Spatial autocorrelation and the dynamics of the mean center of COVID-19 infections in Lebanon

TL;DR

This study analyzes the spatial and temporal spread of COVID-19 in Lebanon, revealing clustering patterns related to population and proximity, and tracks the shifting mean center of infections to inform targeted health policies.

Contribution

It applies Moran's I statistics to identify spatial clustering of COVID-19 in Lebanon and examines the temporal dynamics of the infection's mean center across districts.

Findings

Infection spread is spatially clustered except for poverty rate.

Clustering correlates with adjacency, proximity, population, and poverty density.

The mean center of infections shifted over time, indicating changing hotspots.

Abstract

In this paper we study the spatial spread of the COVID-19 infection in Lebanon. We inspect the spreading of the daily new infections across the 26 administrative districts of the country, and implement Moran's $I$ statistics in order to analyze the tempo-spatial clustering of the infection in relation to various variables parameterized by adjacency, proximity, population, population density, poverty rate and poverty density, and we find out that except for the poverty rate, the spread of the infection is clustered and associated to those parameters with varying magnitude for the time span between July (geographic adjacency and proximity) or August (population, population density and poverty density) through October. We also determine the temporal dynamics of geographic location of the mean center of new and cumulative infections since late March. The results obtained allow for regionally and locally adjusted health policies and measures that would provide higher levels of public health safety in the country.