Quantifying the rebound of influenza epidemics after the adjustment of zero-COVID policy in China.

The coexistence of coronavirus disease 2019 (COVID-19) and seasonal influenza epidemics has become a potential threat to human health, particularly in China in the oncoming season. However, with the relaxation of nonpharmaceutical interventions (NPIs) during the COVID-19 pandemic, the rebound extent of the influenza activities is still poorly understood. In this study, we constructed a susceptible-vaccinated-infectious-recovered-susceptible (SVIRS) model to simulate influenza transmission and calibrated it using influenza surveillance data from 2018 to 2022. We projected the influenza transmission over the next 3 years using the SVIRS model. We observed that, in epidemiological year 2021-2022, the reproduction numbers of influenza in southern and northern China were reduced by 64.0 and 34.5%, respectively, compared with those before the pandemic. The percentage of people susceptible to influenza virus increased by 138.6 and 57.3% in southern and northern China by October 1, 2022, respectively. After relaxing NPIs, the potential accumulation of susceptibility to influenza infection may lead to a large-scale influenza outbreak in the year 2022-2023, the scale of which may be affected by the intensity of the NPIs. And later relaxation of NPIs in the year 2023 would not lead to much larger rebound of influenza activities in the year 2023-2024. To control the influenza epidemic to the prepandemic level after relaxing NPIs, the influenza vaccination rates in southern and northern China should increase to 53.8 and 33.8%, respectively. Vaccination for influenza should be advocated to reduce the potential reemergence of the influenza epidemic in the next few years.

Global influenza vaccination rates and factors associated with influenza vaccination.

OBJECTIVES: Influenza vaccination is an effective method for preventing influenza virus infection. Herein, we performed a meta-analysis to quantify global influenza vaccination rates (IVRs) and the factors influencing its uptake in the general population, individuals with chronic diseases, pregnant women, and healthcare workers. METHODS: Related articles were obtained from online databases and screened according to the inclusion criteria. The pooled IVRs were calculated using the random effects model. Subgroup analyses and multivariate meta-regression were performed to determine the factors associated with influenza vaccine uptake. RESULTS: e included 522 studies from 68 countries/regions. Most studies were conducted in the European region (247 studies), followed by the Western Pacific (135 studies) and American regions (100 studies). The IVRs with 95% confidence intervals (CIs) in the general population were lower (24.96%, 23.45%-26.50%) than in individuals with chronic diseases (41.65%, 40.08%-43.23%), healthcare workers (36.57%, 33.74%-39.44%), and pregnant women (25.92%, 23.18%-28.75%). The IVRs in high-income countries/regions were significantly higher than that in middle-income countries/regions. A free national or regional vaccination policy, perception of influenza vaccine efficacy and disease severity, a recommendation from healthcare workers, and having a history of influenza vaccination were positive factors for vaccine uptake (P <0.01). CONCLUSION: Overall, global IVRs were low, especially in the general population. The studies on the IVRs, especially for priority populations, should be strengthened in Eastern Mediterranean, South-East Asian, and African regions. Free vaccination policies and the dissemination of continuous awareness campaigns are effective measures to enhance vaccination uptake.

Genetic Risk and Chronic Obstructive Pulmonary Disease Independently Predict the Risk of Incident Severe COVID-19.

