WITHDRAWN: Analysis of ‘earlyR’ epidemic model and time series model for prediction of COVID-19 registered cases

Kanagarathinam, Karthick; Algehyne, Ebrahem A.; Sekar, K. R.

doi:10.1016/j.matpr.2020.10.086

Cited by 7 publications

(8 citation statements)

References 10 publications

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“…[21,24,43,49,50,63] The R package earlyR is the frequentist equivalent of EpiEstim, and therefore returns estimates that are only data-driven, but it is not set up to estimate time-varying reproduction numbers. [21,43,49,50,74] EpiFilter can use data before and beyond time t to estimate R t , which means that in addition to real-time estimation it can facilitate more robust retrospective estimation, particularly in low incidence settings. [24,63] In EpiEstim, the SI distribution can be specified as parametric (where only the mean and standard deviation are required), non-parametric (where the whole discrete distribution is provided), or "uncertain", where a range of potential SIs are considered.…”

Section: Other Input Data or Methods Modificationmentioning

confidence: 99%

“…[45] The scoping review identified additional methodological or data-related issues. These led to the development of methods that are more appropriate for use during periods of low incidence, [21,24,43,49,50,63] or offer alternatives to allow less subjective and more flexible inputs into EpiEstim, e.g. the prior for R t , the SI distribution, or an alternative way of temporally smoothing R t estimates.…”

Section: Plos Digital Healthmentioning

confidence: 99%

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Real-time estimation of the epidemic reproduction number: Scoping review of the applications and challenges

2022

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The time-varying reproduction number (Rt) is an important measure of transmissibility during outbreaks. Estimating whether and how rapidly an outbreak is growing (Rt > 1) or declining (Rt < 1) can inform the design, monitoring and adjustment of control measures in real-time. We use a popular R package for Rt estimation, EpiEstim, as a case study to evaluate the contexts in which Rt estimation methods have been used and identify unmet needs which would enable broader applicability of these methods in real-time. A scoping review, complemented by a small EpiEstim user survey, highlight issues with the current approaches, including the quality of input incidence data, the inability to account for geographical factors, and other methodological issues. We summarise the methods and software developed to tackle the problems identified, but conclude that significant gaps remain which should be addressed to enable easier, more robust and applicable estimation of Rt during epidemics.

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Section: Other Input Data or Methods Modificationmentioning

confidence: 99%

Section: Plos Digital Healthmentioning

confidence: 99%

Real-time estimation of the epidemic reproduction number: Scoping review of the applications and challenges

2022

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“…The COVID-19 is fast and lasts a long time [ 32 ], so this paper selects quarterly economic data before and after the COVID-19(September 2019 to December 2020). The data used for calculating the economic indicators were obtained from the Statistical Yearbook of Beijing, Tianjin and Hebei Province.…”

Section: Methodsmentioning

confidence: 99%

An Empirical Perception of Economic Resilience Responded to the COVID-19 Epidemic Outbreak in Beijing–Tianjin–Hebei Urban Agglomeration, China: Characterization and Interaction

Zhang

Wang

et al. 2021

IJERPH

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The COVID-19 has caused a serious impact on the global economy, and all countries are in a predicament of fighting the epidemic and recovering their economies. Aiming to discuss the impact of the COVID-19 on the economic resilience of urban agglomerations, the economic data of each quarter from June 2019 to September 2020 of the Beijing–Tianjin–Hebei Urban Agglomeration are selected, and the economic development index (EDI) is calculated based on the entropy method. Combining the fundamental conditions of urban agglomerations and industrial policies during the COVID-19, urban economic resilience is discussed by the changing trend of the economic development index (EDI) and dividing into resistance and restoration. The results show that: (1) The economic development level of the urban agglomeration has been affected by the epidemic and has changed significantly. The change of endogenous power is the main cause of change.; (2) During the outbreak of the COVID-19, the economic resilience of the Beijing–Tianjin–Hebei urban agglomeration shows four different development types: high resistance and restoration, high resistance but low restoration, low resistance but high restoration, low resistance and restoration cities; (3) High resistance but low restoration, low resistance but high restoration, and low resistance and restoration cities influence each other, but the relationship between cities is mainly dependent ; (4) The economic restoration within the urban agglomeration forms a synergy, which promotes the economic recovery and development of the urban agglomeration during the recovery period of the COVID-19. Urban agglomerations should enhance the combined effect of resistance and increase the impact of high resistance and restoration cities on surrounding cities in the future.

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“…The ARIMA model was more capable in the prediction of COVID-19 cases compared to other prediction models, including instance support vector machine (SVM) and wavelet neural network (WNN). Existing India COVID-19 data were also used for forecasting new daily confirmed cases using two models, earlyR and ARIMA [16]. A comparison between the two models showed that the ARIMA model provided better accuracy than that of the earlyR model.…”

Section: Literature Reviewmentioning

confidence: 99%

“…They were used to predict the future dynamics of malaria [5,6], influenza [7][8][9], tuberculosis [10,11], and other infectious diseases [12,13]. Recently, timeseries models were used to forecast the dynamics of COVID-19 in the USA [14], Italy [14], India [15,16], and several other countries [17,18].…”

Section: Introductionmentioning

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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WITHDRAWN: Analysis of ‘earlyR’ epidemic model and time series model for prediction of COVID-19 registered cases

Cited by 7 publications

References 10 publications

Real-time estimation of the epidemic reproduction number: Scoping review of the applications and challenges

Real-time estimation of the epidemic reproduction number: Scoping review of the applications and challenges

An Empirical Perception of Economic Resilience Responded to the COVID-19 Epidemic Outbreak in Beijing–Tianjin–Hebei Urban Agglomeration, China: Characterization and Interaction

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

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