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<i title="Cold Spring Harbor Laboratory">
<span class="release-stage" >pre-print</span>
Although the COVID-19 disease burden is heterogeneous across space, the U.S. National Academies of Sciences, Engineering, and Medicine recommends an equitable spatial allocation of pharmaceutical interventions based, for example, on population size, in the interest of speed and workability. Utilizing economic--epidemiological modeling, we benchmark the performance of ad hoc allocation rules of scarce vaccines and drugs by comparing them to the rules for a vaccine and for a drug treatment that<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1101/2020.12.18.20248439">doi:10.1101/2020.12.18.20248439</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/nnbbaitjdzbpnl4dgibyvm5nbm">fatcat:nnbbaitjdzbpnl4dgibyvm5nbm</a> </span>
more »... nimize the economic damages and expenditures over time, including a penalty cost representing the social costs of deviating from an ad hoc allocation. Under different levels of vaccine and drug scarcity, we consider scenarios where length of immunity and compliance to travel restrictions vary, and consider the robustness of the rules when assumptions regarding these factors are incorrect. Because drugs and vaccines attack different points in the disease pathology, the benefits from deviating from the ad hoc rule differ. For drug treatment, optimal policies often allocate all available treatments to one jurisdiction for a period of time, while ad hoc rules act to spread out treatments across jurisdictions. For vaccines, the benefits from deviating are especially high when immunity is permanent, when there is compliance to travel restrictions, and when the supply of vaccine is low. Interestingly, a lack of compliance to travel restrictions pushes the optimal allocations of vaccine towards the ad hoc and improves the relative robustness of the ad hoc rules, as the mixing of the populations reduces the spatial heterogeneity in disease burden.
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