Presenting characteristics and clinical outcome of patients with COVID-19 in South Korea: A nationwide retrospective observational study

Mathematical modeling of contact tracing and stability analysis to inform its impact on disease outbreaks; an application to COVID-19

We develop a mathematical model to investigate the effect of contact tracing on containing epidemic outbreaks and slowing down the spread of transmissible diseases. We propose a discrete-time epidemic model structured by disease-age which includes general features of contact tracing. The model is fitted to data reported for the early spread of COVID-19 in South Korea, Brazil, and Venezuela. The calibrated values for the contact tracing parameters reflect the order pattern observed in its performance intensity within the three countries. Using the fitted values, we estimate the effective reproduction number R e and investigate its responses to varied control scenarios of contact tracing. Alongside the positivity of solutions, and a stability analysis of the disease-free equilibrium are provided.

Development and external validation of the DOAT and DOATS scores: simple decision support tools to identify disease progression among nonelderly patients with mild/moderate COVID-19

BACKGROUND

During the fifth wave of the coronavirus disease 2019 (COVID-19) pandemic in Japan, which took place between June and September 2021, a significant number of COVID-19 cases with deterioration occurred in unvaccinated individuals < 65 years old. However, the risk factors for COVID-19 deterioration in this specific population have not yet been determined. This study developed a prediction method to identify COVID-19 patients < 65 years old who are at a high risk of deterioration.

METHODS

This retrospective study analyzed data from 1,675 patients < 65 years old who were admitted to acute care institutions in Fukushima with mild-to-moderate-1 COVID-19 based on the Japanese disease severity criteria prior to the fifth wave. For validation, 324 similar patients were enrolled from 3 hospitals in Yamagata. Logistic regression analyses using cluster-robust variance estimation were used to determine predictors of disease deterioration, followed by creation of risk prediction scores. Disease deterioration was defined as the initiation of medication for COVID-19, oxygen inhalation, or mechanical ventilation starting one day or later after admission.

RESULTS

The patients whose condition deteriorated (8.6%) tended to be older, male, have histories of smoking, and have high body temperatures, low oxygen saturation values, and comorbidities, such as diabetes/obesity and hypertension. Stepwise variable selection using logistic regression to predict COVID-19 deterioration retained comorbidities of diabetes/obesity (DO), age (A), body temperature (T), and oxygen saturation (S). Two predictive scores were created based on the optimism-corrected regression coefficients: the DOATS score, including all of the above risk factors, and the DOAT score, which was the DOATS score without oxygen saturation. In the original cohort, the areas under the receiver operating characteristic curve (AUROCs) of the DOATS and DOAT scores were 0.81 (95% confidence interval [CI] 0.77-0.85) and 0.80 (95% CI 0.76-0.84), respectively. In the validation cohort, the AUROCs for each score were both 0.76 (95% CI 0.69-0.83), and the calibration slopes were both 0.80. A decision curve analysis confirmed the clinical practicability of both scores in the validation cohort.

CONCLUSIONS

We established two prediction scores that can quickly evaluate the risk of COVID-19 deterioration in mild/moderate patients < 65 years old.

Clinical epidemiological applicability of real-time polymerase chain reaction for COVID-19

OBJECTIVES

Real-time polymerase chain reaction is currently used as a confirmatory test for coronavirus disease 2019 (COVID-19). The test results are interpreted as positive, negative, or inconclusive, and are used only for a qualitative classification of patients. However, the test results can be quantitated using threshold count (Ct) values to determine the amount of virus present in the sample. Therefore, this study investigated the diagnostic usefulness of Ct results through various quantitative analyzes, along with an analysis of clinical and epidemiological characteristics.

METHODS

Clinical and epidemiological data from 4,642 COVID-19 patients in April 2021 were analyzed, including the Ct values of the RNA-dependent RNA polymerase (RdRp), envelope (E), and nucleocapsid (N) genes. Clinical and epidemiological data (sex, age, underlying diseases, and early symptoms) were collected through a structured questionnaire. A correlation analysis was used to examine the relationships between variables.

