Objective To provide a comprehensive and systematic analysis of demographic characteristics, clinical symptoms, laboratory findings and imaging features of coronavirus disease 2019 (COVID‐19) in pediatric patients. Methods A meta‐analysis was carried out to identify studies on COVID‐19 from December 25, 2019 to April 30, 2020. Results A total of 48 studies with 5829 pediatric patients were included. Children at all ages were at risk for COVID‐19. The main illness classification ranged as: 20% (95% CI: 14 to 26%, I 2 =91.4%) asymptomatic, 33% (95% CI: 23 to 43%, I 2 =95.6%) mild and 51% (95% CI: 42 to 61%, I 2 =93.4%) moderate. The typical clinical manifestations were fever 51% (95% CI: 45 to 57%, I 2 =78.9%) and cough 41% (95% CI: 35 to 47%, I 2 =81.0%). The common laboratory findings were normal white blood cell 69% (95% CI: 64 to 75%, I 2 =58.5%), lymphopenia 16% (95% CI: 11 to 21%, I 2 =76.9%) and elevated creatine‐kinase MB (CK‐MB) 37% (95% CI: 25 to 48%, I 2 =59.0%). The frequent imaging features were normal images 41% (95% CI: 30 to 52%, I 2 =93.4%) and ground‐glass opacity 36% (95% CI: 25 to 47%, I 2 =92.9%). Among children under 1‐year old, critical cases account for 14% (95% CI: 13 to 34%, I 2 =37.3%) that should be of concern. In addition, vomiting occurred in 33% (95% CI: 18 to 67%, I 2 =0.0%) cases that may also need attention. Conclusions Pediatric patients with COVID‐19 may experience milder illness with atypical clinical manifestations and rare lymphopenia. High incidence of critical illness and vomiting symptoms reward attention in children under 1‐year old. This article is protected by copyright. All rights reserved.

medRxiv preprint generally longer incubation and serial interval of less severe cases suggests a high risk of longterm epidemic in the absence of appropriate control measures.

Background: The aim of this study was to analyze and summarize the clinical characteristics of elderly patients with coronavirus disease 2019 (COVID-19) and compare the differences of young-old patients (60–74 years old) and old-old patients (≥75 years old). Methods: In thisretrospective, multicenter study, the medical records of elderly patients who were diagnosed with COVID-19 in Hunan province, China, from January 21 to February 19, 2020 were reviewed. The characteristics of young-old patients and old-old patients were compared. Results: Of the 105 elderly patientsconfirmed withCOVID-19, 81.0% were young-old patients, and 19.0% were old-old patients; 54.3% of elderly patients were females. Overall, 69.5% of elderly patients had underlying diseases, and the most common comorbidities included hypertension (43.8%), diabetes (25.7%), and cardiac disease (16.2%). Of the elderly patients, 22.9% were severe and 10.5% were critical severe cases. On admission, the most frequent symptoms in elderly patients included fever (66.7%), cough (64.8%), and fatigue (33.3%). Lymphopenia (31.4%), increased D-dimer (38.1%), depressed albumin (36.2%), elevated lactate dehydrogenase (41.0%), and a high level of C-reactive protein (79.0%) were common among elderly patients with COVID-19. The median prothrombin time (PT) and the activated partial thromboplastin time (APTT) were longer in old-old patients than young-old patients (PT median 12.3 vs. 13.1 s, p = 0.007; APTT median 39.0 vs. 33.5 s, p = 0.045). Young-old patients showed fewer complications (14.1%) than old-old patients (40.0%; p = 0.0014) and fewer received invasive ventilator support (3.5 vs. 25.0%, p = 0.006). As of March 11, 2020, 85.7% of elderly patients had been discharged, 3 deaths had occurred, and 11.4% were still hospitalized. Conclusions: Elderly patients usually have chronic medical illness and are likely to have a severe or critically severe condition. They could show atypical symptoms without fever or cough and multiple organ dysfunction. Old-old patients tend to have more complications than young-old patients during hospitalization. Careful nursing, observation, and systemic treatment are very important in elderly patients.

