Quarantine Rate Research Articles

Research on the Impact of COVID-19 on Import and Export Strategies

Due to the spread of COVID-19, the public health crisis is bound to have a huge impact on the world economy and international trade. How to study the import and export strategies under the coronavirus pandemic has become a major issue that many scholars need to solve urgently. Therefore, a two-stage game model is constructed, and the reverse solution method is used to obtain the optimal output of enterprises in importing countries and exporting countries before and after the outbreak of pandemic, as well as the optimal subsidies for enterprises from exporting countries and the optimal import quarantine rate for importing countries. Based on the game between the two countries without the pandemic outbreak, the impact of the pandemic on the output, profits, and social welfare of enterprises in the two countries was compared. Enterprises in exporting countries face double threats from the pandemic and import quarantine fees. The increase in import quarantine fees reduces the social welfare of exporting countries. In order to effectively control the spread of the pandemic, subsidies are an effective means to restore exports to normal. Reasonable collection of import quarantine fees by importing countries can promote bilateral trade, but an excessive collection will be counterproductive. The governments of exporting countries should establish emergency mechanisms and relevant subsidy policies, and enterprises should continuously improve their competitiveness. At the same time, countries should abandon the concept of trade protection and negotiate and cooperate to jointly deal with the pandemic.

A switching dynamic model based on phased COVID-19 data in Chongqing and its evaluation.

ObjectivesAlthough COVID-19 has been controlled in China, the risk of invasion of imported cases remains. We aimed to characterize the impact of the number of imported cases and the implementation of first-level emergency response (FLER) policy.MethodsA SCQIHR switching model was constructed and verified by the complete phased data of COVID-19 in Chongqing in 2020. Then it was used to investigate the impact of the number of imported cases and the timing of FLER. Lastly, it was evaluated by three actual scenarios in Chongqing in 2021.ResultsThe proposed model can fit the multidimensional time series well. After the implementation of FLER, the mean effective reproduction number, contact rate and misdetection rate were decreased significantly, but the quarantine rate for close contacts and isolation rate for non-hospitalized infectious cases were increased significantly. The peaks of quarantined close contacts and hospitalized infectious cases increased linearly with the increase of the number of imported cases and the lag of FLER time, which was verified by three actual scenarios in Chongqing in 2021.ConclusionsThese findings can provide guidance for local public health policy-making and allocation of medical resources, reduce the impact of COVID-19 on the local population.

A spread model of COVID-19 with some strict anti-epidemic measures.

In the absence of specific drugs and vaccines, the best way to control the spread of COVID-19 is to adopt and diligently implement effective and strict anti-epidemic measures. In this paper, a mathematical spread model is proposed based on strict epidemic prevention measures and the known spreading characteristics of COVID-19. The equilibria (disease-free equilibrium and endemic equilibrium) and the basic regenerative number of the model are analyzed. In particular, we prove the asymptotic stability of the equilibria, including locally and globally asymptotic stability. In order to validate the effectiveness of this model, it is used to simulate the spread of COVID-19 in Hubei Province of China for a period of time. The model parameters are estimated by the real data related to COVID-19 in Hubei. To further verify the model effectiveness, it is employed to simulate the spread of COVID-19 in Hunan Province of China. The mean relative error serves to measure the effect of fitting and simulations. Simulation results show that the model can accurately describe the spread dynamics of COVID-19. Sensitivity analysis of the parameters is also done to provide the basis for formulating prevention and control measures. According to the sensitivity analysis and corresponding simulations, it is found that the most effective non-pharmaceutical intervention measures for controlling COVID-19 are to reduce the contact rate of the population and increase the quarantine rate of infected individuals.

A global report on the dynamics of COVID-19 with quarantine and hospitalization: A fractional order model with non-local kernel

