Behavioral responses to risk promote vaccinating high‐contact individuals first

Abstract

How should communities prioritize COVID-19 vaccinations? Prior studies found that prioritizing the elderly and most vulnerable minimizes deaths. However, prior research has ignored how behavioral responses to risk of disease endogenously change transmission rates. We show that incorporating risk-driven behavioral responses enhances fit to data and may change prioritization to vaccinating high-contact individuals. Behavioral responses matter because deaths grow exponentially until communities are compelled to reduce contacts, with deaths stabilizing at levels that oblige higher-contact groups to sufficiently cut their interactions and slow transmissions. More lives may be saved by vaccinating and taking those high-contact groups out of transmission chains earlier because the remaining groups will take more precautions while waiting for their turn for vaccination. These findings are especially important considering the need for further vaccination in many countries, the emergence of new variants, and the expected challenge of distributing new vaccines in the coming months and years.

Generative agent‐based modeling: an introduction and tutorial

“…Fifth, the specific example used in this paper also brings insights into the body of system dynamics modeling literature that deals with dynamic diffusion models. Such models include a wide range of market diffusion (Barabba et al ., 2002; Ghaffarzadegan et al ., 2023), norm diffusion (Ulli‐Beer et al ., 2010), organizational change (Wunderlich et al ., 2014) and epidemic models (Darabi and Hosseinichimeh, 2020; Rahmandad, 2022; Rahmandad and Sterman, 2022). It also provides a case of path dependence behavior, a problem commonly studied in dynamic models (Sterman, 2000).…”

Learning from the COVID-19 pandemic: a systematic review of mathematical vaccine prioritization models

“…Several studies employ this or a similar procedure (see e. Several studies assume that the population-wide contact level depends on the number of recent infections, hospitalizations and/or deaths (Rahmandad, 2022;Althobaity et al, 2022;Gozzi et al, 2021), the number of currently active cases (Jentsch et al, 2021;Islam et al, 2021), recent changes in these numbers, or a combination of these factors. For example in Islam et al…”

“…Several studies assume that the population-wide contact level depends on the number of recent infections, hospitalizations and/or deaths (Rahmandad, 2022; Althobaity et al, 2022; Gozzi et al, 2021), the number of currently active cases (Jentsch et al, 2021; Islam et al, 2021), recent changes in these numbers, or a combination of these factors. For example in Islam et al (2021), the population-wide contact level has been modeled to depend on the number of active cases, using a sigmoidal function.…”

A missing behavioural feedback in COVID-19 models is the key to several puzzles

“…So, counterintuitively, vaccinating the youth first, by bringing down the tolerated death rates among the remaining unvaccinated, may save more lives than vaccinating the elderly first. 17 In fact a recent study 18 after replicating a well-known prior model 16 shows that just adding the risk-driven response to the original analysis (and even without prioritising on responsiveness) reverses the optimal allocation from elderly-first to high-contact-first in early vaccine administration in the USA.…”