Researchers Map Key Factors in the Spread of COVID-19

A group of researchers recently identified potential factors to predict the inconsistency of COVID-19.
As COVID-19 continues to spread across the United States, it is beginning to show inconsistency in the impacts it has on various communities. Some communities have become hotbeds for the virus such as the states of Washington and New York, while others continue to be relatively unscathed. To unravel the reason behind this inconsistency, a group of researchers from Baldwin Wallace University and University of California identified 35 potential factors, and used powerful data mapping software known as GIS to see which key factors most closely predicted the spread of COVID-19. The study found that four factors were the strongest predictors of COVID-19’s spread and inconsistency.

What is GIS?
Geographic Information Systems, also known as GIS is a set of programs and practices commonly used by scientists for interpreting and displaying data through geographic means such as maps.

GIS includes a variety of different systems and mediums

This group of researchers used the GIS software ARCGIS to test their potential factors. Via this ARCGIS environment they created several mathematical models with which to test, and map each of the potential factors and its prediction of the spread of COVID-19.

The Big Four Factors
The study compiled 35 factors for testing, which were divided into five general themes: socioeconomic, environmental, behavioral, topographical, and demographic.

The study came to the conclusion that out of the 35 factors tested, four factors had the largest correlation with predicting the spread of COVID-19: a combination of median household income, income inequality, percentage of nurse practitioners, and percentage of black female population. These four factors when combined into a single model could predict the high variability of COVID-19. Three of these factors were consistent across both models used to calculate the correlation. However, the percentage of black female population had differing results between the two models, meaning that it is not a fully consistent predictor of COVID-19 even though it had strong correlation.

From these four factors influence on COVID-19’s spread the study drew several conclusions. For one, the categories which had the greatest influence were socioeconomic, and demographic factors. This means that the types of people in a geographic region, and their social and economic conditions were among the greatest predictors of the spread of COVID-19. High income inequality, and low median household income are the telltale signs of a region that could be decimated by COVID-19 due to many people lacking health insurance coverage or the necessary funds to treat the disease. On the other hand, a community with more healthcare infrastructure and healthcare professionals is likely to be more resilient to the disease

How can GIS help us fight COVID-19?
The study proposes that using GIS data to help map socioeconomic, and demographic data could help us predict the spread of COVID-19. Several other studies are cited by the researchers which came to similar conclusions on other factors correlations to COVID-19 such as rates of smoking, and air pollution.
However, GIS is not a perfect predictor. A lack of data was cited as being a major issue for the study. Though several factors showed significant correlation to the spread of COVID-19, relatively low correlation rates could mean that almost 90% of variability is due to factors not yet accounted for. Similarly, the constantly changing landscape of lockdowns, laws, and regulations makes creating consistent data sets difficult. However, the researchers believe that GIS remains as an untapped resource for monitoring and predicting the spread of this deadly disease.

Resources
https://www.sciencedirect.com/science/article/pii/S0048969720324013