Stochastic simulation and the detection of immunity to schistosome infections.
In this paper we address the question of detecting immunity to helminth infections from patterns of infection in endemic communities. We use stochastic simulations to investigate whether it would be possible to detect patterns predicted by theoretical models, using typical field data. Thus, our technique is to simulate a theoretical model, to generate the data that would be obtained in field surveys and then to analyse these data using methods usually employed for field data. The general behaviour of the model, and in particular the levels of variability of egg counts predicted, show that the model is capturing most of the variability present in field data. However, analysis of the data in detail suggests that detection of immunity patterns in real data may be very difficult even if the underlying patterns are present. Analysis of a real data set does show patterns consistent with acquired immunity and the implications of this are discussed.
Chan
M S
MS
Wellcome Trust Centre for the Epidemiology of Infectious Disease, University of Oxford, UK. mansuen.chan@ceid.ox.ac.uk
Mutapi
F
F
Woolhouse
M E
ME
Isham
V S
VS
D000293:Adolescent; D000328:Adult; D000367:Age Factors; D000368:Aged; D000818:Animals; D002648:Child; D002675:Child, Preschool; D003198:Computer Simulation; D003430:Cross-Sectional Studies; D006801:Humans; D007110:Immunity, Active / Q000276:immunology; D007223:Infant; D008875:Middle Aged; D008954:Models, Biological; D009010:Monte Carlo Method; D010270:Parasite Egg Count; D015995:Prevalence; D012548:Schistosoma haematobium / Q000276:immunology*; D012553:Schistosomiasis haematobia / Q000453:epidemiology / Q000276:immunology*; D013269:Stochastic Processes; D014867:Water / Q000469:parasitology; D015030:Zimbabwe / Q000453:epidemiology
D014867:Water