Distribution equality as an optimal epidemic mitigation strategy

Adar Hacohen, Reuven Cohen, Sol Efroni, Ido Bachelet, Baruch Barzel

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


Upon the development of a therapeutic, a successful response to a global pandemic relies on efficient worldwide distribution, a process constrained by our global shipping network. Most existing strategies seek to maximize the outflow of the therapeutics, hence optimizing for rapid dissemination. Here we find that this intuitive approach is, in fact, counterproductive. The reason is that by focusing strictly on the quantity of disseminated therapeutics, these strategies disregard the way in which this quantity distributes across destinations. Most crucially—they overlook the interplay of the therapeutic spreading patterns with those of the pathogens. This results in a discrepancy between supply and demand, that prohibits efficient mitigation even under optimal conditions of superfluous flow. To solve this, we design a dissemination strategy that naturally follows the predicted spreading patterns of the pathogens, optimizing not just for supply volume, but also for its congruency with the anticipated demand. Specifically, we show that epidemics spread relatively uniformly across all destinations, prompting us to introduce an equality constraint into our dissemination that prioritizes supply homogeneity. This strategy may, at times, slow down the supply rate in certain locations, however, thanks to its egalitarian nature, which mimics the flow of the pathogens, it provides a dramatic leap in overall mitigation efficiency, potentially saving more lives with orders of magnitude less resources.

Original languageEnglish
Article number10430
JournalScientific Reports
Issue number1
StatePublished - Dec 2022

Bibliographical note

Funding Information:
This work was supported by the Israel Science Foundation (Grant No. 499/19), the bi-national Israel-China ISF-NSFC joint research program (Grant No. 3552/21) and the Bar-Ilan University Data Science Institute grant for COVID-19 related research.

Publisher Copyright:
© 2022, The Author(s).


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