| This dissertation presents new mathematical models of malaria transmission in order to clarify the epidemiological role of acquired immunity. Three dynamic and statistical aspects of infection are discussed, with particular reference to Plasmodium falciparum: seasonal variation, age-specific variation, and poor detectability.; Seasonal parasitological changes can be explained without seasonal changes in acquired immunity. In a model without changes in immunity, seasonal changes in vector emergence produce two features typical of parasitological observations. First, the proportion of infected vectors reaches its seasonal peak after the peak of host infection. Second, if there are intrinsic differences in susceptibility in the host population, measures of susceptibility for the host population decline during a seasonal epidemic. The second feature can also be caused by infections which spontaneously relapse during the season of low transmission.; Acquisition of immunity to malaria is reflected in the pattern of prevalence of malaria in different age groups. Acquisition is, however, slow. In addition, the most striking characteristic is that adult prevalence is greatest at intermediate rates of infection. A model of immunity generates this pattern by assuming that immunity is maintained by repeated exposure to infection. The model not only supports the intuition of epidemiologists that partial control may increase the prevalence of infection in adults, but also suggests that long-range evaluation of malaria control requires study of the dynamics of loss of immunity.; However, quantitative data on the rate of loss of immunity are inadequate. Conventional malaria surveys based on the microscopic examination of blood do not always detect chronic, low-grade infections caused by immunity. A model incorporating differences in detectability within the host population shows how the false negative tests caused by poor detectability confound measures of prevalence and the rates of infection and recovery. The model can also use multiple surveys on one group of individuals to roughly estimate the true prevalence. The form of data required is the number of times parasites are detected for each person; simply noting the cumulative proportion of individuals who show positive tests, as often reported in the literature, yields little information. Improved estimation of epidemiological variables awaits additional work on how best to analyze chronic malarial infection. |
