Table 1 Literature review on epidemic prediction using statistical, mathematical, and deep learning methods.
References | Journal | Epidemics/Diseases Studied | Methods/Algorithms |
|---|---|---|---|
Koike et al.63 | Global Ecology and Biogeography | H1N1 Flu | ANN |
Agarwal et al.64 | Current Science | Dengue Adopted | Multi-regression |
Anno et al.65 | Geospatial health | Dengue fever | ANN |
Chenar et al.66 | Environment international | Oyster norovirus | ANN |
Chenar et al.67 | Water research | Oyster norovirus | Genetic Programming |
Liang et al.68 | Transboundary and Emerging Diseases | Swine fever | Random Forest |
Raja et al.69 | Malaysian Journal of Public health Medicine | Dengue/Aedes | Bayesian Network |
Tapak et al.70 | BMC research notes | Influenza | Random Forest |
Chen et al.71 | Epidemiology & Infection | Influenza | SARIMA |
Fank et al.72 | BMC Infectious Diseases | Infectious Diarrhea | ARIMAX |
Polwiang et al.73 | BMC Infectious Diseases | Dengue Fever | ARIMA, ANN |
Cao et al.74 | Science of The Total Environment | Brucellosis | ARIMA |
Zhang et al.75 | medRxiv | COVID-19 | Seq2Seq |
Kondo et al.76 | arxiv | Influenza | Seq2Seq |
Zhu et al.77 | BMC bioinformatics | Influenza | LSTM |
Yang et al.78 | The Journal of Supercomputing | Influenza | LSTM |
Kara et al.79 | Expert Systems with Applications | Influenza | LSTM |
Venna et al.80 | IEEE Access | Influenza | LSTM |
Heidrich et al.81 | Progress in Industrial Mathematics at ECMI | Dengue | SIR (compartmental model) |
Syafruddin et al.82 | International Journal of Modern Physics | Dengue | SEIR (compartmental model) |
Rosenkrantz et al.83 | Proceedings of National Academia of Science | General epidemic forecasting (theoretical) | Network models |
Petropoulos et al.84 | International Journal of Forecasting | COVID-19 | Simple time series model |
Punyapornwithaya et al.85 | Preventive Veterinary Medicine | Lumpy Skin Disease | Fuzzy logic time series, NNAR, ARIMA |
Panja et al.86 | Neural Networks | General epidemics | Ensemble wavelet neural network |
Rodr´ıguez et al.87 | Nature Machine Intelligence | General epidemics | Machine learning for data-centric forecasting |
Xue et al.88 | Chaos, Solitons & Fractals | General epidemic spreading processes | Deep transfer learning |
Zhang et al.89 | Geophysical Journal International | Earthquake “epidemictype” | Deep learning + ETAS model (adapted for seismic forecasting) |
Feng et al.90 | Communications Physics | General epidemic dynamics | Markovian modelling (mathematical model) |
Charniga et al.91 | Epidemics | Mpox outbreak (2022 NYC) | Mathematical methods |
Kaftan et al.92 | Journal of Medical Virology | Mpox outbreak (2022 NYC) | Mathematical methods |
