Exploring the socio-economic and environmental components of infectious diseases using multivariate geovisualization: West Nile Virus

Abstract

Background This study postulates that underlying environmental conditions and a susceptible population’s socio-economic status should be explored simultaneously to adequately understand a vector borne disease infection risk. Here we focus on West Nile Virus (WNV), a mosquito borne pathogen, as a case study for spatial data visualization of environmental characteristics of a vector’s habitat alongside human demographic composition for understanding potential public health risks of infectious disease. Multiple efforts have attempted to predict WNV environmental risk, while others have documented factors related to human vulnerability to the disease. However, analytical modeling that combines the two is difficult due to the number of potential explanatory variables, varying spatial resolutions of available data, and differing research questions that drove the initial data collection. We propose that the use of geovisualization may provide a glimpse into the large number of potential variables influencing the disease and help distill them into a smaller number that might reveal hidden and unknown patterns. This geovisual look at the data might then guide development of analytical models that can combine environmental and socio-economic data. Methods Geovisualization was used to integrate an environmental model of the disease vector’s habitat alongside human risk factors derived from socio-economic variables. County level WNV incidence rates from California, USA, were used to define a geographically constrained study area where environmental and socio-economic data were extracted from 1,133 census tracts. A previously developed mosquito habitat model that was significantly related to WNV infected dead birds was used to describe the environmental components of the study area. Self-organizing maps found 49 clusters, each of which contained census tracts that were more similar to each other in terms of WNV environmental and socio-economic data. Parallel coordinate plots permitted visualization of each cluster’s data, uncovering patterns that allowed final census tract mapping exposing complex spatial patterns contained within the clusters. Results Our results suggest that simultaneously visualizing environmental and socio-economic data supports a fuller understanding of the underlying spatial processes for risks to vector-borne disease. Unexpected patterns were revealed in our study that would be useful for developing future multilevel analytical models. For example, when the cluster that contained census tracts with the highest median age was examined, it was determined that those census tracts only contained moderate mosquito habitat risk. Likewise, the cluster that contained census tracts with the highest mosquito habitat risk had populations with moderate median age. Finally, the cluster that contained census tracts with the highest WNV human incidence rates had unexpectedly low mosquito habitat risk.

Spatial patterns of West Nile virus distribution in the Volgograd region of Russia, a territory with long-existing foci

“…WNV infections were less likely in hot steppe areas (due to the low number of larval mosquito habitats), and more likely at lower elevations of the floodplain. The use of LST as one of the predictors of WNV distribution has been confirmed in other studies [ 79 , 113 ]. A negative correlation with altitude has been found in various areas [ 41 , 114 , 115 ].…”

Cascading effects of COVID-19 on population mobility and air quality: An exploration including place characteristics using geovisualization

“…Geovisualization techniques may provide valuable insights for identifying variables and associated processes that contribute to variations in disease risk across space and time. Geovisualization was used in this study following procedures outlined in Kala et al (2020) to provide a glimpse into the large number of potential variables influencing the COVID-19 cases and help distil them into a smaller number that might reveal hidden and unknown patterns.…”