Spatio-temporal estimation of the daily cases of COVID-19 in worldwide using random forest machine learning algorithm
| dc.authorid | 0000-0002-7508-7548 | en_US |
| dc.contributor.author | Yeşilkanat, Cafer Mert | |
| dc.date.accessioned | 2020-09-10T07:38:46Z | |
| dc.date.available | 2020-09-10T07:38:46Z | |
| dc.date.issued | 2020 | |
| dc.department | AÇÜ, Eğitim Fakültesi | en_US |
| dc.description.abstract | Novel Coronavirus pandemic, which negatively affected public health in social, psychological and economical terms, spread to the whole world in a short period of 6 months. However, the rate of increase in cases was not equal for every country. The measures implemented by the countries changed the daily spreading speed of the disease. This was determined by changes in the number of daily cases. In this study, the performance of the Random Forest (RF) machine learning algorithm was investigated in estimating the near future case numbers for 190 countries in the world and it is mapped in comparison with actual confirmed cases results. The number of confirmed cases between 23/01/2020 - 17/06/2020 were divided into 3 main sub-datasets: training sub-data, testing sub-data (interpolation data) and estimating sub-data (extrapolation data) for the random forest model. At the end of the study, it has been found that R2 values for testing sub-data of RF model estimates range between 0.843 and 0.995 (average R2= 0.959), and RMSE values between 141.76 and 526.18 (mean RMSE = 259.38); and that R2 values for estimating sub-data range between 0.690 and 0.968 (mean R2 = 0.914), and RMSE values between 549.73 and 2500.79 (mean RMSE = 909.37). These results show that the random forest machine learning algorithm performs well in estimating the number of cases for the near future in case of an epidemic like Novel Coronavirus, which outbreaks suddenly and spreads rapidly. | |
| dc.identifier.citation | Yeşilkanat, C. M. (2020). Spatio-temporal estimation of the daily cases of COVID-19 in worldwide using random forest machine learning algorithm. Chaos, Solitons & Fractals, 140, doi: 10.1016/j.chaos.2020.110210. | en_US |
| dc.identifier.doi | 10.1016/j.chaos.2020.110210 | |
| dc.identifier.scopusquality | N/A | |
| dc.identifier.uri | https://hdl.handle.net/11494/2173 | |
| dc.identifier.volume | 140 | en_US |
| dc.identifier.wosquality | Q1 | |
| dc.indekslendigikaynak | Web of Science | |
| dc.indekslendigikaynak | Scopus | |
| dc.indekslendigikaynak | PubMed | |
| dc.institutionauthor | Yeşilkanat, Cafer Mert | |
| dc.language.iso | en | en_US |
| dc.publisher | Elsevier | en_US |
| dc.relation.ispartof | Chaos, Solitons & Fractals | |
| dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | en_US |
| dc.rights | info:eu-repo/semantics/closedAccess | en_US |
| dc.subject | COVID-19 | en_US |
| dc.subject | Random forest | en_US |
| dc.subject | Machine learning | en_US |
| dc.subject | Estimating Mapping | en_US |
| dc.title | Spatio-temporal estimation of the daily cases of COVID-19 in worldwide using random forest machine learning algorithm | en_US |
| dc.type | Article |
