An investigation into the statistical properties of TB episodes in a South African community with high HIV prevalence.
Continuous differential equations are often applied to small populations with little time spent on understanding uncertainty brought about by small-population effects. Despite large numbers of individuals being latently infected with Mycobacterium tuberculosis (TB), progression from latent infection to observable disease is a relatively rare event. For small communities, this means case counts are subject to stochasticity, and deterministic models may not be appropriate tools for interpreting transmission trends. Furthermore, the nonlinear nature of the underlying dynamics means that fluctuations are autocorrelated, which can invalidate standard statistical analyses which assume independent fluctuations. Here we extend recent work using a system of differential equations to study the HIV-TB epidemic in Masiphumelele, a community near Cape Town in South Africa [Bacaër, et al., J. Mol. Biol. 57(4), 557-593] by studying the statistical properties of active TB events. We apply van Kampen's system-size (or population-size) expansion technique to obtain an approximation to a master equation describing the dynamics. We use the resulting Fokker-Planck equation and point-process theory to derive two-time correlation functions for active TB events. This method can be used to gain insight into the temporal aspect of cluster identification, which currently relies on DNA classification only.
SACEMA, c/o StIAS, DST/NRF Centre of Excellence in Epidemiological Modelling and Analysis, Stellenbosch University, 19 Jonkershoek Road, Stellenbosch 7600, South Africa. c.d.pretorius@gmail.com
Carel
C
Pretorius
Peter
P
Dodd
Robin
R
Wood
D000293:Adolescent; D000328:Adult; D000465:Algorithms; D016000:Cluster Analysis; D003198:Computer Simulation; D015658:HIV Infections / Q000150:complications / Q000453:epidemiology*; D006801:Humans; D055985:Latent Tuberculosis / Q000150:complications / Q000453:epidemiology; D008875:Middle Aged; D008954:Models, Biological; D009169:Mycobacterium tuberculosis / Q000235:genetics / Q000302:isolation & purification; D015995:Prevalence; D012111:Residence Characteristics; D013019:South Africa / Q000453:epidemiology; D013269:Stochastic Processes; D014376:Tuberculosis / Q000150:complications / Q000453:epidemiology* / Q000209:etiology; D055815:Young Adult