While they are rare, superspreading events (SSEs), wherein a few primary cases infect an extraordinarily large number of secondary cases, are recognized as a prominent determinant of aggregate infection rates (R0). Existing stochastic SIR models incorporate SSEs by fitting distributions with thin tails, or finite variance, and therefore predicting almost deterministic epidemiological outcomes in large populations. This paper documents evidence from recent coronavirus outbreaks, including SARS, MERS, and COVID-19, that SSEs follow a power law distribution with fat tails, or infinite variance. We then extend an otherwise standard SIR model with the fat-tailed power law distributions, and show that idiosyncratic uncertainties in SSEs will lead to large aggregate uncertainties in infection dynamics, even with large populations. That is, the timing and magnitude of outbreaks will be unpredictable. While such uncertainties have social costs, we also find that they on average decrease the herd immunity thresholds and the cumulative infections because per-period infection rates have decreasing marginal effects. Our findings have implications for social distancing interventions: targeting SSEs reduces not only the average rate of infection (R0) but also its uncertainty. To understand this effect, and to improve inference of the average reproduction numbers under fat tails, estimating the tail distribution of SSEs is vital.
Fukui, M and C Furukawa (2020), ‘Power Laws in Superspreading Events: Evidence from Coronavirus Outbreaks and Implications for SIR Models‘, COVID Economics 30, CEPR Press, Paris & London. https://cepr.org/publications/covid-economics-issue-30#392514_392906_390519
Social distancing is important to slow the community spread of infectious disease, but it creates enormous economic and social cost. It is thus important to quantify the benefits of different measures. We study the ban of mass gatherings, an intervention with comparably low cost. We exploit exogenous spatial and temporal variation in NBA and NHL games, which arise due to the leagues' predetermined schedules, and the suspension of the 2019-20 seasons. This allows us to estimate the impact of these mass gatherings on the spread of COVID-19 in affected US counties. One additional mass gathering increased the cumulative number of COVID-19 deaths in affected counties by 13 percent.
Ahammer, A, M Halla and M Lackner (2020), ‘Mass Gatherings Contributed to Early COVID-19 Spread: Evidence from US Sports‘, COVID Economics 30, CEPR Press, Paris & London. https://cepr.org/publications/covid-economics-issue-30#392514_392906_390520
Since late 2019, Covid-19 has devastated the global economy, with indirect implications for the environment. As governmentsâ€™ prioritized health and implemented measures such as isolation, the closure of non-essential businesses and social distancing, many workers lost their jobs, were furloughed, or started working from home. Consequently, the world of work has drastically transformed and this period is likely to have major implications for mobility, transportation and the environment. We have estimated the variability of people to engage in remote work and social distancing using O*NET data and Irish Census data. We show that while those who commute by car have a relatively high potential for remote work, they are less likely to be able to engage in social distancing in their workplace. While this may be negative for employment prospects in the short run, this dynamic has the potential for positive environmental implications in the short and long run.
Crowley, F, H Daly, J Doran and G Ryan (2020), ‘COVID-19, Social Distancing, Remote Work and Transport Choice‘, COVID Economics 30, CEPR Press, Paris & London. https://cepr.org/publications/covid-economics-issue-30#392514_392906_390521
We apply the SIR-macro model proposed by Eichenbaum et al. (2020) in its complete version to comparatively study the interaction between economic decisions and COVID-19 epidemics in five different Brazilian states: SÃ£o Paulo (SP), Amazonas (AM), CearÃ¡ (CE), Rio de Janeiro (RJ) and Pernambuco (PE). Our objective is to analyze qualitatively how the main intrinsic differences of each of these states can affect the epidemic dynamics and its consequences. For this purpose, we compute and compare the model for each of the states, both in competitive equilibrium and under optimal containment policy adoption, and analyze the implications of optimal policy adoption. We conclude that the intrinsic characteristics of the five different states imply relevant differences in the general dynamics of the epidemic, in the optimal containment policies, in the effect of the adoption of these policies and the severity of the economic recessions. Our study can serve as an alert for policymakers of countries of huge dimensions and interstate heterogeneity as Brazil for the necessity of discriminating policies by states or regions instead of adopting a single unified policy for the whole country.
Borelli, L and G Góes (2020), ‘Macroeconomics of Epidemics: Interstate Heterogeneity in Brazil‘, COVID Economics 30, CEPR Press, Paris & London. https://cepr.org/publications/covid-economics-issue-30#392514_392906_390522
This paper uses a macroeconomic model to analyse the transmission of the COVID19-pandemic and its associated lockdown and quantify the stabilising effects of the economic policy response. Our simulations identify firm liquidity problems as crucial for shock propagation and amplification. We then quantify the effects of short-term work allowances and liquidity guarantees - central policy strategies in the European Union. The measures reduce the output loss of COVID19 and its associated lockdown by about one fourth. However, they cannot prevent a sharp but temporary decline in production.
in t Veld, J, P Pfeiffer and W Roeger (2020), ‘The COVID19-Pandemic in the EU: Macroeconomic Transmission and Economic Policy Response‘, COVID Economics 30, CEPR Press, Paris & London. https://cepr.org/publications/covid-economics-issue-30#392514_392906_390523