The effect of control measures on COVID-19 transmission in South Korea

Countries around the world have taken control measures to mitigate the spread of COVID-19, including Korea. Social distancing is considered an essential strategy to reduce transmission in the absence of vaccination or treatment. While interventions have been successful in controlling COVID-19 in Korea, maintaining the current restrictions incurs great social costs. Thus, it is important to analyze the impact of different polices on the spread of the epidemic. To model the COVID-19 outbreak, we use an extended age-structured SEIR model with quarantine and isolation compartments. The model is calibrated to age-specific cumulative confirmed cases provided by the Korea Disease Control and Prevention Agency (KDCA). Four control measures—school closure, social distancing, quarantine, and isolation—are investigated. Because the infectiousness of the exposed has been controversial, we study two major scenarios, considering contributions to infection of the exposed, the quarantined, and the isolated. Assuming the transmission rate would increase more than 1.7 times after the end of social distancing, a second outbreak is expected in the first scenario. The epidemic threshold for increase of contacts between teenagers after school reopening is 3.3 times, which brings the net reproduction number to 1. The threshold values are higher in the second scenario. If the average time taken until isolation and quarantine reduces from three days to two, cumulative cases are reduced by 60% and 47% in the first scenario, respectively. Meanwhile, the reduction is 33% and 41%, respectively, for rapid isolation and quarantine in the second scenario. Without social distancing, a second wave is possible, irrespective of whether we assume risk of infection by the exposed. In the non-infectivity of the exposed scenario, early detection and isolation are significantly more effective than quarantine. Furthermore, quarantining the exposed is as important as isolating the infectious when we assume that the exposed also contribute to infection.

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The effect of non-pharmaceutical interventions on COVID-19 cases, deaths and demand for hospital services in the UK: a modelling study

10.1101/2020.04.01.20049908 ◽

2020 ◽

Cited By ~ 52

Author(s):

Nicholas G. Davies ◽

Adam J. Kucharski ◽

Rosalind M. Eggo ◽

Amy Gimma ◽

W. John Edmunds ◽

...

Keyword(s):

Intensive Care ◽

Healthcare System ◽

Control Measures ◽

Transmission Model ◽

School Closures ◽

Social Distancing ◽

Age Structured ◽

The Uk ◽

The Impact ◽

Pharmaceutical Interventions

AbstractBackgroundNon-pharmaceutical interventions have been implemented to reduce transmission of SARS-CoV-2 in the UK. Projecting the size of an unmitigated epidemic and the potential effect of different control measures has been critical to support evidence-based policymaking during the early stages of the epidemic.MethodsWe used a stochastic age-structured transmission model to explore a range of intervention scenarios, including the introduction of school closures, social distancing, shielding of elderly groups, self-isolation of symptomatic cases, and extreme “lockdown”-type restrictions. We simulated different durations of interventions and triggers for introduction, as well as combinations of interventions. For each scenario, we projected estimated new cases over time, patients requiring inpatient and critical care (intensive care unit, ICU) treatment, and deaths.FindingsWe found that mitigation measures aimed at reducing transmission would likely have decreased the reproduction number, but not sufficiently to prevent ICU demand from exceeding NHS availability. To keep ICU bed demand below capacity in the model, more extreme restrictions were necessary. In a scenario where “lockdown”-type interventions were put in place to reduce transmission, these interventions would need to be in place for a large proportion of the coming year in order to prevent healthcare demand exceeding availability.InterpretationThe characteristics of SARS-CoV-2 mean that extreme measures are likely required to bring the epidemic under control and to prevent very large numbers of deaths and an excess of demand on hospital beds, especially those in ICUs.Research in ContextEvidence before this studyAs countries have moved from early containment efforts to planning for the introduction of large-scale non-pharmaceutical interventions to control COVID-19 outbreaks, epidemic modelling studies have explored the potential for extensive social distancing measures to curb transmission. However, it remains unclear how different combinations of interventions, timings, and triggers for the introduction and lifting of control measures may affect the impact of the epidemic on health services, and what the range of uncertainty associated with these estimates would be.Added value of this studyUsing a stochastic, age-structured epidemic model, we explored how eight different intervention scenarios could influence the number of new cases and deaths, as well as intensive care beds required over the projected course of the epidemic. We also assessed the potential impact of local versus national targeting of interventions, reduction in leisure events, impact of increased childcare by grandparents, and timing of triggers for different control measures. We simulated multiple realisations for each scenario to reflect uncertainty in possible epidemic trajectories.Implications of all the available evidenceOur results support early modelling findings, and subsequent empirical observations, that in the absence of control measures, a COVID-19 epidemic could quickly overwhelm a healthcare system. We found that even a combination of moderate interventions – such as school closures, shielding of older groups and self-isolation – would be unlikely to prevent an epidemic that would far exceed available ICU capacity in the UK. Intermittent periods of more intensive lockdown-type measures are predicted to be effective for preventing the healthcare system from being overwhelmed.

