Impact of antibody-level on viral shedding in B.1.617.2 (Delta) variant-infected patients analyzed using a joint model of longitudinal and time-to-event data.

Knowledge of viral shedding remains limited. Repeated measurement data have been rarely used to explore the influencing factors. In this study, a joint model was developed to explore and validate the factors influencing the duration of viral shedding based on longitudinal data and survival data. We divided 361 patients infected with Delta variant hospitalized in Nanjing Second Hospital into two groups (≤ 21 days group and > 21 days group) according to the duration of viral shedding, and compared their baseline characteristics. Correlation analysis was performed to identify the factors influencing the duration of viral shedding. Further, a joint model was established based on longitudinal data and survival data, and the Markov chain Monte Carlo algorithm was used to explain the influencing factors. In correlation analysis, patients having received vaccination had a higher antibody level at admission than unvaccinated patients, and with the increase of antibody level, the duration of viral shedding shortened. The linear mixed-effects model showed the longitudinal variation of logSARS-COV-2 IgM sample/cutoff (S/CO) values, with a parameter estimate of 0.193 and a standard error of 0.017. Considering gender as an influencing factor, the parameter estimate of the Cox model and their standard error were 0.205 and 0.1093 (P = 0.608), the corresponding OR value was 1.228. The joint model output showed that SARS-COV-2 IgM (S/CO) level was strongly associated with the risk of a composite event at the 95% confidence level, and a doubling of SARS-COV-2 IgM (S/CO) level was associated with a 1.38-fold (95% CI: [1.16, 1.72]) increase in the risk of viral non-shedding. A higher antibody level in vaccinated patients, as well as the presence of IgM antibodies in serum, can accelerate shedding of the mutant virus. This study provides some evidence support for vaccine prevention and control of COVID-19 variants.

Impact of inactivated COVID-19 vaccines on lung injury in B.1.617.2 (Delta) variant-infected patients.

BACKGROUND: Chest computerized tomography (CT) scan is an important strategy that quantifies the severity of COVID-19 pneumonia. To what extent inactivated COVID-19 vaccines could impact the COVID-19 pneumonia on chest CT is not clear. METHODS: This study recruited 357 SARS-COV-2 B.1.617.2 (Delta) variant-infected patients admitted to the Second Hospital of Nanjing from July to August 2021. An artificial intelligence-assisted CT imaging system was used to quantify the severity of COVID-19 pneumonia. We compared the volume of infection (VOI), percentage of infection (POI) and chest CT scores among patients with different vaccination statuses. RESULTS: Of the 357 Delta variant-infected patients included for analysis, 105 were unvaccinated, 72 were partially vaccinated and 180 were fully vaccinated. Fully vaccination had the least lung injuries when quantified by VOI (median VOI of 222.4 cm3, 126.6 cm3 and 39.9 cm3 in unvaccinated, partially vaccinated and fully vaccinated, respectively; p < 0.001), POI (median POI of 7.60%, 3.55% and 1.20% in unvaccinated, partially vaccinated and fully vaccinated, respectively; p < 0.001) and chest CT scores (median CT score of 8.00, 6.00 and 4.00 in unvaccinated, partially vaccinated and fully vaccinated, respectively; p < 0.001). After adjustment for age, sex, comorbidity, time from illness onset to hospitalization and viral load, fully vaccination but not partial vaccination was significantly associated with less lung injuries quantified by VOI {adjust coefficient[95%CI] for "full vaccination": - 106.10(- 167.30,44.89); p < 0.001}, POI {adjust coefficient[95%CI] for "full vaccination": - 3.88(- 5.96, - 1.79); p = 0.001} and chest CT scores {adjust coefficient[95%CI] for "full vaccination": - 1.81(- 2.72, - 0.91); p < 0.001}. The extent of reduction of pulmonary injuries was more profound in fully vaccinated patients with older age, having underlying diseases, and being female sex, as demonstrated by relatively larger absolute values of adjusted coefficients. Finally, even within the non-severe COVID-19 population, fully vaccinated patients were found to have less lung injuries. CONCLUSION: Fully vaccination but not partially vaccination could significantly protect lung injury manifested on chest CT. Our study provides additional evidence to encourage a full course of vaccination.

