Abstract
We present a statistical model which can be employed to understand the contagion dynamics of the COVID-19. The model is a Poisson autoregression, and can reveal whether contagion has a trend, and where is each country on that trend. Model results are presented from the observed series of China, Iran, Italy and South Korea.
Highlights
We propose to adapt this model to the COVID-19 contagion
We have applied the model to the available data, which cover the period from January 20 to March 8, 2020
With the aim of better interpreting the still short time series of the other countries, we repeatedly fit the model to the Chinese data, using increasing amounts of data, in a retrospective way
Summary
It follows that it would be ideal to model newly infected counts as a function of both a short-term and a long-term component. The α component represents the short-term dependence on the previous time point.
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