Adaptive Susceptibility and Heterogeneity in Contagion Models on Networks

Pagliara, Renato; Leonard, Naomi Ehrich

doi:10.1109/tac.2020.2985300

Cited by 19 publications

(12 citation statements)

References 40 publications

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“…A network model of COVID-19.. We consider a simple model (similar to models in literature [32,27,8,39,13,47]) of COVID-19 spreading that breaks infected individuals into two types: asymptomatic and symptomatic. This model allows individuals transmit the infection at different rates:…”

Section: 1mentioning

confidence: 99%

Optimal Vaccine Allocation for Pandemic Stabilization

Ma¹,

Liu²,

Olshevsky³

2021

Preprint

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How to strategically allocate the available vaccines is a crucial issue for pandemic control. In this work, we propose a mathematical framework for optimal stabilizing vaccine allocation, where our goal is to send the infections to zero as soon as possible with a fixed number of vaccine doses. This framework allows us to efficiently compute the optimal vaccine allocation policy for general epidemic spread models including SIS/SIR/SEIR and a new model of COVID-19 transmissions. By fitting the real data in New York State to our framework, we found that the optimal stabilizing vaccine allocation policy suggests offering vaccines priority to locations where there are more susceptible people and where the residents spend longer time outside the home. Besides, we found that offering vaccines priority to young adults (20-29) and middle-age adults (20-44) can minimize the cumulative infected cases and the death cases. Moreover, we compared our method with five age-stratified strategies in [11] based on their epidemics model. We also found it's better to offer vaccine priorities to young people to curb the disease and minimize the deaths when the basic reproduction number R 0 is moderately above one, which describes the most world during COVID-19. Such phenomenon has been ignored in [11].

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Section: 1mentioning

confidence: 99%

Optimal Vaccine Allocation for Pandemic Stabilization

Ma¹,

Liu²,

Olshevsky³

2021

Preprint

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“…A natural extension of the homogeneous population setting is the introduction of heterogeneities of various kinds. Characterizing population heterogeneity in terms of infection and/or recovery rates has been discussed both in population subgroups (Anderson and May (1992)) and in networks (Hethcote and Yorke (1984), Pagliara and Leonard (2020)). In the following, we review the network SIS model that was originally introduced as the multi-group SIS model in Lajmanovich and Yorke (1976).…”

Section: Network Sis Modelmentioning

confidence: 99%

“…Such information, however, often involves estimations that are delayed and unavoidably omits details in the finer time scale. We are motivated in part by the model and study of change in susceptibility after first infection as presented Pagliara and Leonard (2020).…”

Section: Homogeneous Populationmentioning

confidence: 99%

Active Control and Sustained Oscillations in actSIS Epidemic Dynamics

Zhou¹,

Levin²,

Leonard³

2020

Preprint

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An actively controlled Susceptible-Infected-Susceptible (actSIS) contagion model is presented for studying epidemic dynamics with continuous-time feedback control of infection rates. Our work is inspired by the observation that epidemics can be controlled through decentralized disease-control strategies such as quarantining, sheltering in place, social distancing, etc., where individuals actively modify their contact rates with others in response to observations of infection levels in the population. Accounting for a time lag in observations and categorizing individuals into distinct sub-populations based on their risk profiles, we show that the actSIS model manifests qualitatively different features as compared with the SIS model. In a homogeneous population of risk-averters, the endemic equilibrium is always reduced, although the transient infection level can exhibit overshoot or undershoot. In a homogeneous population of risk-tolerating individuals, the system exhibits bistability, which can also lead to reduced infection. For a heterogeneous population comprised of risk-tolerators and risk-averters, we prove conditions on model parameters for the existence of a Hopf bifurcation and sustained oscillations in the infected population.

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“…Theory has been used to show how group structure and communication affect behavior, determining when differences in information drive a group to split apart, or the fraction of informed individuals needed to lead a group to a known location [20, 21]. Other studies have used models of contagion [22, 23] to examine how a behavior spreads through a group [24]. One observed advantage of information sharing in groups is that multiple estimates of the same quantity (e.g., chemical gradients or food density) reduce uncertainty arising from measurement or internal noise [25, 26].…”

Section: Introductionmentioning

confidence: 99%

“…Other studies have used models of contagion [22,23] to examine how a behavior spreads through a group [24]. One observed advantage of information sharing in groups is that multiple estimates of the same quantity (e.g., chemical gradients or food density) reduce uncertainty arising from measurement or internal noise [25,26].…”

mentioning

confidence: 99%

Stochastic dynamics of social patch foraging decisions

Bidari

Hady

Davidson

et al. 2022

Preprint

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Animals typically forage in groups. Social foraging can help animals avoid predation and decrease their uncertainty about the richness of food resources. Despite this, theoretical mechanistic models of patch foraging have overwhelmingly focused on the behavior of single foragers. In this study, we develop a mechanistic model that accounts for the behavior of individuals foraging together and departing food patches following an evidence accumulation process. Each individual's belief about patch quality is represented by a stochastically accumulating variable which is coupled to others' belief to represent the transfer of information. We consider a cohesive group, and model information sharing by considering both intermittent pulsatile coupling (only communicate decision to leave) and continuous diffusive coupling (communicate throughout the evidence accumulation process). We find that foraging efficiency under pulsatile coupling has a stronger dependence on the coupling strength parameter compared to diffusive. Despite employing minimal information transfer, pulsatile coupling can still provide similar or higher foraging efficiency compared to diffusive coupling. Conversely, since diffusive coupling is more robust to parameter choices, it performs better when individuals have heterogeneous departure criteria and social information weighting. Efficiency is measured by a reward rate function that balances the amount of energy accumulated against the time spent in a patch, computed by solving an ordered first passage time problem for the patch departures of each individual. Using synthetic data we show that we can distinguish between the two modes of communication and identify the model parameters. Our model establishes a social patch foraging framework to parse and identify deliberative decision strategies, to distinguish different forms of social communication, and to allow model fitting to real world animal behavior data.

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Adaptive Susceptibility and Heterogeneity in Contagion Models on Networks

Cited by 19 publications

References 40 publications

Optimal Vaccine Allocation for Pandemic Stabilization

Optimal Vaccine Allocation for Pandemic Stabilization

Active Control and Sustained Oscillations in actSIS Epidemic Dynamics

Stochastic dynamics of social patch foraging decisions

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