ArticleInternational journal of environmental research and public health2020
Optimized Forecasting Method for Weekly Influenza Confirmed Cases.
Article in International journal of environmental research and public health, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Time series forecasting of COVID-19 infections and deaths in Alpha and Delta variants using LSTM networks.PloS one · 2023Article
- Evaluating the Applications of Dendritic Neuron Model with Metaheuristic Optimization Algorithms for Crude-Oil-Production Forecasting.Entropy (Basel, Switzerland) · 2022Article
- Advance artificial time series forecasting model for oil production using neuro fuzzy-based slime mould algorithm.Journal of petroleum exploration and production technology · 2022Article
- Analysis of Delayed Vaccination Regimens: A Mathematical Modeling Approach.Epidemiologia (Basel, Switzerland) · 2021Article
- Forecasting Weekly Influenza Outpatient Visits Using a Two-Dimensional Hierarchical Decision Tree Scheme.International journal of environmental research and public health · 2020Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Influenza epidemic is a serious threat to the entire world, which causes thousands of death every year and can be considered as a public health emergency that needs to be more addressed and investigated. Forecasting influenza incidences or confirmed cases is very important to do the necessary policies and plans for governments and health organizations. In this paper, we present an enhanced adaptive neuro-fuzzy inference system (ANFIS) to forecast the weekly confirmed influenza cases in China and the USA using official datasets. To overcome the limitations of the original ANFIS, we use two metaheuristics, called flower pollination algorithm (FPA) and sine cosine algorithm (SCA), to enhance the prediction of the ANFIS. The proposed FPASCA-ANFIS is evaluated using two datasets collected from the CDC and WHO websites. Furthermore, it was compared to some previous state-of-the-art approaches. Experimental results confirmed that the FPASCA-ANFIS outperformed the compared methods using variant measures, including RMSRE, MAPE, MAE, and R 2 .
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Registered trials
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