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Purpose: The purpose of this paper is to develop a system dynamics approach based on Susceptible, Exposed, Infected, Recovered (SEIR) model to investigate the coronavirus pandemic and the impact of therapeutic and preventive interventions on epidemic disaster. Design/methodology/approach: To model the behavior of COVID-19 disease, a system dynamics model is developed in this paper based on SEIR model. In the proposed model, the impact of people's behavior, contact reduction, isolation of the sick people as well as public quarantine on the spread of diseases is analyzed. In this model, data collected by the Iran Ministry of Health have been used for modeling and verification of the results. Findings: The results show that besides the intervening policies, early application of them is also of utmost priority and makes a significant difference in the result of the system. Also, if the number of patients with extreme conditions passes available hospital intensive care capacity, the death rate increases dramatically. Intervening policies play an important role in reducing the rate of infection, death and consequently control of pandemic. Also, results show that if proposed policies do not work before the violation of the hospital capacity, the best policy is to increase the hospital's capacity by adding appropriate equipment. Research limitations/implications: The authors also had some limitations in the study including the lack of access to precise data regarding the epidemic of coronavirus, as well as accurate statistics of death rate and cases in the onset of the virus due to the lack of diagnostic kits in Iran. These parameters are still part of the problem and can negatively influence the effectiveness of intervening policies introduced in this paper. Originality/value: The contribution of this paper includes the development of SEIR model by adding more policymaking details and considering the constraint of the hospital and public health capacity in the rate of coronavirus infection and death within a system dynamics modeling framework. [ABSTRACT FROM AUTHOR]

In this paper, the MGIW-ARIMA model is proposed to predict the newly diagnosed cases of COVID-19. The MGIW model is used to predict the trend components, and the ARIMA model is used to predict the random components. Finally, the prediction results of trend components and random components are added together to get the estimation results. The data of COVID-19 in the United States is used to verify and evaluate the model. In the short-term prediction, the combined model of MGIW-ARIMA is better than the single MGIW model. In the long-term prediction, the prediction accuracy of the MGIW model is slightly higher than that of the MGIW-ARIMA model. The MGIW-ARIMA model proposed in this paper makes up for the shortcoming of the lack of randomness in the estimated values of the MGIW model. The combination model can effectively capture the short-term changes of the epidemic while grasping the long-term development trend. [ABSTRACT FROM AUTHOR]

In this paper, the MGIW-ARIMA model is proposed to predict the newly diagnosed cases of COVID-19. The MGIW model is used to predict the trend components, and the ARIMA model is used to predict the random components. Finally, the prediction results of trend components and random components are added together to get the estimation results. The data of COVID-19 in the United States is used to verify and evaluate the model. In the short-term prediction, the combined model of MGIW-ARIMA is better than the single MGIW model. In the long-term prediction, the prediction accuracy of the MGIW model is slightly higher than that of the MGIW-ARIMA model. The MGIW-ARIMA model proposed in this paper makes up for the shortcoming of the lack of randomness in the estimated values of the MGIW model. The combination model can effectively capture the short-term changes of the epidemic while grasping the long-term development trend. [ABSTRACT FROM AUTHOR]

