Social Proximity As a Tool to Fight Pandemics

Galanis, Spyros

doi:10.2139/ssrn.3581802

Cited by 4 publications

(9 citation statements)

References 18 publications

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“…We assume that the epidemic is fast relative to 6 For more related literature, see the excellent discussion in Toxvaerd (2020). Galanis (2020) study in a static model the potential to use social proximity, and within-group random testing and anonymous notification of positive cases as a tool to induce social distancing. 7 See, however, Rowthorn and Toxvaerd (2017) for a problem of managing an infection in an SIS model (where recovered can become infected again) with homogenous population and no risk of infection-induced mortality.…”

Section: The Standard Sir Modelmentioning

confidence: 99%

Covid and social distancing with a heterogenous population

Makris

2021

Econ Theory

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Motivated by the Covid-19 epidemic, we build a SIR model with private decisions on social distancing and population heterogeneity in terms of infection-induced fatality rates, and calibrate it to UK data to understand the quantitative importance of these assumptions. Compared to our model, the calibrated benchmark version with constant mean contact rate significantly over-predicts the mean contact rate, the death toll, herd immunity and prevalence peak. Instead, the calibrated counterfactual version with endogenous social distancing but no heterogeneity massively under-predicts these statistics. We use our calibrated model to understand how the impact of mitigating policies on the epidemic may depend on the responses these policies induce across the various population segments. We find that policies that shut down some of the essential sectors have a stronger impact on the death toll than on infections and herd immunity compared to policies that shut down non-essential sectors. Furthermore, there might not be an after-wave after policies that shut down some of the essential sectors are lifted. Restrictions on social distancing can generate welfare gains relative to the case of no intervention. Milder but longer restrictions on less essential activities might be better in terms of these welfare gains than stricter but shorter restrictions, whereas the opposite might be the case for restrictions on more essential activities. Finally, shutting down some of the more essential sectors might generate larger welfare gains than shutting down the less essential sectors.

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Section: The Standard Sir Modelmentioning

confidence: 99%

Covid and social distancing with a heterogenous population

Makris

2021

Econ Theory

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“…the probability of being infected by j if all i's neighbors had active links to j. As a result, bδ n−1 ≥ (1 − λ)θ(1 − φ)(1 − σ ij )L ≥ (1 − λ)θ(1 − φ)φ n−2 L which, again from (9), implies that in a collection of isolated cliques where every node has n − 1 active links no node wants to cut a link. As a result, a collection of isolated cliques with n members must be stable.…”

Section: B Further Claimsmentioning

confidence: 87%

“…As a result, a slow intervention is always dominated by a fast one. In contrast, in the largest 9 Optimality requires that n-member cliques should acquire a new member whenever the benefit from an additional active link bδ n is larger than θ(1 − φ). However, for λ = 0 (i.e.…”

Section: Tracing Fast and Slowmentioning

confidence: 99%

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Social Distance, Speed of Containment and Crowding In/Out in a Network Model of Contagion

Adriani

2020

SSRN Journal

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We study the effects of an intervention aimed at identifying and containing outbreaks in a network model of contagion where social distance is endogenous. The intervention induces a fall in the risk of infection, to which agents optimally respond by reducing social distance. If the intervention relies on infrequent or inaccurate testing, this crowding out effect may fully offset the intervention's direct effect, so that the risk of infection increases. In these circumstances, we show that "slow" interventions-which allow the outbreak to spread to immediate neighbors before being contained-may generate higher ex-ante welfare than "fast" ones and may even "crowd in" social distance. The theory thus identifies a trade off between (i) the swiftness of the intervention and (ii) the scope for crowding out. We show that the nature of this trade off crucially depends on the structure of the underlying social network and prevailing social norms.

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“…In the smallest equilibrium, cliques tend to be too small relative to the social optimum. 9 In this case, even an intervention causing full crowding out always increases welfare. In contrast, in the largest equilibrium there are typically too many active links and the intervention will typically exacerbate a situation of excessive risk taking.…”

Section: Welfarementioning

confidence: 99%

Social distance, speed of containment and crowding in/out in a network model of contagion

Adriani

Ladley

2021

Journal of Economic Behavior & Organization

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We study the effects of an intervention aimed at identifying and containing outbreaks in a network model of contagion where social distance is endogenous. The intervention induces a fall in the risk of infection, to which agents optimally respond by reducing social distance. If the intervention relies on infrequent or inaccurate testing, this crowding out effect may fully offset the intervention's direct effect, so that the risk of infection increases. In these circumstances, we show that "slow" interventions -which allow the outbreak to spread to immediate neighbors before being contained -may generate higher ex-ante welfare than "fast" ones and may even "crowd in" social distance. The theory thus identifies a trade off between (i) the swiftness of the intervention and (ii) the scope for crowding out. We show that the nature of this trade off crucially depends on the structure of the underlying social network and prevailing social norms.

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Social Proximity As a Tool to Fight Pandemics

Cited by 4 publications

References 18 publications

Covid and social distancing with a heterogenous population

Covid and social distancing with a heterogenous population

Social Distance, Speed of Containment and Crowding In/Out in a Network Model of Contagion

Social distance, speed of containment and crowding in/out in a network model of contagion

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