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The annual epidemics of seasonal influenza is partly attributed to the continued virus evolution. It is challenging to evaluate the effect of influenza virus mutations on evading population immunity. In this study, we introduce a novel statistical and computational approach to measure the dynamic molecular determinants underlying epidemics using effective mutations (EMs), and account for the time of waning mutation advantage against herd immunity by measuring the effective mutation periods (EMPs). Extensive analysis is performed on the sequencing and epidemiology data of H3N2 epidemics in ten regions from season to season. We systematically identified 46 EMs in the hemagglutinin (HA) gene, in which the majority were antigenic sites. Eight EMs were located in immunosubdominant stalk domain, an important target for developing broadly reactive antibodies. The EMs might provide timely information on key substitutions for influenza vaccines antigen design. The EMP suggested that major genetic variants of H3N2 circulated in South-east Asia for an average duration of 4.5 years (SD 2.4) compared to a significantly shorter 2.0 years (SD 1.0) in temperate regions. The proposed method bridges population epidemics and molecular characteristics of infectious diseases, and would find broad applications in various pathogens mutation estimations.

Background The COVID-19 pandemic poses serious threats to public health globally, and the emerging mutations in SARS-CoV-2 genomes has become one of the major challenges of disease control. In the second epidemic wave in Nigeria, the roles of co-circulating SARS-CoV-2 Alpha (ie, B.1.1.7) and Eta (ie, B.1.525) variants in contributing to the epidemiological outcomes were of public health concerns for investigation. Methods We developed a mathematical model to capture the transmission dynamics of different types of strains in Nigeria. By fitting to the national-wide COVID-19 surveillance data, the transmission advantages of SARS-CoV-2 variants were estimated by likelihood-based inference framework. Results The reproduction numbers were estimated to decrease steadily from 1.5 to 0.8 in the second epidemic wave. In December 2020, when both Alpha and Eta variants were at low prevalent levels, their transmission advantages (against the wild type) were estimated at 1.51 (95% credible intervals (CrI) = 1.48, 1.54), and 1.56 (95% CrI = 1.54, 1.59), respectively. In January 2021, when the original variants almost vanished, we estimated a weak but significant transmission advantage of Eta against Alpha variants with 1.14 (95% CrI = 1.11, 1.16). Conclusions Our findings suggested evidence of the transmission advantages for both Alpha and Eta variants, of which Eta appeared slightly more infectious than Alpha. We highlighted the critical importance of COVID-19 control measures in mitigating the outbreak size and relaxing the burdens to health care systems in Nigeria.

Background: Asymptomatic infection of SARS-CoV-2 may lead to silent community transmission and compromise pandemic control measures of COVID-19. We aimed to estimate the rate of asymptomatic COVID-19 infection from published studies, and compare this rate among different patient groups. Methods: The electronic databases including Medline, Embase, PubMed, and three Chinese electronic databases (The Chinese National Knowledge Infrastructure (CNKI), WanFang Data, and VIP) were searched. Studies with sample size (or number of subjects) not less than 5 were included. The STATA command ‘Metaprop’ was implemented to conduct meta-analysis for the pooled rate estimates of asymptomatic infections with exact binomial and score test-based 95% confidence intervals (CIs). Results: A total of 12,713 COVID-19 patients in 136 studies were included in the meta-analysis, including 2,785 asymptomatic infections. The overall rate of asymptomatic infection was 15.1% (95% CI: 12.0%-18.4%). Subgroup analysis showed that the rate was significantly higher in pregnant women (36.3%, 95% CI: 15.7%-59.6%), children (29.4%, 17.4%-42.9%), and studies for screening settings (25.3%, 15.4%-36.5%) conducted on or after 01 March 2020 (27.8%, 15.7%-41.7%). In terms of geographical regions, the rate was the highest in Asia (excluding China) (27.4%, 14.3%-42.6%), followed by Europe (22.7%, 6.3%-44.9%), the US (15.9%, 8.9%-24.3%), and China (13.1%, 10.2%-16.3%). Conclusions: High proportion of asymptomatic infection were observed in pregnant women, children, European residents, screening programmes, and in studies conducted in and after March 2020. Our findings help inform the true burden of COVID-19 among different groups of cases, and provide information on cost-effective strategies of identifying and tracing asymptomatic infections.

