A spatial agent-based model to assess the spread of malaria in relation to anti-malaria interventions in Southeast Iran

Malaria threatens the lives of many people throughout the world. To counteract its spread, knowledge of the prevalence of malaria and the effectiveness of intervention strategies is of great importance. The aim of this study was to assess (1) the spread of malaria by means of a spatial agent-based model (ABM) and (2) the effectiveness of several interventions in controlling the spread of malaria. We focused on Sarbaz county in Iran, a malaria-endemic area where the prevalence rate is high. Our ABM, which was carried out in two steps, considers humans and mosquitoes along with their attributes and behaviors as agents, while the environment is made up of diverse environmental factors, namely air temperature, relative humidity, vegetation, altitude, distance from rivers and reservoirs, and population density, the first three of which change over time. As control interventions, we included long-lasting insecticidal nets (LLINs) and indoor residual spraying (IRS). The simulation results showed that applying LLINs and IRS in combination, rather than separately, was most efficient in reducing the number of infected humans. In addition, LLINs and IRS with moderate or high and high coverage rates, respectively, had significant effects on reducing the number of infected humans when applied separately. Our results can assist health policymakers in selecting appropriate intervention strategies in Iran to reduce malaria transmission

A spatial agent-based model to assess the spread of malaria in relation to anti-malaria interventions in Southeast Iran

Malaria threatens the lives of many people throughout the world. To counteract its spread, knowledge of the prevalence of malaria and the effectiveness of intervention strategies is of great importance. The aim of this study was to assess (1) the spread of malaria by means of a spatial agent-based model (ABM) and (2) the effectiveness of several interventions in controlling the spread of malaria. We focused on Sarbaz county in Iran, a malaria-endemic area where the prevalence rate is high. Our ABM, which was carried out in two steps, considers humans and mosquitoes along with their attributes and behaviors as agents, while the environment is made up of diverse environmental factors, namely air temperature, relative humidity, vegetation, altitude, distance from rivers and reservoirs, and population density, the first three of which change over time. As control interventions, we included long-lasting insecticidal nets (LLINs) and indoor residual spraying (IRS). The simulation results showed that applying LLINs and IRS in combination, rather than separately, was most efficient in reducing the number of infected humans. In addition, LLINs and IRS with moderate or high and high coverage rates, respectively, had significant effects on reducing the number of infected humans when applied separately. Our results can assist health policymakers in selecting appropriate intervention strategies in Iran to reduce malaria transmission