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

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

This study hypothesizes that public health responses to coronavirus disease 2019 (COVID-19), including a mandated restriction of activity (commonly called a 'lockdown') resulted in reduced transportation activities and changes in air quality in Texas, USA. This presented a natural experiment where population mobility and air quality before and after the lockdown could be compared. Changes in mobility were measured by SafeGraph mobility data (from opt-in smart phone applications that transmit location data) and air quality changes were based on NO2 concentrations measured by the European Space Agency's Sentinel-5 Precursor satellite (from the TROPOspheric Monitoring Instrument). The changes in population mobility and NO2 concentration between mid-March 2020 (lockdown initiated) and the end of 2020, as compared to the same time window in 2019, were the basis of exploring the lockdown hypothesis. Additionally, numerous socio-economic (place based) indicators were hypothesized to follow public health vulnerability assumptions based on COVID- 19 incidence patterns. This hypothesis was subjected to geovisualization techniques in order to find potential patterns and insights into the complex combinations of these place-based data. Our results suggest that simultaneously visualizing COVID-19, mobility, air quality and socio-economic data yields insights in underlying spatial processes related to public health policy decisions. The hypothesis that the lockdown resulted in reduced mobility and NO2 concentrations was found partially correct - this trend was observed in highly urbanized areas, but not in less populated areas. Data related public health vulnerability assumptions (e.g. a region's age, poverty, education, etc.) were agreed with in part, but disagreed with in part.

Ecological Predictors of Human Malaria Risk During Different Phases of the Elimination: An Analysis of Historical Data

To understand the evolutionary ecology of disease dynamics, it is crucial to identify the environmental factors that mediate the spread and abundance of parasites and their vectors. However, human-mediated changes in the biotic and abiotic environment and intervention programs are intensifying in the past 30-40 years at a rate that masks the causal effect of the original ecological predictors. In this study, we used archived epidemiological data spanning over 100 years on malaria risk in Hungary to demonstrate that different associations exist between infection risk and environmental predictors during different phases of the elimination program. In the early 20th century, when malaria was quite common in the country and no defense program was operating, as predicted, there was a positive relationship between the area of flooded habitats and the intensity of malaria infection. In contrast, this relationship was absent during middle of the century, when an effective elimination program was already in effect. Furthermore, malaria morbidity in a given year was predicted by the degree of stagnant water cover of the previous year when considering the period before the launch of a drastic mosquito control program by dichloro-diphenyl-trichloroethane (DDT), whereas such relationship could not be revealed for a latter period. Our results highlight that human-induced alterations of the socioecological environment considerably reorganizes the ecological landscape of pathogens and their vectors.