1 Oct 2020

Understanding the Disease Vector Operational Environment by Predicting Presence of Anopheles Mosquito Breeding Sites Using Maximum Entropy Modeling and the Maxent Software Platform

Purpose: This technical note (TN) describes research using the maximum entropy model to predict the presence of breeding sites for mosquitos of the genus Anopheles throughout the Korean peninsula. This methodology is also applicable to many other types of ecological niche modeling problems where analysts only have access to data related to the location a species has been found. The purpose of this study is to help address the need for new and innovative methods that promote military readiness through better understanding of vector-borne disease threats in familiar and unfamiliar operational environments. These methods can be used to provide military planners with valuable information to support their operations, particularly when operations expand into areas lacking direct disease vector surveillance. Disease vector risk information is vital for force readiness, because historically, soldiers are more likely to be unable to perform warfighting due to disease and non-combat injuries than as a direct result of combat (U.S. Department of the Army 2015).

20 Apr 2020

PUBLICATION NOTICE: New and Enhanced Tools for Civil Military Operations (NET-CMO)

Abstract: Civil Military Operations (CMO) associated geospatial modeling is intended to enable increased knowledge of regional stability, assist in Foreign Humanitarian Assistance (FHA), and provide support to Force Health Protection (FHP) operational planning tasks. However, current geoenabled methodologies and technologies are lacking in their overall capacity to support complex mission analysis efforts focused on understanding these important stability factors and mitigating threats to Army soldiers and civilian populations. CMO analysts, planners, and decision-makers do not have a robust capability to both spatially and quantitatively identify Regions of Interest (ROI), which may experience a proliferation in health risks such as vector-borne diseases in areas of future conflict. Additionally, due to this general absence of geoenabled health assessment models and derived end-products, CMO stakeholders are adversely impacted in their Military Decision Making Process (MDMP) capabilities to develop comprehensive area studies and plans such as Course of Action (COA). The NET-CMO project is focused on fostering emerging geoenabling capabilities and technologies to improve military situational awareness for assessment and planning of potential health threat-risk vulnerabilities.

14 Apr 2020

PUBLICATION NOTICE: Spatial Downscaling Disease Risk Using Random Forests Machine Learning

 Link: http://dx.doi.org/10.21079/11681/35618Report Number: ERDC/GRL TN-20-1Title: Spatial Downscaling Disease Risk Using Random Forests Machine LearningBy Sean P. GriffinApproved for Public Release; Distribution is Unlimited February 2020Purpose: Mosquito-borne illnesses are a significant public health concern, both to the Department of Defense