Epidemiologic Characteristics of COVID-19 in Guizhou Province, China

Cross-sectional study on the epidemiological investigation ability of professional staff from Centers for Disease Control and Prevention in Guizhou Province

Introduction

This study aims to evaluate the qualifications and identify skill enhancement areas for epidemiological investigators in Centers for Disease Control and Prevention (CDCs) in Guizhou's, informing future training and policy initiatives to strengthen public health responses.

Methods

A cross-sectional survey was conducted in August 2022, and an online, self-designed questionnaire on the Epidemiological Dynamic Data Collection platform was administered to evaluate the professional staff in CDCs. The responses were scored and presented using descriptive statistical methods, and the factors influencing the total score were analyzed by one-way ANOVA and linear regression.

Results and discussion

A total of 1321 questionnaires were collected, yielding an average score of 14.86±3.49 and a qualification rate of 29.9%. The scoring rate of ability of individual protection and coordination in epidemic control was high (87.25%). Meanwhile, improvements in further training were needed in areas such as data analysis ability (23.67%), knowledge of site disinfection (40.40%), and epidemiological investigation skills (42.50%). No significant difference was observed between the scores of city and county CDCs, (t = 1.071, p =0.284). The effects of gender and age could be disregarded, and the experience in epidemiological work and training (including investigation on COVID-19 cases and contacts), educational background, and professional title partially explained the survey outcome (R Square of the linear regression model was 0.351). The survey indicated the need for additional well-trained epidemiologic investigators in Guizhou. Specified training was effective in improving epidemiologic investigation, and enhancement in data analysis ability and knowledge of field disinfection are recommended in professional staff cultivation.

Assessing changes in incubation period, serial interval, and generation time of SARS-CoV-2 variants of concern: a systematic review and meta-analysis

BACKGROUND

After the first COVID-19 wave caused by the ancestral lineage, the pandemic has been fueled from the continuous emergence of new SARS-CoV-2 variants. Understanding key time-to-event periods for each emerging variant of concern is critical as it can provide insights into the future trajectory of the virus and help inform outbreak preparedness and response planning. Here, we aim to examine how the incubation period, serial interval, and generation time have changed from the ancestral SARS-CoV-2 lineage to different variants of concern.

METHODS

We conducted a systematic review and meta-analysis that synthesized the estimates of incubation period, serial interval, and generation time (both realized and intrinsic) for the ancestral lineage, Alpha, Beta, and Omicron variants of SARS-CoV-2.

RESULTS

Our study included 280 records obtained from 147 household studies, contact tracing studies, or studies where epidemiological links were known. With each emerging variant, we found a progressive shortening of each of the analyzed key time-to-event periods, although we did not find statistically significant differences between the Omicron subvariants. We found that Omicron BA.1 had the shortest pooled estimates for the incubation period (3.49 days, 95% CI: 3.13-4.86 days), Omicron BA.5 for the serial interval (2.37 days, 95% CI: 1.71-3.04 days), and Omicron BA.1 for the realized generation time (2.99 days, 95% CI: 2.48-3.49 days). Only one estimate for the intrinsic generation time was available for Omicron subvariants: 6.84 days (95% CrI: 5.72-8.60 days) for Omicron BA.1. The ancestral lineage had the highest pooled estimates for each investigated key time-to-event period. We also observed shorter pooled estimates for the serial interval compared to the incubation period across the virus lineages. When pooling the estimates across different virus lineages, we found considerable heterogeneities (I2 > 80%; I2 refers to the percentage of total variation across studies that is due to heterogeneity rather than chance), possibly resulting from heterogeneities between the different study populations (e.g., deployed interventions, social behavior, demographic characteristics).

CONCLUSIONS

Our study supports the importance of conducting contact tracing and epidemiological investigations to monitor changes in SARS-CoV-2 transmission patterns. Our findings highlight a progressive shortening of the incubation period, serial interval, and generation time, which can lead to epidemics that spread faster, with larger peak incidence, and harder to control. We also consistently found a shorter serial interval than incubation period, suggesting that a key feature of SARS-CoV-2 is the potential for pre-symptomatic transmission. These observations are instrumental to plan for future COVID-19 waves.

Evaluation of the EsteR Toolkit for COVID-19 Decision Support: Sensitivity Analysis and Usability Study

BACKGROUND

During the COVID-19 pandemic, local health authorities were responsible for managing and reporting current cases in Germany. Since March 2020, employees had to contain the spread of COVID-19 by monitoring and contacting infected persons as well as tracing their contacts. In the EsteR project, we implemented existing and newly developed statistical models as decision support tools to assist in the work of the local health authorities.

