Year: 2015
Type: dissertation
Abstract: African animal trypanosomiasis (AAT), restricted to parts of the KwaZulu-Natal Province, \nis a disease which contributes signi cantly to the disease burden of cattle. Drug resistance \nis a constraint and dipping of cattle using insecticides has proved to be unsustainable. Even \nthough the incidence of AAT has increased, little is known about the epidemiology of the \ndisease in the region. To better understand the dynamics of AAT, mathematical modelling \nwas done to investigate the interactions between the cattle, tsetse \nies and bu aloes which \nare considered to be the reservoir host. In addition, a statistical analysis of the data \ncollected from three sites around the Hluhluwe-iMfolozi Game Park was done to assess the \ninteractions between the variables. \nA susceptible-infected (SI) model was constructed for the di erent classes of the population \ni.e. susceptible and infected cattle and tsetse \nies and infected bu aloes. The basic reproduction \nnumber R0, a threshold determining whether the disease will die out or persist in \nthe population, was derived using the next-generation matrix since we had two-hosts and \none vector. R0 was used to assess which elements contribute to R0 (i.e. transmission of \nAAT from the bu aloes and cattle to the tsetse \nies or tsetse \nies to the cattle and buffaloes). \nThe important element was found to be the transmission of AAT from bu aloes to \n \nies. Sensitivity analysis was done using the partial rank correlation coe cients (PRCC) \nmeasure. PRCC values can show which parameters to target when looking at intervention \nmeasures and determine how to e ciently reduce AAT. The mortality rate of tsetse \nies \nand their biting rate were determined to be the most important parameters. \nGeneralized linear models (GLMs) were used to analyse the data since we had binary and \ncount data. The AAT prevalence data was modelled using a binomial GLM, using the \npacked cell volume (PCV), which is an indicator of whether a cow has AAT or not, region \ni.e. whether the cow is located near or further away from the game park and month as the \nexplanatory variables. PCV and region were found to be signi cant, so where the cattle \nare located seems to be important. The tsetse abundance data was modelled using Poisson \nGLMs, however the problem of overdispersion was evident and so alternative models were \nconsidered. Since there were excess zeroes for G. austeni, zero-in \nated models were used \nand the best t was found to be the zero-in \nated negative binomial, whereas the negative \nbinomial model was used for G. brevipalpis to account for the overdispersion. Months \n7 and 8 and year were found to be statistically signi cant for G.austeni. This could be \nbecause month 7 has the lowest minimum and maximum temperatures during the year \nand at lower temperatures, tsetse \nies become less active and the pupal stage lengthens to \naround 50 days and the reproductive rate decreases. For G.brevipalpis only year was found \nto be statistically signi cant. \nThe AAT prevalence data was t to the mathematical model using least squares, and \nthe input parameters were estimated and used to calculate R0 again so that it is more \nsite-speci c. Climate change was also brie \ny addressed, since it is predicted to a ect the \ngeographical distribution of tsetse \nies. Higher temperatures could have a big impact \non the AAT situation because tsetse \nies might modify their behaviour and shift their \ngeographical range to regions that are cooler, which might put cattle populations in other \nregions at risk of AAT outbreaks.
Author: Nada Abdelatif
Language: English
Subfield: Epidemiology
Field: Medicine
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W2336889704