Attitudes Regarding Influenza Vaccination Among Public Health Workers during COVID-19 Pandemic - China, September 2022.

What is already known about this topic?: Public health workers (PHWs) were listed as a priority group recommended for influenza vaccination during the coronavirus disease 2019 (COVID-19) pandemic. Understanding the drivers of influenza vaccine hesitancy among PHWs can promote influenza vaccination in the COVID-19 pandemic. What is added by this report?: The study found that 10.7% of PHWs were hesitant to get an influenza vaccination. Drivers associated with vaccine hesitancy were assessed based on "3Cs model." The absence of a government or workplace requirement and concerns about vaccine safety were the biggest obstacles for PHWs to recommend influenza vaccination. What are the implications for public health practice?: Interventions are needed to improve PHWs' influenza vaccine coverage to prevent the co-circulation of influenza and COVID-19.

Efficacy of SARS-CoV-2 vaccines and the dose-response relationship with three major antibodies: a systematic review and meta-analysis of randomised controlled trials.

BACKGROUND: The efficacy of SARS-CoV-2 vaccines in preventing severe COVID-19 illness and death is uncertain due to the rarity of data in individual trials. How well the antibody concentrations can predict the efficacy is also uncertain. We aimed to assess the efficacy of these vaccines in preventing SARS-CoV-2 infections of different severities and the dose-response relationship between the antibody concentrations and efficacy. METHODS: We did a systematic review and meta-analysis of randomised controlled trials (RCTs). We searched PubMed, Embase, Scopus, Web of Science, Cochrane Library, WHO, bioRxiv, and medRxiv for papers published between Jan 1, 2020 and Sep 12, 2022. RCTs on the efficacy of SARS-CoV-2 vaccines were eligible. Risk of bias was assessed using the Cochrane tool. A frequentist, random-effects model was used to combine efficacy for common outcomes (ie, symptomatic and asymptomatic infections) and a Bayesian random-effects model was used for rare outcomes (ie, hospital admission, severe infection, and death). Potential sources of heterogeneity were investigated. The dose-response relationships of neutralising, spike-specific IgG and receptor binding domain-specific IgG antibody titres with efficacy in preventing SARS-CoV-2 symptomatic and severe infections were examined by meta-regression. This systematic review is registered with PROSPERO, CRD42021287238. FINDINGS: 28 RCTs (n=286 915 in vaccination groups and n=233 236 in placebo groups; median follow-up 1-6 months after last vaccination) across 32 publications were included in this review. The combined efficacy of full vaccination was 44·5% (95% CI 27·8-57·4) for preventing asymptomatic infections, 76·5% (69·8-81·7) for preventing symptomatic infections, 95·4% (95% credible interval 88·0-98·7) for preventing hospitalisation, 90·8% (85·5-95·1) for preventing severe infection, and 85·8% (68·7-94·6) for preventing death. There was heterogeneity in the efficacy of SARS-CoV-2 vaccines against asymptomatic and symptomatic infections but insufficient evidence to suggest whether the efficacy could differ according to the type of vaccine, age of the vaccinated individual, and between-dose interval (p>0·05 for all). Vaccine efficacy against symptomatic infection waned over time after full vaccination, with an average decrease of 13·6% (95% CI 5·5-22·3; p=0·0007) per month but can be enhanced by a booster. We found a significant non-linear relationship between each type of antibody and efficacy against symptomatic and severe infections (p<0·0001 for all), but there remained considerable heterogeneity in the efficacy, which cannot be explained by antibody concentrations. The risk of bias was low in most studies. INTERPRETATION: The efficacy of SARS-CoV-2 vaccines is higher for preventing severe infection and death than for preventing milder infection. Vaccine efficacy wanes over time but can be enhanced by a booster. Higher antibody titres are associated with higher estimates of efficacy but precise predictions are difficult due to large unexplained heterogeneity. These findings provide an important knowledge base for interpretation and application of future studies on these issues. FUNDING: Shenzhen Science and Technology Programs.

Tailored Text Augmentation for sentiment analysis

In synonym replacement-based data augmentation techniques for natural language processing tasks, words in a sentence are often sampled randomly with equal probability. In this paper, we propose a novel data augmentation technique named Tailored Text Argumentation (TTA) for sentiment analysis. It has two main operations. The first operation is the probabilistic word sampling for synonym replacement based on the discriminative power and relevance of the word to sentiment. The second operation is the identification of words irrelevant to sentiment but discriminative for the training data, and application of zero masking or contextual replacement to these words. The first operation expands the coverage of discriminative words, while the second operation alleviates the problem of misfitting. Both operations tend to improve the model’s generalization capability. Extensive experiments on simulated low-data regimes demonstrate that TTA yields notable improvements over six strong baselines. Finally, TTA is applied to public sentiment analysis on measures against Covid-19, which again proves the effectiveness of the new data augmentation algorithm.