BackgroundDuring the pandemic of coronavirus disease 2019 (COVID-19), the genetic mutations occurred in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) cumulatively or sporadically. In this study, we employed a computational approach to identify and trace the emerging patterns of the SARS-CoV-2 mutations, and quantify accumulative genetic distance across different periods and proteins.MethodsFull-length human SARS-CoV-2 strains in United Kingdom were collected. We investigated the temporal variation in the evolutionary genetic distance defined by the Hamming distance since the start of COVID-19 pandemic.FindingsOur results showed that the SARS-CoV-2 was in the process of continuous evolution, mainly involved in spike protein (S protein), the RNA-dependent RNA polymerase (RdRp) region of open reading frame 1 (ORF1) and nucleocapsid protein (N protein). By contrast, mutations in other proteins were sporadic and genetic distance to the initial sequenced strain did not show an increasing trend.

Virus evolution drives the annual influenza epidemics in human population worldwide. However, it has been challenging to evaluate the mutation effect of the influenza virus on evading the population immunity. In this study, we introduce a novel statistical and computational approach to measure the dynamic molecular determinants underlying epidemics by the effective mutations (EMs), and account for the time of waning mutation advantage against herd immunity by the effective mutation periods (EMPs). Extensive analysis is performed on the genome and epidemiology data of 13-year worldwide H3N2 epidemics involving nine regions in four continents. We showed that the identified EM processed similar profile in geographically adjacent regions, while only 40% are common to Europe, North America, Asia and Oceania, indicating that the regional specific mutations also contributed significantly to the global H3N2 epidemics. The mutation dynamics calibrated that around 90% of the common EMs underlying global epidemics were originated from South East Asia, led by Thailand and India, and the rest were originated from North America. New Zealand was found to be the dominate sink region of H3N2 circulation, followed by UK. All regions might act as the intersection in the H3N2 transmission network. The proposed methodology provided a way to characterize key amino acids from the genetic epidemiology point of view. This approach is not restricted by the genomic region or type of the virus, and will find broad applications in identifying therapeutic targets for combating infectious diseases.

Shi Zhao,1,2,* Xiujuan Tang,3,* Xue Liang,4,* Marc KC Chong,1,2 Jinjun Ran,5 Salihu S Musa,6 Guangpu Yang,7 Peihua Cao,8 Kai Wang,9 Benny CY Zee,1,2 Xin Wang,3 Daihai He,6 Maggie H Wang1,2 1Division of Biostatistics, JC School of Public Health and Primary Care, Chinese University of Hong Kong, Hong Kong, People’s Republic of China; 2Shenzhen Research Institute of Chinese University of Hong Kong, Shenzhen, People’s Republic of China; 3Shenzhen Center for Disease Control and Prevention, Shenzhen, People’s Republic of China; 4Department of Hematology, The 989th Hospital of the Joint Logistics Support Force of Chinese PLA, Luoyang 471031, People’s Republic of China; 5School of Public Health, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong, People’s Republic of China; 6Department of Applied Mathematics, Hong Kong Polytechnic University, Hong Kong, People’s Republic of China; 7Department of Orthopaedics and Traumatology, Chinese University of Hong Kong, Hong Kong, People’s Republic of China; 8Clinical Research Centre, Zhujiang Hospital, Southern Medical University, Guangzhou, People’s Republic of China; 9Department of Medical Engineering and Technology, Xinjiang Medical University, Urumqi 830011, People’s Republic of China*These authors contributed equally to this workCorrespondence: Shi Zhao; Daihai He Email zhaoshi.cmsa@gmail.com; daihai.he@polyu.edu.hkBackground: Measles, a highly contagious disease, still poses a huge burden worldwide. Lately, a trend of resurgence threatened the developed countries. A measles outbreak occurred in the Hong Kong International Airport (HKIA) between March and April 2019, which infected 29 airport staff. During the outbreak, multiple measures were taken including daily situation updates, setting up a public enquiry platform on March 23, and an emergent vaccination program targeting unprotected staff. The outbreak was put out promptly. The effectiveness of these measures was unclear.Methods: We quantified the transmissibility of outbreak in HKIA by the effective reproduction number, Reff(t), and basic reproduction number, R0(t). The reproduction number was modelled as a function of its determinants that were statistically examined, including lags in hospitalization, situation update, and level of public awareness. Then, we considered a hypothetical no-measure scenario when improvements in reporting and public enquiry were absent and calculated the number of infected airport staff.Results: Our estimated average R0 is 10.09 (95% CI: 1.73− 36.50). We found that R0(t) was positively associated with lags in hospitalization and situation update, while negatively associated with the level of public awareness. The average predicted basic reproduction number, r0, was 14.67 (95% CI: 9.01− 45.32) under the no-measure scenario, which increased the average R0 by 77.57% (95% CI: 1.71− 111.15). The total number of infected staff would be 179 (IQR: 90− 339, 95% CI: 23− 821), namely the measure induced 8.42-fold (95% CI: 0.21− 42.21) reduction in the total number of infected staff.Conclusion: Timely reporting on outbreak situation and public awareness measured by the number of public enquiries helped to control the outbreak.Keywords: measles, outbreak, reproduction number, statistical modelling, public awareness, airport

