Query: covid-19 cumulative confirmed cases investors

title: Examining the Psychological State Analysis Relationship Between Bitcoin Prices and COVID 19
abstract: The rapid worldwide spread of COVID 19 forced many countries to enforce complete lockdown and strict quarantine policies. The strict lockdown and quarantine affect the psychological state of people toward cryptocurrency. The current research aims to examine the effect of COVID 19 on Bitcoin prices concerning cumulative deaths and confirmed cases. The research comprises daily data from January 20, 2020, to April 30, 2020, during the initial worldwide breakout of COVID 19. This research employed the augmented Dickey Fuller test to check the stationarity of data, the co integration test for the interdependency of variables, and the vector error correction model for identifying the direction and long or short run relationship between Bitcoin prices and COVID 19. The research results show that Bitcoin prices are negatively significant and related to COVID 19 in the short run. A unidirectional relationship between Bitcoin prices and cumulative deaths is also observed. Investors and the public's psychological state were positively significant to Bitcoin prices in the long term because of cashless transactions, unbanked, and less risky virus traveling. The second reason behind the positive psychological relation is un centralization and easy to make payments by Bitcoin. This study's finding provides timely evidence to decision makers on Bitcoin price volatility and its impacts on the public's psychological states regarding COVID 19.

title: Short term forecasting COVID 19 cumulative confirmed cases: Perspectives for Brazil
abstract: The new Coronavirus (COVID 19) is an emerging disease responsible for infecting millions of people since the first notification until nowadays. Developing efficient short term forecasting models allow forecasting the number of future cases. In this context, it is possible to develop strategic planning in the public health system to avoid deaths. In this paper, autoregressive integrated moving average (ARIMA) cubist regression (CUBIST) random forest (RF) ridge regression (RIDGE) support vector regression (SVR) and stacking ensemble learning are evaluated in the task of time series forecasting with one, three, and six days ahead the COVID 19 cumulative confirmed cases in ten Brazilian states with a high daily incidence. In the stacking ensemble learning approach, the CUBIST regression, RF, RIDGE, and SVR models are adopted as base learners and Gaussian process (GP) as meta learner. The models' effectiveness is evaluated based on the improvement index, mean absolute error, and symmetric mean absolute percentage error criteria. In most of the cases, the SVR and stacking ensemble learning reach a better performance regarding adopted criteria than compared models. In general, the developed models can generate accurate forecasting, achieving errors in a range of 0.87% 3.51% 1.02% 5.63% and 0.95% 6.90% in one, three, and six days ahead, respectively. The ranking of models, from the best to the worst regarding accuracy, in all scenarios is SVR, stacking ensemble learning, ARIMA, CUBIST, RIDGE, and RF models. The use of evaluated models is recommended to forecasting and monitor the ongoing growth of COVID 19 cases, once these models can assist the managers in the decision making support systems.

title: Forecasting Models of the Coronavirus COVID 19 Cumulative Confirmed Cases Using a Hybrid Genetic Programming Method
abstract: The coronavirus disease 2019 (COVID 19) diffusion process, starting in China, caused more than 4600 lives until June 2020 and became a major threat to global public health systems. In Greece, the phenomenon started on February 2020 and it is still being developed. This paper presents the implementation of a hybrid Genetic Programming (hGP) method in finding fitting models of the Coronavirus (COVID 19) for the cumulative confirmed cases in China for the first saturation level until May 2020 and it proposes forecasting models for Greece before summer tourist season. The specific hGP method encapsulates the use of some wellknown diffusion models for forecasting purposes, epidemiological models and produces time dependent models with high performance statistical indices. A retrospective study confirmed the excellent forecasting performance of the method until 3 June 2020.