Study on the COVID-19 epidemic in mainland China between November 2022 and January 2023, with prediction of its tendency.

The prediction system EpiSIX was used to study the COVID-19 epidemic in mainland China between November 2022 and January 2023, based on reported data from December 9, 2022, to January 30, 2023, released by The Chinese Center for Disease Control and Prevention on February 1, 2023. Three kinds of reported data were used for model fitting: the daily numbers of positive nucleic acid tests and deaths, and the daily number of hospital beds taken by COVID-19 patients. It was estimated that the overall infection rate was 87.54% and the overall case fatality rate was 0.078%-0.116% (median 0.100%). Assuming that a new COVID-19 epidemic outbreak would start in March or April of 2023, induced by a slightly more infectious mutant strain, we predicted a possible large rebound between September and October 2023, with a peak demand of between 800,000 and 900,000 inpatient beds. If no such new outbreak was induced by other variants, then the current COVID-19 epidemic course in mainland China would remain under control until the end of 2023. However, it is suggested that the necessary medical resources be prepared to manage possible COVID-19 epidemic emergencies in the near future, especially for the period between September and October 2023.

A dynamic modeling study on the effects of Wuhan traffic control and centralized quarantine measures on COVID-19 epidemic. (Special Issue: Spatio-temporal dynamics analysis, risk assessment and emergency management for COVID-19 epidemic.) [Chinese]

Objective: To quantitatively evaluate the effects of traffic control and centralized quarantine measures on COVID-19 epidemic in Wuhan, so as to provide scientific basis for epidemic prevention and control. Methods The SEIAHR model was established based on SEIR dynamic model, which took into account the characteristics of asymptomatic carriers and unconfirmed quarantined patients. Based on the timing of prevention measures, the epidemic was divided into three stages, the parameters were fitted, the basic reproduction numbers of different stages were calculated, and the development trend of epidemic was predicted. Results The R0 decreased dramatically. The R0 of the three stages were 3.684 1(95%CI: 3.106 1-4.048 0), 2.178 8(95%CI: 1.725 8-3.577 6)and 0.362 5(95%CI: 0.349 9-0.367 6), respectively. Due to the traffic control travel and centralized quarantine, the peak of the disease moved forward from April 19 to March 14, 2020. The scale of the epidemic had also been reduced by prevention and control measures. Conclusion The traffic control and centralized quarantine measures implemented in Wuhan were effective for the epidemic control, which can provide reference for other countries.

A tentative assessment of the changes in transmissibility and fatality risk associated with Beta SARS-CoV-2 variants in South Africa: an ecological study.

The circulation of SARS-CoV-2 Beta (B.1.351) variants challenged the control of COVID-19 pandemic. The numbers of COVID-19 cases and deaths and SARS-CoV-2 sequences in South Africa were collected. We reconstructed the variant-specified reproduction numbers (R t) and delay-adjusted case fatality ratio (CFR) to examine the changes in transmissibility and fatality risk of Beta over non-Beta variants. We estimated that Beta variants were 41% (95%CI: 16, 73) more transmissible and 53% (95%CI: 6, 108) more fatal than non-Beta variants. Higher risks of infection and fatality might lead to increasing volumes of infections and critical patients.

Impacts The circulation of SARS-CoV-2 Beta (B.1.351) variants, which were firstly reported in South Africa, challenged the control of COVID-19 pandemic.Using the national-wide COVID-19 cases and SARS-CoV-2 sequences data, Beta variants were estimated 41% more transmissible and 53% more fatal than non-Beta variants in South Africa.Higher risks of infection and fatality might lead to increasing volumes of infections and critical patients.

Environmental sampling of the severe acute respiratory syndrome coronavirus 2 Delta variant in the inpatient wards of a hospital in Nanjing.