Rationale: Both genetic variants and chronic obstructive pulmonary disease (COPD) contribute to the risk of incident severe coronavirus disease (COVID-19). Whether genetic risk of incident severe COVID-19 is the same regardless of preexisting COPD is unknown. Objectives: In this study, we aimed to investigate the potential interaction between genetic risk and COPD in relation to severe COVID-19. Methods: We constructed a polygenic risk score for severe COVID-19 by using 112 single-nucleotide polymorphisms in 430,582 participants from the UK Biobank study. We examined the associations of genetic risk and COPD with severe COVID-19 by using logistic regression models. Results: Of 430,582 participants, 712 developed severe COVID-19 as of February 22, 2021, of whom 19.8% had preexisting COPD. Compared with participants at low genetic risk, those at intermediate genetic risk (odds ratio [OR], 1.34; 95% confidence interval [CI], 1.09-1.66) and high genetic risk (OR, 1.50; 95% CI, 1.18-1.92) had higher risk of severe COVID-19 (P for trend = 0.001), and the association was independent of COPD (P for interaction = 0.76). COPD was associated with a higher risk of incident severe COVID-19 (OR, 1.37; 95% CI, 1.12-1.67; P = 0.002). Participants at high genetic risk and with COPD had a higher risk of severe COVID-19 (OR, 2.05; 95% CI, 1.35-3.04; P < 0.001) than those at low genetic risk and without COPD. Conclusions: The polygenic risk score, which combines multiple risk alleles, can be effectively used in screening for high-risk populations of severe COVID-19. High genetic risk correlates with a higher risk of severe COVID-19, regardless of preexisting COPD.

Impact of Coinfection With SARS-CoV-2 and Influenza on Disease Severity: A Systematic Review and Meta-Analysis.

Background: Although coinfection with influenza in COVID-19 patients has drawn considerable attention, it is still not completely understood whether simultaneously infected with these two viruses influences disease severity. We therefore aimed to estimate the impact of coinfected with SARS-CoV-2 and influenza on the disease outcomes compared with the single infection of SARS-CoV-2. Materials and Methods: We searched the PubMed, Web of Science, Embase, Cochrane Library, China National Knowledge Infrastructure Database (CNKI) to identify relevant articles up to July 9, 2021. Studies that assessed the effect of SARS-CoV-2 and influenza coinfection on disease outcomes or those with sufficient data to calculate risk factors were included. Risk effects were pooled using fixed or random effects model. Results: We ultimately identified 12 studies with 9,498 patients to evaluate the risk effects of SARS-CoV-2 and influenza coinfection on disease severity. Results indicated that coinfection was not significantly associated with mortality (OR = 0.85, 95%CI: 0.51, 1.43; p = 0.55, I2 = 76.00%). However, mortality was found significantly decreased in the studies from China (OR = 0.51, 95%CI: 0.39, 0.68; I2 = 26.50%), while significantly increased outside China (OR = 1.56, 95%CI: 1.12, 2.19; I2 = 1.00%). Moreover, a lower risk for critical outcomes was detected among coinfection patients (OR = 0.64, 95%CI: 0.43, 0.97; p = 0.04, I2 = 0.00%). Additionally, coinfection patients presented different laboratory indexes compared with the single SARS-CoV-2 infection, including lymphocyte counts and APTT. Conclusion: Our study revealed that coinfection with SARS-CoV-2 and influenza had no effect on overall mortality. However, risk for critical outcomes was lower in coinfection patients and different associations were detected in the studies from different regions and specific laboratory indexes. Further studies on influenza strains and the order of infection were warranted. Systematic testing for influenza coinfection in COVID-19 patients and influenza vaccination should be recommended.

Epidemiological features and spatial-temporal distribution of visceral leishmaniasis in mainland China: a population-based surveillance study from 2004 to 2019.

