title: The effect of intrinsic stochasticity on transmitted HIV drug resistance patterns.
abstract: Estimates of transmitted HIV drug-resistance prevalence vary widely among and within epidemiological surveys. Interpretation of trends from available survey data is therefore difficult. Because the emergence of drug-resistance involves small populations of infected drug-resistant individuals, the role of stochasticity (chance events) is likely to be important. The question addressed here is: how much variability in transmitted HIV drug-resistance prevalence patterns arises due to intrinsic stochasticity alone, i.e., if all starting conditions in the different epidemics surveyed were identical? This 'thought experiment' gives insight into the minimum expected variabilities within and among epidemics. A simple stochastic mathematical model was implemented. Our results show that stochasticity alone can generate a significant degree of variability and that this depends on the size and variation of the pool of new infections when drug treatment is first introduced. The variability in transmitted drug-resistance prevalence within an epidemic (i.e., the temporal variability) is large when the annual pool of all new infections is small (fewer than 200, typical of the HIV epidemics in Central European and Scandinavian countries) but diminishes rapidly as that pool grows. Epidemiological surveys involving hundreds of new infections annually are therefore needed to allow meaningful interpretation of temporal trends in transmitted drug-resistance prevalence within individual epidemics. The stochastic variability among epidemics shows a similar dependence on the pool of new infections if treatment is introduced after endemic equilibrium is established, but can persist even when there are more than 10,000 new infections annually if drug therapy is introduced earlier. Stochastic models may therefore have an important role to play in interpreting differences in transmitted drug-resistance prevalence trends among epidemiological surveys.
authors.affiliation: Bradford University, UK. d.pillay@ucl.ac.uk
authors.forename: Alison J
authors.initials: AJ
authors.lastname: Marks
authors.forename: Deenan
authors.initials: D
authors.lastname: Pillay
authors.forename: Angela R
authors.initials: AR
authors.lastname: McLean
mesh_terms: D019380:Anti-HIV Agents / Q000627:therapeutic use; D023241:Antiretroviral Therapy, Highly Active / Q000706:statistics & numerical data; D003625:Data Collection; D004198:Disease Susceptibility / Q000453:epidemiology; D024882:Drug Resistance, Viral; D005260:Female; D015658:HIV Infections / Q000188:drug therapy / Q000453:epidemiology* / Q000635:transmission*; D015497:HIV-1 / Q000187:drug effects / Q000502:physiology; D006716:Homosexuality / Q000706:statistics & numerical data; D006801:Humans; D008297:Male; D008962:Models, Theoretical; D009010:Monte Carlo Method; D015995:Prevalence; D013269:Stochastic Processes
chemical_list: D019380:Anti-HIV Agents