To comprehensively understand the application progress of ensemble forecast technology in influenza forecast based on infectious disease model, so as to provide scientific references for further research. In this study, two keywords of "influenza" and "ensemble forecast" are selected to search and select the relevant literatures, which are then outlined and summarized. It is found that: In recent years, some studies about ensemble forecast technology for influenza have been reported in the literature, and some well-performed influenza ensemble forecast systems have already been operationally implemented and provide references for scientific prevention and control. In general, ensemble forecast can well represent various uncertainties in forecasting influenza cases based on infectious disease models, and can achieve more accurate forecasts and more valuable information than single deterministic forecast. However, there are still some shortcomings in the current studies, it is suggested that scientists engaged in influenza forecast based on infectious disease models strengthen cooperation with scholars in the field of numerical weather forecast, which is expected to further improve the skills and application level of ensemble forecast for influenza.
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http://dx.doi.org/10.3389/fpubh.2023.1335499 | DOI Listing |
Sci Rep
December 2024
College of Water Resources Science and Engineering, Taiyuan University of Technology, Taiyuan, 030024, China.
Accurate prediction of runoff is of great significance for rational planning and management of regional water resources. However, runoff presents non-stationary characteristics that make it impossible for a single model to fully capture its intrinsic characteristics. Enhancing its precision poses a significant challenge within the area of water resources management research.
View Article and Find Full Text PDFHealth Care Sci
December 2024
School of Computer Science and Engineering, Vellore Institute of Technology Vellore India.
Background: The global impact of the highly contagious COVID-19 virus has created unprecedented challenges, significantly impacting public health and economies worldwide. This research article conducts a time series analysis of COVID-19 data across various countries, including India, Brazil, Russia, and the United States, with a particular emphasis on total confirmed cases.
Methods: The proposed approach combines auto-regressive integrated moving average (ARIMA)'s ability to capture linear trends and seasonality with long short-term memory (LSTM) networks, which are designed to learn complex nonlinear dependencies in the data.
Surv Geophys
April 2024
Department of Atmospheric and Oceanic Science, University of Wisconsin, Madison, WI 53706 USA.
Accurate diagnosis of regional atmospheric and surface energy budgets is critical for understanding the spatial distribution of heat uptake associated with the Earth's energy imbalance (EEI). This contribution discusses frameworks and methods for consistent evaluation of key quantities of those budgets using observationally constrained data sets. It thereby touches upon assumptions made in data products which have implications for these evaluations.
View Article and Find Full Text PDFJ Intell
December 2024
School of Computer and Artificial Intelligence, Huaihua University, Huaihua 418000, China.
Universities and schools rely heavily on the ability to forecast student performance, as it enables them to develop efficient strategies for enhancing academic results and averting student attrition. The automation of processes and the management of large datasets generated by technology-enhanced learning tools can facilitate the analysis and processing of these data, which provides crucial insights into the knowledge of students and their engagement with academic endeavors. The method under consideration aims to forecast the academic achievement of students through an ensemble of deep neural networks.
View Article and Find Full Text PDFCureus
November 2024
Department of Cardiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, CHN.
Background Cardiovascular diseases (CVD), including coronary artery disease, ischemic heart disease, stroke, cardiomyopathy, and atrial fibrillation and flutter, are the leading cause of mortality worldwide, resulting in significant economic and health costs. Recognizing trends and geographical differences in the global burden of CVD facilitates health authorities in particular nations to assess the disease burden and forecast future epidemiological trends. Public health authorities in each country can better understand the differences in disease data and, by learning from the experiences and practices of successful countries and considering the characteristics of their diseases, allocate health resources more rationally and formulate more targeted healthcare strategies to reduce the disease burden.
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