We used data released by the government to analyze the epidemiological distribution of pulmonary tuberculosis in mainland China from 2004 to 2015, in order to provide a deeper understanding of trends in the epidemiology of pulmonary tuberculosis in China and a theoretical basis to assess the effectiveness of government interventions and develop more targeted prevention and control strategies. A discrete dynamic model was designed based on the epidemiological characteristics of pulmonary tuberculosis and fitted to data published by the government to estimate changes in indicators such as adequate contact rate, prevalence of non-treated pulmonary tuberculosis (abbreviated as prevalence), and infection rate. Finally, we performed sensitivity analyses of the effects of parameters on the population infection rate. The epidemiological features of pulmonary tuberculosis in China include a pattern of seasonal fluctuations, with the highest rates of infection in autumn and winter. The adequate contact rate has increased slightly from an average of 0.12/month in 2010 to an average of 0.21/month in 2015. The prevalence in the population has continued to decrease from 3.4% in early 2004 to 1.7% in late 2015. The Mycobacterium tuberculosis (M. tuberculosis) infection rate in the population decreased gradually from 42.3% at the beginning of 2004 to 36.7% at the end of 2015. The actual number of new infections gradually decreased from 1,300,000/year in 2010 to 1,100,000/year in 2015. The actual number of new patients each year has been relatively stable since 2010 and remains at approximately 2,600,000/year. The population prevalence and the M. tuberculosis infection rate have decreased year by year since 2004, indicating that the tuberculosis epidemic in China has been effectively controlled. However, pulmonary tuberculosis has become increasingly contagious since 2010. China should focus on the prevention and control of pulmonary tuberculosis during autumn and winter.

In order to accurately grasp the timing for the prevention and control of diseases, we established an artificial neural network model to issue early warning signals. The real-time recurrent learning (RTRL) and extended Kalman filter (EKF) methods were performed to analyse four types of respiratory infectious diseases and four types of digestive tract infectious diseases in China to comprehensively determine the epidemic intensities and whether to issue early warning signals. The numbers of new confirmed cases per month between January 2004 and December 2017 were used as the training set; the data from 2018 were used as the test set. The results of RTRL showed that the number of new confirmed cases of respiratory infectious diseases in September 2018 increased abnormally. The results of the EKF showed that the number of new confirmed cases of respiratory infectious diseases increased abnormally in January and February of 2018. The results of these two algorithms showed that the number of new confirmed cases of digestive tract infectious diseases in the test set did not have any abnormal increases. The neural network and machine learning can further enrich and develop the early warning theory.

We established a stochastic individual-based model and simulated the whole process of occurrence, development, and control of the coronavirus disease epidemic and the infectors and patients leaving Hubei Province before the traffic was closed in China. Additionally, the basic reproduction number (R₀) and number of infectors and patients who left Hubei were estimated using the coordinate descent algorithm. The median R₀ at the initial stage of the epidemic was 4.97 (95% confidence interval [CI] 4.82–5.17). Before the traffic lockdown was implemented in Hubei, 2000 (95% CI 1982–2030) infectors and patients had left Hubei and traveled throughout the country. The model estimated that if the government had taken prevention and control measures 1 day later, the cumulative number of laboratory-confirmed patients in the whole country would have increased by 32.1%. If the lockdown of Hubei was imposed 1 day in advance, the cumulative number of laboratory-confirmed patients in other provinces would have decreased by 7.7%. The stochastic model could fit the officially issued data well and simulate the evolution process of the epidemic. The intervention measurements nationwide have effectively curbed the human-to-human transmission of severe acute respiratory syndrome coronavirus 2.

Background: Outbreaks of respiratory infectious diseases often occur in crowded places. To understand the pattern of spread of an outbreak of a respiratory infectious disease and provide a theoretical basis for targeted implementation of scientific prevention and control, we attempted to establish a stochastic model to simulate an outbreak of a respiratory infectious disease at a military camp. This model fits the general pattern of disease transmission and further enriches theories on the transmission dynamics of infectious diseases. Methods: We established an enclosed system of 500 people exposed to adenovirus type 7 (ADV 7) in a military camp. During the infection period, the patients transmitted the virus randomly to susceptible people. The spread of the epidemic under militarized management mode was simulated using a computer model named “the random collision model”, and the effects of factors such as the basic reproductive number (R₀), time of isolation of the patients (TOI), interval between onset and isolation (IOI), and immunization rates (IR) on the developmental trend of the epidemic were quantitatively analysed. Results: Once the R₀ exceeded 1.5, the median attack rate increased sharply; when R₀ = 3, with a delay in the TOI, the attack rate increased gradually and eventually remained stable. When the IOI exceeded 2.3 days, the median attack rate also increased dramatically. When the IR exceeded 0.5, the median attack rate approached zero. The median generation time was 8.26 days, (95% confidence interval [CI]: 7.84–8.69 days). The partial rank correlation coefficients between the attack rate of the epidemic and R₀, TOI, IOI, and IR were 0.61, 0.17, 0.45, and − 0.27, respectively. Conclusions: The random collision model not only simulates how an epidemic spreads with superior precision but also allows greater flexibility in setting the activities of the exposure population and different types of infectious diseases, which is conducive to furthering exploration of the epidemiological characteristics of epidemic outbreaks.

We established an individual-based computer model to simulate the occurrence, infection, discovery, quarantine, and quarantine release (recovery) of asymptomatic SARS-CoV-2 infected individuals or patients within the community. The model was used to explore the effects of control measures, such as active tracing, laboratory testing, active treatment, and home quarantine on the epidemic. Considering the condition that R₀ = 1.2, when a case of an imported asymptomatic infected individual (AII) was reported in the community, the implementation of control measures reduced the number of AIIs and patients by 62.2% and 62.4%, respectively. The number of undetected AIIs and patients peaked at 302 days of the epidemic, reaching 53 and 20 individuals, respectively. The implementation of sustained active tracing, laboratory testing, active treatment, and home quarantine can significantly reduce the probability of disease outbreaks and block the spread of the COVID-19 epidemic caused by AIIs in the community.

Family feasting during the Spring Festival is a Chinese tradition. However, close contact during this period is likely to promote the spread of coronavirus disease 2019 (COVID-19). This study developed a dynamic infectious disease model in which the feast gatherings of families were considered the sole mode of transmission. The model simulates COVID-19 transmission via family feast gatherings through a social contact network. First, a kinship-based, virtual social contact network was constructed, with nodes representing families and connections representing kinships. Families in kinship with each other comprised of the largest globally coupled network, also known as a clique, in which a feast gathering was generated by randomly selecting two or more families willing to gather. The social contact network in the model comprised of 215 cliques formed among 608 families with 1517 family members. The modelling results indicated that when there is only one patient on day 0, the number of new infections will reach a peak on day 29, and almost all families and their members in the social contact network will be infected by day 60. This study demonstrated that COVID-19 can spread rapidly through continuous feast gatherings through social contact networks and that the disease will run rampant throughout the network.