COVID-19 in South Korea.

A novel coronavirus (severe acute respiratory syndrome-CoV-2) that initially originated from Wuhan, China, in December 2019 has already caused a pandemic. While this novel coronavirus disease (COVID-19) frequently induces mild diseases, it has also generated severe diseases among certain populations, including older-aged individuals with underlying diseases, such as cardiovascular disease and diabetes. As of 31 March 2020, a total of 9786 confirmed cases with COVID-19 have been reported in South Korea. South Korea has the highest diagnostic rate for COVID-19, which has been the major contributor in overcoming this outbreak. We are trying to reduce the reproduction number of COVID-19 to less than one and eventually succeed in controlling this outbreak using methods such as contact tracing, quarantine, testing, isolation, social distancing and school closure. This report aimed to describe the current situation of COVID-19 in South Korea and our response to this outbreak.

Comparative Study of Government Response Measures and Epidemic Trends for COVID-19 Global Pandemic.

The ongoing novel coronavirus (COVID-19) epidemic has evolved into a full range of challenges that the world is facing. Health and economic threats caused governments to take preventive measures against the spread of the disease. This study aims to provide a correlation analysis of the response measures adopted by countries and epidemic trends since the COVID-19 outbreak. This analysis picks 13 countries for quantitative assessment. We select a trusted model to fit the epidemic trend curves in segments and catch the characteristics based on which we explore the key factors of COVID-19 spread. This review generates a score table of government response measures according to the Likert scale. We use the Delphi method to obtain expert judgments about the government response in the Likert scale. Furthermore, we find a significant negative correlation between the epidemic trend characteristics and the government response measure scores given by experts through correlation analysis. More stringent government response measures correlate with fewer infections and fewer waves in the infection curves. Stringent government response measures curb the spread of COVID-19, limit the number of total infectious cases, and reduce the time to peak of total cases. The clusters of the results categorize the countries into two specific groups. This study will improve our understanding of the prevention of COVID-19 spread and government response.

Effective Lockdown and Role of Hospital-Based COVID-19 Transmission in Some Indian States: An Outbreak Risk Analysis.

Several reports in India indicate hospitals and quarantined centers are COVID-19 hotspots. To study the transmission occurring from the hospitals and as well as from the community, we developed a mechanistic model with a lockdown effect. Using daily COVID-19 cases data from six states and overall India, we estimated several important parameters of our model. Moreover, we provided an estimation of the effective (RT ), the basic (R0 ), the community (RC ), and the hospital (RH ) reproduction numbers. We forecast COVID-19 notified cases from May 3, 2020, till May 20, 2020, under five different lockdown scenarios in the seven locations. Our analysis suggests that 65% to 99% of the new COVID-19 cases are currently asymptomatic in those locations. Besides, about 1-16% of the total COVID-19 transmission are currently occurring from hospital-based contact and these percentage can increase up to 69% in some locations. Furthermore, the hospital-based transmission rate (ß2 ) has significant positive (0.65 to 0.8) and negative (-0.58 to -0.23) correlation with R0 and the effectiveness of lockdown, respectively. Therefore, a much larger COVID-19 outbreak may trigger from the hospital-based transmission. In most of the locations, model forecast from May 3, 2020, till May 20, 2020, indicates a two-times increase in cumulative cases in comparison to total observed cases up to April 29, 2020. Based on our results, we proposed a containment policy that may reduce the threat of a larger COVID-19 outbreak in the future.

Quantifying SARS-CoV-2 Infection Risk Within the Google/Apple Exposure Notification Framework to Inform Quarantine Recommendations.

Most early Bluetooth-based exposure notification apps use three binary classifications to recommend quarantine following SARS-CoV-2 exposure: a window of infectiousness in the transmitter, ≥15 minutes duration, and Bluetooth attenuation below a threshold. However, Bluetooth attenuation is not a reliable measure of distance, and infection risk is not a binary function of distance, nor duration, nor timing. We model uncertainty in the shape and orientation of an exhaled virus-containing plume and in inhalation parameters, and measure uncertainty in distance as a function of Bluetooth attenuation. We calculate expected dose by combining this with estimated infectiousness based on timing relative to symptom onset. We calibrate an exponential dose-response curve based on infection probabilities of household contacts. The probability of current or future infectiousness, conditioned on how long postexposure an exposed individual has been symptom-free, decreases during quarantine, with shape determined by incubation periods, proportion of asymptomatic cases, and asymptomatic shedding durations. It can be adjusted for negative test results using Bayes' theorem. We capture a 10-fold range of risk using six infectiousness values, 11-fold range using three Bluetooth attenuation bins, ∼sixfold range from exposure duration given the 30 minute duration cap imposed by the Google/Apple v1.1, and ∼11-fold between the beginning and end of 14 day quarantine. Public health authorities can either set a threshold on initial infection risk to determine 14-day quarantine onset, or on the conditional probability of current and future infectiousness conditions to determine both quarantine and duration.