Many severe epidemics and pandemics have hit human civilizations throughout history. The recent Sever Actuate Respiratory disease SARS-CoV-2 known as COVID-19 became a global disease and is still growing around the globe. It has severely affected the world's economy and ways of life. It necessitates predicting the spread in advance and considering various control policies to avoid the country's complete closure. In this paper, we propose deep learning-based stacked Bi-directional long short-term memory (Stacked Bi-LSTM) network that forecasts COVID-19 more accurately for the country of South Korea. The paper's main objectives are to present a lightweight, accurate, and optimized model to predict the spread considering restriction policies such as school closure, workspace closing, and the canceling of public events. Based on the fourteen parameters (including control policies), we predict and forecast the future value of the number of positive, dead, recovered, and quarantined cases. In this paper, we use the dataset of South Korea comprised of several control policies implemented for minimizing the spread of COVID-19. We compare the performance of the stacked Bi-LSTM with the traditional time-series models and LSTM model using the performance metrics mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE). Moreover, we study the impact of control policies on forecasting accuracy. We further study the impact of changing the Bi-LSTM default activation functions Tanh with ReLU on forecasting accuracy. The research provides insight to policymakers to optimize the pooling of resources more optimally on the correct date and time prior to the event and to control the spread by employing various strategies in the meantime. • Proposed Policies based COVID-19 Spread Forecasting Model to examine the impact of spread in South Korea. • Proposed Stacked Bi-LSTM Novel Forecasting Model for COVID-19 Spread Control. • A comparison between Stacked Bi-LSTM and LSTM is presented. • Examined individual and collective 10 policies such as school closure, restriction on travel and vice versa. • Provided the multiple combination of COVID-19 Spread control policies analysis for future Forecasting to minimize the spread. [ABSTRACT FROM AUTHOR]

Community group buying is the preferred shopping method for many consumers during the COVID-19 epidemic in China. This new Internet pre-sale mode has a lead- lag effect and non-linear characteristics. Therefore, studying the prediction of community group buying has practical significance. In this paper, the lead-lag term of vector autoregressive model is coupled with the nonlinear term in the GM (1, 1, t α ) model. Then, lead-lag factor τ and nonlinear factor t μ are added in the grey modelling process, and a novel hybrid grey forecasting model of lead-lag and nonlinearity (GAPM) is established by means of optimising the background value. Firstly, the least squares method and derivation method are used to obtain the model parameter values and time response expressions, and the stability of GAPM is studied on the basis of condition number theory of matrices. Secondly, the grey evolutionary algorithm is introduced to obtain the optimal solution of the background value and nonlinear factor, and the weighted geometric average weakening buffer operator is used to preprocess the original data. Finally, the new model is used to fit and predict the daily sales of Orange Optimization Company in Wuhan, China for two consecutive months of community group buying. Results show that the model has good stability and high prediction accuracy. Compared with the other five common grey models that do not consider the lead-lag influence of the system, GAPM has better predictive ability. Therefore, the introduction of the new model constructed by using the lead-lag factor is reasonable and effective in the case of community group buying with lead-lag effect. • On the basis of the characteristics of community group buying, a new lead-lag grey prediction model (GAPM) is proposed. • The stability of GAPM is examined on the basis of condition number theory of matrices. • The WGAWB operator and the GPE algorithm are employed to process raw data and optimize model parameters. • GAPM is used to fit and predict the daily sales of Orange Optimization Company in Wuhan, China for two consecutive months of community group buying. [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]

• Estimation period covers the last 12 months' data (March 2020-March 2021) to forecast new COVID-19 cases in top nine affected countries. • Validation periods for one-day ahead forecasting performance include April, 2021 and three waves of COVID-19 pandemic. • The conditional variance and asymmetric effects were taken into consideration by employing ARMA-GARCH, ARMA-TGARCH and ARMA-EGARCH models. • Time series forecasting models should include the conditional variance effect to rise their forecasting power. • Asymmetric effect has mixed results in regards to improving forecasting power. COVID-19 pandemic has affected more than a hundred fifty million people and killed over three million people worldwide over the past year. During this period, different forecasting models have tried to forecast time path of COVID-19 pandemic. Unlike the COVID-19 forecasting literature based on Autoregressive Integrated Moving Average (ARIMA) modelling, in this paper new COVID-19 cases were modelled and forecasted by conditional variance and asymmetric effects employing Generalized Autoregressive Conditional Heteroscedasticity (GARCH), Threshold GARCH (TARCH) and Exponential GARCH (EGARCH) models. ARMA, ARMA-GARCH, ARMA-TGARCH and ARMA-EGARCH models were employed for one-day ahead forecasting performance for April, 2021 and three waves of COVID-19 pandemic in nine most affected countries USA, India, Brazil, France, Russia, UK, Italy, Spain and Germany. Empirical results show that ARMA-GARCH models have better forecast performance than ARMA models by modelling both the conditional heteroskedasticity and the heavy-tailed distributions of the daily growth rate of the new confirmed cases; and asymmetric GARCH models show mixed results in terms of reducing the root mean squared error (RMSE). [ABSTRACT FROM AUTHOR]

