Count Data Time Series Modelling in Julia-The CountTimeSeries.jl Package and Applications

A log linear Poisson autoregressive model to understand COVID-19 dynamics in Saudi Arabia

Background

On March 2, 2020, the first case of COVID-19 infection in Saudi Arabia was identified and announced by the health authorities. From first week of March, the number of new confirmed COVID-cases has gradually increased, reaching 2932 confirmed cases on April 9, 2020. A period of increasing infection cases was noticed in June and July 2020. Many methods have been taken to model and predict the new confirmed cases of COVID-19, such as the traditional time series forecasting method and other several methods.

Results

We present two statistical models, namely the log linear autoregressive Poisson model and the ARIMA model. The COVID-19 infectious dynamics were evaluated using models in Saudi Arabia, which can affect health, economics, finance, and other fields. We applied both models to daily confirmed cases of COVID-19 count time series data. Moreover, we compare the log linear Poisson autoregressive model with the automatic ARIMA model.

Conclusions

The result of this study showed that a log linear Poisson Autoregressive model gives better forecasting and the predicted results of the log linear Poisson Autoregressive model can be used as the baseline for additional interference to avoid future COVID-19 pandemic incidents. Moreover, the application of a log linear Poisson Autoregressive can be comprehensive to other cases in Saudi Arabia.

Time Series Modelling

Time series consist of data observed sequentially in time, and they are assumed to stem from an underlying stochastic process [...].