Intensifying the fumigation policy to minimize the risk of mosquito-borne diseases implementing a stochastic optimization approach

Abstract

This paper presents a stochastic mathematical model for the planning, dosage, and strategic scheduling of fumigation policies to reduce mosquito-borne diseases in a geographic location. Multiple scenarios were generated to account for uncertainty in the existing mosquito population based on a random normal distribution. In addition, the model includes occupational health, considering the permissible limits of each insecticide applied and their residual effect. This stochastic mathematical model applies a Pareto solution method, using a Conditional Value at Risk approach to select solutions that trade off the different objective functions. Results show that the optimal solutions found by the model provide a compromise between the expected infected people and the total cost of applying the insecticides. This methodology improves the current practice by an intensified approach that considers optimal scheduling alternatives that minimize cost while considering the permissible concentration limits to guarantee the health and comfort of the population to minimize the possible incidences of infections.