Optimized Forecasting Method for Weekly Influenza Confirmed Cases
Bibliographic Data
| ID | 15459872 |
|---|---|
| Authors | Mohammed A A Al‐qaness (0000-0002-6956-7641, Wuhan University), Ahmed A Ewees (0000-0002-0666-7055, Damietta University), Hong Fan (0000-0003-2826-5564, Wuhan University, corresponding author), Mohamed Abd Elaziz (0000-0002-7682-6269, Zagazig University) |
| Year | 2020 |
| Volume | 17 |
| Issue | 10 |
| Pages | 3510-3510 |
| Publication date | 2020-05-18 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | International Journal of Environmental Research and Public Health (JOURNAL) |
| Journal identifiers | ISSN: 1661-7827 • E-ISSN: 1660-4601 |
| Publisher | Multidisciplinary Digital Publishing Institute (PUBLISHER • CH) |
| DOI | 10.3390/ijerph17103510 |
| PMID | 32443409 |
| OpenAlex | W3025882126 |
| Language | EN |
| Citations received | 1 |
| References cited | 44 |
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
Adaptive neuro fuzzy inference system · China · Data mining · Fuzzy control system · Fuzzy logic · Geography · Inference system · Machine learning · Operations research · Statistics · Anomaly Detection Techniques and Applications · Computer Science · COVID-19 epidemiological studies · Engineering · Influenza Virus Research Studies · Mathematics · Artificial Intelligence
| Citations per year | 0.17 |
|---|---|
| Citation span | 2020 - 2020 (1) |
| Highly cited | No |