ModInterv COVID-19: An online platform to monitor the evolution of epidemic curves

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

doi:10.1016/j.asoc.2023.110159

Cited by 3 publications

References 25 publications

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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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Multiple waves of COVID-19: a pathway model approach

et al. 2022

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State of health prediction of lithium-ion batteries using particle swarm optimization with Levy flight and generalized opposition-based learning

Zhang,

Liu,

Cai

et al. 2024

Journal of Energy Storage

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Tracking of COVID-19 Pandemic for Multi-Waves Using a Compartmental Model With Time-Dependent Parameters

Jdid,

Chana,

Bouazi

et al. 2023

Advances in Systems Analysis, Software Engineering, and High Performance Computing

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Estimating and controlling the COVID-19 pandemic is essential to reduce the spread of the disease and help decision-making efforts in combating public health crises. However, the potential presence of multiple dynamic changes in the reported count data or the occurrence of another wave of the pandemic emerges as a challenge for simulating the evolution of the disease over a long period. In this chapter, to account for the dynamic changes in the COVID-19 curves, the authors propose a rate function based on multiple branches of a logistic function. They assumed in a compartmental model that the recovery and disease transmission rates are time-dependent, and they assign to each the rate function. Then, they apply the model to daily COVID-19 data on infection counts in Morocco between March 2, 2020 and December 31, 2021 using curve fitting through the Nelder-Mead optimization method. The simulation outcomes demonstrate the model's ability to replicate the COVID-19 pandemic in Morocco over two waves, with the goodness of fit depending on the number of logistic branches composing the rate function.

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ModInterv COVID-19: An online platform to monitor the evolution of epidemic curves

Cited by 3 publications

References 25 publications

Multiple waves of COVID-19: a pathway model approach

Multiple waves of COVID-19: a pathway model approach

State of health prediction of lithium-ion batteries using particle swarm optimization with Levy flight and generalized opposition-based learning

Tracking of COVID-19 Pandemic for Multi-Waves Using a Compartmental Model With Time-Dependent Parameters

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