Sim SW, Moey KSP, Tan NC. The use of facemasks to prevent respiratory infection: a literature review in the context of the health belief model. Singapore Med J. 2014;55(3):160.

Choi Y, Kim JS, Kim JE, Choi H, Lee CH. Vaccination prioritization strategies for covid-19 in korea: a mathematical modeling approach. Int J Environ Res Public Health. 2021;18(8):4240.

Zhou X, Li Y. Forecasting the covid-19 vaccine uptake rate: an infodemiological study in the us. Hum Vacc Immunother. 2022;18(1):2017216.

Article  Google Scholar 

Tamilselvan S, Kumar S, Priya J, Ramesh R. Covid vaccination analysis and prediction using machine learning. In Smart Technologies, Communication and Robotics (STCR), 2021; pp. 1–5. IEEE.

Kane MJ, Price N, Scotch M, Rabinowitz P. Comparison of arima and random forest time series models for prediction of avian influenza h5n1 outbreaks. BMC Bioinform. 2014;15(1):1–9.

Siami-Namini S, Tavakoli N, Namin AS. A comparison of arima and lstm in forecasting time series. In 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), 2018; pp. 1394–1401. IEEE.

Wang W, Mao W, Tong X, Gang X. A novel recursive model based on a convolutional long short-term memory neural network for air pollution prediction. Remote Sens. 2021;13(7):1284.

Article  Google Scholar 

Kühnert C, Gonuguntla NM, Krieg H, Nowak D, Thomas JA. Application of lstm networks for water demand prediction in optimal pump control. Water. 2021;13(5):644.

Hochreiter S, Schmidhuber J. Long short-term memory. Neural Comput. 1997;9(8):1735–80.

Article  Google Scholar 

Zhang X, Mahadevan S. Bayesian neural networks for flight trajectory prediction and safety assessment. Decis Support Syst. 2020;131:113246.

Article  Google Scholar 

Xu J, Rahmatizadeh R, Bölöni L, Turgut D. A sequence learning model with recurrent neural networks for taxi demand prediction. In 2017 IEEE 42nd Conference on Local Computer Networks (LCN), 2017; pp. 261–268. IEEE.

Patol D, Mehta R, Mehta C, Naik G, Agrawal H. Taxi demand prediction using lstm and optimized taxi geo-distribution. In Soft Computing and Signal Processing, 2022; pp 163–172. Springer.

Niu K, Wang C, Zhou X, Zhou T. Predicting ride-hailing service demand via rpa-lstm. IEEE Trans Veh Technol. 2019;68(5):4213–22.

Article  Google Scholar 

Afrin T, Yodo N. A lstm-based correlated traffic data prediction framework. Knowl Based Syst. 2022;237:107755.

Article  Google Scholar 

Yun KK, Yoon SW, Won D. Prediction of stock price direction using a hybrid ga-xgboost algorithm with a three-stage feature engineering process. Expert Syst Appl. 2021;186:115716.

Sahisnu JS, Natalia F, Ferdinand FV, Sudirman S, Ko CS. Vaccine prediction system using arima method. ICIC Express Lett B Appl. 2020;11(6):567–575.

Kim K-W, Li G, Park S-T, Ko M-H. A study on birth prediction and bcg vaccine demand prediction using arima analysis. Indian J Sci Technol. 2016;9(24):1–7.

Article  Google Scholar 

Arifin B, Anas T. Lessons learned from covid-19 vaccination in indonesia: experiences, challenges, and opportunities. Hum Vacc Immunother. 2021;17(11):3898–906.

Article  Google Scholar 

Zheng Y-J, Chen X, Song Q, Yang J, Wang L. Evolutionary optimization of covid-19 vaccine distribution with evolutionary demands. IEEE Trans Evol Comput. 2022.

Rathinasamy M, Kandhasamy S. An exploratory analysis of covid-19 vaccination administration. Proc Indian National Sci Acad. 2022;88(1):43–52.

Article  Google Scholar 

Yamak PT, Yujian L, Gadosey PK. A comparison between arima, lstm, and gru for time series forecasting. In Proceedings of the 2019 2nd International Conference on Algorithms, Computing and Artificial Intelligence. 2019; pp. 49–55.

Rathnayaka RK, Seneviratna DM, Jianguo W, Arumawadu HI. A hybrid statistical approach for stock market forecasting based on artificial neural network and arima time series models. In 2015 International Conference on Behavioral, Economic and Socio-cultural Computing (BESC), 2015; pp. 54–60. IEEE.

