Malaria remains an extremely important infectious disease and the development of drug resistant strains is currently significantly exacerbating the problem. This project will develop stochastic simulation (computer) models that will mimic and predict the growth and spread of malaria in individual infections (in-host) and through communities (populations) in endemic areas. The effects of mutations leading to drug resistance will be specifically examined. The computer models to be developed include """"""""virtual"""""""" people and a """"""""virtual"""""""" village. Parasites and vector (mosquito) will be programmed to cause and spread infection according to the best current understanding of the factors involved. The approach is based on the hypothesis that the in-host dynamics of chronic malaria infections is dominated by a negative-feedback loop involving the production of successive variant surface antigens, production of antibody to these antigens, the consequent elimination of nearly all parasites with those specific antigens, and the escape of those parasites that have switched to express an alternative surface antigen. This model differs from previous models since it implies a significant genetic bottleneck at the time of antibody clearing of most parasites expressing a particular antigen type. The consequences of this new model are that diverse processes such as the survival of a drug resistant parasite in a single host, the length of time a person remains infected, the degree of diversity of parasites in a single host, the probability of mixed gametocyte infection and therefore the rate of outcrossing, all depend on the switching/recrudescence rate of the parasites. Experimental baselines for the model will be derived in this project by: 1) determining the level of parasitemia which triggers anti-variant surface antibody and the kinetics of production of the antibody; 2) determining the mutation rates to drug resistance of malaria parasites for several drugs; 3) to compare the fitness of drug resistant and mutant parasites; and 4) to examine filed parasite populations and lab isolates for strain specific markers which could provide the basis for strain structuring that would affect the rates at which drug resistance alleles will spread or be lost in a host population.
Showing the most recent 10 out of 20 publications
