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Background: Hand, foot, and mouth disease (HFMD) mostly affects the health of infants and preschool children. Many studies of HFMD in different regions have been published. However, the epidemiological characteristics and space-time patterns of individual-level HFMD cases in a major city such as Beijing are unknown. The objective of this study was to investigate epidemiological features and identify high relative risk space-time HFMD clusters at a fine spatial scale. Methods: Detailed information on age, occupation, pathogen and gender was used to analyze the epidemiological features of HFMD epidemics. Data on individual-level HFMD cases were examined using Local Indicators of Spatial Association (LISA) analysis to identify the spatial autocorrelation of HFMD incidence. Spatial filtering combined with scan statistics methods were used to detect HFMD clusters. Results: A total of 157,707 HFMD cases (60.25% were male, 39.75% were female) reported in Beijing from 2008 to 2012 included 1465 severe cases and 33 fatal cases. The annual average incidence rate was 164.3 per 100,000 (ranged from 104.2 in 2008 to 231.5 in 2010). Male incidence was higher than female incidence for the 0 to 14-year age group, and 93.88% were nursery children or lived at home. Areas at a higher relative risk were mainly located in the urban-rural transition zones (the percentage of the population at risk ranged from 33.89% in 2011 to 39.58% in 2012) showing High-High positive spatial association for HFMD incidence. The most likely space-time cluster was located in the mid-east part of the Fangshan district, southwest of Beijing. Conclusions: The spatial-time patterns of Beijing HFMD (2008–2012) showed relatively steady. The population at risk were mainly distributed in the urban-rural transition zones. Epidemiological features of Beijing HFMD were generally consistent with the previous research. The findings generated computational insights useful for disease surveillance, risk assessment and early warning. [ABSTRACT FROM AUTHOR]

This paper presents an empirical analysis of the epidemiological data concerning the 18445 HFMD-infected cases in Beijing in 2008. The main findings are as follows. (i) Seasonal variations in incidence were observed, with a peak observed during the summer season, especially in May. Male patients outnumber female patients by 1.57:1. (ii) Most cases occurred in children 4 years old or younger. Outperforming Weibull distribution and Gamma distribution as to model fitness when analyzing patient ages, log-normal distribution indicates that the estimated mean age is 3.4 years. (iii) The age distribution seems to indicate cyclic peaks with roughly one-year intervals. (iv) Correlation analyses (ρ = 0.9864) show that time of birth in different months has an impact on the chance of being infected by HFMD. Birth month seems to present a high risk factor on infants and young children. (v) The morbidity rate is 132.7/100000 during the HFMD epidemic in Beijing in 2008. The morbidity map shows that the risks of HFMD infection in areas close to the city center and suburbans are much lower than those in the urban-rural transition zones. Spatial risks inferred from the morbidity map demonstrate a clear circular pattern. (vi) The prevention and control measures taken by the public health departments seem to be effective during the summer season, resulting in the early ending of the epidemic (one month earlier than the natural season) and reduced outbreak size. [ABSTRACT FROM AUTHOR]

1 Transmission chain of COVID-19 outbreak in Beijing was reconstructed. 2 The Bayesian data augmentation approaches account for uncertain data. 3 The risk of infection among different populations was quantified. 4 The source and captured transmission patterns of COVID-19 were effectively traced. 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. 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. 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. 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. [ABSTRACT FROM AUTHOR]