Spatial Autocorrelation and the Dynamics of the Mean Center of COVID-19 Infections in Lebanon

Frontiers in Applied Mathematics and Statistics

Omar El Deeb¹ 

[1] Aabey, Lebanon;Beirut, Lebanon;

关键词: COVID-19;    spatial autocorrelation;    mean center of infection;    Lebanon;    mathematical modelling;   

DOI  :  10.3389/fams.2020.620064

来源: Frontiers

【 摘 要 】

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 the univariate 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. 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 understanding of the spatial, demographic and geographic aspects of the disease spread over time allows for regionally and locally adjusted health policies and measures that would provide higher levels of social and health safety in the fight against the pandemic in Lebanon.

【 授权许可】

CC BY