[Epidemiological investigation of a family clustering of COVID-19]

Clinical Phenotypes and Mortality Biomarkers: A Study Focused on COVID-19 Patients with Neurological Diseases in Intensive Care Units

Purpose: To identify clinical phenotypes and biomarkers for best mortality prediction considering age, symptoms and comorbidities in COVID-19 patients with chronic neurological diseases in intensive care units (ICUs). Subjects and Methods: Data included 1252 COVID-19 patients admitted to ICUs in Cuba between January and August 2021. A k-means algorithm based on unsupervised learning was used to identify clinical patterns related to symptoms, comorbidities and age. The Stable Sparse Classifiers procedure (SSC) was employed for predicting mortality. The classification performance was assessed using the area under the receiver operating curve (AUC). Results: Six phenotypes using a modified v-fold cross validation for the k-means algorithm were identified: phenotype class 1, mean age 72.3 years (ys)-hypertension and coronary artery disease, alongside typical COVID-19 symptoms; class 2, mean age 63 ys-asthma, cough and fever; class 3, mean age 74.5 ys-hypertension, diabetes and cough; class 4, mean age 67.8 ys-hypertension and no symptoms; class 5, mean age 53 ys-cough and no comorbidities; class 6, mean age 60 ys-without symptoms or comorbidities. The chronic neurological disease (CND) percentage was distributed in the six phenotypes, predominantly in phenotypes of classes 3 (24.72%) and 4 (35,39%); χ² (5) 11.0129 p = 0.051134. The cerebrovascular disease was concentrated in classes 3 and 4; χ² (5) = 36.63, p = 0.000001. The mortality rate totaled 325 (25.79%), of which 56 (17.23%) had chronic neurological diseases. The highest in-hospital mortality rates were found in phenotypes 1 (37.22%) and 3 (33.98%). The SSC revealed that a neurological symptom (ageusia), together with two neurological diseases (cerebrovascular disease and Parkinson's disease), and in addition to ICU days, age and specific symptoms (fever, cough, dyspnea and chilliness) as well as particular comorbidities (hypertension, diabetes and asthma) indicated the best prediction performance (AUC = 0.67). Conclusions: The identification of clinical phenotypes and mortality biomarkers using practical variables and robust statistical methodologies make several noteworthy contributions to basic and experimental investigations for distinguishing the COVID-19 clinical spectrum and predicting mortality.

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.

Comprehensive estimation for the length and dispersion of COVID-19 incubation period: a systematic review and meta-analysis

PURPOSE

To estimate the central tendency and dispersion for incubation period of COVID-19 and, in turn, assess the effect of a certain length of quarantine for close contacts in active monitoring.

METHODS

Literature related to SARS-CoV-2 and COVID-19 was searched through April 26, 2020. Quality was assessed according to Agency for Healthcare Research and Quality guidelines. Log-normal distribution for the incubation period was assumed to estimate the parameters for each study. Incubation period median and dispersion were estimated, and distribution was simulated.

RESULTS

Fifty-six studies encompassing 4095 cases were included in this meta-analysis. The estimated median incubation period for general transmissions was 5.8 days [95% confidence interval (95% CI): 5.3, 6.2]. Incubation period was significantly longer for asymptomatic transmissions (median: 7.7 days; 95% CI 6.3, 9.4) than for general transmissions (P = 0.0408). Median and dispersion were higher for SARS-CoV-2 incubation compared to other viral respiratory infections. Furthermore, about 12 in 10,000 contacts in active monitoring would develop symptoms after 14 days, or below 1 in 10,000 for asymptomatic transmissions. Meta-regression suggested that each 10-year increase in age resulted in an average 16% increment in length of median incubation (incubation period ratio, 1.16, 95% CI 1.01, 1.32; P = 0.0250).

CONCLUSION

This study estimated the median and dispersion of the SARS-CoV-2 incubation period more precisely. A 14-day quarantine period is sufficient to trace and identify symptomatic infections.

Epidemiological investigation of a COVID-19 family cluster outbreak transmitted by a 3-month-old infant

Objective

To investigate the clinical characteristics, epidemiological characteristics, and transmissibility of coronavirus disease 2019 (COVID-19) in a family cluster outbreak transmitted by a 3-month-old confirmed positive infant.

Methods

Field-based epidemiological methods were used to investigate cases and their close contacts. Real-time fluorescent reverse transcription polymerase chain reaction (RT-PCR) was used to detect Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) for all collected specimens. Serum SARS-CoV-2 IgM and IgG antibodies were detected by Chemiluminescence and Gold immnnochromatography (GICA).

Results

The outbreak was a family cluster with an attack rate of 80% (4/5). The first case in this family was a 3-month-old infant. The transmission chain was confirmed from infant to adults (her father, mother and grandmother). Fecal tests for SARS-CoV-2 RNA remained positive for 37 days after the infant was discharged. The infant's grandmother was confirmed to be positive 2 days after the infant was discharged from hospital. Patients A (3-month-old female), B (patient A's father), C (patient A's grandmother), and D (patient A's mother) had positive serum IgG and negative IgM, but patients A's grandfather serum IgG and IgM were negative.

Conclusion

SARS-CoV-2 has strong transmissibility within family settings and presence of viral RNA in stool raises concern for possible fecal-oral transmission. Hospital follow-up and close contact tracing are necessary for those diagnosed with COVID-19.

Comorbidities, clinical signs and symptoms, laboratory findings, imaging features, treatment strategies, and outcomes in adult and pediatric patients with COVID-19: A systematic review and meta-analysis

INTRODUCTION

Since December 2019, a novel coronavirus (SARS-CoV-2) has triggered a world-wide pandemic with an enormous medical and societal-economic toll. Thus, our aim was to gather all available information regarding comorbidities, clinical signs and symptoms, outcomes, laboratory findings, imaging features, and treatments in patients with coronavirus disease 2019 (COVID-19).