Risk Factors for Death Among the First 80 543 Coronavirus Disease 2019 (COVID-19) Cases in China: Relationships Between Age, Underlying Disease, Case Severity, and Region.

BACKGROUND: Knowledge of COVID-19 epidemiology remains incomplete and crucial questions persist. We aimed to examine risk factors for COVID-19 death. METHODS: A total of 80 543 COVID-19 cases reported in China, nationwide, through 8 April 2020 were included. Risk factors for death were investigated by Cox proportional hazards regression and stratified analyses. RESULTS: Overall national case-fatality ratio (CFR) was 5.64%. Risk factors for death were older age (≥80: adjusted hazard ratio, 12.58; 95% confidence interval, 6.78-23.33), presence of underlying disease (1.33; 1.19-1.49), worse case severity (severe: 3.86; 3.15-4.73; critical: 11.34; 9.22-13.95), and near-epicenter region (Hubei: 2.64; 2.11-3.30; Wuhan: 6.35; 5.04-8.00). CFR increased from 0.35% (30-39 years) to 18.21% (≥70 years) without underlying disease. Regardless of age, CFR increased from 2.50% for no underlying disease to 7.72% for 1, 13.99% for 2, and 21.99% for ≥3 underlying diseases. CFR increased with worse case severity from 2.80% (mild) to 12.51% (severe) and 48.60% (critical), regardless of region. Compared with other regions, CFR was much higher in Wuhan regardless of case severity (mild: 3.83% vs 0.14% in Hubei and 0.03% elsewhere; moderate: 4.60% vs 0.21% and 0.06%; severe: 15.92% vs 5.84% and 1.86%; and critical: 58.57% vs 49.80% and 18.39%). CONCLUSIONS: Older patients regardless of underlying disease and patients with underlying disease regardless of age were at elevated risk of death. Higher death rates near the outbreak epicenter and during the surge of cases reflect the deleterious effects of allowing health systems to become overwhelmed.

[Consideration on implementation of co-administration of Seasonal Influenza and COVID-19 vaccines during pandemic in China].

Influenza is an infectious respiratory disease caused by the influenza viruses. Older people, infants and people with underlying medical conditions could have a higher risk of severe influenza symptoms and complications. The co-infection of Coronavirus Diseases 2019 (COVID-19) with influenza viruses could lead to the complication of prevention, diagnosis, control, treatment, and recovery of COVID-19. Influenza vaccine and COVID-19 vaccine overlapped in target populations, vaccination time, and inoculation units. Although there was insufficient evidence on the immunogenicity and safety of co-administration of influenza vaccine and COVID-19 vaccine, World Health Organization and some countries recommended co-administration of inactivated influenza vaccine and COVID-19 vaccine. This review summarized domestic and international vaccination policies and research progress, and put forward corresponding suggestions in order to provide scientific support for the formulation of vaccination strategy on seasonal influenza vaccine and COVID-19 vaccine.

Clinical characteristics of coronavirus disease 2019 cases in different age groups

Objective: To analyze the early clinical characteristics of coronavirus disease 2019 (COVID-19) cases in different age groups, and provide references for the early identification, treatment and management of COVID-19 cases, prevention of the progression of illness and further effective prevention and control of COVID-19.

Transmission of Severe Acute Respiratory Syndrome Coronavirus 2 to Close Contacts, China, January-February 2020.

We estimated the symptomatic, PCR-confirmed secondary attack rate (SAR) for 2,382 close contacts of 476 symptomatic persons with coronavirus disease in Yichang, Hubei Province, China, identified during January 23-February 25, 2020. The SAR among all close contacts was 6.5%; among close contacts who lived with an index case-patient, the SAR was 10.8%; among close-contact spouses of index case-patients, the SAR was 15.9%. The SAR varied by close contact age, from 3.0% for those <18 years of age to 12.5% for those >60 years of age. Multilevel logistic regression showed that factors significantly associated with increased SAR were living together, being a spouse, and being >60 years of age. Multilevel regression did not support SAR differing significantly by whether the most recent contact occurred before or after the index case-patient's onset of illness (p = 0.66). The relatively high SAR for coronavirus disease suggests relatively high virus transmissibility.

Long Distance Transmission of SARS-CoV-2 from Contaminated Cold Chain Products to Humans - Qingdao City, Shandong Province, China, September 2020.