BackgroundSince December 8, 2019, an epidemic of coronavirus disease 2019 (COVID‐19) has spread rapidly, but information about children with COVID‐19 is limited.MethodsThis retrospective and the single‐center study were done at the Public Health Clinic Center of Changsha, Hunan, China. We identified all hospitalized children diagnosed with COVID‐19 between January 8, 2019 and February 19, 2020, in Changsha. Epidemiological and clinical data of these children were collected and analyzed. Outcomes were followed until February 26th, 2020.ResultsBy February 19, 2020, nine pediatric patients were identified as having 2019‐nCoV infection in Changsha. Six children had a family exposure and could provide the exact dates of close contact with someone who was confirmed to have 2019‐nCoV infection, among whom the median incubation period was 7.5 days. The initial symptoms of the nine children were mild, including fever (3/9), diarrhea (2/9), cough (1/9), and sore throat (1/9), two had no symptoms. Two of the enrolled patients showed small ground‐glass opacity of chest computed tomography scan. As of February 26, six patients had a negative RT‐PCR for 2019‐nCoV and were discharged. The median time from exposure to a negative RT‐PCR was 14 days.ConclusionsThe clinical symptoms of the new coronavirus infection in children were not typical and showed a less aggressive clinical course than teenage and adult patients. Children who have a familial clustering or have a family member with a definite diagnosis should be reported to ensure a timely diagnosis.

After the 2019 novel coronavirus (2019-nCoV) outbreak, we estimated the distribution and scale of more than 5 million migrants residing in Wuhan after they returned to their hometown communities in Hubei Province or other provinces at the end of 2019 by using the data from the 2013-2018 China Migrants Dynamic Survey (CMDS). We found that the distribution of Wuhan's migrants is centred in Hubei Province (approximately 75%) at a provincial level, gradually decreasing in the surrounding provinces in layers, with obvious spatial characteristics of circle layers and echelons. The scale of Wuhan's migrants, whose origins in Hubei Province give rise to a gradient reduction from east to west within the province, and account for 66% of Wuhan's total migrants, are from the surrounding prefectural-level cities of Wuhan. The distribution comprises 94 districts and counties in Hubei Province, and the cumulative percentage of the top 30 districts and counties exceeds 80%. Wuhan's migrants have a large proportion of middle-aged and high-risk individuals. Their social characteristics include nuclear family migration (84%), migration with families of 3-4 members (71%), a rural household registration (85%), and working or doing business (84%) as the main reason for migration. Using a quasi-experimental analysis framework, we found that the size of Wuhan's migrants was highly correlated with the daily number of confirmed cases. Furthermore, we compared the epidemic situation in different regions and found that the number of confirmed cases in some provinces and cities in Hubei Province may be underestimated, while the epidemic situation in some regions has increased rapidly. The results are conducive to monitoring the epidemic prevention and control in various regions.

Background The estimates of several key epidemiological parameters of the COVID-19 pandemic are often based on small sample sizes or are inaccurate for various reasons. Objective The aim of this study is to obtain more robust estimates of the incubation period, serial interval, frequency of presymptomatic transmission, and basic reproduction number (R0) of COVID-19 based on a large case series. Methods We systematically retrieved and screened 20,658 reports of laboratory-confirmed COVID-19 cases released by the health authorities of China, Japan, and Singapore. In addition, 9942 publications were retrieved from PubMed and China National Knowledge Infrastructure (CNKI) through April 8, 2020. To be eligible, a report had to contain individual data that allowed for accurate estimation of at least one parameter. Widely used models such as gamma distributions were fitted to the data sets and the results with the best-fitting values were presented. Results In total, 1591 cases were included for the final analysis. The mean incubation period (n=687) and mean serial interval (n=1015 pairs) were estimated to be 7.04 (SD 4.27) days and 6.49 (SD 4.90) days, respectively. In 40 cases (5.82%), the incubation period was longer than 14 days. In 32 infector-infectee pairs (3.15%), infectees’ symptom onsets occurred before those of infectors. Presymptomatic transmission occurred in 129 of 296 infector-infectee pairs (43.58%). R0 was estimated to be 1.85 (95% CI 1.37-2.60). Conclusions This study provides robust estimates of several epidemiological parameters of COVID-19. The findings support the current practice of 14-day quarantine of persons with potential exposure, but also suggest the need for additional measures. Presymptomatic transmission together with the asymptomatic transmission reported by previous studies highlight the importance of adequate testing, strict quarantine, and social distancing.

This study examines the direct effect of social support and the mediating effects of coping styles on loneliness and depression of older elderly people in China using data from the 2014 China Longitudinal Aging Social Survey. Our sample includes 905 males and 741 females aged 75 years and over. The mean age of the sample is 79.71 (standard deviation = 4.01). We use structural equation modeling to show that social support is significantly negatively associated with the incidence of loneliness and depression among older elderly people. Higher levels of social support are also significantly negatively associated with the use of negative coping styles and consequently predict fewer symptoms of loneliness and depression. A higher level of social support is significantly positively associated with positive coping styles and consequently predicts fewer depressive symptoms. However, positive coping styles are not significantly associated with loneliness. These findings emphasize the importance of social networks in resilience and have significant implications for gerontological social work practice in China.

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