Socioeconomic determinants of pandemics: a spatial methodological approach with evidence from COVID-19 in Nice, France.

Abstract

During the period 4 January 4 - 14 February 2021 the spread of the COVID-19 epidemic peaked in the city of Nice, France with a worrying number of infected cases. This article focuses on analyzing the explicit, spatial pattern of virus spread and assessing the geographical factors influencing this distribution. Spatial modelling was carried out to examine geographical disparities in terms of distribution, incidence and prevalence of the virus, while taking socio-economic factors into account. A multiple linear regression model was used to identify the key socio-economic variables. Global and local spatial autocorrelation were measured using Moran and LISA indices, followed by spatial autocorrelation analysis of the residuals. Similarly, we used the Geographically Weighted Regression (GWR) model and the Multiscale Geographically Weighted Regression (MGWR) model to assess the influence of socio-economic factors that vary on a global and local scale. Our results reveal a marked geographical polarization, with affluent areas in the Southeast of the city contrasting sharply with disadvantaged neighbourhoods in the Northwest. Neighbourhoods with low Localized Human Development Index (LHDI), low levels of education, social housing and immigrant populations all pointed to worrying values. On the other hand, people who use public transport were significantly more likely to be contaminated by the virus. These results underline the importance of geographically predicting COVID-19 distribution patterns to guide targeted interventions and health policies. Understanding these spatial patterns using models such as MGWR can help guide public health interventions and inform future health policies, particularly in the context of pandemics.