Araştırma Makalesi
BibTex RIS Kaynak Göster
Yıl 2020, Cilt: 8 Sayı: 2, 144 - 150, 21.12.2020
https://doi.org/10.51354/mjen.769763

Öz

Kaynakça

  • 1. World Health Organization (2020) Coronavirus disease (COVID-19) Situation Report–139. World Health Organization,
  • 2. El Zowalaty ME, Järhult JD (2020) From SARS to COVID-19: A previously unknown SARS-CoV-2 virus of pandemic potential infecting humans–Call for a One Health approach. One Health:100124
  • 3. Findlater A, Bogoch II (2018) Human mobility and the global spread of infectious diseases: a focus on air travel. Trends in parasitology 34 (9):772-783
  • 4. Phan LT, Nguyen TV, Luong QC, Nguyen TV, Nguyen HT, Le HQ, Nguyen TT, Cao TM, Pham QD (2020) Importation and human-to-human transmission of a novel coronavirus in Vietnam. New England Journal of Medicine 382 (9):872-874
  • 5. Riou J, Althaus CL (2020) Pattern of early human-to-human transmission of Wuhan 2019 novel coronavirus (2019-nCoV), December 2019 to January 2020. Eurosurveillance 25 (4):2000058
  • 6. Muhammad S, Long X, Salman M (2020) COVID-19 pandemic and environmental pollution: A blessing in disguise? Science of The Total Environment 728:138820. doi:https://doi.org/10.1016/j.scitotenv.2020.138820
  • 7. Kraemer MUG, Yang C-H, Gutierrez B, Wu C-H, Klein B, Pigott DM, du Plessis L, Faria NR, Li R, Hanage WP, Brownstein JS, Layan M, Vespignani A, Tian H, Dye C, Pybus OG, Scarpino SV (2020) The effect of human mobility and control measures on the COVID-19 epidemic in China. Science 368 (6490):493. doi:10.1126/science.abb4218
  • 8. Tagliazucchi E, Balenzuela P, Travizano M, Mindlin GB, Mininni PD (2020) Lessons from being challenged by COVID-19. Chaos, Solitons & Fractals 137:109923. doi:https://doi.org/10.1016/j.chaos.2020.109923
  • 9. Yousaf M, Zahir S, Riaz M, Hussain SM, Shah K (2020) Statistical analysis of forecasting COVID-19 for upcoming month in Pakistan. Chaos, Solitons & Fractals 138:109926. doi:https://doi.org/10.1016/j.chaos.2020.109926
  • 10. Salgotra R, Gandomi M, Gandomi AH (2020) Time Series Analysis and Forecast of the COVID-19 Pandemic in India using Genetic Programming. Chaos, Solitons & Fractals 138:109945. doi:https://doi.org/10.1016/j.chaos.2020.109945
  • 11. Ayinde K, Lukman AF, Rauf RI, Alabi OO, Okon CE, Ayinde OE (2020) Modeling Nigerian Covid-19 cases: A comparative analysis of models and estimators. Chaos, Solitons & Fractals 138:109911. doi:https://doi.org/10.1016/j.chaos.2020.109911
  • 12. Mandal M, Jana S, Nandi SK, Khatua A, Adak S, Kar TK (2020) A model based study on the dynamics of COVID-19: Prediction and control. Chaos, Solitons & Fractals 136:109889. doi:https://doi.org/10.1016/j.chaos.2020.109889
  • 13. Parbat D, Chakraborty M (2020) A python based support vector regression model for prediction of COVID19 cases in India. Chaos, Solitons & Fractals 138:109942. doi:https://doi.org/10.1016/j.chaos.2020.109942
  • 14. Chimmula VKR, Zhang L (2020) Time series forecasting of COVID-19 transmission in Canada using LSTM networks. Chaos, Solitons & Fractals 135:109864. doi:https://doi.org/10.1016/j.chaos.2020.109864
  • 15. Fanelli D, Piazza F (2020) Analysis and forecast of COVID-19 spreading in China, Italy and France. Chaos, Solitons & Fractals 134:109761. doi:https://doi.org/10.1016/j.chaos.2020.109761
  • 16. EU Open Data Portal (2020) COVID-19 Coronavirus data EU Open Data Portal. https://www.ecdc.europa.eu/sites/default/files/documents/COVID-19-geographic-disbtribution-worldwide.xlsx. Accessed 5/29/2020 2020
  • 17. Şahin M (2020) Impact of weather on COVID-19 pandemic in Turkey. Science of The Total Environment 728:138810. doi:https://doi.org/10.1016/j.scitotenv.2020.138810
  • 18. Williams CKI, Rasmussen CE (2006) Gaussian processes for machine learning, vol 2. MIT press Cambridge, MA,
  • 19. Thissen U, van Brakel R, de Weijer AP, Melssen WJ, Buydens LMC (2003) Using support vector machines for time series prediction. Chemometrics and Intelligent Laboratory Systems 69 (1):35-49. doi:https://doi.org/10.1016/S0169-7439(03)00111-4
  • 20. Moisen GG (2008) Classification and Regression Trees. In: Jørgensen SE, Fath BD (eds) Encyclopedia of Ecology. Academic Press, Oxford, pp 582-588. doi:https://doi.org/10.1016/B978-008045405-4.00149-X
  • 21. Wu X, Kumar V, Ross Quinlan J, Ghosh J, Yang Q, Motoda H, McLachlan GJ, Ng A, Liu B, Yu PS, Zhou Z-H, Steinbach M, Hand DJ, Steinberg D (2008) Top 10 algorithms in data mining. Knowledge and Information Systems 14 (1):1-37. doi:10.1007/s10115-007-0114-2
  • 22. Arias Velásquez RM, Mejía Lara JV (2020) Forecast and evaluation of COVID-19 spreading in USA with reduced-space Gaussian process regression. Chaos, Solitons & Fractals 136:109924. doi:https://doi.org/10.1016/j.chaos.2020.109924
  • 23. Ribeiro MHDM, da Silva RG, Mariani VC, Coelho LdS (2020) Short-term forecasting COVID-19 cumulative confirmed cases: Perspectives for Brazil. Chaos, Solitons & Fractals 135:109853. doi:https://doi.org/10.1016/j.chaos.2020.109853
  • 24. Sujath R, Chatterjee JM, Hassanien AE (2020) A machine learning forecasting model for COVID-19 pandemic in India. Stochastic Environmental Research and Risk Assessment. doi:10.1007/s00477-020-01827-8
  • 25. Liu J, Zhou J, Yao J, Zhang X, Li L, Xu X, He X, Wang B, Fu S, Niu T, Yan J, Shi Y, Ren X, Niu J, Zhu W, Li S, Luo B, Zhang K (2020) Impact of meteorological factors on the COVID-19 transmission: A multi-city study in China. Science of The Total Environment 726:138513. doi:https://doi.org/10.1016/j.scitotenv.2020.138513
  • 26. Goswami K, Bharali S, Hazarika J (2020) Projections for COVID-19 pandemic in India and effect of temperature and humidity. Diabetes & Metabolic Syndrome: Clinical Research & Reviews. doi:https://doi.org/10.1016/j.dsx.2020.05.045
  • 27. Li H, Xu X-L, Dai D-W, Huang Z-Y, Ma Z, Guan Y-J (2020) Air Pollution and temperature are associated with increased COVID-19 incidence: a time series study. International Journal of Infectious Diseases. doi:https://doi.org/10.1016/j.ijid.2020.05.076
  • 28. Wu Y, Jing W, Liu J, Ma Q, Yuan J, Wang Y, Du M, Liu M (2020) Effects of temperature and humidity on the daily new cases and new deaths of COVID-19 in 166 countries. Science of The Total Environment 729:139051. doi:https://doi.org/10.1016/j.scitotenv.2020.139051
  • 29. Farzan AN (2020) U.S. coronavirus cases surpass 1.8 million as concern over potential spread rises with turmoil. The Washington Post, June 3, 2020,