BACKGROUND: Little is known about severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Delta variant of concern (VOC)-contaminated environmental surfaces and air in hospital wards admitting COVID-19 cases. Our study was designed to identify high-risk areas of Delta VOC contamination in the hospital and provide suggestions to in-hospital infection control. We analyzed the SARS-CoV-2 Delta VOC contamination in the air and environmental surface samples collected from a hospital in Nanjing, China. METHODS: We collected data on clinical features, laboratory tests, swab tests, and hospital wards, identified the factors associated with environmental contamination, and analyzed patients' hygiene behaviors during hospitalization. RESULTS: A total of 283 environmental surface and air samples were collected from a hospital admitting 36 COVID-19 patients. Twelve swab samples from ten patients were positive. Toilet seats had the highest contamination rate (11.8%), followed by bedside tables (8.2%), garbage bins (5.9%), and bedrails (1.6%). The median time of symptom onset to surface sampling was shorter in the positive environment group than in the negative environment group (11 vs. 18 days; P=0.001). The results indicated that environmental surface contamination was associated with positive anal swabs [odds ratio (OR) 27.183; 95% CI: 2.359-226.063; P=0.003] and the time from symptom onset to surface sampling (OR 0.801; 95% CI: 0.501-0.990; P=0.046). The survey revealed that 33.3% of the patients never cleaned or disinfected their bedside tables or toilets, and 8.3% of them only cleaned their bedside tables or toilets. More than half of the patients often (25%) or always (30.6%) put the used masks on their bedside tables. Only 16.7% of the patients threw the masks into the specific garbage bin for used masks. CONCLUSIONS: The SARS-CoV-2 Delta VOC was detected on environmental surfaces, especially toilet seats and bedside tables, within a median time of 11 days after symptom onset. Our study provided potential predictors for environmental surface contamination, including positive anal swabs and the time from symptom onset to sampling. Disinfecting high-risk environmental surfaces should be emphasized in hospital wards, especially for patients in the early stage of COVID-19.

Impact of interventions on the incidence of natural focal diseases during the outbreak of COVID-19 in Jiangsu Province, China.

BACKGROUND: During the period of the coronavirus disease 2019 (COVID-19) outbreak, strong intervention measures, such as lockdown, travel restriction, and suspension of work and production, may have curbed the spread of other infectious diseases, including natural focal diseases. In this study, we aimed to study the impact of COVID-19 prevention and control measures on the reported incidence of natural focal diseases (brucellosis, malaria, hemorrhagic fever with renal syndrome [HFRS], dengue, severe fever with thrombocytopenia syndrome [SFTS], rabies, tsutsugamushi and Japanese encephalitis [JE]). METHODS: The data on daily COVID-19 confirmed cases and natural focal disease cases were collected from Jiangsu Provincial Center for Disease Control and Prevention (Jiangsu Provincial CDC). We described and compared the difference between the incidence in 2020 and the incidence in 2015-2019 in four aspects: trend in reported incidence, age, sex, and urban and rural distribution. An autoregressive integrated moving average (ARIMA) (p, d, q) × (P, D, Q)s model was adopted for natural focal diseases, malaria and severe fever with thrombocytopenia syndrome (SFTS), and an ARIMA (p, d, q) model was adopted for dengue. Nonparametric tests were used to compare the reported and the predicted incidence in 2020, the incidence in 2020 and the previous 4 years, and the difference between the duration from illness onset date to diagnosed date (DID) in 2020 and in the previous 4 years. The determination coefficient (R2) was used to evaluate the goodness of fit of the model simulation. RESULTS: Natural focal diseases in Jiangsu Province showed a long-term seasonal trend. The reported incidence of natural focal diseases, malaria and dengue in 2020 was lower than the predicted incidence, and the difference was statistically significant (P < 0.05). The reported incidence of brucellosis in July, August, October and November 2020, and SFTS in May to November 2020 was higher than that in the same period in the previous 4 years (P < 0.05). The reported incidence of malaria in April to December 2020, HFRS in March, May and December 2020, and dengue in July to November 2020 was lower than that in the same period in the previous 4 years (P < 0.05). In males, the reported incidence of malaria in 2020 was lower than that in the previous 4 years, and the reported incidence of dengue in 2020 was lower than that in 2017-2019. The reported incidence of malaria in the 20-60-year age group was lower than that in the previous 4 years; the reported incidence of dengue in the 40-60-year age group was lower than that in 2016-2018. The reported cases of malaria in both urban and rural areas were lower than in the previous 4 years. The DID of brucellosis and SFTS in 2020 was shorter than that in 2015-2018; the DID of tsutsugamushi in 2020 was shorter than that in the previous 4 years. CONCLUSIONS: Interventions for COVID-19 may help control the epidemics of natural focal diseases in Jiangsu Province. The reported incidence of natural focal diseases, especially malaria and dengue, decreased during the outbreak of COVID-19 in 2020. COVID-19 prevention and control measures had the greatest impact on the reported incidence of natural focal diseases in males and people in the 20-60-year age group.

