In the past few years, malaria cases have increased dramatically in Panama from 4,181 cases in 2021 to 7,083 in 2022--and they continue to surge (PAHO). With funding support from NASA, the Pan Lab is partnering with Dr. Jose Loaiza at the Instituto de Investigaciones Científicas y Servicios de Alta Tecnología de Panamá AIP (INDICISAT), Johns Hopkins University, and the Clinton Health Access Initiative (CHAI) to establish a Malaria Early Warning System (MEWS) in Panama. Understanding malaria vector makeup and behavior is key to accurate modeling. However, historically, funding gaps have made it challenging to maintain as robust data on malaria vector presence as many other malaria-affected regions globally. We hypothesize that modeling the relationships between common Panamanian malaria vectors' presence and climate variables may help produce more accurate MEWS predictions and support continued entomological surveillance. This project seeks to apply a Maximum Entropy (MaxEnt) spatial distribution model to NASA land cover data and a historical vector presence dataset collated by Dr. Loaiza's team to begin to investigate if and how climate and land cover variables influence Panamanian malaria vector presence.