In this paper, a compartmental mathematical model has been utilized to gain a better insight about the future dynamics of COVID-19. The total human population is divided into eight various compartments including susceptible, exposed, pre-asymptomatic, asymptomatic, symptomatic, quarantined, hospitalized and recovered or removed individuals. The problem was modeled in terms of highly nonlinear coupled system of classical order ordinary differential equations (ODEs) which was further generalized with the Atangana-Balaeanu (ABC) fractional derivative in Caputo sense with nonlocal kernel. Furthermore, some theoretical analyses have been done such as boundedness, positivity, existence and uniqueness of the considered. Disease-free and endemic equilibrium points were also assessed. The basic reproduction was calculated through next generation technique. Due to high risk of infection, in the present study, we have considered the reported cases from three continents namely Americas, Europe, and south-east Asia. The reported cases were considered between 1st May 2021 and 31st July 2021 and on the basis of this data, the spread of infection is predicted for the next 200 days. The graphical solution of the considered nonlinear fractional model was obtained via numerical scheme by implementing the MATLAB software. Based on the fitted values of parameters, the basic reproduction number ℜ0 for the case of America, Asia and Europe were calculated as ℜ0≈2.92819, ℜ0≈2.87970 and ℜ0≈2.23507 respectively. It is also observed that the spread of infection in America is comparatively high followed by Asia and Europe. Moreover, the effect of fractional parameter is shown on the dynamics of spread of infection among different classes. Additionally, the effect of quarantined and treatment of infected individuals is also shown graphically. From the present analysis it is observed that awareness of being quarantine and proper treatment can reduce the infection rate dramatically and a minimal variation in quarantine and treatment rates of infected individuals can lead us to decrease the rate of infection.

Quantitative Analysis of COVID-19 Pandemic Responses Based on an Improved SEIR-SD Model

In late 2019, the COVID-19 pandemic began to spread over the world, causing millions of deaths. In the first few months of the pandemic, several countries (such as China) prevented the spread of the pandemic successfully. By contrast, the pandemic in many other countries was not controlled well. For example, India encountered a second serious outbreak of COVID-19 from April 2021 due to the poor resistance measures implemented by the government. To figure out the effective countermeasures to the pandemic, this research proposes a COVID-19 pandemic and its response system, which consists of the infection subsystem, the quarantine subsystem, and the medical subsystem. On this basis, an improved SEIR-SD model is established which is utilized to analyze the response measures to the pandemic quantitatively. This model successfully simulates the actual epidemic scenarios in Wuhan, which verifies its effectiveness. Afterward, the impact of hospital administration rate, quarantine rate, average contact number, and contact infection rate on the cumulative number of infections and deaths are analyzed by simulation. The results show that both the medical and administrative efforts, especially in the early stage of the epidemic, are significant in reducing the number of infections and shortening the epidemic period. In the medical aspect, the more stringent quarantine brings the earlier inflection point of the epidemic; more importantly, improving the treatment rate significantly reduces the scale of the epidemic. In the administrative aspect, enforcing individual protection and strict community closure can effectively cut off the transmission of the virus and curb the spread of the epidemic. Finally, this research proposes several practical suggestions in response to the COVID-19 pandemic. The main contribution of this research is that the effects of different response measures on the number of new infections daily and the cumulative number of deaths of a country or region in the COVID-19 pandemic are estimated quantitatively based on modeling and simulation.

Vaccination and Quarantine Effect on COVID-19 Transmission Dynamics Incorporating Chinese-Spring-Festival Travel Rush: Modeling and Simulations

The COVID-19 pandemic has adversely affected the entire world. The effective implementation of vaccination strategy is critical to prevent the resurgence of the pandemic, especially during large-scale population migration. We establish a multiple patch coupled model based on the transportation network among the 31 provinces in China, under the combined strategies of vaccination and quarantine during large-scale population migration. Based on the model, we derive a critical quarantine rate to control the pandemic transmission and a vaccination rate to achieve herd immunity. Furthermore, we evaluate the influence of passenger flow on the effective reproduction number during the Chinese-Spring-Festival travel rush. Meanwhile, the spread of the COVID-19 pandemic is investigated for different control strategies, viz. global control and local control. The impact of vaccine-related parameters, such as the number, the effectiveness and the immunity period of vaccine, are explored. It is believed that the articulated models as well as the presented simulation results could be beneficial to design of feasible strategies for preventing COVID-19 transmission during the Chinese-Spring-Festival travel rush or the other future events involving large-scale population migration.

