Forecast and evaluation of COVID-19 spreading in USA with reduced-space Gaussian process regression

Velásquez, Ricardo Manuel Arias; Lara, Jennifer Vanessa Mejía

doi:10.1016/j.chaos.2020.109924

Cited by 87 publications

(55 citation statements)

References 21 publications

(23 reference statements)

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“…This could be explained by the heterogeneity of the clusters (countries) included in the model, with different pandemic start date, different socioeconomic characteristics, and public health and political actions against the pandemic; therefore, this produces potentially an overlap of multiple normal distributions curves. This also could be true for large countries with independent states such as USA (implementing multiple political actions and public health strategies at different moments) [ 23 ]. However, for more homogenous clusters (such as New Zealand, Australia, and some Asian countries), the predicted curves were accurate.…”

Section: Discussionmentioning

confidence: 99%

“…Previous studies have reported behavior predictions for the COVID-19 pandemic; most of them focus on specific countries, such as China [ 24 ], Chile [ 25 ], Italy [ 24 ], France [ 24 ], and USA [ 23 , 26 ]. The estimation of end date varies from May 12 (for Chile) to June 15 (for Italy), these dates from more complex models (most of them from a SIR model) are consistent with our predictions for those countries ( Table 1 ), suggestion an acceptable accuracy to describe the epidemic dynamics with a simpler model.…”

Section: Discussionmentioning

confidence: 99%

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COVID-19 pandemic and Farr’s law: A global comparison and prediction of outbreak acceleration and deceleration rates

et al. 2020

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The COVID-19 outbreak has forced most of the global population to lock-down and has put in check the health services all over the world. Current predictive models are complex, region-dependent, and might not be generalized to other countries. However, a 150-year old epidemics law promulgated by William Farr might be useful as a simple arithmetical model (percent increase [R1] and acceleration [R2] of new cases and deaths) to provide a first sight of the epidemic behavior and to detect regions with high predicted dynamics. Thus, this study tested Farr’s Law assumptions by modeling COVID-19 data of new cases and deaths. COVID-19 data until April 10, 2020, was extracted from available countries, including income, urban index, and population characteristics. Farr’s law first (R 1 ) and second ratio (R 2 ) were calculated. We constructed epidemic curves and predictive models for the available countries and performed ecological correlation analysis between R 1 and R 2 with demographic data. We extracted data from 210 countries, and it was possible to estimate the ratios of 170 of them. Around 42·94% of the countries were in an initial acceleration phase, while 23·5% already crossed the peak. We predicted a reduction close to zero with wide confidence intervals for 56 countries until June 10 (high-income countries from Asia and Oceania, with strict political actions). There was a significant association between high R 1 of deaths and high urban index. Farr’s law seems to be a useful model to give an overview of COVID-19 pandemic dynamics. The countries with high dynamics are from Africa and Latin America. Thus, this is a call to urgently prioritize actions in those countries to intensify surveillance, to re-allocate resources, and to build healthcare capacities based on multi-nation collaboration to limit onward transmission and to reduce the future impact on these regions in an eventual second wave.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

COVID-19 pandemic and Farr’s law: A global comparison and prediction of outbreak acceleration and deceleration rates

et al. 2020

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“…In statistic, regression methods is a set of statistical modeling to forecast the link between a dependent variable and independent variable (s) (Cook andWeisberg 1982, Freedman 2009). As a power tool to forecast the pandemic, various regression methods have been addressed by researchers against COVID-19 (Almeshal, Almazrouee et al 2020, Ji, Zhang et al 2020, Ribeiro, da Silva et al 2020, Sujath, Chatterjee et al 2020, Velásquez, Lara et al 2020.…”

Section: Regression Methodsmentioning

confidence: 99%

A Review on COVID-19 Forecasting Models

Rahimi

Chen

Gandomi

2020

Preprint

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The Novel coronavirus (COVID-19) has distributed to more than 200 territory worldwide leading to about 24 million confirmed cases as of August 25, 2020. Several models have been released that forecast the outbreak globally. This work presents a review of the most important forecasting models against COVID-19 and shows a short analysis of each one. The work presented in this study possesses two parts. A detailed scientometric analysis was done in the first section that provides an influential tool for describing bibliometric analyses. The analysis was performed on data corresponding to COVID-19 using the Scopus and Web of Science databases. For analysis, keywords and subject areas were addressed while classification of forecasting models, criteria evaluation and comparison of solution approaches were done in the second section of the work. Conclusion and discussion are provided as the final sections of this study.

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“…The incorporation between reduced-space Gaussian process regression and chaotic Bayesian approach to predict the cases of COVID-19 and deaths in USA was presented by ( Arias Velásquez and Mejía Lara, 2020 ). The prevalence of the outbreak was expected to dramatically increase and thus new restricted quarantine actions should be implemented.…”

Section: Introductionmentioning

confidence: 99%