modelling of the impacts of climate (variability and change) on vector-borne and infectious diseases. He has completed his Masters and Doctoral degrees in Mathematics at the University of the Western Cape (UWC) in 2012 and 2016, respectively. He has worked extensively on infectious diseases and published peer-reviewed papers in high-profile international journals.
Abstract:
The recent resurgence of malaria incidence across epidemic regions in South Africa has been linked to climatic and environmental factors. An in-depth investigation of the impact of climate variability and mosquito abundance on malaria parasite incidence may therefore offer useful insight towards the control of this life-threatening disease. In this study we investigate the influence of climatic factors on malaria transmission over Nkomazi municipality. The variability and interconnectedness between the variables were analyzed using wavelet coherence analysis. Time-series analyses revealed that malaria cases significantly declined after the outbreak in early 2000, but with a slight increase from 2015. Furthermore, the wavelet coherence and time-lagged correlation analyses identified rainfall and abundance of Anopheles arabiensis as the major variables responsible for malaria transmission over the study region. The analysis further highlights a high malaria intensity with the variables from 1998 – 2002, 2004 – 2006, and 2010 – 2013 and a noticeable periodicity value of 256 - 512 days. Also, malaria transmission shows a time lag of between one month to three months with respect to mosquito abundance and the different climatic variables. The findings from this study offer a better understanding of the importance of climatic factors on the transmission of malaria. The study further highlights the significant roles of An. arabiensis on malaria occurrence over Nkomazi. Implementing the mosquito model to predict mosquito abundance could provide more insight on malaria elimination or control in Africa.