Country-specific optimization strategy for testing through contact tracing can help maintain a low reproduction number (R_{0}) during unlock

In response to the COVID19 pandemic, many countries have implemented lockdowns in multiple phases to ensure social distancing and quarantining of the infected subjects. Subsequent unlocks to reopen the economies started next waves of infection and imposed an extra burden on quarantine to keep the reproduction number (R_{0}) < 1. However, most countries could not effectively contain the infection spread, suggesting identification of the potential sources weakening the effect of lockdowns could help design better informed lockdown-unlock cycles in the future. Here, through building quantitative epidemic models and analyzing the metadata of 50 countries from across the continents we first found that the estimated value of R_{0}, adjusted w.r.t the distribution of medical facilities and virus clades correlates strongly with the testing rates in a country. Since the testing capacity of a country is limited by its medical resources, we investigated if a cost–benefit trade-off can be designed connecting testing rate and extent of unlocking. We present a strategy to optimize this trade-off in a country specific manner by providing a quantitative estimate of testing and quarantine rates required to allow different extents of unlocks while aiming to maintain R_{0} < 1. We further show that a small fraction of superspreaders can dramatically increase the number of infected individuals even during strict lockdowns by strengthening the positive feedback loop driving infection spread. Harnessing the benefit of optimized country-specific testing rates would critically require minimizing the movement of these superspreaders via strict social distancing norms, such that the positive feedback driven switch-like exponential spread phase of infection can be avoided/delayed.

THE DYNAMIC EFFECTS OF DIFFERENT QUARANTINE MEASURES ON THE SPREAD OF COVID-19

<abstract> COVID-19 is pandemic worldwide, and different countries have adopted different measures to stop the spread of the epidemic. In order to study the impact of quarantining close contacts on the spread of coronavirus disease 2019 (COVID-19), based on data published by Beijing Municipal Health Commission, World Health Organization (WHO) and Korea Central Disaster Control Headquarters (KCDC), SEIR dynamic models of virus transmission in Beijing and South Korea were set up respectively; the Genetic algorithm was used to fit the important parameters such as transmission rate, recovery rate and quarantine rate; calculated the control reproduction number; we discuss the impact of quarantining close contacts on daily new cases in South Korea, the daily new cases decrease after a week, and drop to 16.93 after 30 days. When close contacts were quarantined, the maximum value of daily new cases $ I_{max}=57.4 $ obtained by simulation is only 13% of the actual maximum value actual $ I_{max}=441 $; the influences of different quarantine rates and the number of the susceptible on the number of daily new cases are also discussed, the quarantine of close contacts has significant effect on reducing the number of daily new cases compared with less stringent control measures. Vigorous control measures reduce the number of daily new cases to single digits in just 17 days in Beijing, effectively curbing the transmission of COVID-19. It has vital significance for the prevention and control of the epidemic in other countries and regions. </abstract>

A nonstandard finite difference scheme for the SVICDR model to predict COVID-19 dynamics.

In the context of 2019 coronavirus disease (COVID-19), considerable attention has been paid to mathematical models for predicting country- or region-specific future pandemic developments. In this work, we developed an SVICDR model that includes a susceptible, an all-or-nothing vaccinated, an infected, an intensive care, a deceased, and a recovered compartment. It is based on the susceptible-infectious-recovered (SIR) model of Kermack and McKendrick, which is based on ordinary differential equations (ODEs). The main objective is to show the impact of parameter boundary modifications on the predicted incidence rate, taking into account recent data on Germany in the pandemic, an exponential increasing vaccination rate in the considered time window and trigonometric contact and quarantine rate functions. For the numerical solution of the ODE systems a model-specific non-standard finite difference (NSFD) scheme is designed, that preserves the positivity of solutions and yields the correct asymptotic behaviour.

Mathematical modeling of mutated COVID-19 transmission with quarantine, isolation and vaccination.

Multiple variants of SARS-CoV-2 have emerged but the effectiveness of existing COVID-19 vaccines against variants has been reduced, which bring new challenges to the control and mitigation of the COVID-19 pandemic. In this paper, a mathematical model for mutated COVID-19 with quarantine, isolation and vaccination is developed for studying current pandemic transmission. The basic reproduction number $ \mathscr{R}_{0} $ is obtained. It is proved that the disease free equilibrium is globally asymptotically stable if $ \mathscr{R}_{0} < 1 $ and unstable if $ \mathscr{R}_{0} > 1 $. And numerical simulations are carried out to illustrate our main results. The COVID-19 pandemic mainly caused by Delta variant in South Korea is analyzed by using this model and the unknown parameters are estimated by fitting to real data. The epidemic situation is predicted, and the prediction result is basically consistent with the actual data. Finally, we investigate several critical model parameters to access the impact of quarantine and vaccination on the control of COVID-19, including quarantine rate, quarantine effectiveness, vaccination rate, vaccine efficacy and rate of immunity loss.

