ABSTRACT
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.
ABSTRACT
Objective To investigate the causal association between hip circumference (HC) and type 2 diabetes mellitus (T2DM) based on Mendelian randomization. Methods The genetic variants data of the HC and T2DM from the Genetic Investigation of Anthropometric Traits (GIANT) and DIAbetes Genetics Replication And Meta-analysis (DIAGRAM) database were matched according to the single nucleotide polymorphism (SNP) rsID. Genetic loci strongly related to the HC were used as instrumental variables; and the inverse-variance weighting, MR-Egger regression model and weighting median method were carried out to analyze the causal effect of HC on T2DM. Results Fifty-two, nine and fifteen SNPs were matched in the total cohort, female cohort and male cohort, respectively. Heterogeneity test suggested the SNPs were homogeneous. We found HC to be positively associated with T2DM risk (OR=1.065, 95% CI: 1.030-1.100, OR=1.103, 95% CI: 1.057-1.150 and OR=1.583, 95% CI: 1.273-1.968, respectively) in above three cohorts, respectively. Sensitivity analysis showed the results were robust. Conclusions There is a relationship between HC and T2DM of people, and HC may be the risk factor of T2DM.