Covid and social distancing with a heterogenous population

Abstract

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.

The Economic Consequences of R = 1: Towards a Workable Behavioural Epidemiological Model of Pandemics

“…(Gersovitz 2003) and (Chen and Toxvaerd 2014) relaxes those conditions and finds inefficiency especially if individuals can independently acquire immunity. 7 A recent literature on COVID-19 has similarly built on these behavioural foundations with forward-looking rational agents including (Eichenbaum, Rebelo and Trabandt 2020), (Makris 2021) and (Boppart, Harmenberg, Hassler, Krusell and Olsson 2020) (and by extension (Krueger, Uhlig and Xie 2020)) who provide a model of endogenous social distancing in a macroeconomic model; (Farboodi et al 2021) who examine how altruistic preferences (that capture the degree to which individuals choose to self-isolate if they know they are infected) impact on behaviour. (Jones, Philippon and Venkateswaran 2020) use a macro-model and highlight a 'fatalism' effect whereby, when prevalence is high, people do not socially distance as they are likely to become infected anyway; (Bethune and Korinek 2020) who look at what optimal policies look like when the planner has a high degree of information regarding who is infected and who has recovered.…”

Profits, Pandemics, and Lockdown Effectiveness in Nursing Home Networks

“…As of May 20, 2022, the World Health Organization reported more than 521 million confirmed cases of COVID-19 and over 6 million COVID-19 deaths globally (WHO, 2022). The scientific and medical communities agree that individuals over the age of 65 and those who suffer comorbidities have experienced the highest risk of mortality due to COVID-19 (Dessie & Zewotir, 2021;Fallon, Dukelow, Kennelly, & O'Neill, 2020;Makris, 2021). In developed countries, old age individuals are more likely to reside in nursing homes, and these facilities have borne the burden of COVID-19 mortality.…”

“…The epidemiological framework that we use to model the planning problem is a continuous-time individual-based meanfield model which belongs to the class of theoretical approaches for epidemic modeling on undirected heterogeneous networks; Pastor-Satorras, Castellano, Van Mieghem, and Vespignani (2015) provide a review of these epidemiological models, and Boucekkine, Carvajal, Chakraborty, and Goenka (2021), Fajgelbaum et al (2021), Debnam Guzman, Mabeu, and, Pongou, Tchuente, and Tondji (2022a), Pongou et al (2022b), Nganmeni, Pongou, Tchantcho, andTondji (2022), and the references therein highlight the recent economic contributions to the COVID-19 pandemic. 6 Another contribution by Makris (2021) also extends the classical susceptible-infected-recovered (SIR) model by incorporating heterogeneity in infection-induced mortality rates at the population level. Makris assumes that two distinct groups (low-risk versus high-risk individuals) in the population face different epidemiological parameters in terms of infection and deaths and respond differently to social distancing policies enforced by the government.…”

Rather doomed than uncertain: risk attitudes and transmissive behavior under asymptomatic infection

“…Contributions such asMakris (2021) assume perfect health state information and risk neutrality and focus on other aspects of decision making.…”