Epidemiological characteristics, spatial clusters and monthly incidence prediction of hand, foot and mouth disease from 2017 to 2022 in Shanxi Province, China.

Hand, foot and mouth disease (HFMD) is a common infection in the world, and its epidemics result in heavy disease burdens. Over the past decade, HFMD has been widespread among children in China, with Shanxi Province being a severely affected northern province. Located in the temperate monsoon climate, Shanxi has a GDP of over 2.5 trillion yuan. It is important to have a comprehensive understanding of the basic features of HFMD in those areas that have similar meteorological and economic backgrounds to northern China. We aimed to investigate epidemiological characteristics, identify spatial clusters and predict monthly incidence of HFMD. All reported HFMD cases were obtained from the Shanxi Center for Disease Control and Prevention. Overall HFMD incidence showed a significant downward trend from 2017 to 2020, increasing again in 2021. Children aged < 5 years were primarily affected, with a high incidence of HFMD in male patients (relative risk: 1.316). The distribution showed a seasonal trend, with major peaks in June and July and secondary peaks in October and November with the exception of 2020. Other enteroviruses were the predominant causative agents of HFMD in most years. Areas with large numbers of HFMD cases were primarily in central Shanxi, and spatial clusters in 2017 and 2018 showed a positive global spatial correlation. Local spatial autocorrelation analysis showed that hot spots and secondary hot spots were concentrated in Jinzhong and Yangquan in 2018. Based on monthly incidence from September 2021 to August 2022, the mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE) of the long short-term memory (LSTM) and seasonal autoregressive integrated moving average (SARIMA) models were 386.58 vs. 838.25, 2.25 vs. 3.08, and 461.96 vs. 963.13, respectively, indicating that the predictive accuracy of LSTM was better than that of SARIMA. The LSTM model may be useful in predicting monthly incidences of HFMD, which may provide early warnings of HFMD epidemics.

Covid-19 Occupational Risk Incidence and Working Sectors Involved During the Pandemic in Italy.

Background: Starting from March 2020 until December 2021, different phases of Covid-19 pandemic have been identified in Italy, with several containing/lifting measures progressively enforced by the National government. In the present study, we investigate the change in occupational risk during the subsequent pandemic phases and we propose an estimate of the incidence of the cases by economic sector, based on the analysis of insurance claims for compensation for Covid-19. Methods: Covid-19 epidemiological data available for the general population and injury claims of workers covered by the Italian public insurance system in 2020-2021 were analyzed. Monthly Incidence Rate of Covid-19 compensation claims per 100,000 workers (MIRw) was calculated by the economic sector and compared with the same indicator for general population in different pandemic periods. Results: The distribution of Covid-19 MIRw by sector significantly changed during the pandemic related to both the strength of different waves and the mitigation/lifting strategies enforced. The level of occupational fraction was very high at the beginning phase of the pandemic, decreasing to 5% at the end of 2021. Healthcare and related services were continuously hit but the incidence was significantly decreasing in 2021 in all sectors, except for postal and courier activities in transportation and storage enterprises. Conclusion: The analysis of compensation claim data allowed to identify time trends for infection risk in different working sectors. The claim rates were highest for human health and social work activities but the distribution of risk among sectors was clearly influenced by the different stages of the pandemic.

Comparison on predictive capacity of ARIMA model and LSTM model for incidence of hand, foot and mouth disease in Shijiazhuang / 中华疾病控制杂志

Objective To predict the incidence of hand, foot and mouth disease (HFMD) in Shijiazhuang using the multiple seasonal autoregressive integrated moving average model (ARIMA) and long short term memory (LSTM) model, lay theoretical foundation for the prevention and control of HFMD. Methods Multiple seasonal ARIMA model and LSTM model were established separately by using Eviews 8.0 and python 3.7.1 according to the data of monthly incidence of HFMD from January 2013 to May 2018 in Shijiazhuang, and the data from June 2018 to May 2019 were used to verify the prediction precision of model. Finally, the monthly incidence from June to August 2019 was predicted. Results Based on the monthly incidence from January 2013 to May 2018, the optimal models, ARIMA(1,0,0)×(1,1,2)12 and LSTM model were established. Mean absolute percentage of error (MAPE) of ARIMA and LSTM model were 22.14 and 10.03 respectively based on the monthly incidence from June to December 2018, while MAPE of ARIMA and LSTM model were 43.84 and 25.26 respectively based on the monthly incidence from June 2018 to May 2019. These results indicated that LSTM model was superior to ARIMA model in model fitting degree and predicting accuracy, which was relatively consistent with the actual situation. Conclusions LSTM model is able to fit and predict the incidence trend of HFMD well in Shijiazhuang. It can provide guidance to HFMD epidemic prediction and alerting.

Time-series modelling and forecasting of hand, foot and mouth disease cases in China from 2008 to 2018.

