IDEAS home Printed from https://ideas.repec.org/a/arp/ajoams/2020p172-180.html
   My bibliography  Save this article

Forecasting of COVID-19 Cases in Kurdistan Region Using Some Statistical Models

Author

Listed:
  • Shekhmous Hassan Hussen

    (Department of Animal Production, College of Agricultural Engineering Sciences, University of Duhok, KR, Iraq)

Abstract

Nowadays the new universal disease of the coronavirus that is called the epidemic COVID-19 is spread as geometric progression among the people around the world, so, such pathogen considered the most dangerous threat facing humanity. This study aimed to derive the best forecasting models for the close future cases of infected, recovered, and deaths in the four provinces of Kurdistan Region-Iraq to avoid more loss of human lives by applying more health care in certain province. Two forecasting methods were used including Exponential Smoothing and ARIMA models. The results indicate that both ARIMA and Exponential Smoothing models were close to each other for predicting the infected cases of COVID-19 in Kurdistan Region provinces, and the predicting models show that the pandemic might not be under control unless the people apply the government instructions for health care and keep social distances.

Suggested Citation

  • Shekhmous Hassan Hussen, 2020. "Forecasting of COVID-19 Cases in Kurdistan Region Using Some Statistical Models," Academic Journal of Applied Mathematical Sciences, Academic Research Publishing Group, vol. 6(8), pages 172-180, 10-2020.
    • Handle: RePEc:arp:ajoams:2020:p:172-180
    DOI: 10.32861/ajams.68.172.180
    as