Dynamics of COVID-19 Epidemic Model with Asymptomatic Infection, Quarantine, Protection and Vaccination

We discuss the dynamics of new COVID-19 epidemic model by considering asymptomatic infections and the policies such as quarantine, protection (adherence to health protocols), and vaccination. The proposed model contains nine subpopulations: susceptible (S), exposed (E), symptomatic infected (I), asymptomatic infected (A), recovered (R), death (D), protected (P), quarantined (Q), and vaccinated (V ). We first show the non-negativity and boundedness of solutions. The equilibrium points, basic reproduction number, and stability of equilibrium points, both locally and globally, are also investigated analytically. The proposed model has disease-free equilibrium point and endemic equilibrium point. The disease-free equilibrium point always exists and is globally asymptotically stable if basic reproduction number is less than one. The endemic equilibrium point exists uniquely and is globally asymptotically stable if the basic reproduction number is greater than one. These properties have been confirmed by numerical simulations using the fourth order Runge-Kutta method. Numerical simulations show that the disease transmission rate of asymptomatic infection, quarantine rates, protection rate, and vaccination rates affect the basic reproduction number and hence also influence the stability of equilibrium points.

Mathematical modeling of the effect of quarantine rate on controlling the infection of COVID19 in the population of Saudi Arabia

With the development of communications and transportation worldwide, the challenge of controlling epidemiological diseases becomes higher. The COVID19 has put all nations in a lethal confront with a severe disease that needed serious and painful actions. The sooner the actions, the less destructive the impact. In this paper, we incorporate what we believe is crucial but applicable to control the spread of COVID19 in the populations, that is, quarantine. We keep the model as simple as SI Kermack-McKendrick model with an additional compartment of quarantined patients. We established the system’s basic properties and studied the stability of the disease-free equilibrium and its relation to the basic reproduction number R0 in which we calculated its formula. The focus of our study is to measure the effect of quarantine rate on controlling the spread of COVID19. We use the data collected from the Ministry of Health in Saudi Arabia. We studied three different values of the quarantine rate where newly infectious patients are detected and isolated within 14, 7, and 5 days. The simulations show a significant effect of the quarantine where COVID19 can be fully controlled if the newly infected patient enters the quarantine within five days. These results were proposed to the Public Health Authority in Saudi Arabia and approved by the Ministry of Health in which they applied promptly.

Global sensitivity analysis in epidemiological modeling

Operations researchers worldwide rely extensively on quantitative simulations to model alternative aspects of the COVID-19 pandemic. Proper uncertainty quantification and sensitivity analysis are fundamental to enrich the modeling process and communicate correctly informed insights to decision-makers. We develop a methodology to obtain insights on key uncertainty drivers, trend analysis and interaction quantification through an innovative combination of probabilistic sensitivity techniques and machine learning tools. We illustrate the approach by applying it to a representative of the family of susceptible-infectious-recovered (SIR) models recently used in the context of the COVID-19 pandemic. We focus on data of the early pandemic progression in Italy and the United States (the U.S.). We perform the analysis for both cases of correlated and uncorrelated inputs. Results show that quarantine rate and intervention time are the key uncertainty drivers, have opposite effects on the number of total infected individuals and are involved in the most relevant interactions.

Spread and control of COVID-19: A mathematical model

A mathematical model is proposed in this paper to study the transmission and control of COVID-19. The mathematical model is formulated using system of nonlinear ordinary differential equations. The model includes disease-related parameters such as contact rate, disease-induced death rates, immigration rate and transition rates along with parameters for control measures such as implementation of social distancing practices, isolation and quarantine rates. From the stability analysis of the model, it is shown that if the social distancing is practiced by the large number of susceptible population, then the disease will not spread, and it may eventually die out. Further, it is derived from the analysis of the model that if most of the infected populations are isolated or quarantined, then the spread of the disease can be eventually controlled. However, from the analysis of the model, it is observed that if there is constant immigration of asymptomatic infected persons, then the disease will continue to spread and will remain pandemic. For controlling the disease, two more parameters, that is, vaccination and testing rates, are introduced in the original mathematical model and from the numerical analysis of this model, it has been shown that the control strategy involving vaccination and testing in combination can have synergistic effect for minimizing the COVID-19 infected cases.

