Leveraging Artificial Intelligence to Analyze the COVID-19 Distribution Pattern based on Socio-economic Determinants

TL;DR

This study employs machine learning and spatial analysis to explore the relationship between socioeconomic factors and COVID-19 distribution in Dublin, Ireland, identifying demographic patterns and clusters of infection at the electoral division level.

Contribution

It introduces a novel machine learning approach to model COVID-19 spread based on socioeconomic and spatial data at a detailed regional level.

Findings

Identified seven distinct clusters of COVID-19 distribution.

Demographic characteristics significantly influence infection patterns.

Spatial and socioeconomic variables can predict infection hotspots.

Abstract

The spatialization of socioeconomic data can be used and integrated with other sources of information to reveal valuable insights. Such data can be utilized to infer different variations, such as the dynamics of city dwellers and their spatial and temporal variability. This work focuses on such applications to explore the underlying association between socioeconomic characteristics of different geographical regions in Dublin, Ireland, and the number of confirmed COVID cases in each area. Our aim is to implement a machine learning approach to identify demographic characteristics and spatial patterns. Spatial analysis was used to describe the pattern of interest in Electoral Divisions (ED), which are the legally defined administrative areas in the Republic of Ireland for which population statistics are published from the census data. We used the most informative variables of the census data to model the number of infected people in different regions at ED level. Seven clusters detected by implementing an unsupervised neural network method. The distribution of people who have contracted the virus was studied.