BACKGROUND: Although visceral leishmaniasis (VL) was largely brought under control in most regions of China during the previous century, VL cases have rebounded in western and central China in recent decades. The aim of this study was to investigate the epidemiological features and spatial-temporal distribution of VL in mainland China from 2004 to 2019. METHODS: Incidence and mortality data for VL during the period 2004-2019 were collected from the Public Health Sciences Data Center of China and annual national epidemic reports of VL, whose data source was the National Diseases Reporting Information System. Joinpoint regression analysis was performed to explore the trends of VL. Spatial autocorrelation and spatial-temporal clustering analysis were conducted to identify the distribution and risk areas of VL transmission. RESULTS: A total of 4877 VL cases were reported in mainland China during 2004-2019, with mean annual incidence of 0.0228/100,000. VL incidence showed a decreasing trend in general during our study period (annual percentage change [APC] = -4.2564, 95% confidence interval [CI]: -8.0856 to -0.2677). Among mainly endemic provinces, VL was initially heavily epidemic in Gansu, Sichuan, and especially Xinjiang, but subsequently decreased considerably. In contrast, Shaanxi and Shanxi witnessed significantly increasing trends, especially in 2017-2019. The first-level spatial-temporal aggregation area covered two endemic provinces in northwestern China, including Gansu and Xinjiang, with the gathering time from 2004 to 2011 (relative risk [RR] = 13.91, log-likelihood ratio [LLR] = 3308.87, P < 0.001). The secondary aggregation area was detected in Shanxi province of central China, with the gathering time of 2019 (RR = 1.61, LLR = 4.88, P = 0.041). The epidemic peak of October to November disappeared in 2018-2019, leaving only one peak in March to May. CONCLUSIONS: Our findings suggest that VL is still an important endemic infectious disease in China. Epidemic trends in different provinces changed significantly and spatial-temporal aggregation areas shifted from northwestern to central China during our study period. Mitigation strategies, including large-scale screening, insecticide spraying, and health education encouraging behavioral change, in combination with other integrated approaches, are needed to decrease transmission risk in areas at risk, especially in Shanxi, Shaanxi, and Gansu provinces.

Characteristics of Viral Shedding Time in SARS-CoV-2 Infections: A Systematic Review and Meta-Analysis.

Background: The viral shedding time (VST) of SARS-CoV-2 mainly determines its transmission and duration of infectiousness. However, it was heterogeneous in the existing studies. Here, we performed a meta-analysis to comprehensively summarize the VST of SARS-CoV-2. Methods: We searched PubMed, Web of Science, MedRxiv, BioRxiv, CNKI, CSTJ, and Wanfang up to October 25, 2020, for studies that reported VSTs of SARS-CoV-2. Pooled estimates and 95% CIs for the VSTs were calculated using log-transformed data. The VSTs in SARS-CoV-2 infections based on different demographic and clinical characteristics, treatments and specimens were stratified by subgroup analysis. Results: A total of 35 studies involving 3,385 participants met the inclusion criteria. The pooled mean VST was 16.8 days (95% CI: 14.8-19.4, I2 = 99.56%) in SARS-CoV-2 infections. The VST was significantly longer in symptomatic infections (19.7 days, 95% CI: 17.2-22.7, I2 = 99.34%) than in asymptomatic infections (10.9 days, 95% CI: 8.3-14.3, I2 = 98.89%) (P < 0.05). The VST was 23.2 days (95% CI: 19.0-28.4, I2 = 99.24%) in adults, which was significantly longer than that in children (9.9 days, 95% CI: 8.1-12.2, I2 = 85.74%) (P < 0.05). The VST was significantly longer in persons with chronic diseases (24.2 days, 95% CI: 19.2-30.2, I2 = 84.07%) than in those without chronic diseases (11.5 days, 95% CI: 5.3-25.0, I2 = 82.11%) (P < 0.05). Persons receiving corticosteroid treatment (28.3 days, 95% CI: 25.6-31.2, I2 = 0.00%) had a longer VST than those without corticosteroid treatment (16.2 days, 95% CI: 11.5-22.5, I2 = 92.27%) (P = 0.06). The VST was significantly longer in stool specimens (30.3 days, 95% CI: 23.1-39.2, I2 = 92.09%) than in respiratory tract specimens (17.5 days, 95% CI: 14.9-20.6, I2 = 99.67%) (P < 0.05). Conclusions: A longer VST was found in symptomatic infections, infected adults, persons with chronic diseases, and stool specimens.

Predicting the Incident Cases of Emerging Infectious Disease Using a Bayesian Probability Model - China, February 2020.

