A remote sensing and GIS-assisted landscape epidemiology approach to West Nile virus

Abstract

Landscape epidemiology, sometimes called spatial epidemiology, is a sub-discipline of medical geography that uses environmental conditions as explanatory variables in the study of disease or other health phenomena. Machine learning is a sub-discipline of artificial intelligence that can be used to create predictive models from large and complex datasets. West Nile virus (WNV) is a relatively new infectious disease in the United States, and has a fairly well-understood transmission cycle that is believed to be highly dependent on environmental conditions. This study takes a remote sensing and GIS approach to the study of WNV, using both landscape epidemiology and machine learning techniques. A combination of remotely sensed and in situ variables is used to predict WNV incidence with a correlation coefficient as high as 0.86. A novel method of mitigating the effect of limited case numbers is also tested and ultimately discarded. A consistent spatial pattern of model errors is identified, indicating the chosen variables are capable of predicting WNV disease risk across most of the United States, but are inadequate in the northern Great Plains region of the US.