LinkedIn

 آمار

تعداد نشریات20
تعداد شماره‌ها1,093
تعداد مقالات10,023
تعداد مشاهده مقاله27,238,141
تعداد دریافت فایل اصل مقاله7,481,837
Rahmanian, Vahid, Jokar, Mohammad, Mansoorian, Elham. (1402). Forecasting the Spread of the Sixth Wave of COVID-19 Epidemic in Southern Iran: An Application of ARIMA Models. سامانه مدیریت نشریات علمی, 11(3 (Supplement)), 664-669. doi: 10.30476/jhsss.2022.94211.1516
Vahid Rahmanian; Mohammad Jokar; Elham Mansoorian. "Forecasting the Spread of the Sixth Wave of COVID-19 Epidemic in Southern Iran: An Application of ARIMA Models". سامانه مدیریت نشریات علمی, 11, 3 (Supplement), 1402, 664-669. doi: 10.30476/jhsss.2022.94211.1516
Rahmanian, Vahid, Jokar, Mohammad, Mansoorian, Elham. (1402). 'Forecasting the Spread of the Sixth Wave of COVID-19 Epidemic in Southern Iran: An Application of ARIMA Models', سامانه مدیریت نشریات علمی, 11(3 (Supplement)), pp. 664-669. doi: 10.30476/jhsss.2022.94211.1516
Rahmanian, Vahid, Jokar, Mohammad, Mansoorian, Elham. Forecasting the Spread of the Sixth Wave of COVID-19 Epidemic in Southern Iran: An Application of ARIMA Models. سامانه مدیریت نشریات علمی, 1402; 11(3 (Supplement)): 664-669. doi: 10.30476/jhsss.2022.94211.1516

Forecasting the Spread of the Sixth Wave of COVID-19 Epidemic in Southern Iran: An Application of ARIMA Models