RESULTS

All 3 genes showed statistically significant relationships with symptoms and severity levels. The Ct values of the RdRp gene decreased as the severity of the patients increased. Moreover, statistical significance was observed for the presence of underlying diseases and dyspnea.

CONCLUSION

Ct values were found to be related to patients' clinical and epidemiological characteristics. In particular, since these factors are closely related to symptoms and severity, Ct values can be used as primary data for predicting patients' disease prognosis despite the limitations of this method. Conducting follow-up studies to validate this approach might enable using the data from this study to establish policies for preventing COVID-19 infection and spread.

Clinicodemographic profile and predictors of poor outcome in hospitalised COVID-19 patients: a single-centre, retrospective cohort study from India

OBJECTIVES

Primary objective was to study the clinicodemographic profile of hospitalised COVID-19 patients at a tertiary-care centre in India. Secondary objective was to identify predictors of poor outcome.

SETTING

Single centre tertiary-care level.

DESIGN

Retrospective cohort study.

PARTICIPANTS

Consecutively hospitalised adults patients with COVID-19.

PRIMARY AND SECONDARY OUTCOME MEASURES

Primary outcome variable was in-hospital mortality. Covariables were known comorbidities, clinical features, vital signs at the time of admission and on days 3-5 of admission, and initial laboratory investigations.

RESULTS

Intergroup differences were tested using χ² or Fischer's exact tests, Student's t-test or Mann-Whitney U test. Predictors of mortality were evaluated using multivariate logistic regression model. Out of 4102 SARS-CoV-2 positive patients admitted during 1-year period, 3268 (79.66%) survived to discharge and 834 (20.33%) died in the hospital. Mortality rates increased with age. Death was more common among males (OR 1.51, 95% CI 1.25 to 1.81). Out of 261 cases analysed in detail, 55.1% were in mild, 32.5% in moderate and 12.2% in severe triage category. Most common clinical presentations in the subgroup were fever (73.2%), cough/coryza (65.5%) and breathlessness (54%). Hypertension (45.2%), diabetes mellitus (41.8%) and chronic kidney disease (CKD; 6.1%) were common comorbidities. Disease severity on admission (adjusted OR 12.53, 95% CI 4.92 to 31.91, p<0.01), coagulation defect (33.21, 3.85-302.1, p<0.01), CKD (5.67, 1.08-29.64, p=0.04), high urea (11.05, 3.9-31.02, p<0.01), high prothrombin time (3.91, 1.59-9.65, p<0.01) and elevated ferritin (1.02, 1.00-1.03, p=0.02) were associated with poor outcome on multivariate regression. A strong predictor of mortality was disease progression on days 3-5 of admission (adjusted OR 13.66 95% CI 3.47 to 53.68).

CONCLUSION

COVID-19 related mortality in hospitalised adult patients at our center was similar to the developed countries. Progression in disease severity on days 3-5 of admission or days 6-13 of illness onset acts as 'turning point' for timely referral or treatment intensification for optimum use of resources.

Age-specific rate of severe and critical SARS-CoV-2 infections estimated with multi-country seroprevalence studies

BACKGROUND

Knowing the age-specific rates at which individuals infected with SARS-CoV-2 develop severe and critical disease is essential for designing public policy, for infectious disease modeling, and for individual risk evaluation.

METHODS

In this study, we present the first estimates of these rates using multi-country serology studies, and public data on hospital admissions and mortality from early to mid-2020. We combine these under a Bayesian framework that accounts for the high heterogeneity between data sources and their respective uncertainties. We also validate our results using an indirect method based on infection fatality rates and hospital mortality data.

RESULTS

Our results show that the risk of severe and critical disease increases exponentially with age, but much less steeply than the risk of fatal illness. We also show that our results are consistent across several robustness checks.

CONCLUSION

A complete evaluation of the risks of SARS-CoV-2 for health must take non-fatal disease outcomes into account, particularly in young populations where they can be 2 orders of magnitude more frequent than deaths.

