Abstract
Background: Ending the COVID-19 pandemic is arguably one of the most prominent challenges in recent human history. Following closely the growth dynamics of the disease is one of the pillars toward achieving that goal.Objective: We aimed at developing a simple framework to facilitate the analysis of the growth rate (cases/day) and growth acceleration (cases/day2) of COVID-19 cases in real-time.Methods: The framework was built using the Moving Regression (MR) technique and a Hidden Markov Model (HMM). The dynamics of the pandemic was initially modeled via combinations of four different growth stages: lagging (beginning of the outbreak), exponential (rapid growth), deceleration (growth decay), and stationary (near zero growth). A fifth growth behavior, namely linear growth (constant growth above zero), was further introduced to add more flexibility to the framework. An R Shiny application was developed, which can be accessed at https://theguarani.com.br/ or downloaded from https://github.com/adamtaiti/SARS-CoV-2. The framework was applied to data from the European Center for Disease Prevention and Control (ECDC), which comprised 3,722,128 cases reported worldwide as of May 8th 2020.Results: We found that the impact of public health measures on the prevalence of COVID-19 could be perceived in seemingly real-time by monitoring growth acceleration curves. Restriction to human mobility produced detectable decline in growth acceleration within 1 week, deceleration within ~2 weeks and near-stationary growth within ~6 weeks. Countries exhibiting different permutations of the five growth stages indicated that the evolution of COVID-19 prevalence is more complex and dynamic than previously appreciated.Conclusions: These results corroborate that mass social isolation is a highly effective measure against the dissemination of SARS-CoV-2, as previously suggested. Apart from the analysis of prevalence partitioned by country, the proposed framework is easily applicable to city, state, region and arbitrary territory data, serving as an asset to monitor the local behavior of COVID-19 cases.
Highlights
The World Health Organization (WHO) officially declared Coronavirus Disease (COVID-19) a global pandemic on March 11th 2020 [1]
Argument (v) is especially relevant to the analysis of COVID-19 data since the pandemic is ongoing and each country will be at a different stage of the growth curve as time passes
A clear disadvantage of Moving Regression (MR) is that it may over-fit the growth curve to the data, especially if the selected smooth factor is small, in which case accurate prediction of new cases of COVID-19 is limited to very few days in the future
Summary
The World Health Organization (WHO) officially declared Coronavirus Disease (COVID-19) a global pandemic on March 11th 2020 [1]. Worldwide dissemination has been extremely rapid, and by the time this study was completed (May 8th 2020) a total of 3,722,128 cases and 263,288 deaths had been reported across 209 countries and territories according to data from the European Center for Disease Prevention and Control (ECDC) [6]. Extracting daily estimates of growth rate (cases/day) and acceleration (cases/day2) in disease dissemination from real-time case reports can be decisive for an effective and promptly action to restrain further contagion. We report the development of a simple framework dedicated to the real-time analysis of COVID-19 prevalence. This framework was built using a combination of Moving Regression (MR) [8] and Hidden Markov Model (HMM) [9], and was deployed as a Shiny [10] application in R [11]. Following closely the growth dynamics of the disease is one of the pillars toward achieving that goal
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