title: Modeling Palestinian COVID 19 Cumulative Confirmed Cases: A Comparative Study
abstract: COVID 19 is still a major pandemic threatening all the world. In Palestine, there were 26,764 COVID 19 cumulative confirmed cases as of 27th August 2020. In this paper, two statistical approaches, autoregressive integrated moving average (ARIMA) and k th moving averages ARIMA models are used for modeling the COVID 19 cumulative confirmed cases in Palestine. The data was taken from World Health Organization (WHO) website for one hundred seventy six (176) days, from March 5, 2020 through August 27, 2020. We identified the best models for the above mentioned approaches that are ARIMA (1,2,4) and 5 th Exponential Weighted Moving Average ARIMA (2,2,3) Consequently, we recommended to use the 5 th Exponential Weighted Moving Average ARIMA (2,2,3) model in order to forecast new values of the daily cumulative confirmed cases in Palestine. The forecast values are alarming, and giving the Palestinian government a good picture about the next number of COVID 19 cumulative confirmed cases to review her activities and interventions and to provide some robust structures and measures to avoid these challenges.

title: Short term Forecasting of Cumulative Confirmed Cases of Covid 19 Pandemic in Somalia
abstract: Somalia has recorded the first confirmed Covid 19 case and first death case on March 16, and April 08, 2020, respectively. Since its arrival to the country, it had infected 2,603 people and took the lives of 88 people while 577 patients were recovered as of 14 June, 2020. To fight this pandemic, the government requires to make the necessary plans accordingly. To plan effectively, the government needs to answer this question: what will be the effect of Covid 19 cases in the country? To answer this question accurately and objectively, forecasting the spread of confirmed Covid 19 cases will be vital.To this regard, this paper provides real times forecasts of Covid 19 cases employing Holt's linear trend model without seasonality. Provided that the data employed is accurate and the past pattern of the disease will continue in the future, this model is powerful to produce real time forecasts in the future with some degree of uncertainty. With the help of these forecasts,the government can make evidence based decisions by utilizing the scarce resource available at its disposal.

title: Forecasting the cumulative number of confirmed cases of COVID 19 in Italy, UK and USA using fractional nonlinear grey Bernoulli model
abstract: Since the new coronavirus (COVID 19) outbreak spread from China to other countries, it has been a curiosity for how and how long the number of cases will increase. This study aims to forecast the number of confirmed cases of COVID 19 in Italy, the United Kingdom (UK) and the United States of America (USA) In this study, grey model (GM(1,1) nonlinear grey Bernoulli model (NGBM(1,1) and fractional nonlinear grey Bernoulli model (FANGBM(1,1) are compared for the prediction. Therefore, grey prediction models, especially the fractional accumulated grey model, are used for the first time in this topic and it is believed that this study fills the gap in the literature. This model is applied to predict the data for the period 19/03 22/04/2020 (35 days) and forecast the data for the period 23/04 22/05/2020. The number of cases of COVID 19 in these countries are handled cumulatively. The prediction performance of the models is measured by the calculation of root mean square error (RMSE) mean absolute percentage error (MAPE) and R2 values. It is obtained that FANGBM(1,1) gives the highest prediction performance with having the lowest RMSE and MAPE values and the highest R2 values for these countries. Results show that the cumulative number of cases for Italy, UK and USA is forecasted to be about 233000, 184000 and 1090000, respectively, on May 22, 2020 which corresponds to the average daily rate is 0.80% 1.19% and 1.13% respectively, from 22/04/2020 to 22/05/2020. The ) presents that the cumulative number of cases of COVID 19 increases at a diminishing rate from 23/04/2020 to 22/05/2020 for these countries.

