Government Responses Matter: Predicting Covid-19 cases in US using an empirical Bayesian time series framework

Liu, Ziyue; Guo, Wensheng

doi:10.1101/2020.03.28.20044578

Cited by 9 publications

(8 citation statements)

References 14 publications

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“…Recently, several research studies have proposed different mathematical and machine-learning based forecasting models to estimate the spread of the disease, and determine its impact [7] , globally [8] and for specific countries such as the USA [9] , China [10] , Italy [11] , Spain [12] , France [13] , India [14] , Japan [15] , among others. To the best of our knowledge, no previous studies have been conducted to predict the extent of the spread of COVID-19 in Saudi Arabia, where the number of cumulative daily cases is continuing to grow substantially, rising to 11,631 cases in total [16] .…”

Section: Introductionmentioning

confidence: 99%

Forecasting the spread of the COVID-19 pandemic in Saudi Arabia using ARIMA prediction model under current public health interventions

Alzahrani

Aljamaan

Al-Fakih

2020

Journal of Infection and Public Health

229

154

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The substantial increase in the number of daily new cases infected with coronavirus around the world is alarming, and several researchers are currently using various mathematical and machine learning-based prediction models to estimate the future trend of this pandemic. In this work, we employed the Autoregressive Integrated Moving Average (ARIMA) model to forecast the expected daily number of COVID-19 cases in Saudi Arabia in the next four weeks. We first performed four different prediction models; Autoregressive Model, Moving Average, a combination of both (ARMA), and integrated ARMA (ARIMA), to determine the best model fit, and we found out that the ARIMA model outperformed the other models. The forecasting results showed that the trend in Saudi Arabia will continue growing and may reach up to 7668 new cases per day and over 127,129 cumulative daily cases in a matter of four weeks if stringent precautionary and control measures are not implemented to limit the spread of COVID-19. This indicates that the Umrah and Hajj Pilgrimages to the two holy cities of Mecca and Medina in Saudi Arabia that are supposedly scheduled to be performed by nearly 2 million Muslims in mid-July may be suspended. A set of extreme preventive and control measures are proposed in an effort to avoid such a situation.

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Section: Introductionmentioning

confidence: 99%

Forecasting the spread of the COVID-19 pandemic in Saudi Arabia using ARIMA prediction model under current public health interventions

Alzahrani

Aljamaan

Al-Fakih

2020

Journal of Infection and Public Health

229

154

View full text Add to dashboard Cite

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“…2) Bayesian Model: An empirical time series framework is proposed to predict US cases using various countries as reference. On the basis of observed US data and the parameters from the reference countries, the forecast is implemented [55].…”

Section: B Statistical Modelsmentioning

confidence: 99%

A Survey on Mathematical, Machine Learning and Deep Learning Models for COVID-19 Transmission and Diagnosis

John

Ponnusamy

Chandrasekaran

et al. 2022

IEEE Rev. Biomed. Eng.

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COVID-19 is a life threatening disease which has a enormous global impact. As the cause of the disease is a novel coronavirus whose gene information is unknown, drugs and vaccines are yet to be found. For the present situation, disease spread analysis and prediction with the help of mathematical and data driven model will be of great help to initiate prevention and control action, namely lockdown and qurantine. There are various mathematical and machinelearning models proposed for analyzing the spread and prediction. Each model has its own limitations and advantages for a particluar scenario. This article reviews the state-of-the art mathematical models for COVID-19, including compartment models, statistical models and machine learning models to provide more insight, so that an appropriate model can be well adopted for the disease spread analysis. Furthermore, accurate diagnose of COVID-19 is another essential process to identify the infected person and control further spreading. As the spreading is fast, there is a need for quick auotomated diagnosis mechanism to handle large population. Deep-learning and machine-learning based diagnostic mechanism will be more appropriate for this purpose. In this aspect, a comprehensive review on the deep learning models for the diagnosis of the disease is also provided in this article.

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“…A Bayesian time-series framework to predict the number of COVID-19 infection cases in the USA was proposed [21]. The authors used historical USA data and data from other different countries as prior reference, taking into account the difference in population sizes.…”

Section: Literature Reviewmentioning

confidence: 99%

Empirical Evaluation of Alternative Time-Series Models for COVID-19 Forecasting in Saudi Arabia

Al-Turaiki

Almutlaq

Alrasheed

et al. 2021

IJERPH

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COVID-19 is a disease-causing coronavirus strain that emerged in December 2019 that led to an ongoing global pandemic. The ability to anticipate the pandemic’s path is critical. This is important in order to determine how to combat and track its spread. COVID-19 data is an example of time-series data where several methods can be applied for forecasting. Although various time-series forecasting models are available, it is difficult to draw broad theoretical conclusions regarding their relative merits. This paper presents an empirical evaluation of several time-series models for forecasting COVID-19 cases, recoveries, and deaths in Saudi Arabia. In particular, seven forecasting models were trained using autoregressive integrated moving average, TBATS, exponential smoothing, cubic spline, simple exponential smoothing Holt, and HoltWinters. The models were built using publicly available daily data of COVID-19 during the period of 24 March 2020 to 5 April 2021 reported in Saudi Arabia. The experimental results indicate that the ARIMA model had a smaller prediction error in forecasting confirmed cases, which is consistent with results reported in the literature, while cubic spline showed better predictions for recoveries and deaths. As more data become available, a fluctuation in the forecasting-accuracy metrics was observed, possibly due to abrupt changes in the data.

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Government Responses Matter: Predicting Covid-19 cases in US using an empirical Bayesian time series framework

Cited by 9 publications

References 14 publications

Forecasting the spread of the COVID-19 pandemic in Saudi Arabia using ARIMA prediction model under current public health interventions

Forecasting the spread of the COVID-19 pandemic in Saudi Arabia using ARIMA prediction model under current public health interventions

A Survey on Mathematical, Machine Learning and Deep Learning Models for COVID-19 Transmission and Diagnosis

Empirical Evaluation of Alternative Time-Series Models for COVID-19 Forecasting in Saudi Arabia

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