Ribeiro MHDM, da Silva RG, Mariani VC, dos Santos Coelho L. Short-term forecasting COVID-19 cumulative confirmed cases: Perspectives for Brazil. Chaos Solit Fractals. 2020;135:109853. Elsevier.

Tamang SK, Singh PD, Datta B. Forecasting of Covid-19 cases based on prediction using artificial neural network curve fitting technique. Global J Environ Sci Manag. 2020;6(Covid-19):53–64. GJESM Publisher.

Somyanonthanakul R, Warin K, Amasiri W, Mairiang K, Mingmalairak C, Panichkitkosolkul W, Silanun K, Theeramunkong T, Nitikraipot S, Suebnukarn S. Forecasting COVID-19 cases using time series modeling and association rule mining. BMC Med Res Methodol. 2022;22(1):281. Springer.

Chen X, Huang H, Jiandong J, Sun R, Zhang J. Impact of vaccination on the covid-19 pandemic in us states. Sci Rep. 2022;12(1):1–10.

Google Scholar 

Coccia M. Optimal levels of vaccination to reduce covid-19 infected individuals and deaths: a global analysis. Environ Res. 2022;204:112314.

Article  Google Scholar 

Dong E, Ratcliff J, Goyea TD, Katz A, Lau R, Ng TK, Garcia B, Bolt E, Prata S, Zhang D, et al. The johns hopkins university center for systems science and engineering covid-19 dashboard: data collection process, challenges faced, and lessons learned. Lancet Infect Dis. 2022.

Hasell J, et al. A cross-country database of covid-19 testing. Sci Data. 2020;7(1):345.

Gabler J, Raabe T, Röhrl K, von Gaudecker H-M. The effectiveness of testing, vaccinations and contact restrictions for containing the covid-19 pandemic. Sci Rep. 2022;12(1):1–8.

Article  Google Scholar 

Said AB, Erradi A, Aly HA, Mohamed A. Predicting covid-19 cases using bidirectional lstm on multivariate time series. Environ Sci Pollut Res. 2021;28(40):56043–56052.

Borges D, Nascimento MCV. Covid-19 icu demand forecasting: a two-stage prophet-lstm approach. Appl Soft Comput. 2022;125:109181.

Article  Google Scholar 

Das S. Filters, wrappers and a boosting-based hybrid for feature selection. In Icml. 2001;1:74–81. Citeseer.

Sen S, Saha S, Chatterjee S, Mirjalili S, Sarkar R. A bi-stage feature selection approach for covid-19 prediction using chest ct images. Appl Intell. 2021;51(12):8985–9000.

Article  Google Scholar 

Ali RH, Abdulsalam WH. The prediction of covid-19 disease using feature selection techniques. J Phys Conf Series. 2021;1879:022083. IOP Publishing.

Saha A, Mishra V, Rath SK. Prediction of growth in covid-19 cases in india based on machine learning techniques. In 2022 International Conference on Innovative Trends in Information Technology (ICITIIT), 2022; pp. 1–6. IEEE.

Pourhomayoun M, Shakibi M. Predicting mortality risk in patients with covid-19 using machine learning to help medical decision-making. Smart Health. 2021;20:100178.

Article  Google Scholar 

Silva L. A feature engineering approach to wind power forecasting: Gefcom 2012. Int J Forecast. 2014;30(2):395–401.

Article  Google Scholar 

Zheng H, Yanghui W. A xgboost model with weather similarity analysis and feature engineering for short-term wind power forecasting. Appl Sci. 2019;9(15):3019.

Article  Google Scholar 

Pham T, Tao X, Zhang J, Yong J, Li Y, Xie H. Graph-based multi-label disease prediction model learning from medical data and domain knowledge. Knowl Based Syst. 2022;235:107662.

Article  Google Scholar 

Yue W, Yunjie X, Li J. Feature construction for fraudulent credit card cash-out detection. Decis Support Syst. 2019;127:113155.

Article  Google Scholar 

Chandra R, Jain A, Chauhan DS. Deep learning via lstm models for covid-19 infection forecasting in India. PloS One. 2022;17(1):e0262708.

Chen S, Paul R, Janies D, Murphy K, Feng T, Thill J-C. Exploring feasibility of multivariate deep learning models in predicting covid-19 epidemic. Front Public Health. 2021;9:765.

Google Scholar 

Wathore R, Rawlekar S, Anjum S, Gupta A, Bherwani H, Labhasetwar N, Kumar R. Improving performance of deep learning predictive models for covid-19 by incorporating environmental parameters. Gondwana Res. 2022.