Estimating the generation interval and inferring the latent period of COVID-19 from the contact tracing data.

The coronavirus disease 2019 (COVID-19) emerged by end of 2019, and became a serious public health threat globally in less than half a year. The generation interval and latent period, though both are of importance in understanding the features of COVID-19 transmission, are difficult to observe, and thus they can rarely be learnt from surveillance data empirically. In this study, we develop a likelihood framework to estimate the generation interval and incubation period simultaneously by using the contact tracing data of COVID-19 cases, and infer the pre-symptomatic transmission proportion and latent period thereafter. We estimate the mean of incubation period at 6.8 days (95 %CI: 6.2, 7.5) and SD at 4.1 days (95 %CI: 3.7, 4.8), and the mean of generation interval at 6.7 days (95 %CI: 5.4, 7.6) and SD at 1.8 days (95 %CI: 0.3, 3.8). The basic reproduction number is estimated ranging from 1.9 to 3.6, and there are 49.8 % (95 %CI: 33.3, 71.5) of the secondary COVID-19 infections likely due to pre-symptomatic transmission. Using the best estimates of model parameters, we further infer the mean latent period at 3.3 days (95 %CI: 0.2, 7.9). Our findings highlight the importance of both isolation for symptomatic cases, and for the pre-symptomatic and asymptomatic cases.

Evaluating the impact of the travel ban within mainland China on the epidemic of the COVID-19.

OBJECTIVES: The ongoing COVID-19 pandemic expanded its geographic distribution through the movement of humans and caused subsequent local outbreaks. Hence, it is essential to investigate how human mobility and travel ban affect the transmission and spatial spread while minimizing the impact on social activities and national economics. METHODS: We developed a mobility network model for spatial epidemics, explicitly taking into account time-varying inter-province and inner-province population flows, spatial heterogeneity in terms of disease transmission, as well as the impact of media reports. The model is applied to study the epidemic of the dynamic network of 30 provinces of mainland China. The model was calibrated using the publicly available incidence and movement data. RESULTS: We estimated that the second outbreak occurred approximately on February 24, 2020, and the cumulative number of cases as of March 15, 2020, increased by 290.1% (95% CI: (255.3%, 324.9%)) without a travel ban in mainland China (excluding Hubei and Tibet). We found that intra-province travel contributes more to the increase of cumulative number of cases than inter-province travel. CONCLUSION: Our quantitative and qualitative research results suggest that the strict travel ban has successfully prevented a severe secondary outbreak in mainland China, which provides solutions for many countries and regions experiencing secondary outbreaks of COVID-19.

Epidemiology of 631 Cases of COVID-19 Identified in Jiangsu Province Between January 1st and March 20th 2020: Factors Associated with Disease Severity and Analysis of Zero Mortality.

BACKGROUND This retrospective study aimed to investigate the factors associated with disease severity and patient outcomes in 631 patients with COVID-19 who were reported to the Jiangsu Commission of Health between January 1 and March 20, 2020. MATERIAL AND METHODS We conducted an epidemiological investigation enrolling 631 patients with laboratory-confirmed COVID-19 from our clinic from January to March 2020. Patients' information was collected through a standard questionnaire. Then, we described the patients' epidemiological characteristics, analyzed risk factors associated with disease severity, and assessed causes of zero mortality. Additionally, some key technologies for epidemic prevention and control were identified. RESULTS Of the 631 patients, 8.46% (n=53) were severe cases, and no deaths were recorded (n=0). The epidemic of COVID-19 has gone through 4 stages: a sporadic phase, an exponential growth phase, a peak plateau phase, and a declining phase. The proportion of severe cases was significantly different among the 4 stages and 13 municipal prefectures (P<0.001). Factors including age >65 years old, underlying medical conditions, highest fever >39.0°C, dyspnea, and lymphocytopenia (<1.0×109/L) were early warning signs of disease severity (P<0.05). In contrast, earlier clinic visits were associated with better patient outcomes (P=0.029). Further, the viral load was a potentially useful marker associated with COVID-19 infection severity. CONCLUSIONS The study findings from the beginning of the COVID-19 epidemic in Jiangsu Province, China showed that patients who were more than 65 years of age and with comorbidities and presented with a fever of more than 39.0°C developed more severe disease. However, mortality was prevented in this initial patient population by early supportive clinical management.

