A E Kiszewski1, S T Darling. 1. Natural and Applied Sciences, Bentley University, Waltham, MA, USA.
Abstract
BACKGROUND & OBJECTIVES: Probability models for assessing a mosquito repellent's potential to reduce malaria transmission are not readily available to public health researchers. To provide a means for estimating the epidemiological efficacy of mosquito repellents in communities, we developed 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 a personal mosquito repellent 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 evading infection in circumstances where vector biting pressure, repellent efficacy, and product acceptance may vary. [corrected] RESULTS & CONCLUSION: Absolute protection using mosquito repellents alone requires high rates of repellent efficacy and product acceptance. [corrected] 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 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.
BACKGROUND & OBJECTIVES: Probability models for assessing a mosquito repellent's potential to reduce malaria transmission are not readily available to public health researchers. To provide a means for estimating the epidemiological efficacy of mosquito repellents in communities, we developed 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, humaninfection rate, biting pressure, repellent efficacy, and product-acceptance rate. INTERVENTIONS: The model assumes that a certain percentage of the population uses a personal mosquito repellent 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 evading infection in circumstances where vector biting pressure, repellent efficacy, and product acceptance may vary. [corrected] RESULTS & CONCLUSION: Absolute protection using mosquito repellents alone requires high rates of repellent efficacy and product acceptance. [corrected] 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 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.
Authors: Samuel Dadzie; Daniel Boakye; Victor Asoala; Kwadwo Koram; Anthony Kiszewski; Maxwell Appawu Journal: Am J Trop Med Hyg Date: 2012-12-18 Impact factor: 2.345
Authors: Philippe Guyant; Vincent Corbel; Philippe J Guérin; Adeline Lautissier; François Nosten; Sébastien Boyer; Marc Coosemans; Arjen M Dondorp; Véronique Sinou; Shunmay Yeung; Nicholas White Journal: Malar J Date: 2015-07-17 Impact factor: 2.979
Authors: Karel Van Roey; Mao Sokny; Leen Denis; Nick Van den Broeck; Somony Heng; Sovannaroth Siv; Vincent Sluydts; Tho Sochantha; Marc Coosemans; Lies Durnez Journal: PLoS Negl Trop Dis Date: 2014-12-18