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Objectives: The aim of the study was to reconstruct the complete transmission chain of the COVID-19 outbreak in Beijing's Xinfadi Market using data from epidemiological investigations, which contributes to reflecting transmission dynamics and transmission risk factors., Methods: We set up a transmission model, and the model parameters are estimated from the survey data via Markov chain Monte Carlo sampling. Bayesian data augmentation approaches are used to account for uncertainty in the source of infection, unobserved onset, and infection dates., Results: The rate of transmission of COVID-19 within households is 9.2%. Older people are more susceptible to infection. The accuracy of our reconstructed transmission chain was 67.26%. In the gathering place of this outbreak, the Beef and Mutton Trading Hall of Xinfadi market, most of the transmission occurs within 20 m, only 19.61% of the transmission occurs over a wider area (>20 m), with an overall average transmission distance of 13.00 m. The deepest transmission generation is 9. In this outbreak, there were 2 abnormally high transmission events., Conclusions: The statistical method of reconstruction of transmission trees from incomplete epidemic data provides a valuable tool to help understand the complex transmission factors and provides a practical guideline for investigating the characteristics of the development of epidemics and the formulation of control measures., Competing Interests: Conflict of interest statement All authors have no conflict of interests to declare., (Copyright © 2022 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Hand-foot-and-month disease (HFMD), especially the enterovirus A71 (EV-A71) subtype, is a major health problem in Beijing, China. Previous studies mainly used regressive models to forecast the prevalence of HFMD, ignoring its intrinsic age groups. This study aims to predict HFMD of EV-A71 subtype in three age groups (0-3, 3-6 and > 6 years old) from 2011 to 2018 using residual-convolutional-recurrent neural network (CNNRNN-Res), convolutional-recurrent neural network (CNNRNN) and recurrent neural network (RNN). They were compared with auto-regressio, global auto-regression and vector auto-regression on both short-term and long-term prediction. Results showed that CNNRNN-Res and RNN had higher accuracies on point forecast tasks, as well as robust performances in long-term prediction. Three deep learning models also had better skills in peak intensity forecast, and CNNRNN-Res achieved the best results in the peak month forecast. We also found that three age groups had consistent outbreak trends and similar patterns of prediction errors. These results highlight the superior performance of deep learning models in HFMD prediction and can assist the decision-makers to refine the HFMD control measures according to age groups.

Background: Children aged under 5 years are particularly vulnerable to influenza infection. In this study, we aim to estimate the number and incidence of influenza among young children and estimate the impact of childhood vaccination in different scenarios from 2013/14 to 2016/17 seasons., Methods: The number and incidence rate of influenza infections among children aged under 5 years in Beijing was estimated by scaling up observed surveillance data. Then, we used a susceptible-exposed-infected-recovery (SEIR) model to reproduce the weekly number of influenza infections estimated in Beijing during the study seasons, and to estimate the number and proportion of influenza-attributed medically attended acute respiratory infections (I-MAARI) averted by vaccination in each season. Finally, we evaluated the impact of alternative childhood vaccination programs with different coverage and speed of vaccine distribution., Results: The estimated average annual incidence of influenza in children aged under 5 years was 33.9% (95% confidence interval (CI): 27.5%, 47.2%) during the study period. With the actual coverage during the included seasons at around 2.9%, an average of 3.9% (95%CI: 3.5%, 4.4%) I-MAARI was reduced compared to a no-vaccination scenario. Reaching 20%, 40%, 50%, 60%, 80% and 100% vaccine coverage would lead to an overall I-MAARI reduction of 25.3%, 42.7%, 51.9%, 57.0%, 65.3% and 71.2%. At 20% coverage scenario, an average of 28.8% I-MAARI will be prevented if intensive vaccination implemented in 2 months since the vaccine released., Conclusion: In Beijing, the introduction of a program for vaccinating young children, even at relatively low vaccine coverage rates, would considerably reduce I-MAARI, particularly if the vaccines can be quickly delivered.

Background: The benefit of school-based influenza vaccination policy has not been fully addressed in Beijing., Objectives: To evaluate the benefit of school-based influenza vaccination policy launched in Beijing., Methods: Using existing surveillance and immunization data, we developed a dynamic transmission model to assess the impact of influenza vaccination in school-going children. The outcome was defined as the averted number of medically attended influenza illnesses and the prevented disease fraction to all children aged 5-14 years for the 2013/14, 2014/15, and 2015/16 seasons., Results: We estimated that during the three consecutive influenza seasons, the averted number of medically attended influenza illnesses among children aged 5-14 years was around 104 000 (95% CI: 101 000-106 000), 23 000 (95% CI: 22 000-23 000), and 21 000 (95% CI: 21 000-22 000), respectively. Corresponding prevented fractions to all children aged 5-14 years were 76.3%, 38.5%, and 43.9%., Conclusions: In Beijing, school-based vaccinations reduced a substantial number of medically attended influenza illnesses despite seasonal variation in the prevented fraction. This is strong supportive evidence for the continuation of school-based vaccination programs to reduce the influenza burden in this age group., (© 2018 The Authors. Influenza and Other Respiratory Viruses Published by John Wiley & Sons Ltd.)