Inferencing superspreading potential using zero-truncated negative binomial model: exemplification with COVID-19.

BACKGROUND: In infectious disease transmission dynamics, the high heterogeneity in individual infectiousness indicates that few index cases generate large numbers of secondary cases, which is commonly known as superspreading events. The heterogeneity in transmission can be measured by describing the distribution of the number of secondary cases as a negative binomial (NB) distribution with dispersion parameter, k. However, such inference framework usually neglects the under-ascertainment of sporadic cases, which are those without known epidemiological link and considered as independent clusters of size one, and this may potentially bias the estimates. METHODS: In this study, we adopt a zero-truncated likelihood-based framework to estimate k. We evaluate the estimation performance by using stochastic simulations, and compare it with the baseline non-truncated version. We exemplify the analytical framework with three contact tracing datasets of COVID-19. RESULTS: We demonstrate that the estimation bias exists when the under-ascertainment of index cases with 0 secondary case occurs, and the zero-truncated inference overcomes this problem and yields a less biased estimator of k. We find that the k of COVID-19 is inferred at 0.32 (95%CI: 0.15, 0.64), which appears slightly smaller than many previous estimates. We provide the simulation codes applying the inference framework in this study. CONCLUSIONS: The zero-truncated framework is recommended for less biased transmission heterogeneity estimates. These findings highlight the importance of individual-specific case management strategies to mitigate COVID-19 pandemic by lowering the transmission risks of potential super-spreaders with priority.

Estimating the time interval between transmission generations and the presymptomatic period by contact tracing surveillance data from 31 provinces in the mainland of China

The global pandemic of 2019 coronavirus disease (COVID-19) is a great assault to public health. Presymptomatic transmission cannot be controlled with measures designed for symptomatic persons, such as isolation. This study aimed to estimate the interval of the transmission generation (TG) and the presymptomatic period of COVID-19, and compare the fitting effects of TG and serial interval (SI) based on the SEIHR model incorporating the surveillance data of 3453 cases in 31 provinces. These data were allocated into three distributions and the value of AIC presented that the Weibull distribution fitted well. The mean of TG was 5.2 days (95% CI: 4.6-5.8). The mean of the presymptomatic period was 2.4 days (95% CI: 1.5-3.2). The dynamic model using TG as the generation time performed well. Eight provinces exhibited a basic reproduction number from 2.16 to 3.14. Measures should be taken to control presymptomatic transmission in the COVID-19 pandemic.

Immunity and coagulation and fibrinolytic processes may reduce the risk of severe illness in pregnant women with coronavirus disease 2019.