    Download full text from publisher

    File URL: https://www.arpgweb.com/pdf-files/ajams6(8)172-180.pdf
    Download Restriction: no
    File URL: https://www.arpgweb.com/journal/17/archive/10-2020/8/6
    Download Restriction: no
    File URL: https://libkey.io/10.32861/ajams.68.172.180?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Chakraborty, Tanujit & Ghosh, Indrajit, 2020. "Real-time forecasts and risk assessment of novel coronavirus (COVID-19) cases: A data-driven analysis," Chaos, Solitons & Fractals, Elsevier, vol. 135(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Abu Reza Md. Towfiqul Islam & Md. Hasanuzzaman & Md. Abul Kalam Azad & Roquia Salam & Farzana Zannat Toshi & Md. Sanjid Islam Khan & G. M. Monirul Alam & Sobhy M. Ibrahim, 2021. "Effect of meteorological factors on COVID-19 cases in Bangladesh," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 23(6), pages 9139-9162, June.
    2. Crokidakis, Nuno, 2020. "COVID-19 spreading in Rio de Janeiro, Brazil: Do the policies of social isolation really work?," Chaos, Solitons & Fractals, Elsevier, vol. 136(C).
    3. Moghari, Somaye & Ghorani, Maryam, 2022. "A symbiosis between cellular automata and dynamic weighted multigraph with application on virus spread modeling," Chaos, Solitons & Fractals, Elsevier, vol. 155(C).
    4. Srinka Basu & Sugata Sen, 2023. "COVID 19 Pandemic, Socio-Economic Behaviour and Infection Characteristics: An Inter-Country Predictive Study Using Deep Learning," Computational Economics, Springer;Society for Computational Economics, vol. 61(2), pages 645-676, February.
    5. Sadefo Kamdem, Jules & Bandolo Essomba, Rose & Njong Berinyuy, James, 2020. "Deep learning models for forecasting and analyzing the implications of COVID-19 spread on some commodities markets volatilities," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    6. Bhardwaj, Rashmi & Bangia, Aashima, 2020. "Data driven estimation of novel COVID-19 transmission risks through hybrid soft-computing techniques," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    7. Yiannakoulias, Nikolaos & Slavik, Catherine E. & Sturrock, Shelby L. & Darlington, J. Connor, 2020. "Open government data, uncertainty and coronavirus: An infodemiological case study," Social Science & Medicine, Elsevier, vol. 265(C).
    8. Tanujit Chakraborty & Ashis Kumar Chakraborty & Munmun Biswas & Sayak Banerjee & Shramana Bhattacharya, 2021. "Unemployment Rate Forecasting: A Hybrid Approach," Computational Economics, Springer;Society for Computational Economics, vol. 57(1), pages 183-201, January.
    9. Ashwin Muniyappan & Balamuralitharan Sundarappan & Poongodi Manoharan & Mounir Hamdi & Kaamran Raahemifar & Sami Bourouis & Vijayakumar Varadarajan, 2022. "Stability and Numerical Solutions of Second Wave Mathematical Modeling on COVID-19 and Omicron Outbreak Strategy of Pandemic: Analytical and Error Analysis of Approximate Series Solutions by Using HPM," Mathematics, MDPI, vol. 10(3), pages 1-27, January.
    10. Lalmuanawma, Samuel & Hussain, Jamal & Chhakchhuak, Lalrinfela, 2020. "Applications of machine learning and artificial intelligence for Covid-19 (SARS-CoV-2) pandemic: A review," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
    11. Aldila, Dipo & Khoshnaw, Sarbaz H.A. & Safitri, Egi & Anwar, Yusril Rais & Bakry, Aanisah R.Q. & Samiadji, Brenda M. & Anugerah, Demas A. & GH, M. Farhan Alfarizi & Ayulani, Indri D. & Salim, Sheryl N, 2020. "A mathematical study on the spread of COVID-19 considering social distancing and rapid assessment: The case of Jakarta, Indonesia," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
    12. Koutsellis, Themistoklis & Nikas, Alexandros, 2020. "A predictive model and country risk assessment for COVID-19: An application of the Limited Failure Population concept," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    13. Nikola Anđelić & Sandi Baressi Šegota & Ivan Lorencin & Zdravko Jurilj & Tijana Šušteršič & Anđela Blagojević & Alen Protić & Tomislav Ćabov & Nenad Filipović & Zlatan Car, 2021. "Estimation of COVID-19 Epidemiology Curve of the United States Using Genetic Programming Algorithm," IJERPH, MDPI, vol. 18(3), pages 1-26, January.
    14. Gaetano Perone, 2022. "Comparison of ARIMA, ETS, NNAR, TBATS and hybrid models to forecast the second wave of COVID-19 hospitalizations in Italy," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 23(6), pages 917-940, August.
    15. Dalton Garcia Borges de Souza & Erivelton Antonio dos Santos & Francisco Tarcísio Alves Júnior & Mariá Cristina Vasconcelos Nascimento, 2021. "On Comparing Cross-Validated Forecasting Models with a Novel Fuzzy-TOPSIS Metric: A COVID-19 Case Study," Sustainability, MDPI, vol. 13(24), pages 1-25, December.
    16. James, Nick & Menzies, Max & Chan, Jennifer, 2021. "Changes to the extreme and erratic behaviour of cryptocurrencies during COVID-19," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 565(C).
    17. James, Nick, 2021. "Dynamics, behaviours, and anomaly persistence in cryptocurrencies and equities surrounding COVID-19," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 570(C).
    18. Kırbaş, İsmail & Sözen, Adnan & Tuncer, Azim Doğuş & Kazancıoğlu, Fikret Şinasi, 2020. "Comparative analysis and forecasting of COVID-19 cases in various European countries with ARIMA, NARNN and LSTM approaches," Chaos, Solitons & Fractals, Elsevier, vol. 138(C).
    19. Sinitsyn, E. V. & Tolmachev, A. V. & Ovchinnikov, A. S., 2020. "Socio-economic factors in the spread of SARS-COV-2 across Russian regions," R-Economy, Ural Federal University, Graduate School of Economics and Management, vol. 6(3), pages 129-145.
    20. Rasheed, Jawad & Jamil, Akhtar & Hameed, Alaa Ali & Aftab, Usman & Aftab, Javaria & Shah, Syed Attique & Draheim, Dirk, 2020. "A survey on artificial intelligence approaches in supporting frontline workers and decision makers for the COVID-19 pandemic," Chaos, Solitons & Fractals, Elsevier, vol. 141(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:arp:ajoams:2020:p:172-180. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Managing Editor (email available below). General contact details of provider: http://arpgweb.com/index.php?ic=journal&journal=17&info=aims .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.