Study on the impact of meteorological factors on influenza in different periods and prediction based on artificial intelligence RF-Bi-LSTM algorithm: to compare the COVID-19 period with the non-COVID-19 period.

OBJECTIVE: At different times, public health faces various challenges and the degree of intervention measures varies. The research on the impact and prediction of meteorology factors on influenza is increasing gradually, however, there is currently no evidence on whether its research results are affected by different periods. This study aims to provide limited evidence to reveal this issue. METHODS: Daily data on influencing factors and influenza in Xiamen were divided into three parts: overall period (phase AB), non-COVID-19 epidemic period (phase A), and COVID-19 epidemic period (phase B). The association between influencing factors and influenza was analysed using generalized additive models (GAMs). The excess risk (ER) was used to represent the percentage change in influenza as the interquartile interval (IQR) of meteorology factors increases. The 7-day average daily influenza cases were predicted using the combination of bi-directional long short memory (Bi-LSTM) and random forest (RF) through multi-step rolling input of the daily multifactor values of the previous 7-day. RESULTS: In periods A and AB, air temperature below 22 °C was a risk factor for influenza. However, in phase B, temperature showed a U-shaped effect on it. Relative humidity had a more significant cumulative effect on influenza in phase AB than in phase A (peak: accumulate 14d, AB: ER = 281.54, 95% CI = 245.47 ~ 321.37; A: ER = 120.48, 95% CI = 100.37 ~ 142.60). Compared to other age groups, children aged 4-12 were more affected by pressure, precipitation, sunshine, and day light, while those aged ≥ 13 were more affected by the accumulation of humidity over multiple days. The accuracy of predicting influenza was highest in phase A and lowest in phase B. CONCLUSIONS: The varying degrees of intervention measures adopted during different phases led to significant differences in the impact of meteorology factors on influenza and in the influenza prediction. In association studies of respiratory infectious diseases, especially influenza, and environmental factors, it is advisable to exclude periods with more external interventions to reduce interference with environmental factors and influenza related research, or to refine the model to accommodate the alterations brought about by intervention measures. In addition, the RF-Bi-LSTM model has good predictive performance for influenza.

Study of the influence of meteorological factors on HFMD and prediction based on the LSTM algorithm in Fuzhou, China.

BACKGROUND: This study adopted complete meteorological indicators, including eight items, to explore their impact on hand, foot, and mouth disease (HFMD) in Fuzhou, and predict the incidence of HFMD through the long short-term memory (LSTM) neural network algorithm of artificial intelligence. METHOD: A distributed lag nonlinear model (DLNM) was used to analyse the influence of meteorological factors on HFMD in Fuzhou from 2010 to 2021. Then, the numbers of HFMD cases in 2019, 2020 and 2021 were predicted using the LSTM model through multifactor single-step and multistep rolling methods. The root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE) and symmetric mean absolute percentage error (SMAPE) were used to evaluate the accuracy of the model predictions. RESULTS: Overall, the effect of daily precipitation on HFMD was not significant. Low (4 hPa) and high (≥ 21 hPa) daily air pressure difference (PRSD) and low (< 7 °C) and high (> 12 °C) daily air temperature difference (TEMD) were risk factors for HFMD. The RMSE, MAE, MAPE and SMAPE of using the weekly multifactor data to predict the cases of HFMD on the following day, from 2019 to 2021, were lower than those of using the daily multifactor data to predict the cases of HFMD on the following day. In particular, the RMSE, MAE, MAPE and SMAPE of using weekly multifactor data to predict the following week's daily average cases of HFMD were much lower, and similar results were also found in urban and rural areas, which indicating that this approach was more accurate. CONCLUSION: This study's LSTM models combined with meteorological factors (excluding PRE) can be used to accurately predict HFMD in Fuzhou, especially the method of predicting the daily average cases of HFMD in the following week using weekly multifactor data.

Study on the influence of meteorological factors on influenza in different regions and predictions based on an LSTM algorithm.

BACKGROUND: Influenza epidemics pose a threat to human health. It has been reported that meteorological factors (MFs) are associated with influenza. This study aimed to explore the similarities and differences between the influences of more comprehensive MFs on influenza in cities with different economic, geographical and climatic characteristics in Fujian Province. Then, the information was used to predict the daily number of cases of influenza in various cities based on MFs to provide bases for early warning systems and outbreak prevention. METHOD: Distributed lag nonlinear models (DLNMs) were used to analyse the influence of MFs on influenza in different regions of Fujian Province from 2010 to 2021. Long short-term memory (LSTM) was used to train and model daily cases of influenza in 2010-2018, 2010-2019, and 2010-2020 based on meteorological daily values. Daily cases of influenza in 2019, 2020 and 2021 were predicted. The root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE) and symmetric mean absolute percentage error (SMAPE) were used to quantify the accuracy of model predictions. RESULTS: The cumulative effect of low and high values of air pressure (PRS), air temperature (TEM), air temperature difference (TEMD) and sunshine duration (SSD) on the risk of influenza was obvious. Low (< 979 hPa), medium (983 to 987 hPa) and high (> 112 hPa) PRS were associated with a higher risk of influenza in women, children aged 0 to 12 years, and rural populations. Low (< 9 °C) and high (> 23 °C) TEM were risk factors for influenza in four cities. Wind speed (WIN) had a more significant effect on the risk of influenza in the ≥ 60-year-old group. Low (< 40%) and high (> 80%) relative humidity (RHU) in Fuzhou and Xiamen had a significant effect on influenza. When PRS was between 1005-1015 hPa, RHU > 60%, PRE was low, TEM was between 10-20 °C, and WIN was low, the interaction between different MFs and influenza was most obvious. The RMSE, MAE, MAPE, and SMAPE evaluation indices of the predictions in 2019, 2020 and 2021 were low, and the prediction accuracy was high. CONCLUSION: All eight MFs studied had an impact on influenza in four cities, but there were similarities and differences. The LSTM model, combined with these eight MFs, was highly accurate in predicting the daily cases of influenza. These MFs and prediction models could be incorporated into the influenza early warning and prediction system of each city and used as a reference to formulate prevention strategies for relevant departments.

