Spatio-temporal estimation of the daily cases of COVID-19 in worldwide using random forest machine learning algorithm.
TLDR
The 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.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 0843 and 0995 (average R2= 0959), and RMSE values between 14176 and 52618 (mean RMSE = 25938); and that R2 values for estimating sub-data range between 0690 and 0968 (mean R2 = 0914), and RMSE values between 54973 and 250079 (mean RMSE = 90937) 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 rapidlyread more
Citations
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Data analysis of Covid-19 pandemic and short-term cumulative case forecasting using machine learning time series methods.
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