Jun Wu’s research while affiliated with Chinese Center For Disease Control And Prevention and other places

Objectives Early in the outbreak, to predict the final size of the COVID-19 outbreak in Dalian, Liaoning province, China, the finding can be used to provide a scientific reference for timely adjustment of prevention and control strategies. Methods Data from COVID-19 patients were collected from August 26 to September 14 2022. Early in the outbreak, a Susceptible-Exposed-Infectious-Quarantine-Recovered (SEIQR) dynamics model with an epidemic amplifier effect, based on the basic model, was developed to fit the data and predict the final size of the COVID-19 outbreak in Dalian, Liaoning province, China. The mean absolute relative error(MARE), root mean squared error(RMSE) and mean absolute error(MAE) were used to assess the predictive capacity of the model. Results From 26 August to 14 September 2022, 1132 confirmed cases and infected asymptomatic cases of COVID-19 (558 males and 574 females) were reported in Dalian. There were two epidemic amplifiers in this outbreak, namely, T Market and H Hotel. The outbreak size predicted by the combined application of the SEIQR model with these two amplifiers is 1168.34 cases, and MARE, RMSE and MAE compared to the actual value from September 1 to 14 is 1.894%, 21.473 and 17.492 respectively According to the fitting results of the basic SEIQR model, if there was no epidemic amplifier in this outbreak, the final outbreak size was 349.96 cases, which means that the T Market and H Hotel increased 822 infections through amplification. Conclusions Early in the outbreak, it was effective and reliable to use the SEIQR transmission dynamics model with the amplifier effect to predict the final size of the COVID-19 outbreak in Dalian, Liaoning province, China, and the result can provide a theoretical basis for the early closing of the COVID-19 epidemic amplifier sites. Furthermore, the epidemic amplifier effect added to the model can solve the homogeneous mixing hypothesis problem that does not match the actual spread of infectious diseases but commonly used by researchers in the construction process of the dynamic model.

Objectives To forecast the development trend of current outbreak in Dalian, mainly to predict turning points of COVID-19 outbreak in Dalian, Liaoning province, China, the results can be used to provide a scientific reference for timely adjustment of prevention and control strategies. Methods During the outbreak, Bayesian framework was used to calculated the time-dependent reproduction number ( ${R_{t}}$ R t ), and then above acquired ${R_{t}}$ R t and exponential trend equation were used to establish the prediction model, through the model, predict the ${R_{t}}$ R t value of following data and know when ${R_{t}}$ R t smaller than 1. Results From July 22 to August 5, 2020, and from March 14 to April 2, 2022, 92 and 632 confirmed cases and asymptomatic infected cases of COVID-19 were reported (324 males and 400 females) in Dalian. The R square for exponential trend equation were 0.982 and 0.980, respectively which fit the ${R_{t}}$ R t with illness onset between July 19 to July 28, 2020 and between March 5 to March 17, 2022. According to the result of prediction, under the current strength of prevention and control, the ${R_{t}}$ R t of COVID-19 will drop below 1 till August 2, 2020 and March 26, 2022, respectively in Dalian, one day earlier or later than the actual date. That is, the turning point of the COVID-19 outbreak in Dalian, Liaoning province, China will occur on August 2, 2020 and March 26, 2022. Conclusions Using time-dependent reproduction number values to predict turning points of COVID-19 outbreak in Dalian, Liaoning province, China was effective and reliable on the whole, and the results can be used to establish a sensitive early warning mechanism to guide the timely adjustment of COVID-19 prevention and control strategies.

Background Acquired immunodeficiency syndrome (AIDS) is a malignant infectious disease with high mortality caused by HIV (human immunodeficiency virus, and up to now there are no curable drugs or effective vaccines. In order to understand AIDS’s development trend, we establish hybrid EMD-BPNN (empirical modal decomposition and Back-propagation artificial neural network model) model to forecast new HIV infection in Dalian and to evaluate model’s performance. Methods The monthly HIV data series are decomposed by EMD method, and then all decomposition results are used as training and testing data to establish BPNN model, namely BPNN was fitted to each IMF (intrinsic mode function) and residue separately, and the predicted value is the sum of the predicted values from the models. Meanwhile, using yearly HIV data to established ARIMA and using monthly HIV data to established BPNN, and SARIMA (seasonal autoregressive integrated moving average) model to compare the predictive ability with EMD-BPNN model. Results From 2004 to 2017, 3310 cases of HIV were reported in Dalian, including 101 fatal cases. The monthly HIV data series are decomposed into four relatively stable IMFs and one residue item by EMD, and the residue item showed that the incidence of HIV increases firstly after declining. The mean absolute percentage error value for the EMD-BPNN, BPNN, SARIMA (1,1,2) (0,1,1)12 in 2018 is 7.80%, 10.79%, 9.48% respectively, and the mean absolute percentage error value for the ARIMA (3,1,0) model in 2017 and 2018 is 8.91%. Conclusions The EMD-BPNN model was effective and reliable in predicting the incidence of HIV for annual incidence, and the results could furnish a scientific reference for policy makers and health agencies in Dalian.

