Quantifying the Benefits of Targeting for Pandemic Response

57 Pages Posted: 2 Apr 2021 Last revised: 15 Dec 2021

See all articles by Sergio Camelo

Sergio Camelo

Stanford University - Institute for Computational and Mathematical Engineering

Dragos Florin Ciocan

INSEAD

Dan Iancu

Stanford Graduate School of Business

Xavier Warnes

Stanford Graduate School of Business

Spyros I. Zoumpoulis

INSEAD - Decision Sciences

Date Written: March 22, 2021

Abstract

Problem definition: To respond to pandemics such as COVID-19, policy makers have relied on interventions that target specific population groups or activities. Since targeting is potentially contentious, rigorously quantifying its benefits is critical for designing effective and equitable pandemic control policies.

Methodology/results: We propose a flexible modeling framework and a set of associated algorithms that compute optimally targeted, time-dependent interventions that coordinate across two dimensions of heterogeneity: age of different groups and the specific activities that individuals engage in during the normal course of a day. We showcase a complete implementation focused on the Île-de-France region of France, based on commonly available public data. We find that targeted policies generate substantial complementarities that lead to Pareto improvements, reducing the number of deaths and the economic losses, as well as the time in confinement for each age group. Optimized dual-targeted policies are interpretable: by fitting decision trees to our raw policy's decisions across many problem instances, we find that a feature corresponding to the ratio of marginal economic value prorated by social contacts is highly salient in explaining the confinements that any group - activity pair experiences. We also quantify the impact of fairness requirements that explicitly limit the differential treatment of distinct groups, and find that satisfactory trade-offs are achievable through limited targeting.

Implications: Given that some amount of targeting of activities and age groups is already in place in real-world pandemic responses, our framework highlights the significant benefits in explicitly and transparently modelling targeting and identifying the interventions that rigorously optimize overall societal welfare.

Note: Funding Statement: No external funding was used to conduct the submitted research.

Declaration of Interests: No competing interests to declare.

Keywords: Pandemic management, Confinement, Targeted interventions, Optimization, COVID-19

JEL Classification: I18, C61

Suggested Citation

Camelo, Sergio and Ciocan, Dragos Florin and Iancu, Dan and Warnes, Xavier and Zoumpoulis, Spyros, Quantifying the Benefits of Targeting for Pandemic Response (March 22, 2021). Available at SSRN: https://ssrn.com/abstract=3810240 or http://dx.doi.org/10.2139/ssrn.3810240

Sergio Camelo

Stanford University - Institute for Computational and Mathematical Engineering ( email )

Huang Building, 475 Via Ortega
Suite 060 (Bottom level)
Stanford, CA 94305-4042
United States

Dragos Florin Ciocan

INSEAD ( email )

Boulevard de Constance
77305 Fontainebleau Cedex
France

Dan Iancu

Stanford Graduate School of Business ( email )

655 Knight Way
Stanford, CA 94305-5015
United States

Xavier Warnes

Stanford Graduate School of Business ( email )

655 Knight Way
Stanford, CA 94305-5015
United States

Spyros Zoumpoulis (Contact Author)

INSEAD - Decision Sciences ( email )

France

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