Application of a Semi-Empirical Dynamic Model to Forecast the Propagation of the COVID-19 Epidemics in Spain

Mora, Juan C.; Pérez, Sandra; Dvorzhak, Alla

doi:10.3390/forecast2040024

Cited by 4 publications

(3 citation statements)

References 25 publications

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“…Making a comparison with the information provided by other studies, we can see how the already refined statistics from the National Epidemiology Center (CNE) of Spain indicate that the nation-wide first-wave infection peak occurred on 20 March, 6 days after the national lockdown was implemented, and the cases of COVID-19 steadily decreased after then (see [39] for details). Other studies place the peak on 26 March (Guirao, [40]), while in the work of Mora et al [41] an interval is established that goes from 29 March to 3 April. Since the first week of July, Spain experienced an increasing trend in the cumulative incidence.…”

Section: Discussionmentioning

confidence: 90%

Two Multi-Sigmoidal Diffusion Models for the Study of the Evolution of the COVID-19 Pandemic

et al. 2021

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A proposal is made to employ stochastic models, based on diffusion processes, to represent the evolution of the SARS-CoV-2 virus pandemic. Specifically, two diffusion processes are proposed whose mean functions obey multi-sigmoidal Gompertz and Weibull-type patterns. Both are constructed by introducing polynomial functions in the ordinary differential equations that originate the classical Gompertz and Weibull curves. The estimation of the parameters is approached by maximum likelihood. Various associated problems are analyzed, such as the determination of initial solutions for the necessary numerical methods in practical cases, as well as Bayesian methods to determine the degree of the polynomial. Additionally, strategies are suggested to determine the best model to fit specific data. A practical case is developed from data originating from several Spanish regions during the first two waves of the COVID-19 pandemic. The determination of the inflection time instants, which correspond to the peaks of infection and deaths, is given special attention. To deal with this particular issue, point estimation as well as first-passage times have been considered.

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Section: Discussionmentioning

confidence: 90%

Two Multi-Sigmoidal Diffusion Models for the Study of the Evolution of the COVID-19 Pandemic

et al. 2021

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“…It is initially developed to predict population growth (Kendall and Fox, 1998;Lloyd, 1995) and has been used widely in the ecological model (Storch et al, 2017) , cryptography (Moysis et al, 2020) , data encryption (Huang andDing, 2020;Moysis et al, 2020;Raghuvanshi et al, 2020). The application of a logistic map in the study of COVID-19 can be found in past studies (Mora et al, 2020;Koltsova et al, 2020;Pelinovsky et al, 2020). Simple mathematical structure and broad chaotic systems in Logistic map make the model widely used in many elds.…”

Section: Introductionmentioning

confidence: 99%

Chaos Theory of 0-1 Test and Logistic Map in New Confirmed COVID-19 Cases

Yusof

Sapini

Az’hari³

et al. 2022

Sci.Technol.Indones

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This study aims to forecast the new number of positive cases of COVID-19 using the logistic map, where the presence of chaos is determined using the 0-1 Test. There are a small number of investigations on chaos utilizing the 0-1 Test and logistic map in the literature. This study provides a straightforward technique of forecasting and a current way of determining chaos. Information on new confirmed COVID-19 cases and population of ten countries involving China, Vietnam, Korea, Malaysia, Singapore, New Zealand, India, USA, Brazil, and Mexico for six months from January 20 to June 15, 2020, are selected as samples. The logistic map runs through three stages: data training, forecasting, and validation using the Mean Absolute Error method (MAE). The data training procedure is critical for determining the best growth rate, r, for the logistic map. In chaotic investigations of the 0-1 Test, there appears to be an inverse expectation toward a logistic map. The 0-1 Test in the data of new confirmed COVID-19 cases in all the selected countries reveals the presence of non-chaotic. This contrasts with the existence of chaos in the logistic map forecasts for the USA, Brazil, and Mexico. Regardless, the logistic map was found to be capable of forecasting new COVID-19 positive cases with low error instances. Beginning in the middle of May 2020, new COVID-19 positive cases are forecasted to be on the rise in the USA, Brazil, and Mexico.

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“…A new Bayesian indicator is introduced in [2] to forecast the beginning of a new wave. In [3], semi-empirical models based on the logistic map are considered in order to predict the variables in different phases of the pandemic in Spain. Likewise, Ref.…”

Section: Introductionmentioning

confidence: 99%

COVID-19 Data Imputation by Multiple Function-on-Function Principal Component Regression

et al. 2021

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The aim of this paper is the imputation of missing data of COVID-19 hospitalized and intensive care curves in several Spanish regions. Taking into account that the curves of cases, deceases and recovered people are completely observed, a function-on-function regression model is proposed to estimate the missing values of the functional responses associated with hospitalized and intensive care curves. The estimation of the functional coefficient model in terms of principal components’ regression with the completely observed data provides a prediction equation for the imputation of the unobserved data for the response. An application with data from the first wave of COVID-19 in Spain is developed after properly homogenizing, registering and smoothing the data in a common interval so that the observed curves become comparable. Finally, Canonical Correlation Analysis is performed on the functional principal components to interpret the relationship between hospital occupancy rate and illness response variables.

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Application of a Semi-Empirical Dynamic Model to Forecast the Propagation of the COVID-19 Epidemics in Spain

Cited by 4 publications

References 25 publications

Two Multi-Sigmoidal Diffusion Models for the Study of the Evolution of the COVID-19 Pandemic

Two Multi-Sigmoidal Diffusion Models for the Study of the Evolution of the COVID-19 Pandemic

Chaos Theory of 0-1 Test and Logistic Map in New Confirmed COVID-19 Cases

COVID-19 Data Imputation by Multiple Function-on-Function Principal Component Regression

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