Aiken, E.L., et al.: Real-time estimation of disease activity in emerging outbreaks using internet search information. PLoS Comput. Biol. 16(8), e1008117 (2020)

Article  CAS  PubMed  PubMed Central  Google Scholar 

Alamo, T., et al.: Data-driven methods for present and future pandemics: monitoring, modelling and managing. Annu. Rev. Control. 52, 448–464 (2021)

Article  PubMed  PubMed Central  Google Scholar 

Aung, N.N., et al.: A novel bidirectional LSTM deep learning approach for COVID-19 forecasting. Sci. Rep. 13(1), 17953 (2023)

Article  CAS  PubMed  PubMed Central  Google Scholar 

Bambra, C.: Pandemic inequalities: emerging infectious diseases and health equity. Int. J. Equity Health 21(1), 6 (2022)

Article  PubMed  PubMed Central  Google Scholar 

Bandara, K., et al. Towards accurate predictions and causal ‘what-if’ analyses for planning and policy-making: a case study in emergency medical services demand. In: 2020 International Joint Conference on Neural Networks (IJCNN). IEEE (2020)

Beesley, L.J., Osthus, D., Del Valle, S.Y.: Addressing delayed case reporting in infectious disease forecast modeling. PLoS Comput. Biol. 18(6), e1010115 (2022)

Article  CAS  PubMed  PubMed Central  Google Scholar 

Belete, D.M., Huchaiah, M.D.: Grid search in hyperparameter optimization of machine learning models for prediction of HIV/AIDS test results. Int. J. Comput. Appl. 44(9), 875–886 (2022)

Google Scholar 

Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. J. Mach. Learn. Res. 13(2), 281–305 (2012)

Google Scholar 

Bógalo, J., et al.: Seasonality in COVID-19 times. Econ. Lett. 211, 110206 (2022)

Article  PubMed  Google Scholar 

Bonyadi, M.R., Wang, R., Ziaei, M.: Self-punishment and reward backfill for deep Q-learning. IEEE Trans. Neural Netw. Learn. Syst. 34(10), 8086–8093 (2022)

Article  Google Scholar 

Boudreau, M.C., et al.: Temporal and probabilistic comparisons of epidemic interventions. Bull. Math. Biol. 85(12), 118 (2023)

Article  PubMed  PubMed Central  Google Scholar 

Chandra, R., Jain, A., Chauhan, D.S.: Deep learning via LSTM models for COVID-19 infection forecasting in India. PLoS ONE 17(1), e0262708 (2022)

Article  CAS  PubMed  PubMed Central  Google Scholar 

Davydenko, A., Fildes, R.: Forecast error measures: critical review and practical recommendations. In: Business forecasting: practical problems and solutions. 34. (2016)

De Baets, S., Harvey, N.: Incorporating external factors into time series forecasts. In: Judgment in predictive analytics, pp. 265–287. Springer (2023)

Chapter  Google Scholar 

Dehning, J., et al.: Inferring change points in the spread of COVID-19 reveals the effectiveness of interventions. Science 369(6500), eabb9789 (2020)

Article  CAS  PubMed  Google Scholar 

De Myttenaere, A., et al.: Using the mean absolute percentage error for regression models. In Proceedings.

Developers, S. ETS models. n.d. [cited 2024; Available from: https://www.statsmodels.org/dev/examples/notebooks/generated/ets.html.

Ding, Y., et al.: Dynamic ensemble for probabilistic time-series forecasting via deep reinforcement learning (2023)

Fu, Y., Wu, D., Boulet, B.: Reinforcement learning based dynamic model combination for time series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence (2022)

Guo, D., et al.: A mobility-aware deep learning model for long-term COVID-19 pandemic prediction and policy impact analysis. arXiv preprint arXiv:2212.02575 (2022)

Hansen, S.: Using deep q-learning to control optimization hyperparameters. arXiv preprint arXiv:1602.04062, (2016)

Hasan, M.T.: HyperQ-Opt: Q-learning for hyperparameter optimization. arXiv preprint arXiv:2412.17765 (2024)

Health, N.Z.M.O.: covid-cases-counts-location. n.d. [cited 2024; Available from: https://github.com/minhealthnz/nz-covid-data/blob/main/cases/covid-cases-counts-location.xlsx

Homer, J.B., Hirsch, G.B.: System dynamics modeling for public health: background and opportunities. Am. J. Public Health 96(3), 452–458 (2006)

Article  PubMed  PubMed Central  Google Scholar 

Jain, G., Mallick, B.: A study of time series models ARIMA and ETS. Available at SSRN 2898968 (2017)

Jomaa, H.S., Grabocka, J., Schmidt-Thieme, L.: Hyp-rl: Hyperparameter optimization by reinforcement learning. arXiv preprint arXiv:1906.11527 (2019)

Kambali, P.N., Abbasi, A., Nataraj, C.: Nonlinear dynamic epidemiological analysis of effects of vaccination and dynamic transmission on COVID-19. Nonlinear Dyn. 111(1), 951–963 (2023)

Article  PubMed  Google Scholar 

Khashei, M., Bijari, M., Hejazi, S.R.: Combining seasonal ARIMA models with computational intelligence techniques for time series forecasting. Soft. Comput. 16, 1091–1105 (2012)

Article  Google Scholar 

Kong, Y.H., Lim, K.Y., Chin, W.Y.: Time series forecasting using a hybrid prophet and long short-term memory model. In: Soft Computing in Data Science: 6th International Conference, SCDS 2021, Virtual Event, November 2–3, 2021, Proceedings 6. 2021. Springer (2021)