WHAT IS ALREADY KNOWN ABOUT THIS TOPIC?: The exact number of incident cases of emerging infectious diseases on a daily basis is of great importance to the disease control and prevention, but it is not directly available from the current surveillance system in time. WHAT IS ADDED BY THIS REPORT?: In this study, a Bayesian statistical method was proposed to estimate the posterior parameters of the gamma probability distribution of the lag time between the onset date and the reporting time based on the surveillance data. And then the posterior parameters and corresponding cumulative gamma probability distribution were used to predict the actual number of new incident cases and the number of unreported cases per day. The proposed method was used for predicting COVID-19 incident cases from February 5 to February 26, 2020. The final results show that Bayesian probability model predictions based on data reported by February 28, 2020 are very close to those actually reported a month later. WHAT ARE THE IMPLICATIONS FOR PUBLIC HEALTH PRACTICE?: This research provides a Bayesian statistical approach for early estimation of the actual number of cases of incidence based on surveillance data, which is of great value in the prevention and control practice of epidemics.

The value of infectious disease modeling and trend assessment: a public health perspective.

INTRODUCTION: Disease outbreaks of acquired immunodeficiency syndrome, severe acute respiratory syndrome, pandemic H1N1, H7N9, H5N1, Ebola, Zika, Middle East respiratory syndrome, and recently COVID-19 have raised the attention of the public over the past half-century. Revealing the characteristics and epidemic trends are important parts of disease control. The biological scenarios including transmission characteristics can be constructed and translated into mathematical models, which can help to predict and gain a deeper understanding of diseases. AREAS COVERED: This review discusses the models for infectious diseases and highlights their values in the field of public health. This information will be of interest to mathematicians and clinicians, and make a significant contribution toward the development of more specific and effective models. Literature searches were performed using the online database of PubMed (inception to August 2020). EXPERT OPINION: Modeling could contribute to infectious disease control by means of predicting the scales of disease epidemics, indicating the characteristics of disease transmission, evaluating the effectiveness of interventions or policies, and warning or forecasting during the pre-outbreak of diseases. With the development of theories and the ability of calculations, infectious disease modeling would play a much more important role in disease prevention and control of public health.

The epidemiological and radiographical characteristics of asymptomatic infections with the novel coronavirus (COVID-19): A systematic review and meta-analysis.

OBJECTIVES: The role of asymptomatic infections in the transmission of COVID-19 have drawn considerable attention. Here, we performed a meta-analysis to summarize the epidemiological and radiographical characteristics of asymptomatic infections associated with COVID-19. METHODS: Data on the epidemiological and radiographical characteristics of asymptomatic infections were extracted from the existing literature. Pooled proportions with 95% confidence intervals were then calculated using a random effects model. RESULTS: A total of 104 studies involving 20,152 cases were included. The proportion of asymptomatic individuals among those with COVID-19 was 13.34% (10.86%-16.29%), among which presymptomatic and covert infections accounted for 7.64% (4.02%-14.04%) and 8.44% (5.12%-13.62%), respectively. The proportions of asymptomatic infections among infected children and healthcare workers were 32.24% (23.08%-42.13%) and 36.96% (18.51%-60.21%), respectively. The proportion of asymptomatic infections was significantly higher after 2020/02/29 than before (33.53% vs 10.19%) and in non-Asian regions than in Asia (28.76% vs 11.54%). The median viral shedding duration of asymptomatic infections was 14.14 days (11.25-17.04). A total of 47.62% (31.13%-72.87%) of asymptomatic infections showed lung abnormalities, especially ground-glass opacity (41.11% 19.7%-85.79%). CONCLUSIONS: Asymptomatic infections were more commonly found in infected children and healthcare workers and increased after 2020/02/29 and in non-Asian regions. Chest radiographical imaging could be conducive to the early identification of asymptomatic infections.

Contact Settings and Risk for Transmission in 3410 Close Contacts of Patients With COVID-19 in Guangzhou, China : A Prospective Cohort Study.