What is already known about this topic? Though coronavirus disease 2019 (COVID-19) has largely been controlled in China, several outbreaks of COVID-19 have occurred from importation of cases or of suspected virus-contaminated products. Though several outbreaks have been traced to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) isolated on the outer packaging of cold chain products, live virus has not been obtained. What is added by this report? In September 2020, two dock workers were detected as having asymptomatic SARS-CoV-2 infection using throat swabs during routine screening in Qingdao, China. Epidemiological information showed that the two dock workers were infected after contact with contaminated outer packaging, which was confirmed by genomic sequencing. Compared to the Wuhan reference strain, the sequences from the dock workers and the package materials differed by 12-14 nucleotides. Furthermore, infectious virus from the cold chain products was isolated by cell culture, and typical SARS-CoV-2 particles were observed under electron microscopy. What are the implications for public health practice? The international community should pay close attention to SARS-CoV-2 transmission mode through cold chain, build international cooperative efforts in response, share relevant data, and call on all countries to take effective prevention and control measures to prevent virus contamination in cold-chain food production, marine fishing and processing, transportation, and other operations.

Impact of COVID-19 outbreaks and interventions on influenza in China and the United States.

Coronavirus disease 2019 (COVID-19) was detected in China during the 2019-2020 seasonal influenza epidemic. Non-pharmaceutical interventions (NPIs) and behavioral changes to mitigate COVID-19 could have affected transmission dynamics of influenza and other respiratory diseases. By comparing 2019-2020 seasonal influenza activity through March 29, 2020 with the 2011-2019 seasons, we found that COVID-19 outbreaks and related NPIs may have reduced influenza in Southern and Northern China and the United States by 79.2% (lower and upper bounds: 48.8%-87.2%), 79.4% (44.9%-87.4%) and 67.2% (11.5%-80.5%). Decreases in influenza virus infection were also associated with the timing of NPIs. Without COVID-19 NPIs, influenza activity in China and the United States would likely have remained high during the 2019-2020 season. Our findings provide evidence that NPIs can partially mitigate seasonal and, potentially, pandemic influenza.

COVID-19 Outbreak Caused by Contaminated Packaging of Imported Cold-Chain Products - Liaoning Province, China, July 2020.

What is known about this topic? Few major outbreaks of coronavirus disease 2019 (COVID-19) have occurred in China after major non-pharmaceutical interventions and vaccines have been deployed and implemented. However, sporadic outbreaks that had high possibility to be linked to cold chain products were reported in several cities of China.. What is added by this report? In July 2020, a COVID-19 outbreak occurred in Dalian, China. The investigations of this outbreak strongly suggested that the infection source was from COVID-19 virus-contaminated packaging of frozen seafood during inbound unloading personnel contact. What are the implications for public health practice? Virus contaminated paper surfaces could maintain infectivity for at least 17-24 days at -25 ℃. Exposure to COVID-19 virus-contaminated surfaces is a potential route for introducing the virus to a susceptible population. Countries with no domestic transmission of COVID-19 should consider introducing prevention strategies for both inbound travellers and imported goods. Several measures to prevent the introduction of the virus via cold-chain goods can be implemented.

Risk factors for developing severe COVID-19 in China: an analysis of disease surveillance data.

BACKGROUND: COVID-19 has posed an enormous threat to public health around the world. Some severe and critical cases have bad prognoses and high case fatality rates, unraveling risk factors for severe COVID-19 are of significance for predicting and preventing illness progression, and reducing case fatality rates. Our study focused on analyzing characteristics of COVID-19 cases and exploring risk factors for developing severe COVID-19. METHODS: The data for this study was disease surveillance data on symptomatic cases of COVID-19 reported from 30 provinces in China between January 19 and March 9, 2020, which included demographics, dates of symptom onset, clinical manifestations at the time of diagnosis, laboratory findings, radiographic findings, underlying disease history, and exposure history. We grouped mild and moderate cases together as non-severe cases and categorized severe and critical cases together as severe cases. We compared characteristics of severe cases and non-severe cases of COVID-19 and explored risk factors for severity. RESULTS: The total number of cases were 12 647 with age from less than 1 year old to 99 years old. The severe cases were 1662 (13.1%), the median age of severe cases was 57 years [Inter-quartile range(IQR): 46-68] and the median age of non-severe cases was 43 years (IQR: 32-54). The risk factors for severe COVID-19 were being male [adjusted odds ratio (aOR) = 1.3, 95% CI: 1.2-1.5]; fever (aOR = 2.3, 95% CI: 2.0-2.7), cough (aOR = 1.4, 95% CI: 1.2-1.6), fatigue (aOR = 1.3, 95% CI: 1.2-1.5), and chronic kidney disease (aOR = 2.5, 95% CI: 1.4-4.6), hypertension (aOR = 1.5, 95% CI: 1.2-1.8) and diabetes (aOR = 1.96, 95% CI: 1.6-2.4). With the increase of age, risk for the severity was gradually higher [20-39 years (aOR = 3.9, 95% CI: 1.8-8.4), 40-59 years (aOR = 7.6, 95% CI: 3.6-16.3), ≥ 60 years (aOR = 20.4, 95% CI: 9.5-43.7)], and longer time from symtem onset to diagnosis [3-5 days (aOR = 1.4, 95% CI: 1.2-1.7), 6-8 days (aOR = 1.8, 95% CI: 1.5-2.1), ≥ 9 days(aOR = 1.9, 95% CI: 1.6-2.3)]. CONCLUSIONS: Our study showed the risk factors for developing severe COVID-19 with large sample size, which included being male, older age, fever, cough, fatigue, delayed diagnosis, hypertension, diabetes, chronic kidney diasease, early case identification and prompt medical care. Based on these factors, the severity of COVID-19 cases can be predicted. So cases with these risk factors should be paid more attention to prevent severity.