• Compartment model susceptible-exposed-home quarantine-infected-hospital quarantine-recovered is developed to understand the transmitting behaviour of pandemic COVID-19 in human population. • Local and global asymptotic stability established for the conditions of reproduction number. • Statistical Analysis has been performed to analyse the impact of lockdown in different part of the world badly infected by COVID-19 with India. • Also time series forecasting model for the case fatality rate in India and time series forecasting of actual cases versus predicted cases in India is performed. Pandemic COVID-19 which has infected more than 35,027,546 people and death more than 1,034,837 people in 235 countries as on October 05, 2020 has created a chaos across the globe. In this paper, we develop a compartmental epidemic model to understand the spreading behaviour of the disease in human population with a special case of Bhilwara, a desert town in India where successful control measures TTT (tracking, testing and treatment) was adopted to curb the disease in the very early phase of the spread of the disease in India. Local and global asymptotic stability is established for endemic equilibrium. Extensive numerical simulations with real parametric values are performed to validate the analytical results. Trend analysis of fatality rate, infection rate, and impact of lockdown is performed for USA, European countries, Russia, Iran, China, Japan, S. Korea with a comparative assessment by India. Kruskal - Wallis test is performed to test the null hypothesis for infected cases during the four lockdown phases in India. It has been observed that there is a significant difference at both 95% and 99% confidence interval in the infected cases, recovered cases and the case fatality rate during all the four phases of the lockdown. [ABSTRACT FROM AUTHOR]

• Potential advantages of Singular Spectrum Analysis (SSA) for forecasting the number of daily confirmed cases, deaths, and recoveries caused by COVID-19 until 29 October 2020. • Proposing an algorithm to calculate the optimal parameters of SSA. • The results of V-SSA and R-SSA are compared to those from ARIMA, ARFIMA, Exponential Smoothing, TBATS, and NNAR. • SSA technique is found to be viable option for forecasting the number of daily confirmed cases, deaths, and recoveries caused by COVID-19 based on the number of times that it outperforms the competing models. • Providing 40 days ahead point forecasts for top ten countries affected by COVID-19. Coronavirus disease 2019 (COVID-19) is a pandemic that has affected all countries in the world. The aim of this study is to examine the potential advantages of Singular Spectrum Analysis (SSA) for forecasting the number of daily confirmed cases, deaths, and recoveries caused by COVID-19, which are the three main variables of interest. This paper contributes to the literature on forecasting COVID-19 pandemic in several ways. Firstly, an algorithm is proposed to calculate the optimal parameters of SSA including window length and the number of leading components. Secondly, the results of two forecasting approaches in the SSA, namely vector and recurrent forecasting, are compared to those from other commonly used time series forecasting techniques. These include Autoregressive Integrated Moving Average (ARIMA), Fractional ARIMA (ARFIMA), Exponential Smoothing, TBATS, and Neural Network Autoregression (NNAR). Thirdly, the best forecasting model is chosen based on the accuracy measure Root Mean Squared Error (RMSE), and it is applied to forecast 40 days ahead. These forecasts can help us to predict the future behaviour of this disease and make better decisions. The dataset of Center for Systems Science and Engineering (CSSE) at Johns Hopkins University is adopted to forecast the number of daily confirmed cases, deaths, and recoveries for top ten affected countries until October 29, 2020. The findings of this investigation show that no single model can provide the best model for any of the countries and forecasting horizons considered here. However, the SSA technique is found to be viable option for forecasting the number of daily confirmed cases, deaths, and recoveries caused by COVID-19 based on the number of times that it outperforms the competing models. [ABSTRACT FROM AUTHOR]

