An approach based on process mining to assess the quarantine strategies' effect in reducing the COVID-19 spread

PurposeThe feasibility of process mining combined with simulation techniques in estimating the effectiveness of COVID-19 prevention strategies on infection and mortality trends to determine best practices is assessed in this study. The quarantine event log is built from the CUSP (the COVID-19 US State Policy) database, where the dates of implemented social policies in the USA to respond to the COVID-19 pandemic are documented.Design/methodology/approachCOVID-19 is a highly infectious disease leading to a very high death toll worldwide. In most countries, the governments have resorted to a series of drastic strategies to prevent the outbreak by restricting the activities and movement among their population for a predefined time. Heretofore, different approaches have been published to estimate quarantine strategies and the majority signify the positive effect on managing this pandemic. Notably, the process perspective of COVID-19 datasets is of less concern among researchers. The purpose of this paper is to exploit the process mining techniques to model and analyze the quarantine implementation processes.FindingsThe discovered process model has 51 process variants for 51 cases (states), which indicate the quarantine activities were executed in different orders and periods during the pandemic. The time interval analysis between activities reveals the states with the most extended quarantine periods. These primary process mining insights are applied to define scenarios and variables of an agent-based model. The simulation findings indicate a meaningful relation between enforcing quarantine strategies and a declining trend of infection by 90% in the case of following strict quarantine and mask mandates. It is observed that in the post-quarantine period, the disease repeats its ascending trend unless implementation of different intervention strategies likes vaccination.Originality/valueThis study is the first in introducing process mining techniques in analyzing the COVID-19 quarantine strategies impact. The findings provide valuable insights for policymakers to proper control strategies and the process mining research community in expanding more process-related analysis on this pandemic. Also, the results have broad implications for research in other fields like information science to estimate the impact of quarantine strategies on process patterns in library systems.

On the decomposition and analysis of novel simultaneous SEIQR epidemic model

In this manuscript, we are proposing a new kind of modified Susceptible Exposed Infected Quarantined Recovered model (SEIQR) with some assumed data. The novelty imposed here in the study is that we are studying simultaneously SIR, SEIR, SIQR, and SEQR pandemic models with the same data unchanged as the SEIQR model. We are taking this model a step ahead by using a non-helpful transition because it was mostly skipped in the literature. All sorts of features that are essential to study the models, such as basic reproduction number, stability analysis, and numerical simulations have been examined for this modified model with other models.

An approach based on process mining to assess the quarantine strategies' effect in reducing the COVID-19 spread

Purpose The feasibility of process mining combined with simulation techniques in estimating the effectiveness of COVID-19 prevention strategies on infection and mortality trends to determine best practices is assessed in this study. The quarantine event log is built from the CUSP (the COVID-19 US State Policy) database, where the dates of implemented social policies in the USA to respond to the COVID-19 pandemic are documented. Design/methodology/approach COVID-19 is a highly infectious disease leading to a very high death toll worldwide. In most countries, the governments have resorted to a series of drastic strategies to prevent the outbreak by restricting the activities and movement among their population for a predefined time. Heretofore, different approaches have been published to estimate quarantine strategies and the majority signify the positive effect on managing this pandemic. Notably, the process perspective of COVID-19 datasets is of less concern among researchers. The purpose of this paper is to exploit the process mining techniques to model and analyze the quarantine implementation processes. Findings The discovered process model has 51 process variants for 51 cases (states), which indicate the quarantine activities were executed in different orders and periods during the pandemic. The time interval analysis between activities reveals the states with the most extended quarantine periods. These primary process mining insights are applied to define scenarios and variables of an agent-based model. The simulation findings indicate a meaningful relation between enforcing quarantine strategies and a declining trend of infection by 90% in the case of following strict quarantine and mask mandates. It is observed that in the post-quarantine period, the disease repeats its ascending trend unless implementation of different intervention strategies likes vaccination. Originality/value This study is the first in introducing process mining techniques in analyzing the COVID-19 quarantine strategies impact. The findings provide valuable insights for policymakers to proper control strategies and the process mining research community in expanding more process-related analysis on this pandemic. Also, the results have broad implications for research in other fields like information science to estimate the impact of quarantine strategies on process patterns in library systems.

COVID-19 epidemic under the K-quarantine model: Network approach.

