1. Mi YN, Huang TT, Zhang JX, Qin Q, Gong YX, Liu SY, et al. Estimating the instant case fatality rate of COVID-19 in China. Int J Infect Dis. 2020;97:1-6. [
DOI:10.1016/j.ijid.2020.04.055] [
PMID] [
PMCID]
2. Ceylan Z. Estimation of COVID-19 prevalence in Italy, Spain, and France. Sci Total Environ. 2020;729:138817. [
DOI:10.1016/j.scitotenv.2020.138817] [
PMID] [
PMCID]
3. Magalhaes JJF, Mendes RPG, Silva C, Silva S, Guarines KM, Pena L, et al. Epidemiological and clinical characteristics of the first 557 successive patients with COVID-19 in Pernambuco state, Northeast Brazil. Travel Med Infect Dis. 2020;38:101884. [
DOI:10.1016/j.tmaid.2020.101884] [
PMID] [
PMCID]
4. Ozturk T, Talo M, Yildirim EA, Baloglu UB, Yildirim O, Rajendra Acharya U. Automated detection of COVID-19 cases using deep neural networks with X-ray images. Comput Biol Med. 2020;121:103792. [
DOI:10.1016/j.compbiomed.2020.103792] [
PMID] [
PMCID]
5. Faiz SHR, Riahi T, Rahimzadeh P, Nikoubakht N. Commentary: Remote electronic consultation for COVID-19 patients in teaching hospitals in Tehran, Iran. Med J Islam Repub Iran. 2020;34(1):31. [
DOI:10.47176/mjiri.34.31] [
PMID] [
PMCID]
6. Al-Qaness MAA, Ewees AA, Fan H, Abd El Aziz M. Optimization Method for Forecasting Confirmed Cases of COVID-19 in China. J Clin Med. 2020;9(3). [
DOI:10.3390/jcm9030674] [
PMID] [
PMCID]
7. Moftakhar L, Seif M, Safe MS. Exponentially Increasing Trend of Infected Patients with COVID-19 in Iran: A Comparison of Neural Network and ARIMA Forecasting Models. Iran J Public Health. 2020;49(Suppl 1):92-100. [
DOI:10.18502/ijph.v49iS1.3675] [
PMID] [
PMCID]
8. Fan J, Liu X, Shao G, Qi J, Li Y, Pan W, et al. The epidemiology of reverse transmission of COVID-19 in Gansu Province, China. Travel Med Infect Dis. 2020;37:101741. [
DOI:10.1016/j.tmaid.2020.101741] [
PMID] [
PMCID]
9. Pontoh RS, Z S, Hidayat Y, Aldella R, Jiwani NM, Sukono. Covid-19 Modelling in South Korea using A Time Series Approach. Intl J Adv Sci Technol. 2020;29(7):1620 - 32.
10. Maleki M, Mahmoudi MR, Wraith D, Pho K-H. Time series modelling to forecast the confirmed and recovered cases of COVID-19. Travel Med Infect Dis. 2020;37:101742. [
DOI:10.1016/j.tmaid.2020.101742] [
PMCID]
11. Mohammadzadeh rostami F, Nasr Esfahani BN, Ahadi AM, Shalibeik S. A Review of Novel Coronavirus, Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). Iranian Journal of Medical Microbiology. 2020;14(2):154-61. [
DOI:10.30699/ijmm.14.2.154]
12. Papastefanopoulos V, Linardatos P, Kotsiantis S. COVID-19: A Comparison of Time Series Methods to Forecast Percentage of Active Cases per Population. Appl Sci. 2020;10(11):3880. [
DOI:10.3390/app10113880]
13. Parvizi P, Jalilian M, Parvizi H, Amiri S, Mohammad Doust H. The COVID-19 Pandemic: Data Analysis, Impacts and Future Considerations. Iranian Journal of Medical Microbiology. 2021;15(1):1-17. [
DOI:10.30699/ijmm.15.1.1]
14. Ghanbari B. On forecasting the spread of the COVID-19 in Iran: The second wave. Chaos Solitons Fractals. 2020;140:110176. [
DOI:10.1016/j.chaos.2020.110176] [
PMID] [
PMCID]
15. Acevedo ML, Alonso-Palomares L, Bustamante A, Gaggero A, Paredes F, Cortés CP, et al. Infectivity and immune escape of the new SARS-CoV-2 variant of interest Lambda. medRxiv. 2021:2021.06.28.21259673. [
DOI:10.1101/2021.06.28.21259673]
16. Mahase E. Delta variant: What is happening with transmission, hospital admissions, and restrictions? BMJ. 2021;373:n1513. [
DOI:10.1136/bmj.n1513] [
PMID]
17. Zhang Z, Murtagh F, Van Poucke S, Lin S, Lan P. Hierarchical cluster analysis in clinical research with heterogeneous study population: highlighting its visualization with R. Ann Transl Med. 2017;5(4):75. [
DOI:10.21037/atm.2017.02.05] [
PMID] [
PMCID]
18. Renjith S, Sreekumar A, Jathavedan M. Performance evaluation of clustering algorithms for varying cardinality and dimensionality of data sets. Mater Today. 2020;27:627-33. [
DOI:10.1016/j.matpr.2020.01.110]
19. Patel S, Sihmar S, Jatain A. A study of hierarchical clustering algorithms. Int J Inf Comput Technol. 2015;3(11):1225-32.