Widiputra H. Ga-optimized multivariate cnn-lstm model for predicting multi-channel mobility in the covid-19 pandemic. Emerg Sci J. 2021;5(5):619–35.

Article  Google Scholar 

Zain ZM, Alturki NM. Covid-19 pandemic forecasting using cnn-lstm: a hybrid approach. J Control Sci Eng. 2021;2021.

Park S, Yang J-S. Interpretable deep learning lstm model for intelligent economic decision-making. Knowl Based Syst. 2022;248:108907.

Article  Google Scholar 

Srinivasu PN, Sandhya N, Jhaveri RH, Raut R. From blackbox to explainable ai in healthcare: existing tools and case studies. Mob Inf Syst. 2022;2022.

Adadi A, Berrada M. Explainable ai for healthcare: from black box to interpretable models. In Embedded Systems and Artificial Intelligence, 2020; pp. 327–337. Springer.

Tascikaraoglu A. Evaluation of spatio-temporal forecasting methods in various smart city applications. Renew Sust Energy Rev. 2018;82:424–35.

Article  Google Scholar 

Li J, Cheng K, Wang S, Morstatter F, Trevino RP, Tang J, Liu H. Feature selection: a data perspective. ACM Comput Surv (CSUR). 2017;50(6):1–45.

Mathieu E, Ritchie H, Rodés-Guirao L, Appel C, Giattino C, Hasell J, Macdonald B, Dattani S, Beltekian D, Ortiz-Ospina E, Roser M. Coronavirus pandemic (covid-19). Our World in Data. 2020. https://ourworldindata.org/coronavirus.

Achaiah NC, Subbarajasetty SB, Shetty RM. R0 and re of covid-19: can we predict when the pandemic outbreak will be contained? Indian J Crit Care Med. 2020;24(11):1125.

Linka K, Peirlinck M, Kuhl E. The reproduction number of covid-19 and its correlation with public health interventions. Comput Mech. 2020;66(4):1035–50.

Article  MathSciNet  Google Scholar 

Arroyo-Marioli F, Bullano F, Kucinskas S, Rondón-Moreno C. Tracking r of covid-19: a new real-time estimation using the kalman filter. PloS One. 2021;16(1):e0244474.

Article  Google Scholar 

Durbin J, Koopman SJ. Time Series Analysis by State Space Methods. 2012;38. OUP Oxford.

Zhongyi H, Bao Y, Xiong T, Chiong R. Hybrid filter wrapper feature selection for short-term load forecasting. Eng Appl Artif Intell. 2015;40:17–27.

Article  Google Scholar 

Zhang X, Zhang Q, Chen M, Sun Y, Qin X, Li H. A two-stage feature selection and intelligent fault diagnosis method for rotating machinery using hybrid filter and wrapper method. Neurocomputing. 2018;275:2426–39.

Article  Google Scholar 

Wah YB, Ibrahim N, Hamid HA, Abdul-Rahman S, Fong S. Feature selection methods: case of filter and wrapper approaches for maximising classification accuracy. Pertanika J Sci Technol. 2018;26(1).

Krizhevsky A, Sutskever I, Hinton GE. Imagenet classification with deep convolutional neural networks. Commun ACM. 2017;60(6):84–90.

Xie H, Zhang L, Lim CP. Evolving cnn-lstm models for time series prediction using enhanced grey wolf optimizer. IEEE Access. 2020;8:161519–161541.

Livieris IE, Pintelas E, Pintelas P. A cnn-lstm model for gold price time-series forecasting. Neural Comput Appl. 2020;32(23):17351–17360.

Kim T-Y, Cho S-B. Predicting residential energy consumption using cnn-lstm neural networks. Energy. 2019;182:72–81.

Article  Google Scholar 

Ribeiro MT, Singh S, Guestrin C. why should i trust you? explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2016; pp. 1135–1144.

Lundberg SM, Lee S-I. A unified approach to interpreting model predictions. Adv Neural Inf Process Syst. 2017;30.

Ancona M, Oztireli C, Gross M. Explaining deep neural networks with a polynomial time algorithm for shapley value approximation. In International Conference on Machine Learning, 2019; pp. 272–281. PMLR.

Mathieu E, Ritchie H, Ortiz-Ospina E, Roser M, Hasell J, Appel C, Giattino C, Rodés-Guirao L. A global database of covid-19 vaccinations. Nat Hum Behav. 2021;5(7):947–53.

Article  Google Scholar