Forecasting COVID-19 cases based on mobility

Yıl 2020, Cilt: 8 Sayı: 2, 144 - 150, 21.12.2020
https://doi.org/10.51354/mjen.769763

Öz

Countries struggling to overcome the profound and devastating effects of COVID-19 have started taking steps to return to "new normal." Any accurate forecasting can help countries and decision-makers to make plans and decisions in the process of returning normal life. In this regard, it is needless to mention the criticality and importance of accurate forecasting. In this study, daily cases of COVID-19 are estimated based on mobility data, considering the proven human-to-human transmission factor. The data of seven countries, namely Brazil, France, Germany, Italy, Spain, the United Kingdom (UK), and the United States of America (USA) are used to train and test the models. These countries represent around 57% of the total cases in the whole world. In this context, various machine learning algorithms are implemented to obtain accurate predictions. Unlike most studies, the predicted case numbers are evaluated against the actual values to reveal the real performance of the methods and determine the most effective methods. The results indicated that it is unlikely to propose the same algorithm for forecasting COVID-19 cases for all countries. Also, mobility data can be enough the predict the COVID-19 cases in the USA.

Kaynakça

  • 1. World Health Organization (2020) Coronavirus disease (COVID-19) Situation Report–139. World Health Organization,
  • 2. El Zowalaty ME, Järhult JD (2020) From SARS to COVID-19: A previously unknown SARS-CoV-2 virus of pandemic potential infecting humans–Call for a One Health approach. One Health:100124
  • 3. Findlater A, Bogoch II (2018) Human mobility and the global spread of infectious diseases: a focus on air travel. Trends in parasitology 34 (9):772-783
  • 4. Phan LT, Nguyen TV, Luong QC, Nguyen TV, Nguyen HT, Le HQ, Nguyen TT, Cao TM, Pham QD (2020) Importation and human-to-human transmission of a novel coronavirus in Vietnam. New England Journal of Medicine 382 (9):872-874
  • 5. Riou J, Althaus CL (2020) Pattern of early human-to-human transmission of Wuhan 2019 novel coronavirus (2019-nCoV), December 2019 to January 2020. Eurosurveillance 25 (4):2000058
  • 6. Muhammad S, Long X, Salman M (2020) COVID-19 pandemic and environmental pollution: A blessing in disguise? Science of The Total Environment 728:138820. doi:https://doi.org/10.1016/j.scitotenv.2020.138820
  • 7. Kraemer MUG, Yang C-H, Gutierrez B, Wu C-H, Klein B, Pigott DM, du Plessis L, Faria NR, Li R, Hanage WP, Brownstein JS, Layan M, Vespignani A, Tian H, Dye C, Pybus OG, Scarpino SV (2020) The effect of human mobility and control measures on the COVID-19 epidemic in China. Science 368 (6490):493. doi:10.1126/science.abb4218
  • 8. Tagliazucchi E, Balenzuela P, Travizano M, Mindlin GB, Mininni PD (2020) Lessons from being challenged by COVID-19. Chaos, Solitons & Fractals 137:109923. doi:https://doi.org/10.1016/j.chaos.2020.109923
  • 9. Yousaf M, Zahir S, Riaz M, Hussain SM, Shah K (2020) Statistical analysis of forecasting COVID-19 for upcoming month in Pakistan. Chaos, Solitons & Fractals 138:109926. doi:https://doi.org/10.1016/j.chaos.2020.109926
  • 10. Salgotra R, Gandomi M, Gandomi AH (2020) Time Series Analysis and Forecast of the COVID-19 Pandemic in India using Genetic Programming. Chaos, Solitons & Fractals 138:109945. doi:https://doi.org/10.1016/j.chaos.2020.109945
  • 11. Ayinde K, Lukman AF, Rauf RI, Alabi OO, Okon CE, Ayinde OE (2020) Modeling Nigerian Covid-19 cases: A comparative analysis of models and estimators. Chaos, Solitons & Fractals 138:109911. doi:https://doi.org/10.1016/j.chaos.2020.109911
  • 12. Mandal M, Jana S, Nandi SK, Khatua A, Adak S, Kar TK (2020) A model based study on the dynamics of COVID-19: Prediction and control. Chaos, Solitons & Fractals 136:109889. doi:https://doi.org/10.1016/j.chaos.2020.109889