Hand, foot, and mouth disease (HFMD) is a common infectious gastrointestinal disease in children under the age of 5. Many studies have documented that meteorological variables, especially temperature, are associated with HFMD. Since the general climate patterns occur as latitude increases, so latitude may indirectly influence the peak time of HFMD. The objective of this study was to explore the effect of latitude on the starting of an HFMD epidemic in Dalian, which can help in the development of an early warning model of HFMD in difference latitude districts. Spearman's rank correlation coefficient was used to analyze the tendency of HFMD incidence rate over the year. A circular distribution method was used to calculate the gold standard of beginning of the HFMD epidemic. A negative binomial regression model was used to establish the early warning of the starting of the HFMD epidemic. The annualized crude incidence rate of HFMD disease in Dalian, Liaoning Province, China as a whole was 169.14 per 100,000 from 2009 to 2013.The incidence rate of HFMD varied considerably by district during the study period, but there was no significant declining or rising trend in disease incidence over the years by district of Dalian. The circular statistical analysis results showed that there was latitudinal gradient in the starting of the HFMD epidemic except for region B; the starting time of HFMD epidemic of Region A was earlier than other regions range 9 days to 18 days. The starting time of the HFMD epidemic differs from region to region with different latitudes in Dalian, Liaoning Province, China. This result can provide a scientific basis for early warning of HFMD.

Background: The hand foot and mouth disease (HFMD) is a human syndrome caused by intestinal viruses like that coxsackie A virus 16, enterovirus 71 and easily developed into outbreak in kindergarten and school. Scientifically and accurately early detection of the start time of HFMD epidemic is a key principle in planning of control measures and minimizing the impact of HFMD. The objective of this study was to establish a reliable early detection model for start timing of hand foot mouth disease epidemic in Dalian and to evaluate the performance of model by analyzing the sensitivity in detectability. Methods: The negative binomial regression model was used to estimate the weekly baseline case number of HFMD and identified the optimal alerting threshold between tested difference threshold values during the epidemic and non-epidemic year. Circular distribution method was used to calculate the gold standard of start timing of HFMD epidemic. Results: From 2009 to 2014, a total of 62022 HFMD cases were reported (36879 males and 25143 females) in Dalian, Liaoning Province, China, including 15 fatal cases. The median age of the patients was 3 years. The incidence rate of epidemic year ranged from 137.54 per 100,000 population to 231.44 per 100,000population, the incidence rate of non-epidemic year was lower than 112 per 100,000 population. The negative binomial regression model with AIC value 147.28 was finally selected to construct the baseline level. The threshold value was 100 for the epidemic year and 50 for the non- epidemic year had the highest sensitivity(100%) both in retrospective and prospective early warning and the detection time-consuming was 2 weeks before the actual starting of HFMD epidemic. Conclusions: The negative binomial regression model could early warning the start of a HFMD epidemic with good sensitivity and appropriate detection time in Dalian.

Database containing weekly time-series of HFMD and the baseline value obtained from the negative binomial regression during 2009–2012 in Dalian. (XLSX)

This study describes our development of a model to predict the incidence of clinically diagnosed dysentery in Dalian, Liaoning Province, China, using time series analysis. The model was developed using the seasonal autoregressive integrated moving average (SARIMA). Spearman correlation analysis was conducted to explore the relationship between meteorological variables and the incidence of clinically diagnosed dysentery. The meteorological variables which significantly correlated with the incidence of clinically diagnosed dysentery were then used as covariables in the model, which incorporated the monthly incidence of clinically diagnosed dysentery from 2005 to 2010 in Dalian. After model development, a simulation was conducted for the year 2011 and the results of this prediction were compared with the real observed values. The model performed best when the temperature data for the preceding month was used to predict clinically diagnosed dysentery during the following month. The developed model was effective and reliable in predicting the incidence of clinically diagnosed dysentery for most but not all months, and may be a useful tool for dysentery disease control and prevention, but further studies are needed to fine tune the model. © 2015, Southeast Asian Journal of Tropical Medicine and Public Health. All rights reserved.