A generalised SEIRD model with implicit social distancing mechanism: A Bayesian approach for the identification of the spread of COVID-19 with applications in Brazil and Rio de Janeiro state

Volpatto, Diego; Resende, Anna Claudia M.; Anjos, Lucas dos; Silva, J. V. O.; Dias, Claudia Mazza; Almeida, Regina C.; Malta, Sandra M. C.

doi:10.1080/17477778.2021.1977731

Cited by 8 publications

(8 citation statements)

References 46 publications

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“…Following the models proposed by Jia et al [23] and Volpatto et al [57], we develop an extension of the susceptible-exposed-infected-removed (SEIR) model, termed SEIRPD-Q model, which further considers Positively Diagnosed (P) and Dead (D) groups of individuals. To account for social distancing measures, we implicitly consider a mechanism that isolates individuals from the virus transmission (−Q) rather than defining a specific compartment for individuals in quarantine.…”

Section: Compartmental Model Descriptionmentioning

confidence: 99%

“…The SEIRPD-Q model presents some fundamental differences in relation to those on which it was based: first, Jia et al [23] consider that only susceptible individuals are subject to quarantine measures, which is modeled using an explicit compartment and also considering an additional parameter that controls the social distancing relaxation; second, we disregard the asymptomatic compartment, according to Volpatto et al [57], due to the lack of this information associated with limited testing of the population; third, we consider only the mortality rate of positively diagnosed individuals, unlike Volpatto et al [57].…”

Section: Compartmental Model Descriptionmentioning

confidence: 99%

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Framework for enhancing the estimation of model parameters for data with a high level of uncertainty

et al. 2022

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Reliable data are essential to obtain adequate simulations for forecasting the dynamics of epidemics. In this context, several political, economic, and social factors may cause inconsistencies in the reported data, which reflect the capacity for realistic simulations and predictions. In the case of COVID-19, for example, such uncertainties are mainly motivated by large-scale underreporting of cases due to reduced testing capacity in some locations. In order to mitigate the effects of noise in the data used to estimate parameters of models, we propose strategies capable of improving the ability to predict the spread of the diseases. Using a compartmental model in a COVID-19 study case, we show that the regularization of data by means of Gaussian process regression can reduce the variability of successive forecasts, improving predictive ability. We also present the advantages of adopting parameters of compartmental models that vary over time, in detriment to the usual approach with constant values.

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Section: Compartmental Model Descriptionmentioning

confidence: 99%

Section: Compartmental Model Descriptionmentioning

confidence: 99%

Framework for enhancing the estimation of model parameters for data with a high level of uncertainty

et al. 2022

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“…The additional state E corresponds to a natural time delay of the system, which is usual in control-oriented models 8 , 11 and forecasts to a lesser extent 20 , 40 , 41 . Presymptomatic infected individuals are within this state as , but their infection rate is assumed insignificant to simplify parameter estimation.…”

Section: Mathematical Modelmentioning

confidence: 99%

Recursive state and parameter estimation of COVID-19 circulating variants dynamics

Silva

Secchi

2022

Sci Rep

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COVID-19 pandemic response with non-pharmaceutical interventions is an intrinsic control problem. Governments weigh social distancing policies to avoid overload in the health system without significant economic impact. The mutability of the SARS-CoV-2 virus, vaccination coverage, and mobility restriction measures change epidemic dynamics over time. A model-based control strategy requires reliable predictions to be efficient on a long-term basis. In this paper, a SEIR-based model is proposed considering dynamic feedback estimation. State and parameter estimations are performed on state estimators using augmented states. Three methods were implemented: constrained extended Kalman filter (CEKF), CEKF and smoother (CEKF & S), and moving horizon estimator (MHE). The parameters estimation was based on vaccine efficacy studies regarding transmissibility, severity of the disease, and lethality. Social distancing was assumed as a measured disturbance calculated using Google mobility data. Data from six federative units from Brazil were used to evaluate the proposed strategy. State and parameter estimations were performed from 1 October 2020 to 1 July 2021, during which Zeta and Gamma variants emerged. Simulation results showed that lethality increased between 11 and 30% for Zeta mutations and between 44 and 107% for Gamma mutations. In addition, transmissibility increased between 10 and 37% for the Zeta variant and between 43 and 119% for the Gamma variant. Furthermore, parameter estimation indicated temporal underreporting changes in hospitalized and deceased individuals. Overall, the estimation strategy showed to be suitable for dynamic feedback as simulation results presented an efficient detection and dynamic characterization of circulating variants.

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“…The additional state L corresponds to a natural dead time of the system which is usual in control oriented models 7,10 and in forecast to a lesser extent [22][23][24] . Presymptomatics infected are within it as (1 − p)L, but their infection rate is assumed insignificant to simplify parameter estimation.…”

Section: Mathematical Modelmentioning

confidence: 99%

Recursive State and Parameter Estimation of COVID-19 Circulating Variants Dynamics

Silva

Secchi

2021

Preprint

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COVID-19 pandemic response with non-pharmaceutical interventions is an intrinsic control problem. Governments balance social distancing policies to avoid overload on health system without major economic impact. A control strategy requires reliable predictions to be efficient on long-term. SARS-CoV-2 mutability, vaccination coverage and time-varying restrictive measures change virus evolution dynamics frequently. State and parameter estimations are an option do deal with these uncertainties. In this paper, a SIR-based model is proposed considering data available and feedback corrections over time. State and parameter estimations were done on state estimators with augmented states. Three observers were implemented: Constrained Extended Kalman Filter (CEKF), CEKF and Smoother (CEKF&S) and Moving Horizon Estimator (MHE). The parameters estimated therein are based on vaccine efficacy studies regarding transmissibility, severeness of disease and lethality. Social distancing is a measured disturbance calculated with Google mobility data. Six federative units from Brazil are used to evaluate proposed strategy: Amazonas, Mato Grosso do Sul, Rio Grande do Norte, Rio Grande do Sul, Rio de Janeiro and São Paulo. State and parameter estimations were realized from October 1 st 2020 to July 1 st 2021 during which Zeta and Gamma variants emerged. Results showed an efficient detection of circulating variants from proposed parameter estimation. In addition, it asserted dynamics related to virus mutations. Zeta mutations increase lethality between 19 and 45%, and increased transmissibility between 20 and 38%. Gamma mutations, on the other hand, increased lethality between 62 and 110% while increasing transmissibility between 52 and 107%. Furthermore, parameter estimation indicated existence and temporal change of subnotification on hospitalized and deceased individuals. Overall, dynamics estimated were within expectations and are applicable to control theory.

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A generalised SEIRD model with implicit social distancing mechanism: A Bayesian approach for the identification of the spread of COVID-19 with applications in Brazil and Rio de Janeiro state

Cited by 8 publications

References 46 publications

Framework for enhancing the estimation of model parameters for data with a high level of uncertainty

Framework for enhancing the estimation of model parameters for data with a high level of uncertainty

Recursive state and parameter estimation of COVID-19 circulating variants dynamics

Recursive State and Parameter Estimation of COVID-19 Circulating Variants Dynamics

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