Meet Inspiring Speakers and Experts at our 3000+ Global Conference Series Events with over 1000+ Conferences, 1000+ Symposiums
and 1000+ Workshops on Medical, Pharma, Engineering, Science, Technology and Business.

Explore and learn more about Conference Series : World's leading Event Organizer

Back

Tayo Alex Adekiya

Tayo Alex Adekiya

University of Zululand, South Africa

Title: Exploration of the impact of climate change on schistosomiasis transmission dynamics

Biography

Biography: Tayo Alex Adekiya

Abstract

Climate change has been suggested to elicit significant impact on the interactions between pathogens and their hosts. Vector-borne diseases are predominantly sensitive to climatic factors because temperature variability can alter vector development rates, transmission dynamics, as well as cause alteration in their geographical distribution. Schistosomiasis, ranked the second most widespread among neglected tropical diseases is caused by flatworms belonging to the genus Schistosoma. Symptoms of the parasitic infections include acute and chronic diseases, predisposition to cancer of the bladder, as well as pulmonary and portal hypertension and in extreme cases, death. This study employs a deterministic climate-based model using differential equations to investigate the impact of rainfall and temperature on the population dynamics of schistosomes in South Africa. Numerical simulations of the system were done using mathematical models to examine the effect of climate variability on the transmission dynamics of schistosomiasis. Results showed climate variability increases reproduction number of schistosomes and snails. Hence, schistosomiasis transmission was suggested to be seasonal. Snails’ reproduction was found to peak during summer and at the minimum during spring and autumn. So, sensitivity analysis showed reproductive number of schistosomes is more sensitive to the reproduction rate of snails and the probability of infections. Finally, the model used suggested future opportunity for modification and refinement for effective prediction of climate variability on the transmission dynamics of schistosomiasis.