A new view of multiscale stochastic impulsive systems for modeling and control of epidemics

Hernandez‐Vargas, Esteban A.; Alanis, Alma Y.; Tetteh, Josephine N.A.

doi:10.1016/j.arcontrol.2019.06.002

Cited by 24 publications

(18 citation statements)

References 56 publications

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“…The study of the kinetic compartmental system allows to compare the results with that obtained by studying the spread of the epidemic by means of social networks, where contacts between individuals in a given population can be captured by assuming that nodes represent individuals and the edges represent the connections between them (Hernandez-Vargas et al. 2019 ). In connection with this subject, by following a similar approach it was possible to understand how diseases spread in highly heterogeneous social networks (Barthélemy et al.…”

Section: Introductionmentioning

confidence: 99%

Kinetic models for epidemic dynamics with social heterogeneity

et al. 2021

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We introduce a mathematical description of the impact of the number of daily contacts in the spread of infectious diseases by integrating an epidemiological dynamics with a kinetic modeling of population-based contacts. The kinetic description leads to study the evolution over time of Boltzmann-type equations describing the number densities of social contacts of susceptible, infected and recovered individuals, whose proportions are driven by a classical SIR-type compartmental model in epidemiology. Explicit calculations show that the spread of the disease is closely related to moments of the contact distribution. Furthermore, the kinetic model allows to clarify how a selective control can be assumed to achieve a minimal lockdown strategy by only reducing individuals undergoing a very large number of daily contacts. We conduct numerical simulations which confirm the ability of the model to describe different phenomena characteristic of the rapid spread of an epidemic. Motivated by the COVID-19 pandemic, a last part is dedicated to fit numerical solutions of the proposed model with infection data coming from different European countries.

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Section: Introductionmentioning

confidence: 99%

Kinetic models for epidemic dynamics with social heterogeneity

et al. 2021

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“…As a consequence, in recent months there has been a growing research interest on COVID-19 mitigation in different scientific fields. One of the most investigated research area aims at developing dynamical models to predict the evolution of the pandemic Hernandez-Vargas, Alanis, and Tetteh (2019) . In effect, predicting the trend of the epidemic is of paramount importance to plan effective control strategies Giordano et al.…”

Section: Introduction and Paper Positioningmentioning

confidence: 99%

Model predictive control to mitigate the COVID-19 outbreak in a multi-region scenario

Carli

Cavone

Epicoco

et al. 2020

Annual Reviews in Control

100

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The COVID-19 outbreak is deeply influencing the global social and economic framework, due to restrictive measures adopted worldwide by governments to counteract the pandemic contagion. In multi-region areas such as Italy, where the contagion peak has been reached, it is crucial to find targeted and coordinated optimal exit and restarting strategies on a regional basis to effectively cope with possible onset of further epidemic waves, while efficiently returning the economic activities to their standard level of intensity. Differently from the related literature, where modeling and controlling the pandemic contagion is typically addressed on a national basis, this paper proposes an optimal control approach that supports governments in defining the most effective strategies to be adopted during post-lockdown mitigation phases in a multi-region scenario. Based on the joint use of a non-linear Model Predictive Control scheme and a modified Susceptible-Infected-Recovered (SIR)-based epidemiological model, the approach is aimed at minimizing the cost of the so-called non-pharmaceutical interventions (that is, mitigation strategies), while ensuring that the capacity of the network of regional healthcare systems is not violated. In addition, the proposed approach supports policy makers in taking targeted intervention decisions on different regions by an integrated and structured model, thus both respecting the specific regional health systems characteristics and improving the system-wide performance by avoiding uncoordinated actions of the regions. The methodology is tested on the COVID-19 outbreak data related to the network of Italian regions, showing its effectiveness in properly supporting the definition of effective regional strategies for managing the COVID-19 diffusion.

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“…Most of the countries have taken emergency actions, these actions include confinement of their population, travel restrictions, forced use of mask in public spaces, and even a nighttime curfew. In this situations, epidemiological models have been key to mitigating COVID-19 pandemic and many others [15].…”

Section: Introductionmentioning

confidence: 99%

Untangling the cell immune response dynamic for severe and critical cases of SARS-CoV-2 infection

Blanco-Rodríguez¹,

Hernández

2021

Preprint

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COVID-19 is a global pandemic leading high death tolls worldwide day by day. Clinical evidence suggests that COVID-19 patients can be classified as non-severe, severe and critical cases. In particular, studies have highlighted the relationship between the lymphopenia and the severity of the illness, where CD8+ T cells have the lowest levels in critical cases. In this work, we aim to elucidate the key parameters that define the course of the disease deviating from severe to critical case. To this end, several mathematical models are proposed to represent the dynamic of the immune response in patients with SARS-CoV-2 infection. The best model had a good fit to reported experimental data, and in accordance with values found in the literature. Our results suggest that a rapid proliferation of CD8+ T cells is decisive in the severity of the disease.

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A new view of multiscale stochastic impulsive systems for modeling and control of epidemics

Cited by 24 publications

References 56 publications

Kinetic models for epidemic dynamics with social heterogeneity

Kinetic models for epidemic dynamics with social heterogeneity

Model predictive control to mitigate the COVID-19 outbreak in a multi-region scenario

Untangling the cell immune response dynamic for severe and critical cases of SARS-CoV-2 infection

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