20. Yonar H. Modeling and Forecasting for the number of cases of the COVID-19 pandemic with the Curve Estimation Models, the Box-Jenkins and Exponential Smoothing Methods. Eurasian J Med Oncol. 2020;4(2):160-5. [
DOI:10.14744/ejmo.2020.28273]
21. Chaurasia V, Pal S. Covid-19 Pandemic: ARIMA and Regression Model based Worldwide Death Cases Predictions. Research Square; 2020. [
DOI:10.21203/rs.3.rs-49697/v1]
22. Almasarweh M, Wadi SA. ARIMA Model in Predicting Banking Stock Market Data. Mod Appl Sci. 2018;12(11):4. [
DOI:10.5539/mas.v12n11p309]
23. Hyndman R, Koehler AB, Ord JK, Snyder RD. Forecasting with Exponential Smoothing: Springer-Verlag Berlin Heidelberg; 2008. [
DOI:10.1007/978-3-540-71918-2]
24. Awajan AM, Ismail MT, Al Wadi S. Improving forecasting accuracy for stock market data using EMD-HW bagging. PloS one. 2018;13(7):e0199582. [
DOI:10.1371/journal.pone.0199582] [
PMID] [
PMCID]
25. Abdulmajeed K, Adeleke M, Popoola L. Online Forecasting of Covid-19 Cases in Nigeria Using Limited Data. Data Brief. 2020;30:105683. [
DOI:10.1016/j.dib.2020.105683] [
PMID] [
PMCID]
26. Hyndman RJ, Athanasopoulos G. Forecasting: principles and practice: OTexts; 2018.
27. Dhakal CP. A naïve approach for comparing a forecast model. Int J Thesis Projects Dissert. 2017;5(1):1-3.
28. Islam SFN, Sholahuddin A, Abdullah AS. Extreme gradient boosting (XGBoost) method in making forecasting application and analysis of USD exchange rates against rupiah. J Phys Conf Ser. 2021;1722(1):012016. [
DOI:10.1088/1742-6596/1722/1/012016]
29. Dancho M. modeltime: The Tidymodels Extension for Time Series Modeling 2021 [Available from: https://cran.r-project.org/web/packages/modeltime/index.html.
30. Abdullah D, Susilo S, Ahmar AS, Rusli R, Hidayat R. The application of K-means clustering for province clustering in Indonesia of the risk of the COVID-19 pandemic based on COVID-19 data. Qual Quant. 2021:1-9. [
DOI:10.1007/s11135-021-01176-w] [
PMID] [
PMCID]
31. Talkhi N, Akhavan Fatemi N, Ataei Z, Jabbari Nooghabi M. Modeling and forecasting number of confirmed and death caused COVID-19 in IRAN: A comparison of time series forecasting methods. Biomed Signal Process Control. 2021;66:102494. [
DOI:10.1016/j.bspc.2021.102494] [
PMID] [
PMCID]
32. Yadav M, Perumal M, Srinivas M. Analysis on novel coronavirus (COVID-19) using machine learning methods. Chaos Solitons Fractals. 2020;139:110050. [
DOI:10.1016/j.chaos.2020.110050] [
PMID] [
PMCID]
33. Singh PK, Chouhan A, Bhatt RK, Kiran R, Ahmar AS. Implementation of the SutteARIMA method to predict short-term cases of stock market and COVID-19 pandemic in USA. Qual Quant. 2021:1-11. [
DOI:10.1007/s11135-021-01207-6] [
PMID] [
PMCID]
34. Ahmadi A, Fadai Y, Shirani M, Rahmani F. Modeling and forecasting trend of COVID-19 epidemic in Iran until May 13, 2020. Medical Journal of The Islamic Republic of Iran. 2020;34(1):183-95. [
DOI:10.47176/mjiri.34.27]
35. Yang Q, Wang J, Ma H, Wang X. Research on COVID-19 based on ARIMA model(Delta)-Taking Hubei, China as an example to see the epidemic in Italy. J Infect Public Health. 2020;13(10):1415-8. [
DOI:10.1016/j.jiph.2020.06.019] [
PMID] [
PMCID]
36. Farooq J, Bazaz MA. A deep learning algorithm for modeling and forecasting of COVID-19 in five worst affected states of India. Alex Eng J. 2021;60(1):587-96. [
DOI:10.1016/j.aej.2020.09.037] [
PMCID]
37. Christie N, Basri MH. Personal Protective Equipment Demand Forecasting and Inventory Management during COVID-19 Case Study: Public Hospital at Bandung, Indonesia. international conference on management, economics & finance2021. [
DOI:10.33422/3rd.icmef.2021.02.135]
38. Rostami-Tabar B, Rendon-Sanchez JF. Forecasting COVID-19 daily cases using phone call data. Appl Soft Comput. 2021;100:106932. [
DOI:10.1016/j.asoc.2020.106932] [
PMID] [
PMCID]
39. Hu H, van der Westhuysen AJ, Chu P, Fujisaki-Manome A. Predicting Lake Erie wave heights and periods using XGBoost and LSTM. Ocean Model. 2021;164:101832. [
DOI:10.1016/j.ocemod.2021.101832]
40. Paliari I, Karanikola A, Kotsiantis S, editors. A comparison of the optimized LSTM, XGBOOST and ARIMA in Time Series forecasting. 2021 12th International Conference on Information, Intelligence, Systems & Applications (IISA); 2021 12-14 July 2021. [
DOI:10.1109/IISA52424.2021.9555520]