Inactivated vaccine effectiveness against symptomatic COVID-19 in Fujian, China during the Omicron BA.2 outbreak.

Objective: More than 90% of the Chinese population have completed 2 doses of inactivated COVID-19 vaccines in Mainland China. However, after China government abandoned strict control measures, many breakthrough infections appeared, and vaccine effectiveness against Omicron BA.2 infection was uncertain. This study aims to investigate the real-world effectiveness of widely used inactivated vaccines during the wave of Omicron variants. Methods: Test-negative case-control study was conducted in this study to analyze the vaccine effectiveness against symptomatic disease caused by the Omicron variant (BA.2) in Fujian, China. Conditional logistic regression was selected to estimate the vaccine effectiveness. Results: The study found the vaccine effectiveness against symptomatic COVID-19 is 32.46% (95% CI, 8.08% to 50.37%) at 2 to 8 weeks, and 27.05% (95% CI, 1.23% to 46.12%) at 12 to 24 weeks after receiving booster doses of the inactivated vaccine. Notably, the 3-17 years group had higher vaccine effectiveness after 2 doses than the 18-64 years and over 65 years groups who received booster doses. Conclusion: Inactivated vaccines alone may not offer sufficient protection for all age groups before the summer of 2022. To enhance protection, other types of vaccines or bivalent vaccines should be considered.

[A cohort study on the characteristics of the recurrent epidemics on hand, foot and mouth disease, in Fujian province].

OBJECTIVE: To explore the recurrent epidemiological characteristics of hand, foot and mouth disease (HFMD) among children aged <4 years to provide evidence for HFMD prevention and control. METHODS: Principles on historical cohort study were followed when analyzing data related to HFMD surveillance in Fujian province. All the research objects were restricted to patients aged <4, with HFMD and who were permanent residents in Fujian province. Characteristics of the study objects were extracted as potential factors when the patients first showed symptoms of HFMD. These factors might cause the recurrence of HFMD and were filtered by the logistic stepwise regression with SAS 9.0. RESULTS: A total of 82 949 children were included. Among them, 2 612 had repetitiously suffered from HFMD(occupied 3.15%), including 2 510 who had the histories of suffering twice, 98 suffering three times, 3 suffering four times, and 1 even suffering five times. Comparing with the objects who had the first onset at the age of 3, also with the risk increased to 4.39 (95%CI:3.80-5.07) times, when compared to those who had the first onset at the age below 2. Again, the risk among children whose first onset was at the age of 2 had increased to 2.73 (95% CI: 2.35-3.18) times. According to the current residents areas, the morbidities of patients under 6 years old were below 2% when the symptoms first started, but the risk of the objects whose morbidities were higher than 4% , had increased 2.15(95% CI:1.88-2.45)times. Again, risk of the objects whose morbidities were between 3% and 4% had increased to 2.10 (95%CI:1.85-2.38) times. Among those whose specific morbidities were between 2% and 3% , the risk had increased to 1.65 (95% CI: 1.44-1.89) times. Comparing with the objects who never visited any maternal/child care settings when started the first onset, the risk among the ones who had been to the maternal/child care settings, had increased to 1.64 (95% CI:1.51-1.78) times. Boys had the risk 1.34 (95% CI:1.23-1.46)times increase than girls. The preponderant pathogen causing HFMD recurrence was EV71 (33/60). Recurrence might cause more severe symptoms or signs (8/2 612). Pathogens causing the initial infection and recurrence might both belonged to the same-EV71 (3/6). CONCLUSION: Recurrence of the disease were closely related to the opportunities of contacting the pathogens. Interventions should be imposed on patients in time as soon as the disease initiated, especially at the younger age.

[Study on the timeliness of the notifiable communicable diseases surveillance system in Fujian province, China, 2004].

OBJECTIVE: To understand the timeliness of the notifiable communicable diseases surveillance system in Fujian province. METHODS: Database from the internet based communicable diseases reporting system was used. RESULTS: The 50th percentile of time between the disease diagnosed and report recorded in medical faculties was 1 day in 2004 which was 6 days less than that in 2001 - 2003. The timeliness rate of 0 day was 46.46%, a 2.7 times over that in 2001 - 2003. The timeliness of notifiable communicable diseases surveillance system in different administrative areas, reporting units and on different diseases was significantly different. Time between the disease diagnosed and report recorded was the shortest in those cases reported by hospitals and traditional Chinese medicine(TCM) hospitals at the county level and above, with 50th percentile as 0 day, but the timeliness rate of 0 day was 50.76% with 70.04% of the cases were reported from hospitals and TCM hospitals of county level and above. Length between the disease diagnosed and reported was the longest in those cases recorded by Centers for Disease Control and Prevention(CDCs) with the 50th percentile as 3 days. The source of cases recorded by CDCs came from hospitals at the township level, where there was no connection to internet but the reporting cards had to be sent to local CDCs. Time between the disease being diagnosed and reported was 2 days in those cases reported by hospitals at the township level. 21.21% of cases were recorded by hospitals of township level and CDCs. The 50th percentile of time shown between the reported records and confirmed by CDCs was 4 hours The 24 hour timeliness rate was 63.65%. CONCLUSION: The timeliness of the notifiable communicable diseases surveillance system had been improved significantly after the medical personnel recording the cases directly through internet. Timeliness could be further improved through access to internet at the hospitals of township level, training of staff and better hospital management systems.