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Age-Structured Model for COVID-19: Effectiveness of Social Distancing and Contact Reduction

10.21203/rs.3.rs-33480/v1 ◽

2020 ◽

Author(s):

Mark Kimathi ◽

Samuel Mwalili ◽

Viona Ojiambo ◽

Duncan Gathungu

Keyword(s):

Deterministic Model ◽

Age Groups ◽

School Closure ◽

Model Parameters ◽

Suitable Model ◽

Age Group ◽

Social Distancing ◽

First Case ◽

Age Structured ◽

The Impact

Abstract Background: Coronavirus disease 2019 is caused by severe acute respiratory syndrome coronavirus 2. The disease has spread to almost every country in the world. Kenya reported its first case on 13th of March 2020. From 16th March 2020, the country instituted various social distancing strategies to reduce the transmission and flatten the epidemic curve. These strategies include school closure, dusk-to-dawn curfew, and travel restriction across counties, especially Nairobi, Mombasa and Kwale. An age-structured compartmental model was developed to assess the impact of non-pharmaceutical interventions on severity of infections, hospital demands and deaths. Methods: The population is divided into four age-groups and for each age-group there are seven compartments, namely: susceptible , exposed, asymptomatic, mild, severe, critical, death and recovered. The contact matrices between the different ages are integrated into an age-structured deterministic model via the force of infection. This model is represented by ordinary differential equations and solved using Runge–Kutta methods, with suitable model parameters. Simulation results for the unmitigated and mitigated scenarios were depicted, for the different age-groups. Results: The 45% reduction in contacts for 60-days period resulted to between 11.5-13% reduction of infections severity and deaths, while for the 190-days period yielded between 18.8-22.7% reduction. The peak of infections in the 60-days mitigation was higher and happened about 2 months after the relaxation of mitigation as compared to that of the 190-days mitigation, which happened just a month after mitigation were relaxed. Low numbers of cases in children under 15 years was attributed to low susceptibility of persons in this age-group. High numbers of cases are reported in the 15-29 years and 30-59 years age bands since these individuals have wider interaction spheres, and they form a significant percentage of Kenya population. Conclusion: Two mitigation periods, considered in the study, resulted to reductions in severe and critical cases, attack rates, hospital and ICU bed demands, as well as deaths, with the 190-days period giving higher reductions. The study revealed the age-dependency of the key health outputs.

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A descriptive study of the impact of diseases control and prevention on the epidemics dynamics and clinical features of SARS-CoV-2 outbreak in Shanghai, lessons learned for metropolis epidemics prevention

10.1101/2020.02.19.20025031 ◽

2020 ◽

Cited By ~ 20

Author(s):

Hongzhou Lu ◽

Jingwen Ai ◽

Yinzhong Shen ◽

Yang Li ◽

Tao Li ◽

...

Keyword(s):