BACKGROUND: Risk for transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) to close contacts of infected persons has not been well estimated. OBJECTIVE: To evaluate the risk for transmission of SARS-CoV-2 to close contacts in different settings. DESIGN: Prospective cohort study. SETTING: Close contacts of persons infected with SARS-CoV-2 in Guangzhou, China. PARTICIPANTS: 3410 close contacts of 391 index cases were traced between 13 January and 6 March 2020. Data on the setting of the exposure, reverse transcriptase polymerase chain reaction testing, and clinical characteristics of index and secondary cases were collected. MEASUREMENT: Coronavirus disease 2019 (COVID-19) cases were confirmed by guidelines issued by China. Secondary attack rates in different settings were calculated. RESULTS: Among 3410 close contacts, 127 (3.7% [95% CI, 3.1% to 4.4%]) were secondarily infected. Of these 127 persons, 8 (6.3% [CI, 2.1% to 10.5%]) were asymptomatic. Of the 119 symptomatic cases, 20 (16.8%) were defined as mild, 87 (73.1%) as moderate, and 12 (10.1%) as severe or critical. Compared with the household setting (10.3%), the secondary attack rate was lower for exposures in health care settings (1.0%; odds ratio [OR], 0.09 [CI, 0.04 to 0.20]) and on public transportation (0.1%; OR, 0.01 [CI, 0.00 to 0.08]). The secondary attack rate increased with the severity of index cases, from 0.3% (CI, 0.0% to 1.0%) for asymptomatic to 3.3% (CI, 1.8% to 4.8%) for mild, 5.6% (CI, 4.4% to 6.8%) for moderate, and 6.2% (CI, 3.2% to 9.1%) for severe or critical cases. Index cases with expectoration were associated with higher risk for secondary infection (13.6% vs. 3.0% for index cases without expectoration; OR, 4.81 [CI, 3.35 to 6.93]). LIMITATION: There was potential recall bias regarding symptom onset among patients with COVID-19, and the symptoms and severity of index cases were not assessed at the time of exposure to contacts. CONCLUSION: Household contact was the main setting for transmission of SARS-CoV-2, and the risk for transmission of SARS-CoV-2 among close contacts increased with the severity of index cases. PRIMARY FUNDING SOURCE: Guangdong Province Higher Vocational Colleges and Schools Pearl River Scholar Funded Scheme.

Systematic review of the registered clinical trials for coronavirus disease 2019 (COVID-19).

BACKGROUND: Since the outbreak of coronavirus disease 2019 (COVID-19), many researchers in China have performed related clinical research. However, systematic reviews of the registered clinical trials are still lacking. Therefore, we conducted a systematic review of clinical trials for COVID-19 to summarize their characteristics. METHODS: This study is based on the PRISMA recommendations in the Cochrane handbook. The Chinese Clinical Registration Center and the ClinicalTrials.gov databases were searched to identify registered clinical trials related to COVID-19. The retrieval inception date was February 9, 2020. Two researchers independently selected the literature based on the inclusion and exclusion criteria, extracted data, and evaluated the risk of bias. RESULTS: A total of 75 registered clinical trials (63 interventional studies and 12 observational studies) for COVID-19 were identified. The majority of clinical trials were sponsored by Chinese hospitals. Only 11 trials have begun to recruit patients, and none of the registered clinical trials have been completed; 34 trials were early clinical exploratory trials or in the pre-experiment stage, 13 trials were phase III, and four trials were phase IV. The intervention methods included traditional Chinese medicine in 26 trials, Western medicine in 30 trials, and integrated traditional Chinese medicine and Western medicine in 19 trials. The subjects were primarily non-critical adult patients (≥ 18 years old). The median sample size of the trials was 100 (IQR: 60-200), and the median length of the trial periods was 179 d (IQR: 94-366 d). The main outcomes were clinical observation and examinations. Overall, the methodological quality of both the interventional trials and observational studies was low. CONCLUSIONS: Intensive clinical trials on the treatment of COVID-19 using traditional Chinese medicine and Western medicine are ongoing or will be performed in China. However, based on the uncertain methodological quality, small sample size, and long trial duration, we will not be able to obtain reliable, high-quality clinical evidence regarding the treatment of COVID-19 in the near future. Improving the quality of study design, prioritizing promising drugs, and using different designs and statistical methods are worth advocating and recommending for clinical trials of COVID-19 in the future.