Estimating the force of infection from prevalence data : infectious disease modelling.

Abstract

By knowing the incidence of an infectious disease, we can ascertain the high risk factors of the disease as well as the e ectiveness of awareness programmes and treatment strategies. Since the work of Hugo Muench in 1934, many methods of estimating the force of infection have been developed, each with their own advantages and disadvantages. The objective of this thesis is to explore the di erent compartmental models of infectious diseases and establish and interpret the parameters associated with them. Seven models formulated to estimate the force of infection were discussed and applied to data obtained from CAPRISA. The data was agespeci c HIV prevalence data based on antenatal clinic attendees from the Vulindlela district in KwaZulu-Natal. The link between the survivor function, the prevalence and the force of infection was demonstrated and generalized linear model methodology was used i to estimate the force of infection. Parametric and nonparametric force of infection models were used to t the models to data from 2009 to 2010. The best tting model was determined and thereafter applied to data from 2002 to 2010. The occurring trends of HIV incidence and prevalence were then evaluated. It should be noted that the sample size for the year 2002 was considerably smaller than that of the following years. This resulted in slightly inaccurate estimates for the year 2002. Despite the general increase in HIV prevalence (from 54.07% in 2003 to 61.33% in 2010), the rate of new HIV infections was found to be decreasing. The results also showed that the age at which the force of infection peaked for each year increased from 16.5 years in 2003 to 18 years in 2010. Farrington's two parameter model for estimating the force of HIV infection was shown to be the most useful. The results obtained emphasised the importance of HIV awareness campaigns being targeted at the 15 to 19 year old age group. The results also suggest that using only prevalence as a measure of disease can be misleading and should rather be used in conjunction with incidence estimates to determine the success of intervention and control strategies.