key: cord-0426074-4byya8jd
authors: Rahmandad, H.
title: Behavioral Responses to Risk Promote Vaccinating High-contact Individuals First
date: 2021-02-08
journal: nan
DOI: 10.1101/2021.02.05.21251215
sha: 9aae8a407e450fba1300612c62b994c9ed0023da
doc_id: 426074
cord_uid: 4byya8jd

If COVID-19's reproduction number was constant, vaccinating elderly first minimized deaths. However, incorporating risk-driven behavior/policy changes enhances fit to data and prioritizes vaccinating high-contact individuals. Deaths grow exponentially until people are compelled to reduce contacts, stabilizing at levels obliging higher-contact groups to sufficiently cut interactions. Vaccinating those groups out of transmission saves lives and speeds everybody's return to normal life.

-USA deaths (from CDC; dashed lines) vs. calibrated model outputs (solid lines) for three age groups. A) R0 and a weather effect multiplier (4) calibrated in the "leaky vaccine" BL model with otherwise baseline parameters. Excluding weather effect reduces fit and leads to a single-peaked contagion. B) Calibrated extended BL model with g(t). 1 Associate Professor of System Dynamics, MIT Sloan School of Management. Hazhir@mit.edu.

. CC-BY-NC 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)

The copyright holder for this preprint this version posted February 8, 2021. ; https://doi.org/10.1101/2021.02.05.21251215 doi: medRxiv preprint Second, behavioral responses, also absent from most other vaccination analyses (e.g. 5, 6, 7) , change both the magnitude of deaths, and the vaccination priorities, qualitatively. Figure 2 compares alternative prioritization schemes using calibrated BL model (Panel A) and its extension 'Including Behavioral Response' (panel B). Consistent with BL findings Elderly-First policy is best when behavioral response is ignored, but incorporating this consideration flips the priority for any vaccination rollout that takes more than 6 months. Magnitude of the impact is considerable: in 2021 under calibrated BL model vaccinating the population over 250 days leads to 262 vs. 390 thousand deaths for Elderly-First and Highcontact-First policies respectively (blue dot in Panel A is the ratio, 0.67). The corresponding projections accounting for behavioral responses change to 125 and 110 thousands of deaths from vaccination start to the successful suppression of epidemic (ratio of 1.14; Panel B). The large difference in deaths is due to the timing of projected epidemic waves. Absent behavioral responses the increased Re due to winter weather leads to a large "second" wave in the US ( Figure  1A ) raising deaths significantly before vaccination can curb the pandemic. With behavioral response the "third" wave is endogenously brought under control, with under half projected deaths during vaccination period. Exact long-term projections may not be reliable, but the rank ordering of vaccination policies is robust. That ordering switches with inclusion of behavioral response and can be understood in light of two competing mechanisms. First, vaccinating groups based on their IFR reduces deaths among infected, promoting elderly-first policy as BL recommends. The second mechanism operates through a subtler pathway: when people change their behaviors in response to risk, ongoing deaths are determined largely by how responsive to risk different groups in a society are. Deaths stabilize at . CC-BY-NC 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint this version posted February 8, 2021. ;  levels that compel enough reductions in infectious contacts to curtail the exponential growth of the epidemic, i.e. keep Re~1. Higher-contact, or less-responsive, groups would bring down their interactions sufficiently when death rates are higher (than rates tolerable to e.g. lower-contact elderly) (8) . Thus, ongoing deaths could be brought down further by first vaccinating those with higher contact rates (e.g. the High-contact-Frist policy), or those less responsive to risks (a dimension not explicitly modeled here). Taking these groups out of transmission dynamics brings down deaths by shifting the underlying (pseudo-)equilibrium death rates implied by behavioral responses. Initially the first mechanism brings down deaths faster under Elderly-First policy ( Figure 2C ). However, due to the second mechanism remaining high-contact individuals keep the reproduction number higher (than High-contact-First) under Elderly-First prioritization, increasing infections and the later deaths enough to change the overall conclusion. Results are even more pronounced for YLL. Moreover, the life-saving benefits of High-contact-First policy are complemented by the faster return to normal interaction patterns (black lines).

Behavioral responses, missing from existing analysis of vaccination priority, are not only needed for understanding the observed and future trajectories of the pandemic, but also shift the optimal vaccination policy significantly in favor of prioritizing high contact groups first. Moreover, the underlying mechanism is very relevant if one could incorporate into priorities differences in risk-responsiveness across population groups. Prioritization of higher-contact, as well as those least able to change their behaviors in response to COVID-19 risks (e.g. health-care workers, first-responders, incarcerated, low-income service workers, and minority communities that have suffered the greatest COVID-19 deaths to-date) may save many thousands of lives while enabling a faster return to normal life for everybody.

Model-informed COVID-19 vaccine prioritization strategies by age and serostatus

Assessing the age specificity of infection fatality rates for COVID-19: systematic review, meta-analysis, and public policy implications

Estimate of the basic reproduction number for COVID-19: a systematic review and meta-analysis

Weather Conditions and COVID-19 Transmission: Estimates and Projections. medRxiv

Optimal Dynamic Prioritization of Scarce COVID-19 Vaccines. medRxiv

Modelling optimal vaccination strategy for SARS-CoV-2 in the UK. medRxiv

Indirect benefits are a crucial consideration when evaluating SARS-CoV-2 vaccine candidates

Risk-driven responses to COVID-19 eliminate the tradeoff between lives and livelihoods

Navid Ghaffarzadegan, Tse Yang Lim, John Sterman, and Kim Thompson provided helpful comments.

None to declare.

All models, data, and analysis code are available at: https://www.dropbox.com/s/48m5qn9bx697i8r/VaccineExample.zip?dl=0