Clinical Features ◽

Transmission Rate ◽

Early Stage ◽

Disease Onset ◽

Descriptive Study ◽

Lessons Learned ◽

Control Measures ◽

Imported Cases ◽

Control And Prevention ◽

The Impact

AbstractObjectiveTo describe and evaluate the impact of diseases control and prevention on epidemics dynamics and clinical features of SARS-CoV-2 outbreak in Shanghai.DesignA retrospective descriptive studySettingChinaParticipantsEpidemiology information was collected from publicly accessible database. 265 patients admitted to Shanghai Public Health Center with confirmed COVID-19 were enrolled for clinical features analysis.Main outcome measurePrevention and control measures taken by Shanghai government, epidemiological, demographic, clinical, laboratory and radiology data were collected. Weibull distribution, Chi-square test, Fisher’s exact test, t test or Mann-Whitney U test were used in statistical analysis.ResultsCOVID-19 transmission rate within Shanghai had reduced over 99% than previous speculated, and the exponential growth has been stopped so far. Epidemic was characterized by the first stage mainly composed of imported cases and the second stage where >50% of cases were local. The incubation period was 6.4 (95% CI 5.3 to 7.6) days and the mean onset-admission interval was 5.5 days (95% CI, 5.1 to 5.9). Median time for COVID-19 progressed to severe diseases were 8.5 days (IQR: 4.8-11.0 days). By February 11th, proportion of patients being mild, moderate, severe and critically ill were 1.9%(5/265), 89.8%(238/265), 3.8%(10/265), 4.5%(12/265), respectively; 47 people in our cohort were discharged, and 1 patient died.ConclusionStrict controlling of the transmission rate at the early stage of an epidemic in metropolis can quickly prohibit the spread of the diseases. Controlling local clusters is the key to prevent outbreaks from imported cases. Most COVID-19 severe cases progressed within 14 days of disease onset. Multiple systemic laboratory abnormalities had been observed before significant respiratory dysfunction.

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Comparative Analysis of COVID-19 Transmission Patterns in Three Chinese Regions vs. South Korea, Italy and Iran

10.1101/2020.04.09.20053223 ◽

2020 ◽

Cited By ~ 3

Author(s):

Junyu He ◽

Guangwei Chen ◽

Yutong Jiang ◽

Runjie Jin ◽

Mingjun He ◽

...

Keyword(s):

Public Health ◽

Comparative Analysis ◽

South Korea ◽

Transmission Rate ◽

Control Measures ◽

Disease Spread ◽

Normal Distribution Function ◽

General Validity ◽

Social Distancing ◽

The Impact

AbstractBackgroundThe outbreak of Coronavirus 2019 (COVID-19) began in January 2020 in the city of Wuhan (Hubei province, China). It took about 2 months for China to get this infectious disease under control in its epicenter at Wuhan. Since February 2020, COVID-19 has been spreading around the world, becoming widespread in a number of countries. The timing and nature of government actions in response to the pandemic has varied from country to country, and their role in affecting the spread of the disease has been debated.MethodThe present study proposed a modified susceptible-exposed-infected-removed model (SEIR) model to perform a comparative analysis of the temporal progress of disease spread in six regions worldwide: three Chinese regions (Zhejiang, Guangdong and Xinjiang) vs. three countries (South Korea, Italy and Iran). For each region we developed detailed timelines of reported infections and outcomes, along with government- implemented measures to enforce social distancing. Simulations of the imposition of strong social distancing measures were used to evaluate the impact that these measures might have had on the duration and severity of COVID-19 outbreaks in the three countries.ResultsThe main results of this study are as follows: (a) an empirical COVID-19 growth law provides an excellent fit to the disease data in all study regions and potentially could be of more general validity; (b) significant differences exist in the spread characteristics of the disease among the three regions of China and between the three regions of China and the three countries; (c) under the control measures implemented in the Chinese regions (including the immediate quarantine of infected patients and their close contacts, and considerable restrictions on social contacts), the transmission rate of COVID-19 followed a modified normal distribution function, and it reached its peak after 1 to 2 days and then was reduced to zero 11, 11 and 18 days after a 1st-Level Response to Major Public Health Emergency was declared in Zhejiang, Guangdong and Xinjiang, respectively; moreover, the epidemic control times in Zhejiang, Guangdong and Xinjiang showed that the epidemic reached an “inflection point” after 9, 12 and 17 days, respectively, after a 1st-Level Response was issued; (d) an empirical COVID-19 law provided an excellent fit to the disease data in the six study regions, and the law can be potentially of more general validity; and (e) the curves of infected cases in South Korea, Italy and Iran would had been significantly flattened and shrunken at a relatively earlier stage of the epidemic if similar preventive measures as in the Chinese regions had been also taken in the above three countries on February 25th, February 25th and March 8th, respectively: the simulated maximum number of infected individuals in South Korea, Italy and Iran would had been 4480 cases (March 9th, 2020), 2335 cases (March 10th) and 6969 cases (March 20th), instead of the actual (reported) numbers of 7212 cases (March 9th), 8514 cases (March 10th, 2020) and 11466 cases (March 20th), respectively; moreover, up to March 29th, the simulated reduction in the accumulated number of infected cases would be 1585 for South Korea, 93490 for Italy and 23213 for Iran, respectively, accounting for 16.41% (South Korea), 95.70% (Italy) and 60.59% (Iran) of the accumulated number of actual reported infected cases.ConclusionsThe implemented measures in China were very effective for controlling the spread of COVID-19. These measures should be taken as early as possible, including the early identification of all infection sources and eliminating transmission pathways. Subsequently, the number of infected cases can be controlled at a low level, and existing medical resources could be sufficient for maintaining higher cure rates and lower mortality rate compared to the current situations in these countries. The proposed model can account for these prevention and control measures by properly adjusting its parameters, it computes the corresponding variations in disease transmission rate during the outbreak period, and it can provide valuable information for public health decision- making purposes.

