Trends and Prediction in Daily New Cases and Deaths of COVID-19 in the United States: An Internet Search-Interest Based Model

Yuan, Xiaoling; Xu, Jie; Hussain, Sabiha; Wang, He; Gao, Nan; Zhang, Lanjing

doi:10.14218/erhm.2020.00023

Cited by 77 publications

(94 citation statements)

References 10 publications

(4 reference statements)

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“…Several approaches in monitoring and analysis several epidemiological characteristics of the virus have been recorded up to this point using Google Trends data. [32][33][34][35] Google Trends is an open online tool that provides information on the behavior towards selected topics and keywords. Such infodemiology variables can accurately measure the users' online search patterns, and, in this case, assist with exploring the public's perception and interest towards COVID-19 over the examined period.…”

Section: Google Trendsmentioning

confidence: 99%

COVID-19 and Non-Pharmaceutical Interventions: The Greek Paradigm

Mavragani

Gillas

2020

Preprint

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Due to the COVID-19 pandemic originating in China in December 2019, apart from the grave concerns on the exponentially increasing casualties, the affected countries are called to deal with severe repercussions in all aspects of everyday life; from economic recession to national and international movement restrictions. Several regions managed to handle the pandemic more successfully than others in terms of life loss, while ongoing heated debates as to the right course of action for battling COVID-19 have divided the academic community as well as public opinion. To this direction, in this paper, an autoregressive COVID-19 prediction model with heterogeneous explanatory variables is proposed for Greece, taking past COVID-19 data, non-pharmaceutical interventions (NPIs), and Google query data as independent variables, from the day of the first confirmed case -February 26th- to the day before the announcement for the quarantine measures’ softening -April 24th. The analysis indicates that the early measures taken by the Greek officials positively affected the flattening of the curve, with Greece having recorded significantly decreased COVID-19 casualties compared to other countries. In specific, the prediction model identifies not only the seven-day lag that is needed in order for the measures’ results to actually show, that is, in sense, the optimal time-intervention framework for managing the disease’s spread, but the appropriate point during the disease spread where restrictive measures should be applied. Present results have significant implications for effective policy making and in the designing of the NPIs, as the second wave of COVID-19 is expected in fall 2020, and such multidisciplinary analyses are crucial in order to understand the evolution of the Daily Deaths to Daily Cases ratio along with its determinants as soon as possible, for the assessment of the respective domestic health authorities’ policy interventions as well as for the timely health resources allocation.

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Section: Google Trendsmentioning

confidence: 99%

COVID-19 and Non-Pharmaceutical Interventions: The Greek Paradigm

Mavragani

Gillas

2020

Preprint

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“…3,[5][6][7] Also, stakeholders can apply the time series from the past and present outbreaks to forecast prevalence rates and then identifying how to limit the spread of the virus, and ultimately introducing the most effective vaccination policies. 5 Numerous modeling techniques (such as machine learning method, 8 general linear model, 9 spatiotemporal approach, 5 artificial neural networks (ANNs), 10 grey GM (1,1) model, 11 autoregressive integrated moving average (ARIMA), 12 support vector machine (SVM) regression model, 13 multivariate time series analysis, 14 and susceptible-exposed-infectious-recovered (SEIR) model) 15 that serve as helpful policy-supportive tools have been utilized to model and estimate the epidemic patterns and even the outbreak of infectious diseases. Among which, the most commonly adopted methods for various predictive objectives are either the linear models (such as general linear model, GM (1,1), and ARIMA) or the nonlinear models (such as SVM and ANNs).…”

Section: Introductionmentioning

confidence: 99%

“…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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“…There are many more research papers are available not only for Indian but also describes the covid 19 pandemic situations of China, Italy, France and United States, which can be helpful for planning and decision making [18,19,20].…”

Section: Introductionmentioning

confidence: 99%

Analysis and forecast of COVID-19 spreading in India using Nonlinear curve fitting model with machine learning

Pandey¹,

Katoch²,

Kumar³

2020

Preprint

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In this paper, an analysis and forecasting of Indian COVID-19 data is discussed by using scipy optimize curve fitting model of machine learning. We demonstrates the month wise analysis of coming cases, daily recovered cases, death cases and test cases conducted by the Government of India, of COVID-19 from 01st March 2020 to 02nd August 2020, and also forecast for the new cases, recover cases & death cases from 03rd August 2020 to 01st November 2020. Our study show that the total numbers of affected persons due to COVID-19 up to 01st November 2020 will be total cases 13,690,491, recover cases 10,499,593 and death of 129,271.

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Trends and Prediction in Daily New Cases and Deaths of COVID-19 in the United States: An Internet Search-Interest Based Model

Cited by 77 publications

References 10 publications

COVID-19 and Non-Pharmaceutical Interventions: The Greek Paradigm

COVID-19 and Non-Pharmaceutical Interventions: The Greek Paradigm

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

Analysis and forecast of COVID-19 spreading in India using Nonlinear curve fitting model with machine learning

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