The COVID-19 pandemic is still ongoing worldwide, and the damage it has caused is unprecedented. For prevention, South Korea has adopted a local quarantine strategy rather than a global lockdown. This approach not only minimizes economic damage but also efficiently prevents the spread of the disease. In this work, the spread of COVID-19 under local quarantine measures is modeled using the Susceptible-Exposed-Infected-Recovered model on complex networks. In this network approach, the links connected to infected and so isolated people are disconnected and then reinstated when they are released. These link dynamics leads to time-dependent reproduction number. Numerical simulations are performed on networks with reaction rates estimated from empirical data. The temporal pattern of the accumulated number of confirmed cases is then reproduced. The results show that a large number of asymptomatic infected patients are detected as they are quarantined together with infected patients. Additionally, possible consequences of the breakdowns of local quarantine measures and social distancing are considered.

Prevalence and Risk Factors of Home Quarantine Strategy Implementation Among Chinese Residents During the Coronavirus Disease 2019 Pandemic.

Background: Home quarantine is an important strategy to contain the mass spread of the coronavirus disease 2019 (COVID-19) pandemic. However, there are a dearth of studies on the prevalence and risk factors of home quarantine strategy implementation among residents. This study aims to assess the state of home quarantine strategy implementation among Chinese residents, which could provide a reference for quarantine policymakers around the world during the pandemic. Method: We conducted a cross-sectional survey of 3,398 residents in China by adopting a convenience sampling strategy. We measured the prevalence and risk factors of home quarantine strategy implementation with the Center for Epidemiological Studies-Depression Scale (CES-D), 10-item Connor-Davidson Resilience Scale (CD-RISC 10), and Perceived Social Support Scale (PSSS). A multivariable model was used to determine the factors associated with home quarantine strategy implementation. Results: A total of 2,936 (86.4%) respondents carried out home quarantine. There were some factors significantly associated with home quarantine strategy implementation among Chinese residents during the COVID-19 outbreak. Respondents who were male, lived in western and central China, were aware of the primary symptoms of COVID-19, were willing to accept recommendations on relevant protective measures, understood local quarantine measures, had better resilience, and had better social support were more likely to engage in home quarantine. Respondents who were married, were employed, were healthy, and had high depression scores were more likely to refuse to follow home quarantine guidance. Conclusions: Gender, region, marital status, employment status, health status, awareness of the primary symptoms of COVID-19, willingness to accept recommendations on relevant protective measures, understanding of local quarantine measures, depression, psychological resilience, and perceived social support were the main factors affecting the implementation of residents' home quarantine strategy. Health service policymakers should adopt relevant measures to improve the prevalence of home quarantine strategy implementation among residents during the pandemic.

From blanket quarantine in Wuhan to distant centralized quarantine in Shijiazhuang: the evolution of China's COVID-19 quarantine approach.

With COVID-19 spreading globally, the World Health Organization (WHO) declared a pandemic on March 11, 2020. COVID-19 swept many countries and regions worldwide. An effective response to COVID-19 requires newer and more creative tools. In this paper, we discussed the evolution of China's COVID-19 quarantine approach, compared the blanket quarantine in Wuhan and the distant centralized quarantine in rural areas of Shijiazhuang, and analyzed the important issues which authorities will have to pay attention to ensure success from the moment they begin to take close contacts to the single room isolation in a distant quarantine center. The large-scale distant centralized quarantine strategy in Shijiazhuang cut off the transmission of COVID-19 within 1 month. This strategy may inform other countries and regions of a feasible and effective approach to combat the global pandemic of COVID-19.

Joint estimation of case fatality rate of COVID-19 and power of quarantine strategy performed in Wuhan, China.

From the first case of COVID-19 confirmed in Wuhan, the capital of Hubei Province, China, in early December 2019, it has been found in more than 160 countries and caused over 11,000 deaths as of March 20, 2020. Wuhan, as the city where the epidemic first broke out, has made great sacrifices to block the possible transmission. In this research, we estimate the case fatality rate (CFR) of COVID-19 and quantify the effect of quarantine strategy utilized in Wuhan by developing an extended Susceptible-Infected-Recovered (SIR) model. The outcomes suggest that the CFR is 4.4% (95% CI [3.6%, 5.2%]) and the effect of the quarantine strategy is 99.3% (95% CI [99.2%, 99.5%]), which implies that such a method can significantly reduce the number of infections.