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Background: Infectious diseases are a major threat to public health, causing serious medical consumption and casualties. Accurate prediction of infectious diseases incidence is of great significance for public health organizations to prevent the spread of diseases. However, only using historical incidence data for prediction can not get good results. This study analyzes the influence of meteorological factors on the incidence of hepatitis E, which are used to improve the accuracy of incidence prediction., Methods: We extracted the monthly meteorological data, incidence and cases number of hepatitis E from January 2005 to December 2017 in Shandong province, China. We employ GRA method to analyze the correlation between the incidence and meteorological factors. With these meteorological factors, we achieve a variety of methods for incidence of hepatitis E by LSTM and attention-based LSTM. We selected data from July 2015 to December 2017 to validate the models, and the rest was taken as training set. Three metrics were applied to compare the performance of models, including root mean square error(RMSE), mean absolute percentage error(MAPE) and mean absolute error(MAE)., Results: Duration of sunshine and rainfall-related factors(total rainfall, maximum daily rainfall) are more relevant to the incidence of hepatitis E than other factors. Without meteorological factors, we obtained 20.74%, 19.50% for incidence in term of MAPE, by LSTM and A-LSTM, respectively. With meteorological factors, we obtained 14.74%, 12.91%, 13.21%, 16.83% for incidence, in term of MAPE, by LSTM-All, MA-LSTM-All, TA-LSTM-All, BiA-LSTM-All, respectively. The prediction accuracy increased by 7.83%. Without meteorological factors, we achieved 20.41%, 19.39% for cases in term of MAPE, by LSTM and A-LSTM, respectively. With meteorological factors, we achieved 14.20%, 12.49%, 12.72%, 15.73% for cases, in term of MAPE, by LSTM-All, MA-LSTM-All, TA-LSTM-All, BiA-LSTM-All, respectively. The prediction accuracy increased by 7.92%. More detailed results are shown in results section of this paper., Conclusions: The experiments show that attention-based LSTM is superior to other comparative models. Multivariate attention and temporal attention can greatly improve the prediction performance of the models. Among them, when all meteorological factors are used, multivariate attention performance is better. This study can provide reference for the prediction of other infectious diseases., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Feng et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Background: Accurate and reliable predictions of infectious disease can be valuable to public health organizations that plan interventions to decrease or prevent disease transmission. A great variety of models have been developed for this task. However, for different data series, the performance of these models varies. Hepatitis E, as an acute liver disease, has been a major public health problem. Which model is more appropriate for predicting the incidence of hepatitis E? In this paper, three different methods are used and the performance of the three methods is compared., Methods: Autoregressive integrated moving average(ARIMA), support vector machine(SVM) and long short-term memory(LSTM) recurrent neural network were adopted and compared. ARIMA was implemented by python with the help of statsmodels. SVM was accomplished by matlab with libSVM library. LSTM was designed by ourselves with Keras, a deep learning library. To tackle the problem of overfitting caused by limited training samples, we adopted dropout and regularization strategies in our LSTM model. Experimental data were obtained from the monthly incidence and cases number of hepatitis E from January 2005 to December 2017 in Shandong province, China. We selected data from July 2015 to December 2017 to validate the models, and the rest was taken as training set. Three metrics were applied to compare the performance of models, including root mean square error(RMSE), mean absolute percentage error(MAPE) and mean absolute error(MAE)., Results: By analyzing data, we took ARIMA(1, 1, 1), ARIMA(3, 1, 2) as monthly incidence prediction model and cases number prediction model, respectively. Cross-validation and grid search were used to optimize parameters of SVM. Penalty coefficient C and kernel function parameter g were set 8, 0.125 for incidence prediction, and 22, 0.01 for cases number prediction. LSTM has 4 nodes. Dropout and L2 regularization parameters were set 0.15, 0.001, respectively. By the metrics of RMSE, we obtained 0.022, 0.0204, 0.01 for incidence prediction, using ARIMA, SVM and LSTM. And we obtained 22.25, 20.0368, 11.75 for cases number prediction, using three models. For MAPE metrics, the results were 23.5%, 21.7%, 15.08%, and 23.6%, 21.44%, 13.6%, for incidence prediction and cases number prediction, respectively. For MAE metrics, the results were 0.018, 0.0167, 0.011 and 18.003, 16.5815, 9.984, for incidence prediction and cases number prediction, respectively., Conclusions: Comparing ARIMA, SVM and LSTM, we found that nonlinear models(SVM, LSTM) outperform linear models(ARIMA). LSTM obtained the best performance in all three metrics of RSME, MAPE, MAE. Hence, LSTM is the most suitable for predicting hepatitis E monthly incidence and cases number., Competing Interests: The authors have declared that no competing interests exist.

Background: Persons with chronic hepatitis B (CHB) infection were reported to suffer severe disease after hepatitis E virus (HEV) superinfection, but the studies regarding HEV seroprevalence in this population were limited. A recent study in Vietnam found higher HEV seroprevalence among CHB patients compared with healthy controls., Methods: A community-based case-control study was conducted in two counties of Shandong province, China, where hepatitis E incidence was at the highest (Rushan) and lowest (Zhangqiu) in the province based on data from routine public health surveillance. Four townships were selected randomly from each county and all residents in these townships were tested for hepatitis B surface antigen (HBsAg). Those tested positive for HBsAg (CHB group) and the 1:1 age and sex-matched HBsAg-negative residents (control group) were included. Anti-HEV IgM and IgG were tested and positive rates of IgG and IgM were compared between the CHB group and the control group., Results: In total, 2048 CHB participants and 2054 controls were included in the study. In the CHB group, HEV IgG seroprevalence was 9.16% (95% CI: 7.47-11.09) in Zhangqiue and 38.06% (95% CI: 35.07-41.19) in Rushan (P < 0.001); the corresponding rates of IgM were 0.1% (95% CI: 0.002-0.54) and 1.57% (95% CI: 0.90-2.53), respectively (P < 0.001). HEV IgG seroprevalence was similar between CHB group and the control group in both counties (P = 0.21, P = 0.47, respectively) and the same results were found for the positive rate of IgM (P = 0.103, P = 0.262, respectively). Multivariable analysis showed the status of HBsAg was not independently associated with the status of anti-HEV IgG in either Zhangqiu or Rushan [P = 0.187, OR = 1.23(95% CI: 0.90, 1.68); P = 0.609, OR = 1.05 (95% CI: 0.87, 1.26)]., Conclusions: The seroprevalence of HEV varies greatly in different geographic areas, but the seroprevalence is similar between populations with and without CHB. CHB patients residing in high HEV endemic areas might be at higher risk for HBV-HEV superinfection.

Hepatitis E vaccine was available in China in 2012, but the priority population for immunization is not clear. In 2013, a hepatitis E outbreak occurred in a company of Shandong province, China where most employees moved from other provinces and dined at the company's cafeteria. A total of fourteen (19%, 14/73) case-patients were identified, and three of them had symptomatic infection with one death. The proportion of symptomatic infection was much higher among those aged ⩾50years than those aged <50years (2/2 vs. 1/12, P=0.03), and higher in males than females (3/8 vs. 0/6, P=0.21). Food in the company's cafeteria might be the possible source of the outbreak. The findings from this outbreak investigation indicate that individuals aged ⩾50years, particularly males, might be the population of top priority for hepatitis E vaccination in China., (Copyright © 2016 The Author(s). Published by Elsevier Ltd.. All rights reserved.)