OBJECTIVE

The main goal of this study was to validate the EsteR toolkit in two complementary ways: first, investigating the stability of the answers provided by our statistical tools regarding model parameters in the back end and, second, evaluating the usability and applicability of our web application in the front end by test users.

METHODS

For model stability assessment, a sensitivity analysis was carried out for all 5 developed statistical models. The default parameters of our models as well as the test ranges of the model parameters were based on a previous literature review on COVID-19 properties. The obtained answers resulting from different parameters were compared using dissimilarity metrics and visualized using contour plots. In addition, the parameter ranges of general model stability were identified. For the usability evaluation of the web application, cognitive walk-throughs and focus group interviews were conducted with 6 containment scouts located at 2 different local health authorities. They were first asked to complete small tasks with the tools and then express their general impressions of the web application.

RESULTS

The simulation results showed that some statistical models were more sensitive to changes in their parameters than others. For each of the single-person use cases, we determined an area where the respective model could be rated as stable. In contrast, the results of the group use cases highly depended on the user inputs, and thus, no area of parameters with general model stability could be identified. We have also provided a detailed simulation report of the sensitivity analysis. In the user evaluation, the cognitive walk-throughs and focus group interviews revealed that the user interface needed to be simplified and more information was necessary as guidance. In general, the testers rated the web application as helpful, especially for new employees.

CONCLUSIONS

This evaluation study allowed us to refine the EsteR toolkit. Using the sensitivity analysis, we identified suitable model parameters and analyzed how stable the statistical models were in terms of changes in their parameters. Furthermore, the front end of the web application was improved with the results of the conducted cognitive walk-throughs and focus group interviews regarding its user-friendliness.

A Small Surge in Incidence of SARS-CoV-2 Omicron Variant in the "Dynamic Zero" Period

To describe the epidemiological characteristics and transmission dynamics of SARS-CoV-2 Omicron variant during "Dynamic Zero" period, we analyzed data on the 108 laboratory-confirmed SARS-CoV-2 cases during 14 to 30 May 2022 in Beichen district, Tianjin, China. We collected information on demographic characteristics, exposure history, and illness timelines of the 108 cases. We described characteristics of the patients and estimated the key epidemiological parameters, including serial interval and the time-dependent reproduction number of the Omicron variant, Rt. Among the 108 laboratory-confirmed patients, the median age was 38 years old, and 50.9% were females. Obvious symptoms were observed among 67.6% (73/108) of all cases, and major clinical manifestations included fever, sore throat, and cough, which occurred in 31.5%, 26.9%, and 19.4% of the 108 cases, respectively. The mean and standard deviation of the SI were estimated as 2.89 and 0.95 days, the Rt varied from 1.24 to 0.27 for a 7-day timelapse. The low reproduction number and the Omicron outbreak being suppressed within a short time marked the effectiveness of the implemented public health measures, such as nucleic acid screening, social distancing, masking, vaccination, medical treatment of patients, and isolation of close contacts. These measures play an important role in fulfilling the goal of controlling the spread of the disease.

Incubation Period of COVID-19 Caused by Unique SARS-CoV-2 Strains: A Systematic Review and Meta-analysis

IMPORTANCE

Several studies were conducted to estimate the average incubation period of COVID-19; however, the incubation period of COVID-19 caused by different SARS-CoV-2 variants is not well described.

OBJECTIVE

To systematically assess the incubation period of COVID-19 and the incubation periods of COVID-19 caused by different SARS-CoV-2 variants in published studies.

DATA SOURCES

PubMed, EMBASE, and ScienceDirect were searched between December 1, 2019, and February 10, 2022.

STUDY SELECTION

Original studies of the incubation period of COVID-19, defined as the time from infection to the onset of signs and symptoms.

DATA EXTRACTION AND SYNTHESIS

Following the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) reporting guideline, 3 reviewers independently extracted the data from the eligible studies in March 2022. The parameters, or sufficient information to facilitate calculation of those values, were derived from random-effects meta-analysis.

MAIN OUTCOMES AND MEASURES

The mean estimate of the incubation period and different SARS-CoV-2 strains.