Kreinovich, V., Nguyen, H.T., Ouncharoen, R.: How to estimate forecasting quality: a system-motivated derivation of symmetric mean absolute percentage error (SMAPE) and other similar characteristics. (2014)

Kumar, N., Susan, S.: COVID-19 pandemic prediction using time series forecasting models. In 2020 11th international conference on computing, communication and networking technologies (ICCCNT). IEEE (2020)

Kumar, R., Kumar, P., Kumar, Y.: Multi-step time series analysis and forecasting strategy using ARIMA and evolutionary algorithms. Int. J. Inf. Technol. 14(1), 359–373 (2022)

Google Scholar 

Kwuimy, C., et al.: Nonlinear dynamic analysis of an epidemiological model for COVID-19 including public behavior and government action. Nonlinear Dyn. 101, 1545–1559 (2020)

Article  CAS  PubMed  PubMed Central  Google Scholar 

Lopez, V.K., et al.: Challenges of COVID-19 case forecasting in the US, 2020–2021. PLoS Comput. Biol. 20(5), e1011200 (2024)

Article  CAS  PubMed  PubMed Central  Google Scholar 

Maghsoodi, A.I.: Cryptocurrency portfolio allocation using a novel hybrid and predictive big data decision support system. Omega 115, 102787 (2023)

Article  Google Scholar 

Mahmud, A., et al.: Hybrid ARIMA-LSTM for COVID-19 forecasting: a comparative AI modeling study. PeerJ Comput. Sci. 11, e3195 (2025)

Article  PubMed  PubMed Central  Google Scholar 

Malki, Z., et al.: ARIMA models for predicting the end of COVID-19 pandemic and the risk of second rebound. Neural Comput. Appl. 33, 2929–2948 (2021)

Article  PubMed  Google Scholar 

McKinney, W.: Data structures for statistical computing in Python. SciPy. (2010)

Monfared, M.A.S., Ghandali, R., Esmaeili, M.: A new adaptive exponential smoothing method for non-stationary time series with level shifts. J. Ind. Eng. Int. 10, 209–216 (2014)

Article  Google Scholar 

Mozgunov, P., Jaki, T., Gasparini, M.: Loss functions in restricted parameter spaces and their Bayesian applications. J. Appl. Stat. (2019). https://doi.org/10.1080/02664763.2019.1586848

Article  PubMed  Google Scholar 

Nichols, S., Abolmaali, S.: Enhancing COVID-19 case forecasting in the United States: a comparative analysis of ARIMA, SARIMA, and RNN models with grid search optimization. medRxiv,: p. 2024.03. 04.24303713 (2024)

Nunuvero, J., et al.: Modeling the effects of adherence to vaccination and health protocols in epidemic dynamics by means of an SIR model. arXiv preprint arXiv:2308.01038 (2023)

Overton, C.E., et al.: Using statistics and mathematical modelling to understand infectious disease outbreaks: COVID-19 as an example. Infect. Dis. Modell. 5, 409–441 (2020)

Google Scholar 

Padthe, K.K., et al.: Emergency department optimization and load prediction in hospitals. arXiv preprint arXiv:2102.03672 (2021)

Pedregosa, F., et al.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011)

Google Scholar 

Perepu, S.K., et al.: Reinforcement learning based dynamic weighing of ensemble models for time series forecasting. arXiv preprint arXiv:2008.08878 (2020)

Perone, G.: Comparison of ARIMA, ETS, NNAR, TBATS and hybrid models to forecast the second wave of COVID-19 hospitalizations in Italy. Eur. J. Health Econom. 23(6), 1–24 (2021)

Google Scholar 

Prajapati, S., et al.: Comparison of traditional and hybrid time series models for forecasting COVID-19 cases. arXiv preprint arXiv:2105.03266 (2021)

Putatunda, S., Rama, K.: A modified bayesian optimization based hyper-parameter tuning approach for extreme gradient boosting. In: 2019 Fifteenth International Conference on Information Processing (ICINPRO). IEEE (2019)

Qi, X., Xu, B.: Hyperparameter optimization of neural networks based on Q-learning. Signal Image Video Process. 17(4), 1669–1676 (2023)

Article  Google Scholar 

Qi, L., et al.: fETSmcs: Feature-based ETS model component selection. Int. J. Forecast. 39(3), 1303–1317 (2023)

Article  Google Scholar 

Quaedvlieg, R.: Multi-horizon forecast comparison. J. Bus. Econ. Stat. 39(1), 40–53 (2021)

Article  Google Scholar 

Rana, S., et al.: How effective are time series models for pandemic forecasting? In: International Conference on Big Data. Springer (2024)

Rasouli Panah, H., et al.: A novel proof of concept forecasting model for pandemics–A case study in New Zealand. In: International Conference on Information Technology in Disaster Risk Reduction. Springer (2023)

Robeson, S.M., Willmott, C.J.: Decomposition of the mean absolute error (MAE) into systematic and unsystematic components. PLoS ONE 18(2), e0279774 (2023)

Article  CAS  PubMed  PubMed Central  Google Scholar 

Seddighi, H.: COVID-19 as a natural disaster: focusing on exposure and vulnerability for response. Disaster Med. Public Health Prep. 14(4), e42–e43 (2020)

Article  PubMed  Google Scholar 

Shen, J., Valagolam, D., McCalla, S.: Prophet forecasting model: a machine learning approach to predict the concentration of air pollutants (PM2. 5, PM10, O3, NO2, SO2, CO) in Seoul, South Korea. PeerJ 8, e9961 (2020)

Article  PubMed