Author: Saidan, Motasem N.; Shbool, Mohammad A.; Arabeyyat, Omar Suleiman; T Al-Shihabi, Sameh; Abdallat, Yousef Al; Barghash, Mahmoud A.; Saidan, Hakam
Title: Estimation of the probable outbreak size of novel coronavirus (COVID-19) in social gathering events and industrial activities Cord-id: k6mlrrxx Document date: 2020_7_4
ID: k6mlrrxx
Snippet: Abstract Background The reproduction number (R0) is vital in epidemiology to estimate the number infected people and trace the close contacts. The R0 values varies depending on social activity and type of gathering events that induce infection transmissibility, in addition to its pathophysiology dependence. Objectives In this study, we estimated the probable outbreak size of COVID-19 at clusters mathematically using a simple model that can predict the number of COVID-19 cases, as a function of t
Document: Abstract Background The reproduction number (R0) is vital in epidemiology to estimate the number infected people and trace the close contacts. The R0 values varies depending on social activity and type of gathering events that induce infection transmissibility, in addition to its pathophysiology dependence. Objectives In this study, we estimated the probable outbreak size of COVID-19 at clusters mathematically using a simple model that can predict the number of COVID-19 cases, as a function of time. Methods We proposed mathematical model to estimate the R0 of COVID-19 in the outbreak occurring in both of local and international clusters in light of published data. Different types of clusters (religious, wedding, and industrial activity) were selected based on reported events in different countries between February and April 2020. Results The highest R0 values were found in wedding party events (5), followed by religious gathering events (2.5), while the lowest value was found in the industrial cluster (2). This in return, shall enable us to assess the trend coronavirus spread by comparing the model results and observed patterns.. Conclusions This study provides a predictive COVID-19 transmission patterns in different clusters types based on different R0 values. This model offers the decision makers in the contact-tracing task the predicted number of cases, which would help them in epidemiology investigations by knowing when to stop.
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