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An adaptive social distancing SIR model for COVID-19 disease spreading and forecasting

Epidemiologic Methods ◽

10.1515/em-2020-0044 ◽

2021 ◽

Vol 10 (s1) ◽

Author(s):

Said Gounane ◽

Yassir Barkouch ◽

Abdelghafour Atlas ◽

Mostafa Bendahmane ◽

Fahd Karami ◽

...

Keyword(s):

Mathematical Theory ◽

Real Data ◽

Sir Model ◽

Epidemic Threshold ◽

Social Distancing ◽

Sir Epidemic ◽

Proposed Model ◽

Disease Spreading ◽

The Government ◽

The Impact

Abstract Recently, various mathematical models have been proposed to model COVID-19 outbreak. These models are an effective tool to study the mechanisms of coronavirus spreading and to predict the future course of COVID-19 disease. They are also used to evaluate strategies to control this pandemic. Generally, SIR compartmental models are appropriate for understanding and predicting the dynamics of infectious diseases like COVID-19. The classical SIR model is initially introduced by Kermack and McKendrick (cf. (Anderson, R. M. 1991. “Discussion: the Kermack–McKendrick Epidemic Threshold Theorem.” Bulletin of Mathematical Biology 53 (1): 3–32; Kermack, W. O., and A. G. McKendrick. 1927. “A Contribution to the Mathematical Theory of Epidemics.” Proceedings of the Royal Society 115 (772): 700–21)) to describe the evolution of the susceptible, infected and recovered compartment. Focused on the impact of public policies designed to contain this pandemic, we develop a new nonlinear SIR epidemic problem modeling the spreading of coronavirus under the effect of a social distancing induced by the government measures to stop coronavirus spreading. To find the parameters adopted for each country (for e.g. Germany, Spain, Italy, France, Algeria and Morocco) we fit the proposed model with respect to the actual real data. We also evaluate the government measures in each country with respect to the evolution of the pandemic. Our numerical simulations can be used to provide an effective tool for predicting the spread of the disease.

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COVID-19 in South Korea: Proper timing for Easing Mask mandates after COVID-19 Vaccination

Disaster Medicine and Public Health Preparedness ◽

10.1017/dmp.2021.258 ◽

2021 ◽

pp. 1-9

Author(s):

Yun-Jung Kang

Keyword(s):

South Korea ◽

Disease Control ◽

Prevention And Control ◽

Control Measures ◽

Social Distancing ◽

Proper Timing ◽

Control And Prevention ◽

Prevention And Control Measures ◽

And Control

Abstract As of 25 July 2021, the Korea Disease Control and Prevention Agency reported 1,422 new COVID-19 cases, 188,848 total cases, and 2.073 total deaths (1.10% fatality rates). Since the first SARS-CoV-2 case was reported, efforts to find a treatment and vaccine against COVID-19 have been widespread. Four vaccines are on the WHO’s emergency use listing and are approved of their usage; BNT162b2, mRNA-1273, AZD1222, and Ad26.COV2.S. Vaccines against SARS-CoV-2 need at least 14 days to achieve effectiveness. Thus, people should abide by prevention and control measures, including wearing masks, washing hands, and social distancing. However, a lot of new cases were reported after vaccinations, as many people did not follow the prevention control measures before the end of the 14 days period. There is no doubt we need to break free from mask mandates. But let us not decide the timing in haste. Even if the mask mandates are eased, they should be changed depending on the number of reported cases, vaccinations, as well as prevention and control measures on how circumstances are changing under the influence of mutant coronavirus.

