Alessandro Toppeta
Jason Sockin
Todd Schoellman
Paolo Martellini
UCL Policy Lab
Natalia Ramondo
Javier Cravino
Vanessa Alviarez
Natalia Ramondo
Javier Cravino
Vanessa Alviarez
Hugo Reis
Pedro Carneiro
Raul Santaeulalia-Llopis
Diego Restuccia
Chaoran Chen
Brad J. Hershbein
Claudia Macaluso
Chen Yeh
Xuan Tam
Xin Tang
Marina M. Tavares
Adrian Peralta-Alva
Carlos Carillo-Tudela
Felix Koenig
Joze Sambt
Ronald Lee
James Sefton
David McCarthy
Bledi Taska
Carter Braxton
Alp Simsek
Plamen T. Nenov
Gabriel Chodorow-Reich
Virgiliu Midrigan
Corina Boar
Sauro Mocetti
Guglielmo Barone
Steven J. Davis
Nicholas Bloom
José María Barrero
Thomas Sampson
Adrien Matray
Natalie Bau
Darryl Koehler
Laurence J. Kotlikoff
Alan J. Auerbach
Irina Popova
Alexander Ludwig
Dirk Krueger
Nicola Fuchs-Schündeln
Taylor Jaworski
Walker Hanlon
Ludo Visschers
Carlos Carillo-Tudela
Henrik Kleven
Kristian Jakobsen
Katrine Marie Jakobsen
Alessandro Guarnieri
Tanguy van Ypersele
Fabien Petit
Cecilia García-Peñalosa
Yonatan Berman
Nina Weber
Julian Limberg
David Hope
Pedro Tremacoldi-Rossi
Tatiana Mocanu
Marco Ranaldi
Silvia Vannutelli
Raymond Fisman
John Voorheis
Reed Walker
Janet Currie
Roel Dom
Marcos Vera-Hernández
Emla Fitzsimons
José V. Rodríguez Mora
Tomasa Rodrigo
Álvaro Ortiz
Stephen Hansen
Vasco Carvalho
Gergely Buda
Gabriel Zucman
Anders Jensen
Matthew Fisher-Post
José-Alberto Guerra
Myra Mohnen
Christopher Timmins
Ignacio Sarmiento-Barbieri
Peter Christensen
Linda Wu
Gaurav Khatri
Julián Costas-Fernández
Eleonora Patacchini
Jorgen Harris
Marco Battaglini
Ricardo Fernholz
Alberto Bisin
Jess Benhabib

Who should get vaccinated? Individualized allocation of vaccines over SIR network

What is this research about and why did you do it?

Allocation of scarce resources among individuals interacting in a social network is an important policy decision in many fields, including medicine, education, and economics. Our research was motivated by the problem of how to allocate vaccines in the Covid-19 pandemic among heterogeneous individuals to control the spread of the disease and protect the vulnerable. Devising an optimal allocation rule when there are spillovers over the network remains a challenging open question. This research develops a method to identify an individualized allocation of vaccines among individuals who interact in a social network with an epidemiological model of disease transmission (heterogeneous Susceptible-Infectious-Recovered (SIR) model).

How did you answer this question?

Exploiting social network data containing individual demographic characteristics and health status, we develop a procedure to estimate an individualized vaccine  allocation policy when supply is limited. We introduce a heterogeneous-interacted-SIR model to model the spillover effects of an infectious disease and show that the welfare objective function of the vaccine allocation problem satisfies a certain decreasing-returns property (known as sub-modularity). Building on these findings, we propose an algorithm that sequentially allocates a vaccine to an individual in the network who is most influential for improving social welfare and show guaranteed performance in terms of a societal public health criterion. We also show the ‘greedy algorithm’ method can accommodate exogenous capacity constraints across different demographic groups, which policymakers commonly face in reality.

What did you find?

The numbers on each axis reflect the value of welfare (the sum of probabilities of being healthy in the second period). For different parameters (network size and density) used in the simulation, welfare is higher when the ‘greedy algorithm’ is used to allocate the vaccine rather than targeting the vaccine without using the network information. This outcome is shown by the observations lying to the right of the 45-degree line.

Our numerical study shows the effectiveness of individualized assignment of vaccines considering spillovers, and the importance of targeting using network information. Compared with a random assignment policy and a targeting policy without network information, our assignment policy improves welfare by 4%–12%. This result is insensitive to the values of the SIR parameters and the size and density of the network. It suggests that even under the state of strict containment policy in the early phase of the pandemic, where the density of the network is maintained at a relatively low level, the cost of ignoring spillovers is significant for vaccine allocation.

What implications does this have for the research on wealth concentration or economic inequality?

This work highlights the importance of network information in the individualized vaccine allocation problem. Ignoring network information leads to an inefficient allocation of resources. Such inefficiency increases dramatically as the number of vaccines and the size of the network expand. General policy implications of our work are:

  • collecting network information is extremely valuable for designing treatment assignment policy,
  • a computationally attractive greedy algorithm is useful to devise an effective policy.

What are the next steps in your agenda?

We want to extend this analysis to assignment policies that run over multiple allocation periods. We also want to analyze what type of fairness constraints can be incorporated in the individualized treatment allocation problems over the network.


This paper can be cited as follows: Kitagawa, T. and Wang, G. (2023) 'Who Should Get Vaccinated? Individualized Allocation of Vaccines Over SIR Network.' Journal of Econometrics, 232(1): pp. 109-131.

About the authors

Toru Kitagawa
Guanyi Wang
Kitagawa 2021.pdf