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Data from the Garki Project are analyzed to assess how misdiagnosis affects the estimated prevalence of Plasmodium falciparum. Three double-sampling models that account for the fallibility of the expert are derived and applied. The models incorporate information about the density of parasites in the blood to varying degrees. The error in the estimation of prevalence is quantified; and its dependence on calendar time, age, prevalence, and density is investigated. Prevalence and average density are discovered to be good predictors of the error, with the latter being better. Implications of the double-sampling models for the design of epidemiological surveys similar to the one in Garki are investigated.

The Macdonald-Dietz model for superinfection in malaria is a time-dependent infinite-server queue. However, the queue is only partially observable; it can be ascertained only as not empty or empty. Moreover, continuous observation of the queue is impossible. This paper derives likelihoods for the model's parameters with incomplete, multiwave panel data, and numerically maximizes those likelihoods for some data from a field study in Nigeria.

Sampling models are investigated for counts of mosquitoes from a malaria field survey conducted by the World Health Organization in Nigeria. The data can be described by a negative binomial model for two-way classified counted data, where the cell means are constrained to satisfy row-by-column independence and the parameter k is constant across rows. An algorithm, based on iterative proportional fitting, is devised for finding maximum likelihood estimates. Sampling properties of the estimates and likelihood-ratio statistics for the small sample sizes of the data are investigated by Monte Carlo experiments. The WHO reported an observation that the relative efficiencies of four trapping methods vary over time. Out of eight villages in the survey area, this observation is found to be true in only the one village that is near a swamp.