Returning to a normal life via COVID-19 vaccines in the USA: a large-scale agent-based simulation study

摘要: Abstract Background In 2020, COVID-19 has claimed more than 300,000 deaths in the US alone. While non-pharmaceutical interventions were implemented by federal and state governments USA, these efforts have failed to contain virus. Following FDA approval of two vaccines, however, hope for return normalcy is renewed. This rests on an unprecedented nation-wide vaccine campaign, which faces many logistical challenges also contingent several factors whose values are currently unknown. Objective We study effectiveness a campaign response different efficacies, willingness population be vaccinated, daily capacity under plans. To characterize possible outcomes most accurately, we account interactions between through six scenarios that capture range impact from interventions. Methods use large-scale cloud-based agent-based simulations implementing vaccination using Covasim, open-source ABM been used peer-reviewed studies accounts individual heterogeneity as well multiplicity contact networks. Several modifications parameters simulation logic made better align model with current evidence. chose intervention applied following both plan proposed Operation Warp Speed (former Trump administration) one million vaccines per day, Biden administration. accounted unknowns efficacies levels compliance varying parameters. For each experiment, cumulative infection growth fitted logistic model, carrying capacities rates recorded. Results plans all scenarios, presence considerably lowers total number infections when life returns normal, even low at 20%. noted unintended consequence: given availability estimates focus vaccinating individuals age categories, significant reduction results counterintuitive situation higher then leads infections. Conclusions Although potent, alone cannot effectively end pandemic adopted strategy. Non-pharmaceutical need continue enforced ensure high compliance, so rate immunity established outpaces induced