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Chasing up and locking down the virus: Optimal pandemic interventions within a network
Author(s) -
Freiberger Michael,
Grass Dieter,
Kuhn Michael,
Seidl Andrea,
Wrzaczek Stefan
Publication year - 2022
Publication title -
journal of public economic theory
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.809
H-Index - 32
eISSN - 1467-9779
pISSN - 1097-3923
DOI - 10.1111/jpet.12604
Subject(s) - pandemic , psychological intervention , social distance , population , covid-19 , h1n1 pandemic , disease , computer science , epidemic model , distancing , economics , operations research , microeconomics , econometrics , medicine , environmental health , mathematics , infectious disease (medical specialty) , psychiatry , pathology
During the COVID‐19 pandemic countries invested significant amounts of resources into its containment. In early stages of the pandemic most of the (nonpharmaceutical) interventions can be classified into two groups: (i) testing and identification of infected individuals, (ii) social distancing measures to reduce the transmission probabilities. Furthermore, both groups of measures may, in principle, be targeted at certain subgroups of a networked population. To study such a problem, we propose an extension of the SIR model with additional compartments for quarantine and different courses of the disease across several network nodes. We develop the structure of the optimal allocation and study a numerical example of three symmetric regions that are subject to an asymmetric progression of the disease (starting from an initial hotspot). Key findings include that (i) for our calibrations policies are chosen in a “flattening‐the‐curve,” avoiding hospital congestion; (ii) policies shift from containing spillovers from the hotspot initially to establishing a symmetric pattern of the disease; and (iii) testing that can be effectively targeted allows to reduce substantially the duration of the disease, hospital congestion and the total cost, both in terms of lives lost and economic costs.