The outbreak of novel COVID-19 disease elicited a wide range of anti-contagion and economic policies like school closure, income support, contact tracing, and so forth, in the mitigation and suppression of the spread of the SARS-CoV-2 virus. However, a systematic evaluation of these policies has not been made. Here, 17 implemented policies from the Oxford COVID-19 Government Response Tracker dataset employed in 90 countries from December 31, 2019, to August 31, 2020, were analyzed. A Poisson regression model was applied to analyze the relationship between policies and daily confirmed cases using a generalized estimating equations approach. A lag is a fixed time displacement in time series data. With that, lagging (0, 3, 7, 10, and 14 days) was also considered during the analysis since the effects of policies implemented on a given day may affect the number of confirmed cases several days after implementation. The countries were divided into three groups depending on the number of waves of the pandemic observed in each country. Through subgroup analysis, we showed that with and without lagging, contact tracing and containment policies were significant for countries with two waves, while closing, economic, and health policies were significant for countries with three waves. Wave-specific analysis for each wave showed that significant health, economic, and containment policies varied across waves of the pandemic. Emergency investment in healthcare was consistently significant among the three groups of countries, while the Stringency index was significant among all waves of the pandemic. These findings may help in making informed decisions regarding whether, which, or when these policies should be intensified or lifted.

Background Limited information is available about the present characteristics and dynamic clinical changes that occur in patients with COVID-19 during the early phase of the illness. Objective This study aimed to develop and validate machine learning models based on clinical features to assess the risk of severe disease and triage for COVID-19 patients upon hospital admission. Methods This retrospective multicenter cohort study included patients with COVID-19 who were released from quarantine until April 30, 2020, in Korea. A total of 5628 patients were included in the training and testing cohorts to train and validate the models that predict clinical severity and the duration of hospitalization, and the clinical severity score was defined at four levels: mild, moderate, severe, and critical. Results Out of a total of 5601 patients, 4455 (79.5%), 330 (5.9%), 512 (9.1%), and 301 (5.4%) were included in the mild, moderate, severe, and critical levels, respectively. As risk factors for predicting critical patients, we selected older age, shortness of breath, a high white blood cell count, low hemoglobin levels, a low lymphocyte count, and a low platelet count. We developed 3 prediction models to classify clinical severity levels. For example, the prediction model with 6 variables yielded a predictive power of >0.93 for the area under the receiver operating characteristic curve. We developed a web-based nomogram, using these models. Conclusions Our prediction models, along with the web-based nomogram, are expected to be useful for the assessment of the onset of severe and critical illness among patients with COVID-19 and triage patients upon hospital admission.

Since the outbreak of novel SARS-COV-2, each country has implemented diverse policies to mitigate and suppress the spread of the virus. However, no systematic evaluation of these policies in their alleviation of the pandemic has been done. We investigate the impact of five indices derived from 12 policies in the Oxford COVID-19 Government Response Tracker dataset and the Korean government’s index, which is the social distancing level implemented by the Korean government in response to the changing pandemic situation. We employed segmented Poisson model for this analysis. In conclusion, health and the Korean government indices are most consistently effective (with negative coefficients), while the restriction and stringency indexes are mainly effective with lagging (1~10 days), as intuitively daily confirmed cases of a given day is affected by the policies implemented days before, which shows that a period of time is required before the impact of some policies can be observed. The health index demonstrates the importance of public information campaign, testing policy and contact tracing, while the government index shows the importance of social distancing guidelines in mitigating the spread of the virus. These results imply the important roles of these polices in mitigation of the spread of COVID-19 disease.

Increasing evidence shows that many infections of COVID-19 are asymptomatic, becoming a global challenge, since asymptomatic infections have the same infectivity as symptomatic infections. We developed a probabilistic model for estimating the proportion of undetected asymptomatic COVID-19 patients in the country. We considered two scenarios: one is conservative and the other is nonconservative. By combining the above two scenarios, we gave an interval estimation of 0.0001–0.0027 and in terms of the population, 5200–139,900 is the number of undetected asymptomatic cases in South Korea as of February 02, 2021. In addition, we provide estimates for total cases of COVID-19 in South Korea. Combination of undetected asymptomatic cases and undetected symptomatic cases to the number of confirmed cases (78,844 cases on 2 February 2021) shows that 0.17%–0.42% (89,244–218,744) of the population have COVID-19. In conclusion, to control and understand the true ongoing reality of the pandemic, it is of outermost importance to focus on the ratio of undetected asymptomatic cases in the total population.

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