COVID-19 in corrections: Quarantine of incarcerated people.

Carceral settings in the United States have been the source of many single site COVID-19 outbreaks. Quarantine is a strategy used to mitigate the spread of COVID-19 in correctional settings, and specific quarantine practices differ state to state. To better understand how states are using quarantine in prisons, we reviewed each state’s definition of quarantine and compared each state’s definition to the Centers for Disease Control’s (CDC) definition and recommendations for quarantine in jails and prisons. Most prison systems, 45 of 53, define quarantine, but definitions vary widely. No state published definitions of quarantine that align with all CDC recommendations, and only 9 states provide quarantine data. In these states, the highest recorded quarantine rate occurred in Ohio in May 2020 at 843 per 1,000. It is necessary for prison systems to standardize their definitions of quarantine and to utilize quarantine practices in accordance with CDC recommendations. In addition, data transparency is needed to better understand the use of quarantine and its effectiveness at mitigating COVID-19 outbreaks in carceral settings.

Mathematical modeling of COVID-19 in India and Nepal with optimal control and sensitivity analysis.

The pandemic started in the late 2019 and is still waving in claiming millions of lives with virus being mutated to deadlier form. This pandemic has caught attention toward interventions like improved detection of the infected, better quarantine facilities and adequate medical facilities in terms of hospital beds and other medical aid. In this study, we developed a 7-compartment epidemiological model, with inclusion of identified and unidentified infected population along with media factor associated with the aware identified infected population. This is included by using Holling function in the nonlinear incidence, that is responsible for reduction in infection rate via identified infected. The model is fitted to the observed active COVID-19 cases data, collected for a period of 11 months between July 2020 to May 2021 of Nepal and India, and the infection rate as well as the basic reproduction number is obtained for the first wave and second wave of the pandemic in both countries. A comparative analysis on the effect of different parameters on the disease prevalence for both the countries is presented in this work. Sensitivity analysis, time series behavior and optimal control analysis with control parameters equating with reduced infection rate, enhanced detection rate, improved quarantine and hospitalization rate are presented in detail. By means of PRCC, sensitivity analysis is performed and the key parameters influencing the disease prevalence are identified. A detailed study on impact of several parameters in the COVID-19 prevalence, thereby suggesting the interventions to be implemented is discussed in the work. Predictions till June 30, 2021, are obtained using the second wave data for both the countries, and a declining trend is observed for both the countries for the next 30 to 40 days. The estimated values of the infection rates and the hospitalization rates obtained are higher for India compared to Nepal. An optimal control analysis for both the countries is described in detail providing the difference in infectives and recoveries with and without any controls or interventions. The study suggests that improved treatment facilities, testing drives and other non-pharmaceutical interventions would bring down the infected cases to a major extent.

Differential impacts of contact tracing and lockdowns on outbreak size in COVID-19 model applied to China

The COVID-19 pandemic has led to widespread attention given to the notions of “flattening the curve” during lockdowns, and successful contact tracing programs suppressing outbreaks. However a more nuanced picture of these interventions’ effects on epidemic trajectories is necessary. By mathematical modeling each as reactive quarantine measures, dependent on current infection rates, with different mechanisms of action, we analytically derive distinct nonlinear effects of these interventions on final and peak outbreak size. We simultaneously fit the model to provincial reported case and aggregated quarantined contact data from China. Lockdowns compressed the outbreak in China inversely proportional to population quarantine rates, revealing their critical dependence on timing. Contact tracing had significantly less impact on final outbreak size, but did lead to peak size reduction. Our analysis suggests that altering the cumulative cases in a rapidly spreading outbreak requires sustained interventions that decrease the reproduction number close to one, otherwise some type of swift lockdown measure may be needed.

