Abstract
In this paper, we propose a latent pandemic space modeling approach for analyzing coronavirus disease 2019 (COVID-19) pandemic data. We developed a pandemic space concept that locates different regions so that their connections can be quantified according to the distances between them. A main feature of the pandemic space is to allow visualization of the pandemic status over time through the connectedness between regions. We applied the latent pandemic space model to dynamic pandemic networks constructed using data of confirmed cases of COVID-19 in 164 countries. We observed the ways in which pandemic risk evolves by tracing changes in the locations of countries within the pandemic space. Empirical results gained through this pandemic space analysis can be used to quantify the effectiveness of lockdowns, travel restrictions, and other measures in regard to reducing transmission risk across countries.
Highlights
Since early 2020, many countries have been affected by the novel coronavirus disease (COVID-19) pandemic
We present the statistical results from the latent pandemic space model
If we take into account the daily number of confirmed cases at that time, it begins to appear that there were major events that accelerated the pandemic risk during that period
Summary
Since early 2020, many countries have been affected by the novel coronavirus disease (COVID-19) pandemic. To stop the spread of the disease and to reduce the risk from the pandemic, different countries have adopted various levels of control measures, including, but not limited to, quarantining, social distancing regulations, travel restrictions, and the locking down of entire cities. There have been several studies carried out to evaluate the effectiveness and impacts of social distancing measures [14] The findings of these studies have shown that lockdown measures, in particular, appear to be one of the most effective approaches to limiting the spread of infections [15]. Studies have shown that the imposition of travel restrictions has been effective in reducing correlations in the numbers of infected people across different countries [16,17].
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