查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – New research on Liver Diseases and Conditions - V iral Hepatitis is the subject of a report. According to news reporting out of Sh ijiazhuang, People’s Republic of China, by NewsRx editors, research stated “The escalating frequency of environmental pollution incidents has raised significan t concerns regarding the potential health impacts of pollutant fluctuations. Con sequently, a comprehensive study on the role of pollutants in the prevalence of viral hepatitis is indispensable for the advancement of innovative prevention st rategies.” Our news journalists obtained a quote from the research, “Monthly incidence rate s of viral hepatitis from 2005 to 2020 were sourced from the Chinese Center for Disease Control and Prevention Infectious Disease Surveillance Information Syste m. Pollution data spanning 2014-2020 were obtained from the National Oceanic and Atmospheric Administration (NOAA), encompassing pollutants such as CO, NO2, and O3. Time series analysis models, including seasonal auto-regressive integrated moving average (SARIMA), Holt-Winters model, and Generalized Additive Model (GAM ), were employed to explore prediction and synergistic effects related to viral hepatitis. Spearman correlation analysis was utilized to identify pollutants sui table for inclusion in these models. Concurrently, machine learning (ML) algorit hms were leveraged to refine the prediction of environmental pollutant levels. F inally, a weighted quantile sum (WQS) regression framework was developed to eval uate the singular and combined impacts of pollutants on viral hepatitis cases ac ross different demographics, age groups, and environmental strata. The incidence of viral hepatitis in Beijing exhibited a declining trend, primarily characteri zed by HBV and HCV types. In predicting hepatitis prevalence trends, the Holt-Wi nters additive seasonal model outperformed the SARIMA multiplicative model ((1,1 ,0) (2,1,0) ). In the prediction of environmental pollutants, the SVM model demo nstrated superior performance over the GPR model, particularly with Polynomial a nd Besseldot kernel functions. The combined pollutant risk effect on viral hepat itis was quantified as bWQS (95% CI) = 0.066 (0.018, 0.114). Among different groups, PM emerged as the most sensitive risk factor, notably impacti ng patients with HCV and HEV, as well as individuals aged 35-64. CO predominantl y affected HAV patients, showing a risk effect of bWQS (95% CI) = - 0.0355 (- 0.0695, - 0.0016). Lower levels of PM and PM were associated with he ightened risk of viral hepatitis incidence with a lag of five months, whereas el evated levels of PM (100-120 mg/m) and CO correlated with increased hepatitis in cidence risk with a lag of six months. The Holt-Winters model outperformed the S ARIMA model in predicting the incidence of viral hepatitis. Among machine learni ng algorithms, SVM and GPR models demonstrated superior performance for analyzin g pollutant data. Patients infected with HAV and HEV were primarily influenced b y PM and CO, whereas SO and PM significantly impacted others. Individuals aged 3 5-64 years appeared particularly susceptible to these pollutants. Mixed pollutan t exposures were found to affect the development of viral hepatitis with a notab le lag of 5-6 months.”
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