BACKGROUND: There are specific physiological features regarding the immunity and coagulation among pregnant women, which may play important roles in the development of coronavirus disease 2019. OBJECTIVE: This study aimed to determine the key factors associated with the deterioration of patients with coronavirus disease 2019 and the differentiating clinical characteristics of pregnant women with coronavirus disease 2019 to interfere with the progression of coronavirus disease 2019. STUDY DESIGN: A retrospective study of 539 Chinese Han adult patients with coronavirus disease 2019 was conducted, of which 36 cases were pregnant women. In addition, 36 pregnant women without coronavirus disease 2019 were recruited as the control. The characteristics of severe and critical illnesses, which were differentiated from mild and moderate illnesses in patients with coronavirus disease 2019, were analyzed using a machine learning algorithm. In addition, major differences between pregnant women with coronavirus disease 2019 and age-matched nonpregnant women with severe or critical coronavirus disease 2019, paired with pregnant women without coronavirus disease 2019, were explored to identify specific physiological features of pregnant women with coronavirus disease 2019. RESULTS: For the total patient population, the lymphocyte, CD3+, CD4+, CD8+, CD19+, and CD16+CD56+ cell counts were significantly lower, and white blood cell count, neutrophil count, and neutrophil-to-lymphocyte ratio were higher in those with severe or critical illness than those with mild or moderate illness (P<.001). The plasma levels of interleukin-6, interleukin-10, and interleukin-6-to-interleukin-10 ratio were significantly increased in patients with critical illness compared with patients with mild, moderate, and severe illnesses (P<.001). The above immunologic coclusters achieved an area under the receiver operating characteristic curve of 0.801 (95% confidence interval, 0.764-0.838), and its combined model with the coagulation and fibrinolysis indices (prothrombin time, D-dimer) achieved an area under the receiver operating characteristic curve of 0.815 (95% confidence interval, 0.779-0.851) using the random forest regression model to predict severe or critical illness. For pregnant women with coronavirus disease 2019, none had preexisting diseases. Compared with nonpregnant women with mild or moderate coronavirus disease 2019, pregnant women with coronavirus disease 2019 displayed increased white blood cell count, neutrophil count, neutrophil-to-lymphocyte ratio, and levels of D-dimer and fibrinogen, along with decreased lymphocyte and interleukin-4 levels (P<.05). Although they presented similar changes of immunologic markers of lymphocyte; white blood cell count; neutrophil-to-lymphocyte ratio; CD3+, CD4+, CD8+, and CD16+CD56+ cell counts; and interleukin-6-to-interleukin-10 ratio, compared with nonpregnant women with severe or critical coronavirus disease 2019, none of the pregnant women with coronavirus disease 2019 deteriorated into severe or critical illness. There was no significant difference in white blood cell count, lymphocyte count, neutrophil count, neutrophil-to-lymphocyte ratio, immunologic markers, or coagulation and fibrinolysis markers between pregnant women with coronavirus disease 2019 and pregnant women without coronavirus disease 2019. As for the discrepancy of pathophysiological features between pregnant women with coronavirus disease 2019 and nonpregnant women with severe or critical coronavirus disease 2019, the immunologic markers achieved an area under the receiver operating characteristic curve of 0.875 (95% confidence interval, 0.773-0.977), and its combined model with coagulation and fibrinolysis indices achieved an area under the receiver operating characteristic curve of 0.931 (95% confidence interval, 0.850-1.000). CONCLUSION: Immune dysregulation was identified as a crucial feature of patients with coronavirus disease 2019, which developed severe or critical illness, and pregnant women with coronavirus disease 2019 presented with similar immune responses but rarer incidences of severe or critical illness. Immune dysregulation is related to the risks of deterioration into severe or critical illness. The specific coagulation and fibrinolysis systems of pregnancy may reduce the risk of pregnant women with coronavirus disease 2019 without preexisting disease from developing severe illness.

Initial COVID-19 Transmissibility and Three Gaseous Air Pollutants (NO2, SO2, and CO): A Nationwide Ecological Study in China.

In this study, we conducted an ecological study to examine their effects in the early phase of the pandemic (from December 2019 to February 2020) in China. We found that the associations between the average concentrations of NO2, SO2, and CO and the COVID-19 transmissibility are not statistically clear.

Tendency prediction of COVID-19 worldwide

Objective: We give projections of the COVID-19 epidemic course worldwide.

Massive migration promotes the early spread of COVID-19 in China: a study based on a scale-free network.

BACKGROUND: The coronavirus disease 2019 (COVID-19) epidemic met coincidentally with massive migration before Lunar New Year in China in early 2020. This study is to investigate the relationship between the massive migration and the coronavirus disease 2019 (COVID-19) epidemic in China. METHODS: The epidemic data between January 25th and February 15th and migration data between Jan 1st and Jan 24th were collected from the official websites. Using the R package WGCNA, we established a scale-free network of the selected cities. Correlation analysis was applied to describe the correlation between the Spring Migration and COVID-19 epidemic. RESULTS: The epidemic seriousness in Hubei (except the city of Wuhan) was closely correlated with the migration from Wuhan between January 10 and January 24, 2020. The epidemic seriousness in the other provinces, municipalities and autonomous regions was largely affected by the immigration from Wuhan. By establishing a scale-free network of the regions, we divided the regions into two modules. The regions in the brown module consisted of three municipalities, nine provincial capitals and other 12 cities. The COVID-19 epidemics in these regions were more likely to be aggravated by migration. CONCLUSIONS: The migration from Wuhan could partly explain the epidemic seriousness in Hubei Province and other regions. The scale-free network we have established can better evaluate the epidemic. Three municipalities (Beijing, Shanghai and Tianjin), eight provincial capitals (including Nanjing, Changsha et al.) and 12 other cities (including Qingdao, Zhongshan, Shenzhen et al.) were hub cities in the spread of COVID-19 in China.

