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 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.
Highlights
The spread of COVID-19 pandemic has practically affected the entire planet, and created enormous challenges on every aspect of human life and organization, starting with the health sector and with far reaching consequences on the economy, education, sports, transportation and politics
There were only few days when new infections were not clustered in adjacent regions, and only one day where distance was not shown to be a detrimental effect in the spatial spread of new cases
The results of the spatial spread dynamics in relation to population and population density adjacency as shown in Moran’s I and p-value of cases III and IV depicted in Figure 3 reveal that the spread was not clustered with respect to the regional population until late August 2020, where it started achieving a positive value of I with p < 0.05 indicating spatial clustering between regions of adjacent population rank, with several days showing a probability of random spread
Summary
The spread of COVID-19 pandemic has practically affected the entire planet, and created enormous challenges on every aspect of human life and organization, starting with the health sector and with far reaching consequences on the economy, education, sports, transportation and politics. Since the first cases were registered in Wuhan, China in December 2019 [1], the global spatial dynamics of the infection have been changing as the disease swiftly moved toward the West [2] into Europe into the United States, South America, and eventually to the whole world, with nearly 38.1 million cases and 1.1 million deaths registered until October 12, 2020 [3]. Given the global geographic spread of the virus and the local wide spread in many countries, and the nature of the transmission of the virus, it is important to understand the spatial mechanisms of this spread and its dependence on proximity, demographics and social characteristics of infected areas. Spatial analysis provides a better understanding of the routes of transmission of infections [4], it allows the decision-makers to draft and implement effective health and mitigation measures to reduce risks associated with the pandemic. The lift of the international travel ban and the partial easing of measures led to the revival of higher
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