The novel coronavirus disease 2019 (COVID-19) outbreak on the Diamond Princess ship has caused over 634 cases as of February 20, 2020. We model the transmission process on the ship with a stochastic model and estimate the basic reproduction number at 2.2 (95%CI: 2.1−2.4). Through estimating the dispersion parameter which quantifies the superspreading events, we find that stochastic bursts were unlikely to occur if the reproduction number is less than 1. The epidemic doubling time is at 4.6 days (95%CI: 3.0−9.3), thus timely action were crucial. The lesson learn on the ship is generally applicable in other settings

Background: Primary prevention of cardiovascular disease (CVD) requires adequate control of hypertension and diabetes. We designed and implemented pharmaceutical and healthy lifestyle interventions for patients with diabetes and/or hypertension in rural primary care, and assessed their effectiveness at reducing the rate of severe CVD events. Methods: We used a pragmatic, parallel group, two-arm, controlled, superiority, cluster trial design. We randomised 67 township hospitals from rural Zhejiang province, China, to intervention (34) or control (33) arms. Using health records, we recruited patients with hypertension and a 10-year CVD risk of 20% or higher and all patients with type 2 diabetes. The intervention included prescription of a standardised package of medicines, individual advice on lifestyle change and adherence support. Control was usual hypertension and diabetes care. In both arms, as usual in China, most outpatient drug costs were out-of-pocket. The primary outcome was severe CVD events after 36 months of intervention, as recorded by the CVD surveillance system. Findings: We conducted the trial from December 2013 until May 2017. We randomly allocated 13,385 and 14,745 participants to the intervention and control arms respectively. At 36 months there were 762 and 874 severe CVD events in the intervention and control arms respectively (incidence rate = 1.92 and 2.01 per 100 person-years respectively; crude incidence rate ratio = 0.90 [95% CI: 0.74, 1.08; P = 0.259]). Self-reported adherence to recommended medicines was significantly higher in the intervention arm, but adherence was low. Differences in mean blood pressure were significant but small. No safety concerns were identified. Interpretation: The comprehensive package of pharmaceutical and healthy lifestyle interventions did not significantly reduce severe CVD events over 36 months. Improving health system factors such as universal coverage for the cost of essential medicines are required for successful risk-based CVD prevention programmes. Trial Registration: Current Controlled Trials (ISRCTN58988083). Funding: UK DFID (COMDIS-HSD) and Zhejiang Health Bureau. Declaration of Interests: The authors declare no competing interests. Ethics Approval Statement: Ethics approval was obtained from the University of Leeds School of Medicine Research Ethics Committee (reference HSLTLM/12/010) and the Ethics Committee of Zhejiang Provincial Centre for Disease Control and Prevention, China (reference 18/06/2012). Written informed consent was obtained from both hospital directors and all participating individuals.

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.

Background Smoking is an important risk factor of TB. However, no studies have been conducted to identify TB cases from smokers. We assessed the process and initial impact of active case finding among smokers at primary care facilities in a setting with high smoking rates and TB burden. Methods A prospective quasi-experimental study was conducted in para-urban communities in Yunnan China between September 2013 and June 2014. Smokers attending primary care facilities in the intervention group were prescribed chest X-rays if they had diabetes or TB symptoms, or were elders or close contacts of TB patients. Those with X-rays suggestive of TB were referred to TB dispensaries for diagnosis. Passive case finding was practiced in the control group. Results In the intervention group, we screened 471 smokers with high risks of TB, of whom 73% took chest X-ray examinations. Eight TB cases were diagnosed, reflecting a 1.7% yield rate of all screened smokers. Smokers with diabetes (OR 6.003, 95% CI 1.057-34.075) were more likely to have TB compared with those without. In total, the intervention group reported significantly higher TB notification rate compared with the control group (38.6 vs 22.9 per 100 000, p=0.016). Conclusions Active case finding among smokers with high risks of TB was feasible and contributed to improved notification rates.