Risk factors and on-site simulation of environmental transmission of SARS-CoV-2 in the largest wholesale market of Beijing, China.

From June 11, 2020, a surge in new cases of coronavirus disease 2019 (COVID-19) in the largest wholesale market of Beijing, the Xinfadi Market, leading to a second wave of COVID-19 in Beijing, China. Understanding the transmission modes of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and the personal behaviors and environmental factors contributing to viral transmission is of utmost important to curb COVID-19 rise. However, currently these are largely unknown in food markets. To this end, we completed field investigations and on-site simulations in areas with relatively high infection rates of COVID-19 at Xinfadi Market. We found that if goods were tainted or personnel in market was infected, normal transaction behaviors between sellers and customers, daily physiological activities, and marketing activities could lead to viral contamination and spread to the surroundings via fomite, droplet or aerosol routes. Environmental factors such as low temperature and high humidity, poor ventilation, and insufficient hygiene facilities and disinfection practices may contribute to viral transmission in Xinfadi Market. In addition, precautionary control strategies were also proposed to effectively reduce the clustering cases of COVID-19 in large-scale wholesale markets.

Antibody seroprevalence in the epicenter Wuhan, Hubei, and six selected provinces after containment of the first epidemic wave of COVID-19 in China.

BACKGROUND: China implemented containment measures to stop SARS-CoV-2 transmission in response to the COVID-19 epidemic. After the first epidemic wave, we conducted population-based serological surveys to determine extent of infection, risk factors for infection, and neutralization antibody levels to assess the real infections in the random sampled population. METHODS: We used a multistage, stratified cluster random sampling strategy to conduct serological surveys in three areas - Wuhan, Hubei Province outside Wuhan, and six provinces selected on COVID-19 incidence and containment strategy. Participants were consenting individuals >1 year old who resided in the survey area >14 days during the epidemic. Provinces screened sera for SARS-CoV-2-specific IgM, IgG, and total antibody by two lateral flow immunoassays and one magnetic chemiluminescence enzyme immunoassay; positive samples were verified by micro-neutralization assay. FINDINGS: We enrolled 34,857 participants (overall response rate, 92%); 427 were positive by micro-neutralization assay. Wuhan had the highest weighted seroprevalence (4•43%, 95% confidence interval [95%CI]=3•48%-5•62%), followed by Hubei-ex-Wuhan (0•44%, 95%CI=0•26%-0•76%), and the other provinces (<0•1%). Living in Wuhan (adjusted odds ratio aOR=13•70, 95%CI= 7•91-23•75), contact with COVID-19 patients (aOR=7•35, 95%CI=5•05-10•69), and age over 40 (aOR=1•36, 95%CI=1•07-1•72) were significantly associated with SARS-CoV-2 infection. Among seropositives, 101 (24%) reported symptoms and had higher geometric mean neutralizing antibody titers than among the 326 (76%) without symptoms (30±2•4 vs 15±2•1, p<0•001). INTERPRETATION: The low overall extent of infection and steep gradient of seropositivity from Wuhan to the outer provinces provide evidence supporting the success of containment of the first wave of COVID-19 in China. SARS-CoV-2 infection was largely asymptomatic, emphasizing the importance of active case finding and physical distancing. Virtually the entire population of China remains susceptible to SARS-CoV-2; vaccination will be needed for long-term protection. FUNDING: This study was supported by the Ministry of Science and Technology (2020YFC0846900) and the National Natural Science Foundation of China (82041026, 82041027, 82041028, 82041029, 82041030, 82041032, 82041033).