Seasonal autoregressive-integrated moving average (SARIMA) has been widely used to model and forecast incidence of infectious diseases in time-series analysis. This study aimed to model and forecast monthly cases of hand, foot and mouth disease (HFMD) in China. Monthly incidence HFMD cases in China from May 2008 to August 2018 were analysed with the SARIMA model. A seasonal variation of HFMD incidence was found from May 2008 to August 2018 in China, with a predominant peak from April to July and a trough from January to March. In addition, the annual peak occurred periodically with a large annual peak followed by a relatively small annual peak. A SARIMA model of SARIMA (1, 1, 2) (0, 1, 1)12 was identified, and the mean error rate and determination coefficient were 16.86% and 94.27%, respectively. There was an annual periodicity and seasonal variation of HFMD incidence in China, which could be predicted well by a SARIMA (1, 1, 2) (0, 1, 1)12 model.

Application of ARIMA model in predicting monthly incidence of syphilis / 西安交通大学学报(医学版)

Objective To explore the value of the autoregressive integrated moving average model (ARIMA) applied to predict monthly incidence of syphilis so as to provide basis for prevention and control of syphilis . Methods Eviews 8 .0 was used to establish the ARIMA model based on the data of monthly incidence of syphilis in China from January 2009 to December 2015 .Then the data of the first half of 2016 were used to verify the predicted results .The predictions were evaluated by RMSE ,MAE ,MAPE and MRE models .Then the monthly incidence of syphilis in the second half of 2016 was predicted .Results The optimal model for the monthly incidence of syphilis from January 2009 to June 2016 was the model of ARIMA (2 ,1 ,1) × (0 ,1 ,1)12 ,its equation was (1 - B)(1 - B12 ) (1+0 .820 B)(1+0 .566 B2 ) x2t = (1+0 .365 B) (1+0 .897 B12 )εt ,its parameters are as follows :R2 =0 .832 ,RMSE=0 .181 ,MAE=0 .118 ,MAPE=5 .088 .The predicted monthly incidence values (10-5 ) of the second half of 2016 were 3 .124 ,3 .008 ,2 .906 ,2 .691 ,2 .714 ,and 2 .717 .Conclusion ARIMA model has a relatively good prediction precision .Therefore , it can make short-term prediction based on the evolution trend of monthly incidence of syphilis in China .

Seasonal patterns of dengue fever and associated climate factors in 4 provinces in Vietnam from 1994 to 2013.

BACKGROUND: In Vietnam, dengue fever (DF) is still a leading cause of hospitalization. The main objective of this study was to evaluate the seasonality and association with climate factors (temperature and precipitation) on the incidences of DF in four provinces where the highest incidence rates were observed from 1994 to 2013 in Vietnam. METHODS: Incidence rates (per 100,000) were calculated on a monthly basis from during the study period. The seasonal-decomposition procedure based on loess (STL) was used in order to assess the trend and seasonality of DF. In addition, a seasonal cycle subseries (SCS) plot and univariate negative binomial regression (NBR) model were used to evaluate the monthly variability with statistical analysis. Lastly, a generalized estimating equation (GEE) was used to assess the relationship between monthly incidence rates and weather factors (temperature and precipitation). RESULTS: We found that increased incidence rates were observed in the second half of each year (from May through December) which is the rainy season in each province. In Hanoi, the final model showed that 1 °C rise of temperature corresponded to an increase of 13% in the monthly incidence rate of DF. In Khanh Hoa, the final model displayed that 1 °C increase in temperature corresponded to an increase of 17% while 100 mm increase in precipitation corresponded to an increase of 11% of DF incidence rate. For Ho Chi Minh City, none of variables were significant in the model. In An Giang, the final model showed that 100 mm increase of precipitation in the preceding and same months corresponded to an increase of 30% and 22% of DF incidence rate. CONCLUSION: Our findings provide insight into understanding the seasonal pattern and associated climate risk factors.

A Study on Menarchel Age and Monthly Incidence of School Girls in Ansan

PURPOSE: Menarche is an important life event, making the transition from childhood to early womanhood. It is a significant physical and physiological event that adolescent girls feel sexual identity and it affects on psychological development. The onset of menstruation is considered a practical indication of sexual maturity in girls. On a population level, mean menarcheal age is considered to be primarily an indicator of living conditions and health. The purpose of this study is to determine menarcheal age in Ansan, Korea in present time and confirm and analize the seasonal variation in menarche in Ansan, Korea. METHODS: The cross-sectional study was done on menarche in 4,786 junior high and high school girl students (11-20years old) of Ansan city. RESULTS: 1) The mean menarcheal age was 12.4+/-1.1 years. 2) The menarcheal ages of 12-year-old, 16-year-old, and 20-year-old girls were 11.4+/-0.7, 12.3+/-0.9, 13.2+/-1.1 respectively. These data show earlier onset of menarche at the younger age groups with statistical significance at p<0.01. 3) The menarche occurred most frequently on August (14.9%), followed by July (12.1%), January (10.2%), December (9.7%). CONCLUSIONS: The mean menarcheal age of the subjects was 12.4+/-1.1 years and it was not different from the mean age of menarche in Europe and United States. These data showed that there was a seasonal variation in the onset of menarche, peak in the summer and winter and it seems to be affected complicatedly by many factors such as seasonal factor, phylogenetic factor ontogenetic factor, environmental factor, psychologic status, nutritional status etc.