  • 13. Parbat D, Chakraborty M (2020) A python based support vector regression model for prediction of COVID19 cases in India. Chaos, Solitons & Fractals 138:109942. doi:https://doi.org/10.1016/j.chaos.2020.109942
  • 14. Chimmula VKR, Zhang L (2020) Time series forecasting of COVID-19 transmission in Canada using LSTM networks. Chaos, Solitons & Fractals 135:109864. doi:https://doi.org/10.1016/j.chaos.2020.109864
  • 15. Fanelli D, Piazza F (2020) Analysis and forecast of COVID-19 spreading in China, Italy and France. Chaos, Solitons & Fractals 134:109761. doi:https://doi.org/10.1016/j.chaos.2020.109761
  • 16. EU Open Data Portal (2020) COVID-19 Coronavirus data EU Open Data Portal. https://www.ecdc.europa.eu/sites/default/files/documents/COVID-19-geographic-disbtribution-worldwide.xlsx. Accessed 5/29/2020 2020
  • 17. Şahin M (2020) Impact of weather on COVID-19 pandemic in Turkey. Science of The Total Environment 728:138810. doi:https://doi.org/10.1016/j.scitotenv.2020.138810
  • 18. Williams CKI, Rasmussen CE (2006) Gaussian processes for machine learning, vol 2. MIT press Cambridge, MA,
  • 19. Thissen U, van Brakel R, de Weijer AP, Melssen WJ, Buydens LMC (2003) Using support vector machines for time series prediction. Chemometrics and Intelligent Laboratory Systems 69 (1):35-49. doi:https://doi.org/10.1016/S0169-7439(03)00111-4
  • 20. Moisen GG (2008) Classification and Regression Trees. In: Jørgensen SE, Fath BD (eds) Encyclopedia of Ecology. Academic Press, Oxford, pp 582-588. doi:https://doi.org/10.1016/B978-008045405-4.00149-X
  • 21. Wu X, Kumar V, Ross Quinlan J, Ghosh J, Yang Q, Motoda H, McLachlan GJ, Ng A, Liu B, Yu PS, Zhou Z-H, Steinbach M, Hand DJ, Steinberg D (2008) Top 10 algorithms in data mining. Knowledge and Information Systems 14 (1):1-37. doi:10.1007/s10115-007-0114-2
  • 22. Arias Velásquez RM, Mejía Lara JV (2020) Forecast and evaluation of COVID-19 spreading in USA with reduced-space Gaussian process regression. Chaos, Solitons & Fractals 136:109924. doi:https://doi.org/10.1016/j.chaos.2020.109924
  • 23. Ribeiro MHDM, da Silva RG, Mariani VC, Coelho LdS (2020) Short-term forecasting COVID-19 cumulative confirmed cases: Perspectives for Brazil. Chaos, Solitons & Fractals 135:109853. doi:https://doi.org/10.1016/j.chaos.2020.109853
  • 24. Sujath R, Chatterjee JM, Hassanien AE (2020) A machine learning forecasting model for COVID-19 pandemic in India. Stochastic Environmental Research and Risk Assessment. doi:10.1007/s00477-020-01827-8
  • 25. Liu J, Zhou J, Yao J, Zhang X, Li L, Xu X, He X, Wang B, Fu S, Niu T, Yan J, Shi Y, Ren X, Niu J, Zhu W, Li S, Luo B, Zhang K (2020) Impact of meteorological factors on the COVID-19 transmission: A multi-city study in China. Science of The Total Environment 726:138513. doi:https://doi.org/10.1016/j.scitotenv.2020.138513
  • 26. Goswami K, Bharali S, Hazarika J (2020) Projections for COVID-19 pandemic in India and effect of temperature and humidity. Diabetes & Metabolic Syndrome: Clinical Research & Reviews. doi:https://doi.org/10.1016/j.dsx.2020.05.045
  • 27. Li H, Xu X-L, Dai D-W, Huang Z-Y, Ma Z, Guan Y-J (2020) Air Pollution and temperature are associated with increased COVID-19 incidence: a time series study. International Journal of Infectious Diseases. doi:https://doi.org/10.1016/j.ijid.2020.05.076
  • 28. Wu Y, Jing W, Liu J, Ma Q, Yuan J, Wang Y, Du M, Liu M (2020) Effects of temperature and humidity on the daily new cases and new deaths of COVID-19 in 166 countries. Science of The Total Environment 729:139051. doi:https://doi.org/10.1016/j.scitotenv.2020.139051
  • 29. Farzan AN (2020) U.S. coronavirus cases surpass 1.8 million as concern over potential spread rises with turmoil. The Washington Post, June 3, 2020,
Toplam 29 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Mühendislik
Bölüm Araştırma Makalesi
Yazarlar

