A Comparative Analysis between a SIRD Compartmental Model and the Richards Growth Model

Macêdo, A. M. S.; Brum, Arthur A.; Duarte-Filho, Gerson C.; Almeida, Francisco A. G.; Ospina, Raydonal; Vasconcelos, Giovani L.

doi:10.5540/tcam.2021.022.04.00545

Cited by 6 publications

(1 citation statement)

References 12 publications

(18 reference statements)

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“…The pathway approach has, however, the drawback that the model parameters are not easily interpreted in terms of standard epidemiological concepts, as in the case of compartmental models. In this context, it should be noted that some logistic-like growth models can be ‘mapped’ onto respective compartmental models [ 7 , 20 , 21 ], whereby the parameters of the former models can be put into correspondence (albeit in a coupled and nonlinear manner) with the parameters of the latter. A similar comparative study between the pathway approach and compartmental models is therefore an interesting topic for future research.…”

Section: Methodsmentioning

confidence: 99%

Multiple waves of COVID-19: a pathway model approach

et al. 2022

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A generalized pathway model, with time-dependent parameters, is applied to describe the mortality curves of the COVID-19 disease for several countries that exhibit multiple waves of infections. The pathway approach adopted here is formulated explicitly in time, in the sense that the model’s growth rate for the number of deaths or infections is written as an explicit function of time, rather than in terms of the cumulative quantity itself. This allows for a direct fit of the model to daily data (new deaths or new cases) without the need of any integration. The model is applied to COVID-19 mortality curves for ten selected countries and found to be in very good agreement with the data for all cases considered. From the fitted theoretical curves for a given location, relevant epidemiological information can be extracted, such as the starting and peak dates for each successive wave. It is argued that obtaining reliable estimates for such characteristic points is important for studying the effectiveness of interventions and the possible negative impact of their relaxation, as it allows for a direct comparison of the time of adoption/relaxation of control measures with the peaks and troughs of the epidemic curve.

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Section: Methodsmentioning

confidence: 99%

Multiple waves of COVID-19: a pathway model approach

et al. 2022

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Standard and Anomalous Waves of COVID-19: A Multiple-Wave Growth Model for Epidemics

et al. 2021

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We apply a generalized logistic growth model, with time-dependent parameters, to describe the fatality curves of the COVID-19 disease for several countries that exhibit multiple waves of infections. In the case of two waves only, the model parameters vary as a function of time according to a logistic function, whose two extreme values, i.e., for early and late times, characterize the first and second waves, respectively. For the multiple-wave model, the time-dependency of the parameters is likewise described by a multi-step logistic function with N intermediate plateaus, representing the N waves of the epidemic. We show that the theoretical curves are in excellent agreement with the empirical data for all countries considered here, namely: Brazil, Canada, Germany, Iran, Italy, Japan, Mexico, South Africa, Sweden, and the USA. The model also allows for predictions about the time of occurrence and severity of the subsequent waves. It is shown furthermore that the subsequent waves of COVID-19 can be generically classified into two main types, namely, standard and anomalous waves, according as to whether a given wave starts well after or well before the preceding one has subsided, respectively.

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Situation of COVID-19 in Brazil in August 2020: An Analysis via Growth Models as Implemented in the ModInterv System for Monitoring the Pandemic

Vasconcelos

Duarte-Filho

Brum

et al. 2022

J Control Autom Electr Syst

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In this work we introduce a novel methodology to classify the dynamical stages of an epidemic, based on the different acceleration regimes of the corresponding growth curve. Our classification scheme is implemented by fitting the empirical data with a general class of mathematical growth models, from which we compute not only the growth acceleration but also its jerk and jounce (i.e., the first and second derivatives of the acceleration, respectively), thus allowing for a finer distinction of the epidemic stages. Using this methodology, we analyze the cumulative curves of deaths attributed to COVID-19 in the 26 Brazilian States and the Federal District, up until August 21, 2020. The online application ModInterv COVID-19, which automatically implements the classification scheme and which can be accessed via an internet browser or a mobile app, was used to investigate the epidemic stages in each of the Brazilian federal units. The analysis revealed that almost all states in the Northern and Northeastern regions were already in the saturation phase, meaning that the epidemic was relatively under control, whereas in all Southern states and in most states in the Midwest the epidemic was still accelerating or showed only a slight deceleration. The Southeastern region presented a great diversity of epidemic stages, with each state being found at a different stage, ranging from acceleration to saturation. It is argued that understanding this heterogeneous geographical distribution of the epidemic is relevant for public health authorities, as it may help in devising more effective strategies against the COVID-19 pandemic in a continental country like Brazil.

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A Comparative Analysis between a SIRD Compartmental Model and the Richards Growth Model

Cited by 6 publications

References 12 publications

Multiple waves of COVID-19: a pathway model approach

Multiple waves of COVID-19: a pathway model approach

Standard and Anomalous Waves of COVID-19: A Multiple-Wave Growth Model for Epidemics

Situation of COVID-19 in Brazil in August 2020: An Analysis via Growth Models as Implemented in the ModInterv System for Monitoring the Pandemic

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