The Amazon environment has been exposed in the last decades to radical changes that have been accompanied by a remarkable rise of both Plasmodium falciparum and Plasmodium vivax malaria. The malaria transmission process is highly influenced by factors such as spatial and temporal heterogeneities of the environment and individual-based characteristics of mosquitoes and humans populations. All these determinant factors can be simulated effectively trough agent-based models. This paper presents a validated agent-based model of local-scale malaria transmission. The model reproduces the environment of a typical riverine village in the northern Peruvian Amazon, where the malaria transmission is highly seasonal and apparently associated with flooding of large areas caused by the neighbouring river. Agents representing humans, mosquitoes and the two species of Plasmodium (P. falciparum and P. vivax) are simulated in a spatially explicit representation of the environment around the village. The model environment includes: climate, people houses positions and elevation. A representation of changes in the mosquito breeding areas extension caused by the river flooding is also included in the simulation environment. A calibration process was carried out to reproduce the variations of the malaria monthly incidence over a period of 3 years. The calibrated model is also able to reproduce the spatial heterogeneities of local scale malaria transmission. A “what if” eradication strategy scenario is proposed: if the mosquito breeding sites are eliminated through mosquito larva habitat management in a buffer area extended at least 200 m around the village, the malaria transmission is eradicated from the village. The use of agent-based models can reproduce effectively the spatiotemporal variations of the malaria transmission in a low endemicity environment dominated by river floodings like in the Amazon.

This paper analyzes factors affecting the risk of malaria among individuals working in wildcat gold mining camps (garimpos) in northern Mato Grosso State in the Brazilian Amazon. Historically, such mining camps have the locations with the highest malaria prevalence in the Brazilian Amazon. However, little attention has focused on understanding the disease from the internal perspective of the mining camps themselves, such as the mining population's characteristics and its spatial organization. This paper adopts a stepwise logistic model to identify spatial, occupational-exposure, and cultural factors that affect malaria prevalence. According to the results, differences among individuals working and/or living in the gold mining areas could produce different exposure to the disease and thus to different risk of malaria prevalence. Understanding these differences may provide an important tool for identifying risk profiles in the gold mining and related population and for informing programs for prevention and treatment of malaria in the Amazon.