Optimal Investment in Prevention and Recovery for Mitigating Epidemic Risks.

The worldwide healthcare and economic crisis caused by the COVID-19 pandemic highlights the need for a deeper understanding of investing in the mitigation of epidemic risks. To address this, we built a mathematical model to optimize investments into two types of measures for mitigating the risks of epidemic propagation: prevention/containment measures and treatment/recovery measures. The new model explicitly accounts for the characteristics of networks of individuals, as a critical element of epidemic propagation. Subsequent analysis shows that, to combat an epidemic that can cause significant negative impact, optimal investment in either category increases with a higher level of connectivity and intrinsic loss, but it is limited to a fraction of that total potential loss. However, when a fixed and limited mitigation investment is to be apportioned among the two types of measures, the optimal proportion of investment for prevention and containment increases when the investment limit goes up, and when the network connectivity decreases. Our results are consistent with existing studies and can be used to properly interpret what happened in past pandemics as well as to shed light on future and ongoing events such as COVID-19.

Failure in initial stage containment of global COVID-19 epicenters.

With multiple virus epicenters, COVID-19 has been declared a pandemic by the World Health Organization. Consequently, many countries have implemented different policies to manage this crisis including curfew and lockdown. However, the efficacy of individual policies remains unclear with respect to COVID-19 case development. We analyzed available data on COVID-19 cases of eight majorly affected countries, including China, Italy, Iran, Germany, France, Spain, South Korea, and Japan. Growth rates and doubling time of cases were calculated for the first 6 weeks after the initial cases were declared for each respective country and put into context with implemented policies. Although the growth rate of total confirmed COVID-19 cases in China has decreased, those for Japan have remained constant. For European countries, the growth rate of COVID-19 cases considerably increased during the second time interval. Interestingly, the rates for Germany, Spain, and France are the highest measured in the second interval and even surpass the numbers in Italy. Although the initial data in Asian countries are encouraging with respect to case development at the initial stage, the opposite is true for European countries. Based on our data, disease management in the 2 weeks following the first reported cases is of utmost importance.

Knowledge, attitude, prevention practice, and associated factors toward COVID-19 among preparatory school students in Southwest Ethiopia, 2021.

INTRODUCTION: As of February 2021 COVID-19 report in 57 African countries, there were 3,761,512 confirmed cases and 98,088 deaths. Ethiopia reported the highest number of cases in East Africa with a total of 147,092 cases and 2,194 deaths. Over 1.5 billion students from 195 countries across the world separated from school as a consequence of the closure of schools related to the pandemic. This study aimed to determine the level of knowledge, attitude, prevention practices, and determinant factors regarding COVID-19 among preparatory school students in southwest Ethiopia. METHODS: An institution-based cross-sectional study design was used for 422 samples. Each respondent was selected using simple random sampling. Data were collected using a self-administered questionnaire. The collected data were entered and analyzed using Statistical Package for social science software version 25.0. Multivariable binary logistic regression was used to identify factors that were significantly associated with the practice of COVID-19 prevention. RESULTS: The response rate in this study was 96.2%. A higher proportion of the respondents were female (53.9%), Bench (43.6%), and protestant (47.3%). The level of good knowledge, positive attitude, and good practice were 81.8%, 70.9%, and 47.0% respectively. Using social media [AOR: 1.801, 95% CI: 1.005, 3.226], watching television [AOR: 1.884 95% CI: 1.093, 3.247], being knowledgeable [AOR: 5.173 95% CI: 2.276, 11.755], and having a positive attitude [AOR: 4.300 95% CI: 2.351, 7.868] were positively associated with COVID-19 prevention practice. CONCLUSION: Despite the high level of knowledge and a moderate level of positive attitude, the practice of COVID-19 prevention measures was low. Using social media, watching television, being knowledgeable, and having positive attitudes towards COVID-19 increases the tendency to practice COVID-19 prevention measures. School directors and teachers should strictly monitor students for their adherence to COVID-19 prevention measures as directed by the local and national health care departments.

