Managing Public Transit during a Pandemic: The Trade-Off between Safety and Mobility

Luo, Qi; Gee, Marissa; Piccoli, Benedetto; Work, Daniel B.; Samaranayake, Samitha

doi:10.2139/ssrn.3757210

Cited by 8 publications

(7 citation statements)

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“…We get, from the r.h.s. in (87), 𝑓 𝑆 = 𝑆 𝑐 , 𝑓 𝐼 = 𝐼 𝑐 , 𝑓 𝑅 = 𝑅 𝑐 , and, consequently, from the last three equations in ( 90)-( 91) we recover Fick's laws, which inserted into the first three equations in ( 90)-( 91) lead to the diffusion system for the population of commuters [88,109,118]…”

Section: Macroscopic Formulation and Diffusion Limitmentioning

confidence: 77%

“…The hyperbolic transport models here proposed, similarly to other fields of application, like traffic flow models, chemotaxis and cardiovascular modeling, can be embedded into a network of cities following [26,99]. Note that, the approach differs from the classical network modeling in epidemiology based on coupled systems of ODEs [13,39,57,87].…”

Section: Network Modellingmentioning

confidence: 99%

See 1 more Smart Citation

Kinetic modelling of epidemic dynamics: social contacts, control with uncertain data, and multiscale spatial dynamics

Albi¹,

Bertaglia²,

Boscheri³

et al. 2021

Preprint

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In this survey we report some recent results in the mathematical modeling of epidemic phenomena through the use of kinetic equations. We initially consider models of interaction between agents in which social characteristics play a key role in the spread of an epidemic, such as the age of individuals, the number of social contacts, and their economic wealth. Subsequently, for such models, we discuss the possibility of containing the epidemic through an appropriate optimal control formulation based on the policy maker's perception of the progress of the epidemic. The role of uncertainty in the data is also discussed and addressed. Finally, the kinetic modeling is extended to spatially dependent settings using multiscale transport models that can characterize the impact of movement dynamics on epidemic advancement on both one-dimensional networks and realistic two-dimensional geographic settings.

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Section: Macroscopic Formulation and Diffusion Limitmentioning

confidence: 77%

Section: Network Modellingmentioning

confidence: 99%

Kinetic modelling of epidemic dynamics: social contacts, control with uncertain data, and multiscale spatial dynamics

Albi¹,

Bertaglia²,

Boscheri³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…In [10] a model is developed to estimate future case counts in the United States by varying projected vaccine efficacy and NPI policies such as social distancing, mask wearing, and testing. In [44], a spacial epidemic model is coupled with an optimization technique to resolve safety-and-mobility trade-offs in epidemic response plans. In June 2020, [7] built an agent-based model to predict the effects of social distancing and isolation on transmission of a virus.…”

Section: Related Workmentioning

confidence: 99%

Optimization of vaccination for COVID-19 in the midst of a pandemic

Luo¹,

Weightman²,

McQuade³

et al. 2022

Preprint

Self Cite

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During the Covid-19 pandemic a key role is played by vaccination to combat the virus. There are many possible policies for prioritizing vaccines, and different criteria for optimization: minimize death, time to herd immunity, functioning of the health system. Using an age-structured population compartmental finite-dimensional optimal control model, our results suggest that the eldest to youngest vaccination policy is optimal to minimize deaths. Our model includes the possible infection of vaccinated populations. We apply our model to real-life data from the US Census for New Jersey and Florida, which have a significantly different population structure. We also provide various estimates of the number of lives saved by optimizing the vaccine schedule and compared to no vaccination.

show abstract

“…In [10] a model is developed to estimate future case counts in the United States by varying projected vaccine efficacy and NPI policies such as social distancing, mask wearing, and testing. In [42], a spacial epidemic model is coupled with an optimization technique to resolve safety-and-mobility trade-offs in epidemic response plans. In June 2020, [7] built an agent-based model to predict the effects of social distancing and isolation on transmission of a virus.…”

Section: Introductionmentioning

confidence: 99%

Optimization of vaccination for COVID-19 in the midst of a pandemic

Luo

Weightman

McQuade

et al. 2022

NHM

Self Cite

View full text Add to dashboard Cite

<p style='text-indent:20px;'>During the Covid-19 pandemic a key role is played by vaccination to combat the virus. There are many possible policies for prioritizing vaccines, and different criteria for optimization: minimize death, time to herd immunity, functioning of the health system. Using an age-structured population compartmental finite-dimensional optimal control model, our results suggest that the eldest to youngest vaccination policy is optimal to minimize deaths. Our model includes the possible infection of vaccinated populations. We apply our model to real-life data from the US Census for New Jersey and Florida, which have a significantly different population structure. We also provide various estimates of the number of lives saved by optimizing the vaccine schedule and compared to no vaccination.</p>

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Managing Public Transit during a Pandemic: The Trade-Off between Safety and Mobility

Cited by 8 publications

References 0 publications

Kinetic modelling of epidemic dynamics: social contacts, control with uncertain data, and multiscale spatial dynamics

Kinetic modelling of epidemic dynamics: social contacts, control with uncertain data, and multiscale spatial dynamics

Optimization of vaccination for COVID-19 in the midst of a pandemic

Optimization of vaccination for COVID-19 in the midst of a pandemic

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