The Exponentially Increasing Rate of Patients Infected with COVID-19 in Iran

Moftakhar, Leila; Seif, Mozhgan

doi:10.34172/aim.2020.03

Cited by 40 publications

(36 citation statements)

References 15 publications

(24 reference statements)

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“…General characteristics of reviewed studies: We found 13 articles and 10 reports and included eight published articles / preprint manuscripts [16][17][18][19][20][21][22][23] and three reports [13,24,25]. From the 13 found articles, three articles were kept for a next version of this review [8][9][10], one article was excluded because it was not an epidemic estimation study, and one article was excluded because of absent minimum necessary details on methods. From the 10 found reports, three were included, four were excluded because they were not an independent epidemic modeling study, three excluded because of absent minimum necessary details on methods, and one was excluded because it was not an epidemic estimation study.…”

Section: Resultsmentioning

confidence: 99%

Rapid review of COVID-19 epidemic estimation studies for Iran

Pourmalek

Hemami

Janani

et al. 2020

Preprint

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Background: To inform the researchers about the methodology and results of epidemic estimation studies performed for COVID-19 epidemic in Iran, we aimed to perform a rapid review.Methods: We searched for and included published articles, preprint manuscripts and reports that estimated numbers of cumulative or daily deaths or cases of COVID-19 in Iran. We found 23 articles or reports and included eight articles (four published, four preprints) and three reports.Results: The included studies provided outputs for a total of 45 study-scenarios. Seven studies used 3-4 compartmental disease models. At the end of month two of the epidemic (2020-04-19), the lowest (and highest) values of predictions were 3762 (97445) for cumulative deaths, 60720 (1489201) for cumulative cases, and at the end of month four (2020-06-20), were 86931 (3002721) for cumulative deaths, and 1602592 (2917927) for cumulative cases.Conclusions: Meticulousness and degree of details reported for disease modeling and statistical methods used in the included studies varied widely. Greater heterogeneity was observed regarding the results of predicted outcomes. Consideration of minimum and preferred reporting items in epidemic estimation studies might better inform future revisions of the available models and new models to be developed.

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Section: Resultsmentioning

confidence: 99%

Rapid review of COVID-19 epidemic estimation studies for Iran

Pourmalek

Hemami

Janani

et al. 2020

Preprint

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“…There have been many articles using the ARIMA model to predict the trend of COVID-19 in different countries [9,16]. In Iran, Moftakhar et al [17] used the ARIMA model and artificial neural network to predict the daily new COVID-19 infections, which showed that the ARIMA model was more accurate. Ceylan [16] estimated the prevalence of COVID-19 in Italy, Spain, and France, with M A P E ( I t a l y ) = 4 .…”

Section: (4) Predictionmentioning

confidence: 99%

Analysis and Estimation of COVID-19 Spreading in Russia Based on ARIMA Model

Fang

Wang

Pan

2020

SN Compr. Clin. Med.

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Russia has been currently in the "hard-hit" area of the COVID-19 outbreak, with more than 396,000 confirmed cases as of May 30. It is necessary to analyze and predict its epidemic situation to help formulate effective public health policies. Autoregressive integrated moving average (ARIMA) models were developed to predict the cumulative confirmed, dead, and recovered cases, respectively. R 3.6.2 software was used to fit the data from January 31 to May 20, 2020, and predict the data for the next 30 days. The COVID-19 epidemic in Russia was divided into two stages and reached its peak in May. The epidemic began to stabilize on May 19. The case fatality rate has been at an extremely low level. ARIMA (2,2,1), ARIMA (3,2,0), and ARIMA (0,2,1) were the models of cumulative confirmed, dead, and recovered cases, respectively. After testing, the mean absolute percentage error (MAPE) of three models were 0.6, 3.9, and 2.4, respectively. This paper indicates that Russia's health system capacity can effectively respond to the COVID-19 pandemic. Three ARIMA models have a good fitting effect and can be used for short-term prediction of the COVID-19 trend, providing a theoretical basis for Russia to formulate new intervention policies.

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“…Most recently, a large number of models have been used to nowcast and forecast the epidemic patterns of COVID-19 pandemic around the world, such as ARIMA model, 3,[30][31][32] machine-learning model, 8 spatiotemporal approach, 5 Bats-Hosts-Reservoir-People transmission network model, 33 data mining approach based on a 3rd degree polynomial curve, 7 SEIR or SIR model, 18,34 internet search-interest based model, 9 ad hoc, 35 fixed-effects linear model, 36 adaptive neuro-fuzzy inference system (ANFIS) model, 37 etc. However, most of these models are focused on a shortterm estimation for the COVID-19 incidence, mortality, or prevalence, and they can only unearth the linear or nonlinear components in a given series.…”

Section: Introductionmentioning

confidence: 99%

<p>Estimating the Prevalence and Mortality of Coronavirus Disease 2019 (COVID-19) in the USA, the UK, Russia, and India</p>

Wang¹,

Xu²,

Yao³

et al. 2020

IDR

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The aim of this study is to apply the advanced error-trend-seasonal (ETS) framework to forecast the prevalence and mortality series of COVID-19 in the USA, the UK, Russia, and India, and the predictive performance of the ETS framework was compared with the most frequently used autoregressive integrated moving average (ARIMA) model. Materials and Methods: The prevalence and mortality data of COVID-19 in the USA, the UK, Russia, and India between 20 February 2020 and 15 May 2020 were extracted from the WHO website. Then, the data subsamples between 20 February 2020 and 3 May 2020 were treated as the training horizon, and the others were used as the testing horizon to construct the ARIMA models and the ETS models. Results: Based on the model evaluation criteria, the ARIMA (0,2,1) and ETS (M,MD,N), sparse coefficient ARIMA (0,2,(1,6)) and ETS (A,AD,M), ARIMA (1,1,1) and ETS (A,MD, A), together with ARIMA (2,2,1) and ETS (A,M,A) specifications were identified as the preferred ARIMA and ETS models for the prevalence data in the USA, the UK, Russia, and India, respectively; the ARIMA (0,2,1) and ETS (M,A,M), ARIMA (0,2,1) and ETS (M,A, N), ARIMA (0,2,1) and ETS (A,A,N), coupled with ARIMA (0,2,2) and ETS (M,M,N) specifications were selected as the optimal ARIMA and ETS models for the mortality data in these four countries, respectively. Among these best-fitting models, the ETS models produced smaller forecasting error rates than the ARIMA models in all the datasets. Conclusion: The ETS framework can be used to nowcast and forecast the long-term temporal trends of the COVID-19 prevalence and mortality in the USA, the UK, Russia, and India, and which provides a notable performance improvement over the most frequently used ARIMA model. Our findings can aid governments as a reference to prepare for and respond to the COVID-19 pandemic both in restricting the transmission of the disease and in lowering the disease-related deaths in the upcoming days.

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The Exponentially Increasing Rate of Patients Infected with COVID-19 in Iran

Cited by 40 publications

References 15 publications

Rapid review of COVID-19 epidemic estimation studies for Iran

Rapid review of COVID-19 epidemic estimation studies for Iran

Analysis and Estimation of COVID-19 Spreading in Russia Based on ARIMA Model

<p>Estimating the Prevalence and Mortality of Coronavirus Disease 2019 (COVID-19) in the USA, the UK, Russia, and India</p>

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