1277Reasons for quarantine and positivity rate among quarantined Health Care Workers (HCWs) in North India

Abstract Background WHO has declared the COVID-19 as Pandemic on 11th March, 2020. It is important to break the chain of transmission by quarantining the persons with high-risk exposure. Understanding the reasons for quarantine will help in reducing the exposures and thus reducing the chances of quarantine. Methods A validated risk assessment tool based on National Centre for Disease Control guidelines was used for the risk assessment of HCWs. The forms of HCWs who underwent risk assessment between April-November, 2020 were analyzed for reasons of quarantine. The positivity rates among high-risk and low-risk groups were compared. Results Out of 1414 HCWs who were assessed, 345 were categorized as high-risk exposure and were quarantined. The most common reasons for quarantine were performance of aerosol generating procedure without recommended personal protection equipment (PPE) (34%), exposure to COVID-19 positive patient without mask for more than 20 minutes at the distance less than 1 m (30%) and having food/tea together (27%). The positivity rate was 8.4% among high-risk and 1.9% among low-risk exposure group (p-value: &amp;lt;0.001). The positivity among low risk category was more in the second half (19/466; 4.1%) as compared to first half (1/603; 0.2%) of the study period. This might be due to exposure from non-hospital sources as second half coincides with first wave of the pandemic. Conclusion Not using recommended PPE and having tea/food breaks together were the most common reasons for quarantine. Key messages Strict enforcement of recommended PPE and scattered tea and food breaks can reduce high-risk exposures.

Effect of Arrival Quarantine on Subsequent COVID-19 Testing in a Cohort of Military Basic Trainees.

Basic Military Training at Joint Base San Antonio-Lackland implemented several sequential non-pharmaceutical interventions in response to coronavirus disease-2019 (COVID-19). One measure, arrival quarantine, has not been studied as a modern military disease prevention strategy. This study aimed to determine the effect of a 14-day arrival quarantine on symptomatic COVID-19 testing. A retrospective cohort study compared symptomatic COVID-19 testing among all trainees who entered Basic Military Training between March 17, 2020, and April 17, 2020, before the implementation of universal arrival COVID-19 testing, during their first 2 weeks in arrival quarantine compared to the rest of their training. Furthermore, symptomatic COVID-19 testing in the last 5 weeks of training in those who completed arrival quarantine was compared to testing in the last 5 weeks for trainees who arrived between February 16, 2020, and March 16, 2020, and did not undergo arrival quarantine. Nominal variables were compared by chi-square test, and continuous variables were compared by Mann-Whitney U test. This study was approved as a public health surveillance project by the 59th Medical Wing Institutional Review Board. Five thousand five hundred and seventy-six trainees started training between February 16, 2020, and April 17, 2020, with 2,573 trainees undergoing an arrival quarantine compared to 3,003 trainees who did not. Trainees who underwent arrival quarantine had higher rates of COVID-19 testing while in arrival quarantine (10.5 tests per 1,000 trainee-weeks vs. 2.3, P ≤ .001) and higher rates of concomitant influenza testing (74% vs. 38%, P = .001) compared to after they completed quarantine. Trainees that completed quarantine had less symptomatic COVID-19 testing after day 14 of training (2.3 tests per 1,000 trainee-weeks vs. 14.3, P ≤ .001) and influenza testing (38% vs. 74%, P = .001) compared to trainees that did not undergo arrival quarantine. Arrival quarantine appears to be an effective non-pharmaceutical intervention associated with fewer symptomatic COVID-19 tests, especially after completion of quarantine.

Producing Masks Amid COVID-19? How A Pandemic Imprint Affects Firm's New Medical Production

We examine how corporate leaders’ prior pandemic experience influences their response to a later and similar pandemic. Drawing on imprinting theory, we argue that corporate leaders that experienced Severe Acute Respiratory Syndrome (SARS) are more likely to be aware of the severity of a new and similar infectious disease (i.e., COVID-19) and so are quicker to respond to COVID-19 by producing personal protective equipment (PPE). We theorize that this imprinting effect can vary in intensity and so the effect of the pandemic imprint is stronger if the corporate leader experienced the SARS during a more susceptible life stage (i.e., early adulthood). Furthermore, a series of environment-triggered processes will also shape the imprinting intensity. Specifically, population mobility from the epicenter and local COVID-19 spread tend to strengthen the effect of a SARS imprint, whereas local quarantine rate and the abundance of local medical resources likely weaken the effect of a SARS imprint. Using a large sample of publicly traded firms in China, we find evidence that mostly supports our theory. Our study contributes to imprinting theory by highlighting how imprinting intensity can vary depending on individual-level susceptibility and environment-level triggered processes. We also contribute to literature on organizational responses to public crises and provide fresh insights on corporate diversification decisions.