Non-pharmaceutical interventions during the COVID-19 epidemic changed detection rates of other circulating respiratory pathogens in Japan.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has circulated worldwide and causes coronavirus disease 2019 (COVID-19). At the onset of the COVID-19 pandemic, infection control measures were taken, such as hand washing, mask wearing, and behavioral restrictions. However, it is not fully clear how the effects of these non-pharmaceutical interventions changed the prevalence of other pathogens associated with respiratory infections. In this study, we collected 3,508 nasopharyngeal swab samples from 3,249 patients who visited the Yamanashi Central Hospital in Japan from March 1, 2020 to February 28, 2021. We performed multiplex polymerase chain reaction (PCR) using the FilmArray Respiratory Panel and singleplex quantitative reverse transcription PCR targeting SARS-CoV-2 to detect respiratory disease-associated pathogens. At least one pathogen was detected in 246 (7.0%) of the 3,508 samples. Eleven types of pathogens were detected in the samples collected from March-May 2020, during which non-pharmaceutical interventions were not well implemented. In contrast, after non-pharmaceutical interventions were thoroughly implemented, only five types of pathogens were detected, and the majority were SARS-CoV-2, adenoviruses, or human rhinoviruses / enteroviruses. The 0-9 year age group had a higher prevalence of infection with adenoviruses and human rhinoviruses / enteroviruses compared with those 10 years and older, while those 10 years and older had a higher prevalence of infection with SARS-CoV-2 and other pathogens. These results indicated that non-pharmaceutical interventions likely reduced the diversity of circulating pathogens. Moreover, differences in the prevalence of pathogens were observed among the different age groups.

Model-based evaluation of alternative reactive class closure strategies against COVID-19.

There are contrasting results concerning the effect of reactive school closure on SARS-CoV-2 transmission. To shed light on this controversy, we developed a data-driven computational model of SARS-CoV-2 transmission. We found that by reactively closing classes based on syndromic surveillance, SARS-CoV-2 infections are reduced by no more than 17.3% (95%CI: 8.0-26.8%), due to the low probability of timely identification of infections in the young population. We thus investigated an alternative triggering mechanism based on repeated screening of students using antigen tests. Depending on the contribution of schools to transmission, this strategy can greatly reduce COVID-19 burden even when school contribution to transmission and immunity in the population is low. Moving forward, the adoption of antigen-based screenings in schools could be instrumental to limit COVID-19 burden while vaccines continue to be rolled out.

Societal activities associated with SARS-CoV-2 infection: a case-control study in Denmark, November 2020.

Identification of societal activities associated with SARS-CoV-2 infection may provide an evidence base for implementing preventive measures. Here, we investigated potential determinants for infection in Denmark in a situation where society was only partially open. We conducted a national matched case-control study. Cases were recent RT-PCR test-positives, while controls, individually matched on age, sex and residence, had not previously tested positive for SARS-CoV-2. Questions concerned person contact and community exposures. Telephone interviews were performed over a 7-day period in December 2020. We included 300 cases and 317 controls and determined odds ratios (ORs) and 95% confidence intervals (95% CI) by conditional logistical regression with adjustment for household size and country of origin. Contact (OR 4.9, 95% CI 2.4-10) and close contact (OR 13, 95% CI 6.7-25) with a person with a known SARS-CoV-2 infection were main determinants. Contact most often took place in the household or work place. Community determinants included events with singing (OR 2.1, 95% CI 1.1-4.1), attending fitness centres (OR 1.8, 95% CI 1.1-2.8) and consumption of alcohol in a bar (OR 10, 95% CI 1.5-65). Other community exposures appeared not to be associated with infection, these included shopping at supermarkets, travel by public transport, dining at restaurants and private social events with few participants. Overall, the restrictions in place at the time of the study appeared to be sufficient to reduce transmission of disease in the public space, which instead largely took place following direct exposures to people with known SARS-CoV-2 infections.

The first wave of the COVID-19 epidemic in Spain was associated with early introductions and fast spread of a dominating genetic variant.

The coronavirus disease 2019 (COVID-19) pandemic has affected the world radically since 2020. Spain was one of the European countries with the highest incidence during the first wave. As a part of a consortium to monitor and study the evolution of the epidemic, we sequenced 2,170 samples, diagnosed mostly before lockdown measures. Here, we identified at least 500 introductions from multiple international sources and documented the early rise of two dominant Spanish epidemic clades (SECs), probably amplified by superspreading events. Both SECs were related closely to the initial Asian variants of SARS-CoV-2 and spread widely across Spain. We inferred a substantial reduction in the effective reproductive number of both SECs due to public-health interventions (Re < 1), also reflected in the replacement of SECs by a new variant over the summer of 2020. In summary, we reveal a notable difference in the initial genetic makeup of SARS-CoV-2 in Spain compared with other European countries and show evidence to support the effectiveness of lockdown measures in controlling virus spread, even for the most successful genetic variants.

Failures of quarantine systems for preventing COVID-19 outbreaks in Australia and New Zealand.