• A predicting model for the long-term epidemic trend of COVID-19 by using LSTM with rolling update mechanism is proposed. • The 150-days ahead epidemic trend of COVID-19 in Russia, Peru and Iran are estimated by our proposed model. • The results provide that the epidemic of Peru will end in early December. • The number of daily cases in Russia and Iran is expected to fall below 2000 and 1000 by mid-November and early December. • By introducing diffusion index, the effectiveness of preventive measures taken by the government are analyzed. The COVID-19 outbreak in late December 2019 is still spreading rapidly in many countries and regions around the world. It is thus urgent to predict the development and spread of the epidemic. In this paper, we have developed a forecasting model of COVID-19 by using a deep learning method with rolling update mechanism based on the epidemical data provided by Johns Hopkins University. First, as traditional epidemical models use the accumulative confirmed cases for training, it can only predict a rising trend of the epidemic and cannot predict when the epidemic will decline or end, an improved model is built based on long short-term memory (LSTM) with daily confirmed cases training set. Second, considering the existing forecasting model based on LSTM can only predict the epidemic trend within the next 30 days accurately, the rolling update mechanism is embedded with LSTM for long-term projections. Third, by introducing Diffusion Index (DI), the effectiveness of preventive measures like social isolation and lockdown on the spread of COVID-19 is analyzed in our novel research. The trends of the epidemic in 150 days ahead are modeled for Russia, Peru and Iran, three countries on different continents. Under our estimation, the current epidemic in Peru is predicted to continue until November 2020. The number of positive cases per day in Iran is expected to fall below 1000 by mid-November, with a gradual downward trend expected after several smaller peaks from July to September, while there will still be more than 2000 increase by early December in Russia. Moreover, our study highlights the importance of preventive measures which have been taken by the government, which shows that the strict controlment can significantly reduce the spread of COVID-19. [ABSTRACT FROM AUTHOR]

In this paper, a susceptible-infected-removed (SIR) model has been used to track the evolution of the spread of COVID-19 in four countries of interest. In particular, the epidemic model, that depends on some basic characteristics, has been applied to model the evolution of the disease in Italy, India, South Korea and Iran. The economic, social and health consequences of the spread of the virus have been cataclysmic. Hence, it is imperative that mathematical models can be developed and used to compare published datasets with model predictions. The predictions estimated from the presented methodology can be used in both the qualitative and quantitative analysis of the spread. They give an insight into the spread of the virus that the published data alone cannot, by updating them and the model on a daily basis. We show that by doing so, it is possible to detect the early onset of secondary spikes in infections or the development of secondary waves. We considered data from March to August, 2020, when different communities were affected severely and demonstrate predictions depending on the model's parameters related to the spread of COVID-19 until the end of December, 2020. By comparing the published data with model results, we conclude that in this way, it may be possible to reflect better the success or failure of the adequate measures implemented by governments and authorities to mitigate and control the current pandemic. [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]

In this paper, we have conducted analysis based on data obtained from National Institute of Health (NIH) - Islamabad and produced a forecast of COVID-19 confirmed cases as well as the number of deaths and recoveries in Pakistan using the Auto-Regressive Integrated Moving Average Model (ARIMA). The fitted forecasting models revealed high exponential growth in the number of confirmed cases, deaths and recoveries in Pakistan. Based on our model prediction the number of confirmed cases will be increased by 2.7 times, 95% prediction interval for the number of cases at the end of May 2020 = (5681 to 33079). There could be up to 500 deaths, 95% prediction interval = (168 to 885) and there could be eightfold increase in the number of recoveries, 95% prediction interval = (2391 to 16126). The forecasting results of COVID-19 are alarming for May in Pakistan. The health officials and government should adopt new strategies to control the pandemic from further spread until a proper treatment or vaccine is developed. [ABSTRACT FROM AUTHOR]