Abstract On September 26, 2024, a municipal hospital in Jiaxing City reported a maternal case of Listeria monocytogenes infection. In order to clarify the source of infection, the Jiaxing Center for Disease Control and Prevention immediately conducted the epidemiological investigation, laboratory testing and related disposal work. The case presented with fever (37.9 ℃), gradually intensifying paroxysmal abdominal pain without obvious cause, and went to hospital on the day of onset. Due to fetal intrauterine distress, a male infant was delivered by cesarean section on the same day. The epidemiological investigation identified that the case usually consumed fruits, often store fruits such as watermelon and grapes in the refrigerator alongside raw meat, and the refrigerator had never been cleaned or disinfected, posing a risk of cross contamination. Laboratory tests on amniotic fluid sample from the pregnant woman, infant blood sample showed positive results for Listeria monocytogenes infection. One strain of Listeria monocytogenes was detected in a smear sample from the inner wall of the refrigerator, and all the strains were ST2 type. Consuming fruits contaminated with Listeria monocytogenes may be the main source of infection. Food safety education for pregnant women and their family members should be strengthened to reduce the risk of infection.

Objective: To explore the impact of heat wave on the incidence of foodborne diseases, so as to provide the basis for preventing foodborne diseases in summer. Methods: Data on foodborne disease cases from June to September each year in Jiaxing City from 2018 to 2023 were collected through the Zhejiang Provincial Foodborne Disease Surveillance Platform. Meteorological data were obtained from the Jiaxing Meteorological Bureau. Air quality data were collected from the National Urban Air Quality Real-time Release Platform. Heat wave were defined based on temperature thresholds and durations. The impact of heat wave on the incidence of foodborne diseases were analyzed by a time stratified case-crossover design with a distributed lag nonlinear model (DLNM). The cumulative relative risk (CRR) and attributable fraction (AF) were calculated to compare the cumulative lagged risks and disease burdens across different genders, age groups, and disease types. Results: From June to September each year between 2018 and 2023, a total of 13 026 foodborne disease cases were reported in Jiaxing City, including 6 646 male cases and 6 380 female cases. Among them, 746 cases were aged <15 years, 10 910 cases were aged 15-65 years, and 1 370 cases were aged >65 years. There were 1 425 cases of infectious diarrhea, 9 956 cases of acute gastroenteritis, and 1 645 cases of other foodborne diseases. The DLNM analysis revealed that heat waves defined in different ways increased the risk of foodborne diseases (all CRR>1, P<0.05), and the risk of disease incidence rose with increasing temperature thresholds and durations. When the daily mean temperature was not lower than the 95th percentile of the daily mean temperature from 2018 to 2023 and lasted for at least 4 days, the Akaike Information Criterion was minimized, and the cumulative lagged risk and disease burden of foodborne diseases were relatively high, with a CRR value of 1.32 (95%CI: 1.09-1.60) and an AF value of 2.81% (95%CI: 1.05%-4.31%). Among them, females, individuals aged 15-65 years, and cases of acute gastroenteritis were more significantly affected by heat wave days, with CRR values of 1.37 (95%CI: 1.12-1.67), 1.35 (95%CI: 1.06-1.73), and 1.35 (95%CI: 1.09-1.66), respectively, and AF values of 3.00% (95%CI: 0.82%-4.73%), 3.08% (95%CI: 1.27%-4.61%), and 3.00% (95%CI: 1.04%-4.64%), respectively. Conclusions Heat wave have lagged and cumulative effects on the risk of foodborne disease incidence, increasing both the risk and disease burden. Females, individuals aged 15-65 years, and cases of acute gastroenteritis are more significantly affected by heat wave days.

Objective To investigate the modification effect of atmospheric temperature on outpatient visits caused by O₃ in Linzhi City. Methods The daily outpatient data, the daily O₃ concentration and daily meteorological data (including daily average temperature, average relative humidity, etc.) in Linzhi City from 2018 to 2019 were collected. The distributed lag non-liner-model (DLNM) was used to quantitatively evaluate the impact of O₃ in different temperature layers on the risk of outpatient visits. Results At low temperature layers, the cumulative relative risk (CRR) of total outpatient visits and non-injury outpatient visits increased by 53.8%（4.2% -126.9%) and 59.1%（5.8% -139.2%）for every 10 μg/m³ increase of O₃ concentration, respectively. The subgroup analysis showed that for every 10 μg/m³ increase of O₃ concentration at low temperature, the CRR of patients with circulatory diseases, men, women, and people being <14 years old and 14-65 years old increased by 152.1% (15.1% - 451.9%), 58.3% (2.1%-145.5%), 49.2% (3.0% -116.1%), 39.6% (2.5% - 90.3%), and 61% (0.8%-157.1%), respectively. Conclusion The average temperature may have a modifying effect on the outpatient visits caused by O₃ in Linzhi City. In general, the cumulative risk increases as the temperature decreases.

Objective To compare the effects of Autoregressive Integrated Moving Average model-X (ARIMAX) and multivariate Long Short Term Memory Network (multivariate LSTM) in the prediction of daily total death toll in Yancheng City. Methods Based on total death toll data, meteorological data and air quality data from January 1^st, 2014 to June 30^th,2017 in Yancheng City, Jiangsu province, ARIMAX model and multivariate LSTM model were established to predict the daily total death toll from July 1^st,2017 to July 14^th,2017. RMSE, MAE and MAPE were used as evaluation indexes to compare the prediction effects of these two models. Results RMSE, MAE and MAPE of ARIMAX model and multivariate LSTM model were 20.742、15.094、9.921 and 47.182、35.863、19.633, respectively. Conclusion ARIMAX model is better than multivariate LSTM model to predict the daily death toll in Yancheng city.