Age-specific rate of severe and critical SARS-CoV-2 infections estimated with multi-country seroprevalence studies

Background

Knowing the age-specific rates at which individuals infected with SARS-CoV-2 develop severe and critical disease is essential for designing public policy, for infectious disease modeling, and for individual risk evaluation.

Methods

In this study, we present the first estimates of these rates using multi-country serology studies, and public data on hospital admissions and mortality from early to mid-2020. We combine these under a Bayesian framework that accounts for the high heterogeneity between data sources and their respective uncertainties. We also validate our results using an indirect method based on infection fatality rates and hospital mortality data.

Results

Our results show that the risk of severe and critical disease increases exponentially with age, but much less steeply than the risk of fatal illness. We also show that our results are consistent across several robustness checks.

Conclusion

A complete evaluation of the risks of SARS-CoV-2 for health must take non-fatal disease outcomes into account, particularly in young populations where they can be 2 orders of magnitude more frequent than deaths.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12879-022-07262-0.

Fluvoxamine Treatment of Patients with Symptomatic COVID-19 in a Community Treatment Center: A Preliminary Result of Randomized Controlled Trial

Background

This study aimed to evaluate whether fluvoxamine reduces clinical deterioration in adult patients with mild to moderate coronavirus disease 2019 (COVID-19), and to identify risk factors for clinical deterioration in patients admitted to a community treatment center (CTC).

Materials and Methods

A randomized, placebo-controlled trial was conducted in a CTC, in Seoul, Korea from January 15, 2021, to February 19, 2021. Symptomatic adult patients with positive results of severe acute respiratory syndrome coronavirus 2 real time-polymerase chain reaction within 3 days of randomization were assigned at random to receive 100 mg of fluvoxamine or placebo twice daily for 10 days. The primary outcome was clinical deterioration defined by any of the following criteria: oxygen requirement to keep oxygen saturation over 94.0%, aggravation of pneumonia with dyspnea, or World Health Organization clinical progression scale 4 or greater.

Results

Of 52 randomized participants [median (interquartile range) age, 53.5 (43.3 - 60.0) years; 31 (60.0%) men], 44 (85.0%) completed the trial. Clinical deterioration occurred in 2 of 26 patients in each group (P >0.99). There were no serious adverse events in either group. Clinical deterioration occurred in 15 (6.0%) of 271 patients admitted to the CTC, and all of them were transferred to a hospital. In multivariate analysis, age between 55 and 64, fever and pneumonia at admission were independent risk factors for clinical deterioration.

Conclusion

In this study of adult patients with symptomatic COVID-19 who were admitted to the CTC, there was no significant differences in clinical deterioration between patients treated with fluvoxamine and placebo (ClinicalTrials.gov Identifier: NCT04711863).

Prevalence of Coronavirus Disease 2019 (COVID-19) in Different Clinical Stages before the National COVID-19 Vaccination Programme in Malaysia: A Systematic Review and Meta-Analysis

More than 1.75 million COVID-19 infections and 16 thousand associated deaths have been reported in Malaysia. A meta-analysis on the prevalence of COVID-19 in different clinical stages before the National COVID-19 Vaccination Program in Malaysia is still lacking. To address this, the disease severity of a total of 215 admitted COVID-19 patients was initially recorded in the early phase of this study, and the data were later pooled into a meta-analysis with the aim of providing insight into the prevalence of COVID-19 in 5 different clinical stages during the outset of the COVID-19 pandemic in Malaysia. We have conducted a systematic literature search using PubMed, Web of Science, Scopus, ScienceDirect, and two preprint databases (bioRxiv and medRxiv) for relevant studies with specified inclusion and exclusion criteria. The quality assessment for the included studies was performed using the Newcastle–Ottawa Scale. The heterogeneity was examined with an I² index and a Q-test. Funnel plots and Egger’s tests were performed to determine publication bias in this meta-analysis. Overall, 5 studies with 6375 patients were included, and the pooled prevalence rates in this meta-analysis were calculated using a random-effect model. The highest prevalence of COVID-19 in Malaysia was observed in Stage 2 cases (32.0%), followed by Stage 1 (27.8%), Stage 3 (17.1%), Stage 4 (7.6%), and Stage 5 (3.4%). About two-thirds of the number of cases have at least one morbidity, with the highest percentage of hypertension (66.7%), obesity (55.5%), or diabetes mellitus (33.3%) in Stage 5 patients. In conclusion, this meta-analysis suggested a high prevalence of COVID-19 occurred in Stage 2. The prevalence rate in Stage 5 appeared to be the lowest among COVID-19 patients before implementing the vaccination program in Malaysia. These meta-analysis data are critically useful for designing screening and vaccination programs and improving disease management in the country.