Journal of Health Sciences & Surveillance System
دوره 11، 3 (Supplement)، مهر 2023، صفحه 664-669    اصل مقاله (926.68 K)
نوع مقاله: Short Communication
شناسه دیجیتال (DOI): 10.30476/jhsss.2022.94211.1516
نویسندگان
Vahid Rahmanian* 1؛ Mohammad Jokar2؛ Elham Mansoorian3
1Department of Public Health, Torbat Jam Faculty of Medical Sciences, Torbat Jam, Iran
2Faculty of Veterinary Medicine, Karaj Branch, Islamic Azad University, Karaj, Iran
3Research Center for Social Determinants of Health, Jahrom University of Medical Sciences, Jahrom, Iran
چکیده
Background: Through the fifth wave of the Covid-19 outbreak in Jahrom, the fatality and incidence of the virus increased. The quick spread of infection is one of the causes of this dreadful situation. Therefore, recognizing the future epidemic trend can be a useful instrument to decrease mortality and morbidity. This study aimed to determine the time trends and select the best model to predict the sixth wave of the COVID-19 outbreak using ARIMA models.
Methods: We used daily data of 9533 hospital cases (Suspected and PCR-confirmed COVID-19 cases) between 4th March 2020 and 31st December 2021. Nine different ARIMA models were fitted to our data. Autocorrelation functions (ACF) and partial autocorrelation (PACF) plots were used to determine model parameters. Likelihood-ratio test for comparison of the reduced and full model was used. In addition, Akaike Information Criteria (AIC) was also used to choose the final model. Data were analyzed by STATA 14 software with a significant level of 0.05.
Results: The ARIMA (3, 0, 3) model was selected among the potential models, with lower AIC (999) and MAPE (3.18%) values. This model showed that the daily number of hospitalized patients may increase from 5.85 (2.16-15.79) to 8.55 (1.47-49.48) in two months. By March 01, 2022, the predictable daily hospitalized cases could reach 468.36 (03.79-2209.88).
Conclusion: Time series models is a useful tool for predictingthe hospitals’ admission trend during an epidemic. Thus, they can be used as early warning models in the readiness of hospital systems during epidemics.
کلیدواژه‌ها
COVID-19؛ Predicting؛ Sixth wave؛ Spread؛ Time series modeling
مراجع
  1. Huang C, Wang Y, Li X, Ren L, Zhao J, Hu Y, et al. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. lancet 2020; 395(10223): 497-506. doi: 10.1016/S0140-6736(20)30183-5, PMID: 31986264 PMCID: PMC7159299.
  2. Rahmanian V, Rabiee MH, Sharifi H. Case fatality rate of coronavirus disease 2019 (COVID-19) in Iran-a term of caution. Asian Pac J Trop Med 2020: 13(7):  328-330. doi: 10.4103/1995-7645.281617.
  3. Coronavirus disease 2019 (COVID-19) Situation Report –53. 2020. https://www.who.int/docs/default-source/coronaviruse/situation-reports/20200313-sitrep-53-covid-19.pdf.
  4. Coronavirus (COVID-19) Dashboard. 2 January 2022. https://covid19.who.int/.
  5. Pan L, Wang J, Wang X, Ji JS, Ye D, Shen J, et al. Prevention and control of coronavirus disease 2019 (COVID-19) in public places. Environ Pollut 2022; 292: 118273. doi: 10.1016/j.envpol.2021.118273, MID: 34634404 PMCID: PMC8498926.
  6. Anderson RM, Heesterbeek H, Klinkenberg D, Hollingsworth TD. How will country-based mitigation measures influence the course of the COVID-19 epidemic? lancet 2020; 395(10228): 931-4. doi: 10.1016/S0140-6736(20)30567-5, PMID: 32164834 PMCID: PMC7158572.
  7. Rahmanian V, Bokaie S, Rahmanian K, Hosseini S, Taj Firouzeh A. Analysis of temporal trends of human brucellosis during 2013-2018 in Yazd Province to predict future trends in incidence: A time-series study using ARIMA model. Asian Pac J Trop Med 2020; 13(4): 1-6. doi: 10.4103/1995-7645.281528.
  8. Esmaeilzadeh N, Shakeri M, Esmaeilzadeh M, Rahmanian V. ARIMA models forecasting the SARS-COV-2 in the Islamic Republic of Iran. Asian Pac J Trop Med 2020; 13(11): 521. doi: 10.4103/1995-7645.291407.
  9. Dohoo IR, Martin W, Stryhn HE. Veterinary epidemiologic research. 2end ed: AVC Incorporated; 2009.
  10. Kibria HB, Jyoti O, Matin A. Forecasting the spread of the third wave of COVID-19 pandemic using time series analysis in Bangladesh. Inform Med Unlocked. 2022; 28: 100815. doi: 10.1016/j.imu.2021.100815, PMID: 34961844 PMCID: PMC8694818.
  11. Chaurasia V, Pal S. COVID-19 pandemic: ARIMA and regression model-based worldwide death cases predictions. SN Comput Sci 2020; 1(5): 1-12. doi: 10.1007/s42979-020-00298-6, PMID: 33063056 PMCID: PMC7456206.
  12. Moftakhar L, Seif M. The exponentially increasing rate of patients infected with COVID-19 in Iran. Arch Iran Med. 2020; 23(4): 235-8. doi: 10.34172/aim.2020.03, PMID: 32271595.
  13. Konarasinghe K. Forecasting wave-like patterns of COVID-19 daily infected cases in Iran. J New Front  Healthcare  Biol Scie 2021; 2(1): 39-56.doi: 10.5281/zenodo.5071363.
  14. Tran T, Pham L, Ngo Q. Forecasting epidemic spread of SARS-CoV-2 using ARIMA model (Case study: Iran). Global J Enviro Sci Manag 2020; 6(Special Issue (Covid-19)): 1-10. doi: 10.22034/GJESM.2019.06.SI.01.
  15. Rahmanian V, Bokaie S, Haghdoost A, Barouni M. Predicting cutaneous leishmaniasis using SARIMA and Markov switching models in Isfahan, Iran: A time-series study. Asian Pac J Trop Med 2021; 14(2): 83-93. doi: 10.4103/1995-7645.306739.
آمار
تعداد مشاهده مقاله: 204
تعداد دریافت فایل اصل مقاله: 67


سامانه مدیریت نشریات علمی. قدرت گرفته از سیناوب