METHODS

EMBASE, PubMed/Medline, Scopus, and Web of Science were searched for studies published in any language between December 1st, 2019 and March 28th, 2020. Original studies were included if the exposure of interest was an infection with SARS-CoV-2 or confirmed COVID-19. The primary outcome was the risk ratio of comorbidities, clinical signs and symptoms, laboratory findings, imaging features, treatments, outcomes, and complications associated with COVID-19 morbidity and mortality. We performed random-effects pairwise meta-analyses for proportions and relative risks, I2, T2, and Cochrane Q, sensitivity analyses, and assessed publication bias.

RESULTS

148 studies met the inclusion criteria for the systematic review and meta-analysis with 12'149 patients (5'739 female) and a median age of 47.0 [35.0-64.6] years. 617 patients died from COVID-19 and its complication. 297 patients were reported as asymptomatic. Older age (SMD: 1.25 [0.78-1.72]; p < 0.001), being male (RR = 1.32 [1.13-1.54], p = 0.005) and pre-existing comorbidity (RR = 1.69 [1.48-1.94]; p < 0.001) were identified as risk factors of in-hospital mortality. The heterogeneity between studies varied substantially (I2; range: 1.5-98.2%). Publication bias was only found in eight studies (Egger's test: p < 0.05).

CONCLUSIONS

Our meta-analyses revealed important risk factors that are associated with severity and mortality of COVID-19.

Clinical and Demographic Characteristics of Cluster Cases and Sporadic Cases of Coronavirus Disease 2019 (COVID-19) in 141 Patients in the Main District of Chongqing, China, Between January and February 2020

BACKGROUND In December 2019, an outbreak of coronavirus disease 2019 (COVID-19), due to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), occurred in Wuhan, China. Patients with COVID-19 were also identified in Chongqing. This study aimed to investigate the clinical and demographic characteristics of cluster cases and sporadic cases of COVID-19 in 141 patients in the main district of Chongqing during one month, between January and February 2020. MATERIAL AND METHODS A retrospective study included 141 patients with a diagnosis of COVID-19. The diagnosis was confirmed using real-time reverse transcription-polymerase chain reaction (RT-PCR) for SARS-CoV-2. The patients were divided into cluster cases (n=90) and sporadic cases (n=51). Demographic and clinical characteristics were compared between the two study groups and included the presence of comorbidities, the presenting symptoms, chest computed tomography (CT) imaging findings, and laboratory findings. RESULTS The mean age of the 141 patients diagnosed with COVID-19 was 47.3 years, and the most common presenting symptom was a persistent cough (48.9%). The 90 cluster cases (63.8%) were older than the sporadic cases, and cross-infection from family gathering occurred in 82.2%, and cough was more common than fever, and there was an increased prevalence of asymptomatic, mild, and moderate cases. Cluster cases showed fewer typical manifestations of COVID-19 on chest CT. However, the laboratory findings between the cluster and sporadic cases showed no significant differences. CONCLUSIONS There were demographic and clinical differences between cluster cases and sporadic cases of COVID-19 in the main district of Chongqing during the month between January to February 2020.

Mining the Characteristics of COVID-19 Patients in China: Analysis of Social Media Posts

BACKGROUND

In December 2019, pneumonia cases of unknown origin were reported in Wuhan City, Hubei Province, China. Identified as the coronavirus disease (COVID-19), the number of cases grew rapidly by human-to-human transmission in Wuhan. Social media, especially Sina Weibo (a major Chinese microblogging social media site), has become an important platform for the public to obtain information and seek help.

OBJECTIVE

This study aims to analyze the characteristics of suspected or laboratory-confirmed COVID-19 patients who asked for help on Sina Weibo.

METHODS

We conducted data mining on Sina Weibo and extracted the data of 485 patients who presented with clinical symptoms and imaging descriptions of suspected or laboratory-confirmed cases of COVID-19. In total, 9878 posts seeking help on Sina Weibo from February 3 to 20, 2020 were analyzed. We used a descriptive research methodology to describe the distribution and other epidemiological characteristics of patients with suspected or laboratory-confirmed SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) infection. The distance between patients' home and the nearest designated hospital was calculated using the geographic information system ArcGIS.

RESULTS

All patients included in this study who sought help on Sina Weibo lived in Wuhan, with a median age of 63.0 years (IQR 55.0-71.0). Fever (408/485, 84.12%) was the most common symptom. Ground-glass opacity (237/314, 75.48%) was the most common pattern on chest computed tomography; 39.67% (167/421) of families had suspected and/or laboratory-confirmed family members; 36.58% (154/421) of families had 1 or 2 suspected and/or laboratory-confirmed members; and 70.52% (232/329) of patients needed to rely on their relatives for help. The median time from illness onset to real-time reverse transcription-polymerase chain reaction (RT-PCR) testing was 8 days (IQR 5.0-10.0), and the median time from illness onset to online help was 10 days (IQR 6.0-12.0). Of 481 patients, 32.22% (n=155) lived more than 3 kilometers away from the nearest designated hospital.

CONCLUSIONS

Our findings show that patients seeking help on Sina Weibo lived in Wuhan and most were elderly. Most patients had fever symptoms, and ground-glass opacities were noted in chest computed tomography. The onset of the disease was characterized by family clustering and most families lived far from the designated hospital. Therefore, we recommend the following: (1) the most stringent centralized medical observation measures should be taken to avoid transmission in family clusters; and (2) social media can help these patients get early attention during Wuhan's lockdown. These findings can help the government and the health department identify high-risk patients and accelerate emergency responses following public demands for help.