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Modeling the impact of school reopening and contact tracing strategies on Covid-19 dynamics in different epidemiologic settings in Brazil

10.1101/2021.10.22.21264706 ◽

2021 ◽

Author(s):

Marcelo Eduardo Borges ◽

Leonardo Souto Ferreira ◽

Silas Poloni ◽

Ângela Maria Bagattini ◽

Caroline Franco ◽

...

Keyword(s):

Disease Transmission ◽

Transmission Rate ◽

Disease Incidence ◽

Demographic Data ◽

School Closure ◽

Mitigation Measures ◽

Contact Tracing ◽

School Closures ◽

Infection Transmission ◽

The Impact

Among the various non–pharmaceutical interventions implemented in response to the Covid–19 pandemic during 2020, school closures have been in place in several countries to reduce infection transmission. Nonetheless, the significant short and long–term impacts of prolonged suspension of in–person classes is a major concern. There is still considerable debate around the best timing for school closure and reopening, its impact on the dynamics of disease transmission, and its effectiveness when considered in association with other mitigation measures. Despite the erratic implementation of mitigation measures in Brazil, school closures were among the first measures taken early in the pandemic in most of the 27 states in the country. Further, Brazil delayed the reopening of schools and stands among the countries in which schools remained closed for the most prolonged period in 2020. To assess the impact of school reopening and the effect of contact tracing strategies in rates of Covid–19 cases and deaths, we model the epidemiological dynamics of disease transmission in 3 large urban centers in Brazil under different epidemiological contexts. We implement an extended SEIR model stratified by age and considering contact networks in different settings – school, home, work, and elsewhere, in which the infection transmission rate is affected by various intervention measures. After fitting epidemiological and demographic data, we simulate scenarios with increasing school transmission due to school reopening. Our model shows that reopening schools results in a non–linear increase of reported Covid-19 cases and deaths, which is highly dependent on infection and disease incidence at the time of reopening. While low rates of within[&ndash]school transmission resulted in small effects on disease incidence (cases/100,000 pop), intermediate or high rates can severely impact disease trends resulting in escalating rates of new cases even if other interventions remain unchanged. When contact tracing and quarantining are restricted to school and home settings, a large number of daily tests is required to produce significant effects of reducing the total number of hospitalizations and deaths. Our results suggest that policymakers should carefully consider the epidemiological context and timing regarding the implementation of school closure and return of in-person school activities. Also, although contact tracing strategies are essential to prevent new infections and outbreaks within school environments, our data suggest that they are alone not sufficient to avoid significant impacts on community transmission in the context of school reopening in settings with high and sustained transmission rates.

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How did Autistic Children, and their Parents, Experience School Transition during the COVID-19 Pandemic?

10.31234/osf.io/8kzsn ◽

2021 ◽

Author(s):

Aimee Code ◽

Umar Toseeb ◽

Kathryn Asbury ◽

Laura Fox

Keyword(s):

School Transition ◽

School Closure ◽

School Transitions ◽

Autistic Children ◽

Structured Interviews ◽

School Closures ◽

Social Distancing ◽

New School ◽

The Uk ◽

The Impact

Due to the COVID-19 pandemic and resultant school closures, social distancing measures, and restrictions placed on routine activities, the start of the academic year in September 2020 was a unique time for those transitioning to a new school. This study aimed to explore the experiences of parents who supported autistic children making a school transition in 2020, and to examine what impact parents perceived the COVID-19 pandemic had on their child’s school transition. Emphasis was placed on identifying facilitating factors that had benefitted school transitions, and barriers, which had negatively impacted these experiences. Semi-structured interviews were carried out with 13 parents of autistic children in the UK. Reflexive thematic analysis was carried out to identify themes in interview data. Parents reported a variety of experiences, and factors that were perceived as facilitatory to some were observed to be barriers by others. For some parents, the COVID-19 pandemic negatively impacted aspects of school transitions. For example, school closure in March 2020, being unable to visit their child’s new school, and social distancing measures were discussed as being barriers to an easy transition. However, other parents identified these factors as being facilitatory for their child or reported that these circumstances created opportunities to approach the school transition in a unique, improved manner. This paper sheds light on the heterogeneity of experiences and perceptions of parents of autistic children, and highlights the need to examine the impact of COVID-19 on school transitions, including practices which may be advantageous to retain.