Mehmet Şahin 0000-0001-7078-7396

Yayımlanma Tarihi 21 Aralık 2020
Yayımlandığı Sayı Yıl 2020 Cilt: 8 Sayı: 2

Kaynak Göster

APA Şahin, M. (2020). Forecasting COVID-19 cases based on mobility. MANAS Journal of Engineering, 8(2), 144-150. https://doi.org/10.51354/mjen.769763
AMA Şahin M. Forecasting COVID-19 cases based on mobility. MJEN. Aralık 2020;8(2):144-150. doi:10.51354/mjen.769763
Chicago Şahin, Mehmet. “Forecasting COVID-19 Cases Based on Mobility”. MANAS Journal of Engineering 8, sy. 2 (Aralık 2020): 144-50. https://doi.org/10.51354/mjen.769763.
EndNote Şahin M (01 Aralık 2020) Forecasting COVID-19 cases based on mobility. MANAS Journal of Engineering 8 2 144–150.
IEEE M. Şahin, “Forecasting COVID-19 cases based on mobility”, MJEN, c. 8, sy. 2, ss. 144–150, 2020, doi: 10.51354/mjen.769763.
ISNAD Şahin, Mehmet. “Forecasting COVID-19 Cases Based on Mobility”. MANAS Journal of Engineering 8/2 (Aralık 2020), 144-150. https://doi.org/10.51354/mjen.769763.
JAMA Şahin M. Forecasting COVID-19 cases based on mobility. MJEN. 2020;8:144–150.
MLA Şahin, Mehmet. “Forecasting COVID-19 Cases Based on Mobility”. MANAS Journal of Engineering, c. 8, sy. 2, 2020, ss. 144-50, doi:10.51354/mjen.769763.
Vancouver Şahin M. Forecasting COVID-19 cases based on mobility. MJEN. 2020;8(2):144-50.

Manas Journal of Engineering 

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