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A quadratic trend-based time series method to analyze the early incidence pattern of COVID-19

A quadratic trend-based time series method to analyze the early incidence pattern of COVID-19 The ongoing pandemic of Coronavirus disease has already affected more than 300,000 people. In this study, we propose an appropriate auto-regressive integrated moving-average model with time-varying parameters to analyze the trend pattern of the early incidence of COVID-19 outbreak, and subsequently, estimate the basic reproduction number for different countries. We also incorporate information on total or partial lockdown into the model. The model is concise and flexible in structure. For we use the maximum likelihood method and estimate it for different serial interval distributions. Proper diagnostic measures establish that a time-varying quadratic trend successfully captures the incidence pattern of the disease. We find that the number of affected cases starts increasing more rapidly three to four weeks after the first case is identified. Countrywide lockdown has been effective in reducing the growth rate of the disease in Italy. Estimated of the 2019 novel Coronavirus ranges between 1.4 and 3.2 in different countries, except for the United States where it is higher. A much-needed outcome is that the method gives insight into what epidemiological stage a region is in. This has the potential to help in prompting policies to address the COVID-19 pandemic in different countries. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Biostatistics & Epidemiology Taylor & Francis

A quadratic trend-based time series method to analyze the early incidence pattern of COVID-19

Biostatistics & Epidemiology , Volume OnlineFirst: 13 – Jun 1, 2022

A quadratic trend-based time series method to analyze the early incidence pattern of COVID-19

Abstract

The ongoing pandemic of Coronavirus disease has already affected more than 300,000 people. In this study, we propose an appropriate auto-regressive integrated moving-average model with time-varying parameters to analyze the trend pattern of the early incidence of COVID-19 outbreak, and subsequently, estimate the basic reproduction number for different countries. We also incorporate information on total or partial lockdown into the model. The model is concise and flexible in structure. For...
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Publisher
Taylor & Francis
Copyright
© 2022 International Biometric Society – Chinese Region
ISSN
2470-9379
eISSN
2470-9360
DOI
10.1080/24709360.2022.2076529
Publisher site
See Article on Publisher Site

Abstract

The ongoing pandemic of Coronavirus disease has already affected more than 300,000 people. In this study, we propose an appropriate auto-regressive integrated moving-average model with time-varying parameters to analyze the trend pattern of the early incidence of COVID-19 outbreak, and subsequently, estimate the basic reproduction number for different countries. We also incorporate information on total or partial lockdown into the model. The model is concise and flexible in structure. For we use the maximum likelihood method and estimate it for different serial interval distributions. Proper diagnostic measures establish that a time-varying quadratic trend successfully captures the incidence pattern of the disease. We find that the number of affected cases starts increasing more rapidly three to four weeks after the first case is identified. Countrywide lockdown has been effective in reducing the growth rate of the disease in Italy. Estimated of the 2019 novel Coronavirus ranges between 1.4 and 3.2 in different countries, except for the United States where it is higher. A much-needed outcome is that the method gives insight into what epidemiological stage a region is in. This has the potential to help in prompting policies to address the COVID-19 pandemic in different countries.

Journal

Biostatistics & EpidemiologyTaylor & Francis

Published: Jun 1, 2022

Keywords: Coronavirus; epidemiology; reproduction number; ARIMA; COVID-19 pandemic

References