OBJECTIVES: To identify COVID-19 quarantine system failures in Australia and New Zealand. DESIGN, SETTING, PARTICIPANTS: Observational epidemiological study of travellers in managed quarantine in Australia and New Zealand, to 15 June 2021. MAIN OUTCOME MEASURES: Number of quarantine system failures, and failure with respect to numbers of travellers and SARS-CoV-2-positive travellers. RESULTS: We identified 22 quarantine system failures in Australia and ten in New Zealand to 15 June 2021. One failure initiated a COVID-19 outbreak that caused more than 800 deaths (the Victorian "second wave"); nine lockdowns were linked with quarantine system failures. The failure risk was estimated to be 5.0 failures per 100 000 travellers passing through quarantine and 6.1 (95% CI, 4.0-8.3) failures per 1000 SARS-CoV-2-positive travellers. The risk per 1000 SARS-CoV-2-positive travellers was higher in New Zealand than Australia (relative risk, 2.0; 95% CI, 1.0-4.2). CONCLUSIONS: Quarantine system failures can be costly in terms of lives and economic impact, including lockdowns. Our findings indicate that infection control in quarantine systems in Australia and New Zealand should be improved, including vaccination of quarantine workers and incoming travellers, or that alternatives to hotel-based quarantine should be developed.

COVID-19 Pandemic and Pediatric Type 1 Diabetes: No Significant Change in Glycemic Control During The Pandemic Lockdown of 2020.

Importance: There is no consensus on the impact of the 2020 COVID-19 pandemic lockdown on glycemic control in children and adolescents with type 1 diabetes (T1D) in the US. Aim: To determine the impact of the pandemic lockdown of March 15th through July 6th, 2020 on glycemic control after controlling for confounders. Subjects and Methods: An observational study of 110 subjects of mean age 14.8 ± 4.9 years(y), [male 15.4 ± 4.0y, (n=57); female 14.1 ± 3.8y, (n=53), p=0.07] with T1D of 6.31 ± 4.3y (95% CI 1.0-19.7y). Data were collected at 1-4 months before the lockdown and 1-4 months following the lifting of the lockdown at their first post-lockdown clinic visit. Results: There was no significant change in A1c between the pre- and post-pandemic lockdown periods, 0.18 ± 1.2%, (95% CI -0.05 to 0.41), p=0.13. There were equally no significant differences in A1c between the male and female subjects, -0.16 ± 1.2 vs -0.19 ± 1.2%, p=0.8; insulin pump users and non-pump users, -0.25 ± 1.0 vs -0.12 ± 1.4%, p=0.5; and pubertal vs prepubertal subjects, 0.18 ± 1.3 vs -0.11 ± 0.3%, p=0.6. The significant predictors of decrease in A1c were pre-lockdown A1c (p<0.0001) and the use of CGM (p=0.019). The CGM users had significant reductions in point-of-care A1c (0.4 ± 0.6%, p=0.0012), the CGM-estimated A1c (p=0.0076), mean glucose concentration (p=0.022), a significant increase in sensor usage (p=0.012), with no change in total daily dose of insulin (TDDI). The non-CGM users had significantly increased TDDI (p<0.0001) but no change in HbA1c, 0.06 ± 1.8%, p=0.86. Conclusions: There was no change in glycemic control during the pandemic lockdown of 2020 in US children.

Social sentiment segregation: Evidence from Twitter and Google Trends in Chile during the COVID-19 dynamic quarantine strategy.

The Chilean health authorities have implemented a sanitary strategy known as dynamic quarantine or strategic quarantine to cope with the COVID-19 pandemic. Under this system, lockdowns were established, lifted, or prolonged according to the weekly health authorities' assessment of municipalities' epidemiological situation. The public announcements about the confinement situation of municipalities country-wide are made typically on Tuesdays or Wednesdays before noon, have received extensive media coverage, and generated sharp stock market fluctuations. Municipalities are the smallest administrative division in Chile, with each city broken down typically into several municipalities. We analyze social media behavior in response to the confinement situation of the population at the municipal level. The dynamic quarantine scheme offers a unique opportunity for our analysis, given that municipalities display a high degree of heterogeneity, both in size and in the socioeconomic status of their population. We exploit the variability over time in municipalities' confinement situations, resulting from the dynamic quarantine strategy, and the cross-sectional variability in their socioeconomic characteristics to evaluate the impact of these characteristics on social sentiment. Using event study and panel data methods, we find that proxies for social sentiment based on Twitter queries are negatively related (more pessimistic) to increases in the number of confined people, but with a statistically significant effect concentrated on people from the wealthiest cohorts of the population. For indicators of social sentiment based on Google Trends, we found that search intensity during the periods surrounding government announcements is positively related to increases in the total number of confined people. Still, this effect does not seem to be dependent on the segments of the population affected by the quarantine. Furthermore, we show that the observed heterogeneity in sentiment mirrors heterogeneity in stock market reactions to government announcements. We provide evidence that the observed stock market behavior around quarantine announcements can be explained by the number of people from the wealthiest segments of the population entering or exiting lockdown.