COVID-19 outbreak following a single patient exposure at an entertainment site: An epidemiological study.

We investigated an outbreak of COVID-19 infection, which was traced back to a bathing pool at an entertainment venue, to explore the epidemiology of the outbreak, understand the transmissibility of the virus and analyse the influencing factors. Contact investigation and management were conducted to identify potential cases. Epidemiological investigation was carried out to determine the epidemiological and demographic characteristics of the outbreak. We estimated the secondary attack rate (SAR), incubation time and time-dependent reproductive number (Rt ) and explored the predisposing factors for cluster infection. The incubation time was 5.4 days and the serial interval (SI) was 4.4 days, with the rate of negative-valued SIs at 24.5%. The SAR at the bathing pool (3.3%) was relatively low due to its high temperature and humidity. The SAR was higher in the colleagues' cluster (20.5%) than in the family cluster (11.8%). Super-spreaders had a longer isolation delay time (p = .004). The Rt of the cluster decreased from the highest value of 3.88 on January 27, 2020 to 1.22 on February 6. Our findings suggest that the predisposing factors of the outbreak included close contact with an infected person, airtight and crowded spaces, temperature and humidity in the space and untimely isolation of patients and quarantine of contacts at the early stage of transmission. Measures to reduce the risk of infection at these gatherings and subsequent tracking of close contacts were effective.

Clinical Use of Short-Course and Low-Dose Corticosteroids in Patients With Non-severe COVID-19 During Pneumonia Progression.

Background: The emerging coronavirus disease 2019 (COVID-19) has become a serious public health concern with a high number of fatalities. It is unclear whether corticosteroids could be a candidate for an early intervention strategy for patients with COVID-19. Methods: In this retrospective cohort study, we analyzed data from 28 corticosteroid-treated patients with non-severe but advanced COVID-19, in which short-course and low-dose corticosteroids were administered because of unremitting or worsening clinical conditions during hospitalization. To compare the effect of corticosteroids on viral clearance, 44 corticosteroid-untreated patients were included as controls. Results: At the time of admission, corticosteroid-treated patients (n = 28) had a more advanced baseline illness compared with corticosteroid-untreated patients (n = 44), as reflected by poorer blood laboratory parameters (lymphocytes, C-reactive protein, and lactate dehydrogenase) and more extensive chest computed tomography (CT) abnormalities. Corticosteroids were given because of radiological evidence of pneumonia progression (26/28) and/or unremitting fever (22/28) after admission. The median time from illness onset to corticosteroid treatment was 9 days (IQR, 7-10). The median duration and accumulated dose of corticosteroid treatment were 4.5 days [interquartile range (IQR), 3-5] and 140 mg of methylprednisolone (IQR, 120-200). Intravenous immunoglobulin (20 g per day for 3-5 days) was co-administered with corticosteroids. With the corticosteroid treatment, all patients achieved an abatement of fever within 1 day, and 78.6% (22/28) of the patients achieved radiological remission when evaluated about 3 days later. Only one (3.6%) patient progressed to severe COVID-19, and all patients recovered and were discharged without any sequela. The median time from illness onset to viral clearance was similar, as compared with 44 corticosteroid-untreated patients with relatively milder disease [18 (IQR 14.3-23.5) days vs. 17 (IQR, 12-20) days, p = 0.252]. When adjusted for age, sex, underlying comorbidities, baseline blood laboratory parameters, viral load, and chest radiological findings, the causal hazard ratio of corticosteroid treatment for the viral clearance was 0.79 (95%CI, 0.48-1.30, p = 0.34). Conclusion: Short-course and low-dose applications of corticosteroids, when co-administered with intravenous immunoglobulin, in non-severe COVID-19 patients during the stage of clinical deterioration may possibly prevent disease progression, while having a negligible impact on the viral clearance.