A time series method to analyze incidence pattern and estimate reproduction number of COVID-19

Abstract

The ongoing pandemic of Coronavirus disease (COVID-19) emerged in Wuhan, China in the end of 2019. It has already affected more than 300,000 people, with the number of deaths nearing 13000 across the world. As it has been posing a huge threat to global public health, it is of utmost importance to identify the rate at which the disease is spreading. In this study, we propose a time series model to analyze the trend pattern of the incidence of COVID-19 outbreak. We also incorporate information on total or partial lockdown, wherever available, into the model. The model is concise in structure, and using appropriate diagnostic measures, we showed that a time-dependent quadratic trend successfully captures the incidence pattern of the disease. We also estimate the basic reproduction number across different countries, and find that it is consistent except for the United States of America. The above statistical analysis is able to shed light on understanding the trends of the outbreak, and gives insight on what epidemiological stage a region is in. This has the potential to help in prompting policies to address COVID-19 pandemic in different countries.

References in corpus (1)

• Forecast of the evolution of the contagious disease caused by novel coronavirus (2019-nCoV) in China

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• Diagnosis of COVID-19 Using Machine Learning and Deep Learning: A review
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• Monitoring SEIRD model parameters using MEWMA for the COVID-19 pandemic with application to the State of Qatar