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Changes in contact patterns shape the dynamics of the COVID-19 outbreak in China

Science ◽

10.1126/science.abb8001 ◽

2020 ◽

Vol 368 (6498) ◽

pp. 1481-1486 ◽

Cited By ~ 202

Author(s):

Juanjuan Zhang ◽

Maria Litvinova ◽

Yuxia Liang ◽

Yan Wang ◽

Wei Wang ◽

...

Keyword(s):

Confidence Interval ◽

Odds Ratio ◽

School Closure ◽

Hunan Province ◽

Contact Tracing ◽

Transmission Model ◽

Social Distancing ◽

Susceptibility To Infection ◽

Contact Patterns ◽

The Impact

Intense nonpharmaceutical interventions were put in place in China to stop transmission of the novel coronavirus disease 2019 (COVID-19). As transmission intensifies in other countries, the interplay between age, contact patterns, social distancing, susceptibility to infection, and COVID-19 dynamics remains unclear. To answer these questions, we analyze contact survey data for Wuhan and Shanghai before and during the outbreak and contact-tracing information from Hunan province. Daily contacts were reduced seven- to eightfold during the COVID-19 social distancing period, with most interactions restricted to the household. We find that children 0 to 14 years of age are less susceptible to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection than adults 15 to 64 years of age (odds ratio 0.34, 95% confidence interval 0.24 to 0.49), whereas individuals more than 65 years of age are more susceptible to infection (odds ratio 1.47, 95% confidence interval 1.12 to 1.92). Based on these data, we built a transmission model to study the impact of social distancing and school closure on transmission. We find that social distancing alone, as implemented in China during the outbreak, is sufficient to control COVID-19. Although proactive school closures cannot interrupt transmission on their own, they can reduce peak incidence by 40 to 60% and delay the epidemic.

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Socioeconomic Disparities in Social Distancing During the COVID-19 Pandemic in the United States: Observational Study (Preprint)

10.2196/preprints.24591 ◽

2020 ◽

Author(s):

Romain Garnier ◽

Jan R Benetka ◽

John Kraemer ◽

Shweta Bansal

Keyword(s):

United States ◽

Black People ◽

The United States ◽

Control Measures ◽

Mobility Patterns ◽

Poverty Level ◽

Social Distancing ◽

Mobility Data ◽

Black Populations ◽

The Impact

BACKGROUND Eliminating disparities in the burden of COVID-19 requires equitable access to control measures across socio-economic groups. Limited research on socio-economic differences in mobility hampers our ability to understand whether inequalities in social distancing are occurring during the SARS-CoV-2 pandemic. OBJECTIVE We aimed to assess how mobility patterns have varied across the United States during the COVID-19 pandemic and to identify associations with socioeconomic factors of populations. METHODS We used anonymized mobility data from tens of millions of devices to measure the speed and depth of social distancing at the county level in the United States between February and May 2020, the period during which social distancing was widespread in this country. Using linear mixed models, we assessed the associations between social distancing and socioeconomic variables, including the proportion of people in the population below the poverty level, the proportion of Black people, the proportion of essential workers, and the population density. RESULTS We found that the speed, depth, and duration of social distancing in the United States are heterogeneous. We particularly show that social distancing is slower and less intense in counties with higher proportions of people below the poverty level and essential workers; in contrast, we show that social distancing is intensely adopted in counties with higher population densities and larger Black populations. CONCLUSIONS Socioeconomic inequalities appear to be associated with the levels of adoption of social distancing, potentially resulting in wide-ranging differences in the impact of the COVID-19 pandemic in communities across the United States. These inequalities are likely to amplify existing health disparities and must be addressed to ensure the success of ongoing pandemic mitigation efforts.

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