Objectives To explore the trend characteristics of foodborne diseases in Jinan City and apply the seasonal autoregressive integrated moving average model (SARIMA) for prediction. Methods The incidence data of foodborne diseases from two active monitoring sentinel hospitals in Jinan City from 2014 to 2020 were collected to establish a time series. The SARIMA model was used to fit the incidence situation. The numbers of cases in 2021 were compared with the predicted values to validate the model and evaluate the predictive effect. Results The SARIMA (2, 0, 1) (0, 1, 1)₁₂ model was established and fitted the time series of food borne diseases in Jinan well, with AIC=687.22. Using Ljung Box function, P=0.499 was obtained, indicating that the residual error belonged to the white noise series. The data in 2021 was used to test the model extrapolation effect, and the actual values fell within the 95% confidence interval of the predicted value. The model prediction effect was relatively ideal. Conclusion SARIMA (2, 0, 1) (0, 1, 1)₁₂ model can better fit the temporal change of foodborne diseases, and therefore can be used to fit and predict the monthly incidence of foodborne diseases.

Objective To explore the correlation between the incidence of foodborne diseases and meteorological factors in Jinan, and to provide targeted measures for the prevention and control of foodborne diseases. Methods Data from the reporting systems of two sentinel hospitals for active surveillance of foodborne diseases from 2013 to 2021 in Jinan were collected. The meteorological data in the same period in Jinan were also collected. The generalized additive model was used to explore the nonlinear relationship between meteorological factors and the incidence of foodborne diseases, and threshold function analysis was use to perform subsection regression. Results The incidence of foodborne diseases was positively correlated with daily average temperature (r_s=0.23), relative humidity (r_s=0.05), and daily average wind speed (r_s=0.01), and negatively correlated with daily average air pressure (r_s=-0.19). Based on the GAM results and segmented regression analysis of meteorological factors, it was found that when the daily average temperature was below or above the threshold of 24.63°C, for every 1°C increase in daily average temperature, the incidence of foodborne diseases correspondingly increased by 0.04% and 0.18%. When the daily average wind speed was above the threshold of 2.26 m/s, the incidence of foodborne diseases decreased by 0.36% for every 1 m/s increase in the daily average wind speed. Conclusion Nine years of observation and data analysis have shown that meteorological factors such as daily average temperature, relative humidity, air pressure, and wind speed are related to the incidence of foodborne diseases. These findings suggest that meteorological factors may be important factors leading to foodborne diseases, which provides an important scientific basis for formulating effective prevention and control measures.

Objective: To analyze the antimicrobial resistance and multilocus sequence typing (MLST) results of extended-spectrum β-lactamase (ESBLs)-producing Escherichia coli in rural residents in villages with pig breeding farms in a county of Shandong province. Methods: Antimicrobial susceptibility testing was performed with agar dilution method by using 360 ESBLs-producing E. coli strains from fresh stool samples of rural residents in villages with pig breeding farms in a county of Shandong. PCR was conducted to amplify the CTX-M, TEM, SHV genes and capillary electrophoresis was used to screen positive strains in July, 2016. MLST was performed for molecular typing analysis, and eBURST v3.0 software was used for cluster analysis. Results: Among 360 strains of ESBLs-producing E. coli, the resistance rates to cefotaxime, tetracycline, trimethoprim-sulfamethoxazole and florfenicol were 100.0% (360/360), 82.2% (296/360), 81.1% (292/360) and 80.3% (289/360), respectively. The positive rate of CTX-M gene was 99.2% (357/360), in which the positive rate of CTX-M-9 was 35.6% (128/360) and the positive rate of CTX-M-1 was 24.4% (88/360). The positive rate of TEM gene was 26.9% (97/360). A total of 132 STs were identified through MLST. The predominant ST was ST10, accounting for 12.5% (45/360). Cluster analysis showed that CC10 was the most important clone group, including 39 ST clones, involving 148 strains (41.1%). Conclusions The drug resistances of ESBLs-producing E. coli to cefotaxime, tetracycline, trimethoprim-sulfamethoxazole and flurfenicol are serious in this rural area. There is a small-scale clustering of CC10 and transmission mode from animals to humans might exist.