Identifying Malaria Transmission Risk in the Peruvian Amazon: A Geospatial Approach

Thumbnail Image



Journal Title

Journal ISSN

Volume Title

Repository Usage Stats



Peru has endured a long history with malaria, an infectious disease caused by the mosquito-borne transmission of the Plasmodium parasite. Throughout the 20th century, disease prevalence has varied tremendously with a number of factors including Peru’s growth and development, variable support for malaria control measures, and the migration of immunologically naïve populations. However, many researchers believe that anthropogenic deforestation is at the root of a recent resurgence of malaria in the Peruvian Amazon. Deforestation creates favorable conditions for disease transmission by increasing mosquito habitat and placing humans in close proximity to more abundant disease vectors. In addition, rural communities often lack the resources to combat malaria due to the prohibitive cost of conventional technologies and lack of access to health care. Using data derived from field collections and remotely sensed images in the Loreto department of Peru, this study proposes a new method for characterizing malaria risk in the Peruvian Amazon. A variety of novel geospatial and remote sensing techniques were used to develop environmental layers from satellite imagery and produce the species distribution model. A geospatial risk model synthesized the predicted mosquito habitat and associated community risk factors into an assessment of malaria exposure risk. The threat model developed from this study can be used to create maps that will help local communities manage their malaria risk. Management efforts, such as the reduction of available mosquito breeding habitat, can be concentrated in areas identified as high-risk for malaria exposure.





Yin, Elizabeth (2014). Identifying Malaria Transmission Risk in the Peruvian Amazon: A Geospatial Approach. Master's project, Duke University. Retrieved from

Dukes student scholarship is made available to the public using a Creative Commons Attribution / Non-commercial / No derivative (CC-BY-NC-ND) license.