Query: covid-19 new and cumulative confirmed cases

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: 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: 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: COVID 19 pandemic in the African continent: forecasts of cumulative cases, new infections, and mortality
abstract: Africa is the last major region to capitulate to the SARS CoV 2 (COVID 19) pandemic. The first confirmed COVID 19 case in the region was reported on February 14, but what lies ahead in terms of the course and magnitude of infection remains speculative. To the best of our knowledge, no study, using a robust methodology, provides the immediate and long term trajectory of COVID 19 for the entire region or accounts for its local context. This paper is the first systematic attempt to provide estimates on how many people would contract the virus and how many would die in the coming few months across Africa.  The forecasts on caseloads and incidences are from a co variate based instrumental variable regression model. Fatality rates from Italy and China were further applied to generate mortality estimates after adjustments were made for differences in age structure, health service quality, and living standards between each of the African countries and those of the reference population. We cover all countries that reported a confirmed case as of March 31, 2020.  By the end of June, 16,283,085 people will contract COVID 19 (95% CI 718,403 to 98, 358, 799) With a cumulative caseload of 5,413,4517 (95% CI 1,332,953 to 8,489,940) and 906,625 (95% CI 173, 821 to 4,742,917) Northern and Eastern Africa will respectively be the most and least affected sub regions in the continent. Cumulative COVID 19 cases on June 30, 2020 are expected to reach 2,912, 864 (95% CI 465,028 to 18,286,358) in Southern Africa, 2,787, 913 (95% CI 517, 489 to 15,056,314) in Western Africa, and 1,185,742 (95% CI 229, 111 to 6,138,692) in Central Africa. New infections (incidence) for the month of April are expected to be the highest in Djibouti, 32.8 per 1000 (95% CI 6.25 to 171.77) while Morocco 1045 (95% CI 167 to 6,547) will register the highest number of deaths.  Our study shows that countries that are least urbanized and have a low level of socio economic development, hence least connected to the outside world, are likely to register lower and slower transmissions, at least at the early stage of the epidemic. However, the same set of enabling factors that worked for their benefit are likely to go against them in implementing interventions that have lessened the impact of the disease elsewhere.

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: Forecasting new daily confirmed cases infected by COVID 19 in Italy from April 9th to May 18th 2020
abstract: We aim at forecasting the outbreak of COVID 19 in Italy by using a two part time series to model the daily relative increments. Our model is based on the data observed from 22 February to 8 April 2020 and its objective is forecasting 40 days from 9 April to 18 May 2020. All the calculations, simulations, and results in the present paper have been done in MatLab R2015b. The average curve and 80% upper and lower bounds are calculated based on 100 simulations of the fitted models. According to our model, it is expected that by May 18th, 2020, the relative increment (RI) falls to the interval of 0.31% to 1.24% (average equal to 0.78% During the last three days of the studied period, the RI belonged to the interval 2.5% to 3% Accordingly, It is expected that the new daily confirmed cases face a decreasing to around 1900 on average. Finally, our prediction establishes that the cumulative number of confirmed cases reaches 237635 (with 80% confidence interval equal to [226340 248417] by May 18th, 2020.

title: COVID 19 outbreak in Algeria: A mathematical Model to predict cumulative cases
abstract: Since December 29, 2019 a pandemic of new novel coronavirus infected pneumonia named COVID 19 has started from Wuhan, China, has led to 254 996 confirmed cases until midday March 20, 2020. Sporadic cases have been imported worldwide, in Algeria, the first case reported on February 25, 2020 was imported from Italy, and then the epidemic has spread to other parts of the country very quickly with 139 confirmed cases until March 21, 2020.  It is crucial to estimate the cases number growth in the early stages of the outbreak, to this end, we have implemented the Alg COVID 19 Model which allows to predict the incidence and the reproduction number R0 in the coming months in order to help decision makers. The Alg COVIS 19 Model initial equation 1, estimates the cumulative cases at t prediction time using two parameters: the reproduction number R0 and the serial interval SI.  We found R0=2.55 based on actual incidence at the first 25 days, using the serial interval SI= 4,4 and the prediction time t=26. The herd immunity HI estimated is HI=61% Also, The Covid 19 incidence predicted with the Alg COVID 19 Model fits closely the actual incidence during the first 26 days of the epidemic in Algeria Fig. 1.A. which allows us to use it.

title: Predictive Modelling of COVID 19 New Cases in Algeria using An Extreme Learning Machines (ELM)
abstract: In this research, an extreme learning machine (ELM) is proposed to predict the new COVID 19 cases in Algeria. In the present study, public health database from the Algeria health ministry has been used to train and test the ELM models. The input parameters for the predictive models include Cumulative Confirmed COVID 19 Cases (CCCC) Calculated COVID 19 New Cases (CCNC) and Index Day (ID) The predictive accuracy of the seven models has been assessed via several statistical parameters. The results showed that the proposed ELM model achieved an adequate level of prediction accuracy with smallest errors (MSE= 0.16, RMSE=0.4114, and MAE= 0.2912) and highest performances (NSE 0.9999, IO 0.9988, R2 0.9999) Hence, the ELM model could be utilized as a reliable and accurate modeling approach for predicting the new COVIS 19 cases in Algeria.

title: Forecasting Confirmed Cases and Mortalities of COVID 19 in the US
abstract: The wide spread of COVID 19 in the US has placed the country as the most infected population worldwide. This paper aims to forecast the number of confirmed cases and mortalities from 12 April to 21 May, 2020. There has been a large body of literature in 20 forecasting epidemic outbreaks such as C algorithms with shortfall of predicting for long periods and autoregressive integrated moving average models with the limited flexibility. However, the US COVID19 data shows great variety in the relative increments of confirmed cases. This requires a reproductive time series. 25 CC BY ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted November 4, 2020. https:/doi.org/10.1101/2020.10.30.20223412 doi: medRxiv  This  reports new research that has not been certified by peer review and should not be used to guide clinical practice.  This paper suggests a time series based on the relative increments of confirmed cases. The proposed time series assumes the changes in the time series and provides flexibility. The suggested model was applied on the data observed from 27 February to 11 April 2020 and its objective is forecasting 40 days from 12 April to 21 May 2020. 30  It is expected that by May 21, 2020, the accumulative number of confirmed cases of COVID 19 in the US rises to 1,464,729, with 80% confidence interval. Our analysis also shows that by the 21 of May, the cumulative number of mortalities caused by COVID 19 in the US from 18747 cases on 11 April increases to around 73250 cases 35 on 21 May, 2020.  Our results highlight the value of reproductive strategies in time series modelling of COVID 19. Our model benefits from a reproductive strategy from a time point in which the US COVID 19 data demonstrates a sudden fall.

title: Modelling spreading of an infection using time series by a novel family of models; fitting the time series of the confirmed cases of COVID 19 in China
abstract: Time series models are one of the frequently used methods to describe the pattern of spreading an epidemic.  We presented a new family of time series models able to represent the cumulative number of individuals that contracted an infectious disease from the start to the end of the first wave of spreading. This family is flexible enough to model the propagation of almost all infectious diseases. After a general discussion on competent time series to model the outbreak of a communicable disease, we introduced the new family through one of its examples.  We estimated the parameters of two samples of the novel family to model the spreading of COVID 19 in China.  Our model does not work well when the decreasing trend of the rate of growth is absent because it is the main presumption of the model. In addition, since the information on the initial days is of the utmost importance for this model, one of the challenges about this model is modifying it to get qualified to model datasets that lack the information on the first days.