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Due to delay in reporting, the daily national and statewide COVID-19 incidence counts are often unreliable and need to be estimated from recent data. This process is known in economics as nowcasting. We describe in this paper a simple random forest statistical model for nowcasting the COVID-19 daily new infection counts based on historic data along with a set of simple covariates, such as the currently reported infection counts, day of the week, and time since first reporting. We apply the model to adjust the daily infection counts in Ohio, and show that the predictions from this simple data-driven method compare favorably both in quality and computational burden to those obtained from the state-of-the-art hierarchical Bayesian model employing a complex statistical algorithm. The interactive notebook for performing nowcasting is available online at https://tinyurl.com/simpleMLnowcasting. • Delays in reporting often make daily statewide COVID-19 incidence counts unreliable. • We developed a simple model for nowcasting the daily counts from historic data. • To calibrate the model we use historic rates of backfilling COVID-19 cases in Ohio. • On the Ohio COVID-19 dataset our method outperforms the standard model. • The algorithm is implemented in jupyter notebook environment for public use. [ABSTRACT FROM AUTHOR]

• Impact of covariates on COVID-19 response were tested via Granger-causality tests. • Climatic impact on COVID-19 cases-to-mortality ratios were studied in 36 countries. • Relationship equations were established using regression analysis. • Temperature data were factored-in in training forecasting models. • The trained models were used for forecasting COVID-19 cases-to-mortality ratios. There are several recent publications criticizing the failure of COVID-19 forecasting models, with swinging over predictions and underpredictions, which have made it difficult for decision and policy making. Observing the failures of several COVID-19 forecasting models and the alarming spread of the virus, we seek to use some stable response for forecasting COVID-19, viz., ratios of COVID-19 cases to mortalities, rather than COVID-19 cases or fatalities. A trend of low COVID-19 cases-to-mortality ratios calls for urgent attention: the need for vaccines, for instance. Studies have shown that there are influences of weather parameters on COVID-19; and COVID-19 may have come to stay and could manifest a seasonal outbreak profile similar to other infectious respiratory diseases. In this paper, the influences of some weather, geographical, economic and demographic covariates were evaluated on COVID-19 response based on a series of Granger-causality tests. The effect of four weather parameters, viz., temperature, rainfall, solar irradiation and relative humidity, on daily COVID-19 cases-to-mortality ratios of 36 countries from 5 continents of the world were determined through regression analysis. Regression studies show that these four weather factors impact ratios of COVID-19 cases-to-mortality differently. The most impactful factor is temperature which is positively correlated with COVID-19 cases-to-mortality responses in 24 out of 36 countries. Temperature minimally affects COVID-19 cases-to-mortality ratios in the tropical countries. The most influential weather factor – temperature – was incorporated in training random forest and deep learning models for forecasting the cases-to-mortality rate of COVID-19 in clusters of countries in the world with similar weather conditions. Evaluation of trained forecasting models incorporating temperature features show better performance compared to a similar set of models trained without temperature features. This implies that COVID-19 forecasting models will predict more accurately if temperature features are factored in, especially for temperate countries. [ABSTRACT FROM AUTHOR]

• The cumulative coronavirus cases for USA, Germany and Global are forecasted. • Four different machine learning time series methods are employed. • SVM method achieves the best trend. • Largest extreme value distribution fits best for Covid-19 global cumulative weekly cases. The Covid-19 pandemic is the most important health disaster that has surrounded the world for the past eight months. There is no clear date yet on when it will end. As of 18 September 2020, more than 31 million people have been infected worldwide. Predicting the Covid-19 trend has become a challenging issue. In this study, data of COVID-19 between 20/01/2020 and 18/09/2020 for USA, Germany and the global was obtained from World Health Organization. Dataset consist of weekly confirmed cases and weekly cumulative confirmed cases for 35 weeks. Then the distribution of the data was examined using the most up-to-date Covid-19 weekly case data and its parameters were obtained according to the statistical distributions. Furthermore, time series prediction model using machine learning was proposed to obtain the curve of disease and forecast the epidemic tendency. Linear regression, multi-layer perceptron, random forest and support vector machines (SVM) machine learning methods were used. The performances of the methods were compared according to the RMSE, APE, MAPE metrics and it was seen that SVM achieved the best trend. According to estimates, the global pandemic will peak at the end of January 2021 and estimated approximately 80 million people will be cumulatively infected. [ABSTRACT FROM AUTHOR]

• Daily number of COVID-19 cases was estimated by the random forest method. • Case estimates by random forest were mapped and compared to actual data. • Random forest performed well in estimating the number of cases in the near future. 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. [ABSTRACT FROM AUTHOR]

• Hybrid and single models are employed to forecast COVID-19 cases in the Brazilian and USA context. • Models for multi-step-ahead forecasting coupled with climatic variables are evaluated. • Out-of-sample forecasting errors lower than 3.08% are achieved by best models. • Temperature and Precipitation variables play a key role in the forecasting model. • VMD-based models are the most suitable tools to forecast COVID-19 cases six-days-ahead. The novel coronavirus disease (COVID-19) is a public health problem once according to the World Health Organization up to June 24th, 2020, more than 9.1 million people were infected, and more than 470 thousand have died worldwide. In the current scenario, the Brazil and the United States of America present a high daily incidence of new cases and deaths. Therefore, it is important to forecast the number of new cases in a time window of one week, once this can help the public health system developing strategic planning to deals with the COVID-19. The application of the forecasting artificial intelligence (AI) models has the potential of deal with dynamical behavior of time-series like of COVID-19. In this paper, Bayesian regression neural network, cubist regression, k -nearest neighbors, quantile random forest, and support vector regression, are used stand-alone, and coupled with the recent pre-processing variational mode decomposition (VMD) employed to decompose the time series into several intrinsic mode functions. All AI techniques are evaluated in the task of time-series forecasting with one, three, and six-days-ahead the cumulative COVID-19 cases in five Brazilian and American states, with a high number of cases up to April 28th, 2020. Previous cumulative COVID-19 cases and exogenous variables as daily temperature and precipitation were employed as inputs for all forecasting models. The models' effectiveness are evaluated based on the performance criteria. In general, the hybridization of VMD outperformed single forecasting models regarding the accuracy, specifically when the horizon is six-days-ahead, the hybrid VMD–single models achieved better accuracy in 70% of the cases. Regarding the exogenous variables, the importance ranking as predictor variables is, from the upper to the lower, past cases, temperature, and precipitation. Therefore, due to the efficiency of evaluated models to forecasting cumulative COVID-19 cases up to six-days-ahead, the adopted models can be recommended as a promising models for forecasting and be used to assist in the development of public policies to mitigate the effects of COVID-19 outbreak. [ABSTRACT FROM AUTHOR]