title: Relationship between Average Daily Temperature and Average Cumulative Daily Rate of Confirmed Cases of COVID 19
abstract: The rapid outbreak of the new Coronavirus (COVID 19) pandemic and the spread of the virus worldwide, especially in the Northern Hemisphere, have prompted various investigations about the impact of environmental factors on the rate of development of this epidemic. Different studies have called the attention to various parameters that may have influenced the spread of the virus, and in particular, the impact of climatic parameters has been emphasized. The main purpose of this study is to investigate the correlation between the average daily temperature and the rate of coronavirus epidemic growth in the infected regions. The main hypothesis object of our research is that between regions exhibiting a significant difference in the mean daily temperature, a significant difference is also observed in the average cumulative daily rate of confirmed cases, and that this does not happen if there is no significant difference in mean daily temperature. To test this research hypothesis, we carried on the case study of three regions in each of five countries and analyzed the correlation through F test, and Independent Samples T Test. In all five selected countries, we found that when there is a significant difference in the daily mean temperature between two regions of a country, a significant difference exists also in the average cumulative daily rate of confirmed cases. Conversely, if there are no significant differences in the mean daily temperature of two regions in the same country, no significant difference is observed in the average cumulative daily rate of confirmed cases for these regions.

title: A Time Series Forecasting for the Cumulative Confirmed and Critical Cases of the Covid 19 Pandemic in Saudi Arabia using Autoregressive Integrated Moving Average (ARIMA) Model
abstract: Reviews at present, different machine learning techniques and algorithms have been applied for predicting significant factors of the Coronavirus Disease 2019 (COVID 19) such as the outbreak and diagnosis In this study, the most accurate time series forecasting model, namely, the Autoregressive Integrated Moving Average (ARIMA) model is used to forecast the expected cumulative number of confirmed and critical cases in Saudi Arabia for the upcoming months Additionally, the dataset is collected from the King Abdullah Petroleum Studies and Research Centre (KAPSARC) Acquiring the number of expected cases within a short period is considered crucial as it provides an important knowledge that can be applied by the health sector in containing the COVID 19 pandemic and forming the proper precautions and strategies that are concerned on the public health system The main finding of this research is that the number of cumulative confirmed cases is expected to increase at a high rate in the upcoming two months, while the number of critical cases is forecasted to increase at a smaller rate compared to the total number of cases To evaluate the performance of the adopted model, different statistical matrices as the R Squared, Mean Squarer Error, Root Mean Square Error and Mean Absolute Error are used in this research It is found to be proven from the findings that the proposed model generates an accurate prediction of the expected number of cumulative confirmed and critical cases in the upcoming months (c) 2020 Samer H Atawneh, Osamah A M Ghaleb, Ahmad MohdAziz Hussein, Mohammad Al Madi and Bilal Shehabat

title: Short term Forecasting of Cumulative Confirmed Covid 19 Cases Pandemic in Somalia
abstract: Somalia has recorded the first confirmed Covid 19 case and first death case on March 16, and April 08, 2020, respectively. Since its arrival, it had infected 2,603 people and took the lives of 88 people while 577 patients were recovered as of 14 June, 2020. To fight this pandemic, the government requires to make the necessary plans accordingly. To plan effectively, the government needs to answer this question: what will be the effect of Covid 19 cases in the country? To answer this question accurately and objectively, forecasting the spread of confirmed Covid 19 cases will be vital. To this regard, this paper provides real times forecasts of Covid 19 cases employing Holt's linear trend model without seasonality. Provided that the data employed is accurate and the past pattern of the disease will continue in the future, this model is powerful to produce real time forecasts in the future with some degree of uncertainty. With the help of these forecasts, the government can make evidence based decisions by utilizing the scarce resource available at its disposal.

title: The proportion of deaths cases in confirmed patients of COVID 19 is still increasing for cumulative cases reported up to 25 April 2020
abstract: In this work I analyse how proportions of fatal cases after COVID 19 infection change since outbreak of the disease. Using publicity available data I model the change in deaths probability from day 30 of outbreak until 25 April 2020. The global trend is that the proportion of fatal cases is still increasing and that many countries have not yet reached the maximum deaths proportion. However, there are visual differences between countries and in some countries the proportions are clearly below or above the global trend. A positive correlation between deaths cases and recorded infections indicates that a higher infection number results in increased mortality numbers.