A static probability model is presented to simulate malaria infection in a community during a single transmission season. The model includes five parameters—sporozoite rate, human infection rate, biting pressure, repellent efficacy, and product-acceptance rate.

Background & objective: Probability models for assessing a mosquito repellent’s potential to reduce malaria transmission are not readily available to public health researchers. To provide a simple means for estimating the epidemiological efficacy of mosquito repellents in communities, we develop a simple mathematical model.

Study design: A static probability model is presented to simulate malaria infection in a community during a single transmission season. The model includes five parameters—sporozoite rate, human infection rate, biting pressure, repellent efficacy, and product-acceptance rate.

Interventions: The model assumes that a certain percentage of the population uses personal mosquito repellents over the course of a seven-month transmission season and that this repellent maintains a constant rate of protective efficacy against the bites of malaria vectors.

Main outcome measures: This model measures the probability of completely evading infection over a seven-month period at diverse rates of vector biting pressure, repellent efficacy, and product acceptance.

Results & conclusion: Absolute protection using mosquito repellents alone requires high rates of repellent efficacy and product acceptance. Using performance data from a highly effective repellent, the model estimates an 88.9% reduction of infections over a seven-month transmission season. A corresponding and proportional reduction in the incidence of super-infection in community members not completely evading infection can also be presumed. Thus, the model shows that mass distribution of a repellent with >98% efficacy and >98% product acceptance would suppress new malaria infections to levels lower than those achieved with insecticide treated nets (ITNs). A combination of both interventions could create synergies that result in reductions of disease burden significantly greater than with the use of ITNs alone.

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