RESULTS

A total of 142 studies with 8112 patients were included. The pooled incubation period was 6.57 days (95% CI, 6.26-6.88) and ranged from 1.80 to 18.87 days. The incubation period of COVID-19 caused by the Alpha, Beta, Delta, and Omicron variants were reported in 1 study (with 6374 patients), 1 study (10 patients), 6 studies (2368 patients) and 5 studies (829 patients), respectively. The mean incubation period of COVID-19 was 5.00 days (95% CI, 4.94-5.06 days) for cases caused by the Alpha variant, 4.50 days (95% CI, 1.83-7.17 days) for the Beta variant, 4.41 days (95% CI, 3.76-5.05 days) for the Delta variant, and 3.42 days (95% CI, 2.88-3.96 days) for the Omicron variant. The mean incubation was 7.43 days (95% CI, 5.75-9.11 days) among older patients (ie, aged over 60 years old), 8.82 days (95% CI, 8.19-9.45 days) among infected children (ages 18 years or younger), 6.99 days (95% CI, 6.07-7.92 days) among patients with nonsevere illness, and 6.69 days (95% CI, 4.53-8.85 days) among patients with severe illness.

CONCLUSIONS AND RELEVANCE

The findings of this study suggest that SARS-CoV-2 has evolved and mutated continuously throughout the COVID-19 pandemic, producing variants with different enhanced transmission and virulence. Identifying the incubation period of different variants is a key factor in determining the isolation period.

The incubation period of COVID-19: a global meta-analysis of 53 studies and a Chinese observation study of 11 545 patients

BACKGROUND

The incubation period is a crucial index of epidemiology in understanding the spread of the emerging Coronavirus disease 2019 (COVID-19). In this study, we aimed to describe the incubation period of COVID-19 globally and in the mainland of China.

METHODS

The searched studies were published from December 1, 2019 to May 26, 2021 in CNKI, Wanfang, PubMed, and Embase databases. A random-effect model was used to pool the mean incubation period. Meta-regression was used to explore the sources of heterogeneity. Meanwhile, we collected 11 545 patients in the mainland of China outside Hubei from January 19, 2020 to September 21, 2020. The incubation period fitted with the Log-normal model by the coarseDataTools package.

RESULTS

A total of 3235 articles were searched, 53 of which were included in the meta-analysis. The pooled mean incubation period of COVID-19 was 6.0 days (95% confidence interval [CI] 5.6-6.5) globally, 6.5 days (95% CI 6.1-6.9) in the mainland of China, and 4.6 days (95% CI 4.1-5.1) outside the mainland of China (P = 0.006). The incubation period varied with age (P = 0.005). Meanwhile, in 11 545 patients, the mean incubation period was 7.1 days (95% CI 7.0-7.2), which was similar to the finding in our meta-analysis.

CONCLUSIONS

For COVID-19, the mean incubation period was 6.0 days globally but near 7.0 days in the mainland of China, which will help identify the time of infection and make disease control decisions. Furthermore, attention should also be paid to the region- or age-specific incubation period.

Challenges Caused by Imported Cases Abroad for the Prevention and Control of COVID-19 in China

Background: Overseas imported cases of COVID-19 continue to increase in China, so we conducted this study to review the epidemiological characteristics of these patients.

Methods: From February 26 to April 4, 2020, the imported cases from abroad were enrolled in this study. The effect of prevention countermeasures in curbing the spread of COVID-19 was assessed in this study. Moreover, we defined incubation period and confirmed time as from the date of leaving the epicenter to date of symptom onset and date of final diagnosed, respectively, and the interval of symptom onset to final diagnosed time was defined as diagnostic time. Categorical variables were summarized as numbers and percentages, and the difference among the variables were analyzed.

Results: For 670 cases imported from abroad, 555 were Chinese and 115 were foreigners. Apparently, confirmed cases had significantly decreased after China was compelled to temporarily suspend the entry of foreign passport holders with valid visas or residence permits; 6 days after implement of controlled measures, the daily new confirmed cases were reduced to 13 cases. Moreover, about 84.3% of patients (166/197) presented symptoms 1 week after leaving the epicenter, and notably seven patients (3.6%) had symptoms 2 weeks after leaving the epicenter. The median incubation period was 3.0 days (inter quartile range, 1.0 to 6.0), the 95th percentile was 11.6 days. Additionally, most of cases (92.9%) were detected positively of nucleic acid after symptom onset with 4 days, the median diagnostic time was 2.0 days (interquartile range, 1.0 to 3.0), and the 95th percentile of the distribution was 5.0 days. Finally, about 5.8% of patients were healthy carriers, and the median confirmed time of asymptomatic patients was 4.0 days (interquartile range, 2.0 to 9.0). The following variables might be associated with confirmed time: symptom type (P = 0.005), exported regions (P < 0.001), and symptom onset time (P < 0.001).

Conclusions: The prevention countermeasures for imported cases implemented by the Chinese government played an indispensable role in curbing the spread of COVID-19; the time of departure from epicenter could provide an estimate of the incubation period; and a confirmed time, 2-week quarantine period might need to be prolonged, while asymptomatic patients should be closely monitored.