A strategic response map for cascading pandemics: Lessons learned from the response to COVID-19 in the Republic of Korea

COVID 19 is a still on-going fatal risk that affects the whole world. COVID-19 pandemic has been characterized as a systemic risk. Accordingly, this paper aims to identify the features of systemic risk of COVID-19 and draw policy implications for effective response. For this, we traced the COVID-19 related risk in Korea from January 2020 to August 2021 by utilizing the official data of the Korea Disease Control and Prevention Agency. Analyzing the relationship between anticipated, emerging, amplified, and lingering risk and response measure through actual data, it was revealed that the risk flow model for cascading risk proposed by the author can be readily applied. In addition, through the analysis of actual response measures against the risks for 1 year and 8 months, the authors proposed a strategic response map against cascading pandemics. Five policy implication derived through this study can be extended for identifying strategic approach against cascading pandemics and for developing guidelines for effective preparedness, risk reduction, and resilience building.

The ATHENA COVID-19 Study: Cohort profile and first findings for people diagnosed with COVID-19 in Queensland, 1 January to 31 December 2020

Background

To date, there are limited Australian data on characteristics of people diagnosed with COVID-19 and on how these characteristics relate to outcomes. The ATHENA COVID-19 Study was established to describe health outcomes and investigate predictors of outcomes for all people diagnosed with COVID-19 in Queensland by linking COVID-19 notification, hospital, general practice and death registry data. This paper reports on the establishment and first findings for the ATHENA COVID-19 Study.

Methods

Part 1 of the ATHENA COVID-19 Study used Notifiable Conditions System data from 1 January 2020 to 31 December 2020, linked to: Emergency Department Collection data for the same period; Queensland Health Admitted Patient Data Collections (from 1 January 2010 to 30 January 2021); and Deaths Registrations data (from 1 January 2020 to 17 January 2021).

Results

To 31 December 2020, a total of 1,254 people had been diagnosed with SARS-CoV-2 infection in Queensland: half were female (49.8%); two-thirds (67.7%) were aged 20-59 years; and there was an over-representation of people living in less-disadvantaged areas. More than half of people diagnosed (57.6%) presented to an ED; 21.2% were admitted to hospital as an inpatient (median length of stay 11 days); 1.4% were admitted to an intensive care unit (82.4% of these required ventilation); and there were six deaths. Analysis of factors associated with these outcomes was limited due to small case numbers: people living in less-disadvantaged areas had a lower risk of being admitted to hospital (test for trend, p < 0.001), while those living in more remote areas were less likely than people living in major cities to present to an ED (test for trend: p=0.007), which may reflect differential health care access rather than health outcomes per se. Increasing age (test for trend, p < 0.001) and being a current/recent smoker (age-sex-adjusted relative risk: 1.61; 95% confidence interval: 1.00, 2.61) were associated with a higher risk of being admitted to hospital.

Conclusion

Despite uncertainty in our estimates due to small numbers, our findings are consistent with what is known about COVID-19. Our findings reinforce the value of linking multiple data sources to enhance reporting of outcomes for people diagnosed with COVID-19 and provide a platform for longer term follow-up.