Optimal SARS-CoV-2 vaccine allocation using real-time attack-rate estimates in Rhode Island and Massachusetts

Abstract

Background When three SARS-CoV-2 vaccines came to market in Europe and North America in the winter of 2020–2021, distribution networks were in a race against a major epidemiological wave of SARS-CoV-2 that began in autumn 2020. Rapid and optimized vaccine allocation was critical during this time. With 95% efficacy reported for two of the vaccines, near-term public health needs likely require that distribution is prioritized to the elderly, health care workers, teachers, essential workers, and individuals with comorbidities putting them at risk of severe clinical progression. Methods We evaluate various age-based vaccine distributions using a validated mathematical model based on current epidemic trends in Rhode Island and Massachusetts. We allow for varying waning efficacy of vaccine-induced immunity, as this has not yet been measured. We account for the fact that known COVID-positive cases may not have been included in the first round of vaccination. And, we account for age-specific immune patterns in both states at the time of the start of the vaccination program. Our analysis assumes that health systems during winter 2020–2021 had equal staffing and capacity to previous phases of the SARS-CoV-2 epidemic; we do not consider the effects of understaffed hospitals or unvaccinated medical staff. Results We find that allocating a substantial proportion (>75%) of vaccine supply to individuals over the age of 70 is optimal in terms of reducing total cumulative deaths through mid-2021. This result is robust to different profiles of waning vaccine efficacy and several different assumptions on age mixing during and after lockdown periods. As we do not explicitly model other high-mortality groups, our results on vaccine allocation apply to all groups at high risk of mortality if infected. A median of 327 to 340 deaths can be avoided in Rhode Island (3444 to 3647 in Massachusetts) by optimizing vaccine allocation and vaccinating the elderly first. The vaccination campaigns are expected to save a median of 639 to 664 lives in Rhode Island and 6278 to 6618 lives in Massachusetts in the first half of 2021 when compared to a scenario with no vaccine. A policy of vaccinating only seronegative individuals avoids redundancy in vaccine use on individuals that may already be immune, and would result in 0.5% to 1% reductions in cumulative hospitalizations and deaths by mid-2021. Conclusions Assuming high vaccination coverage (>28%) and no major changes in distancing, masking, gathering size, hygiene guidelines, and virus transmissibility between 1 January 2021 and 1 July 2021 a combination of vaccination and population immunity may lead to low or near-zero transmission levels by the second quarter of 2021.

SARS-CoV-2 epidemic after social and economic reopening in three U.S. states reveals shifts in age structure and clinical characteristics

“…We infer eight relative mixing levels for each age class (relative to the 0 to 9 age group) and use a symmetric parametrization where contact rates are described per age group pair (i.e., c ab = c a × c b ) where c a is the mixing rate for age group a ; see equations (section S6). Use of the Belgian CoMix study’s contact rates was evaluated ( 43 , 46 ), but these more highly parameterized matrices did not provide a better fit for our 6-month time series [ΔDIC (deviance information criterion) > 70 for all three states]. Age characteristics of each state epidemic are shown in Fig.…”

SARS-CoV-2 epidemic after social and economic reopening in three US states reveals shifts in age structure and clinical characteristics

“…cab = ca × cb) where ca is the mixing rate for age group a; see equations (S6). Use of the Belgian CoMix study's contact rates was evaluated [46,47], but these more highly parameterized matrices did not provide a better fit for our six-month time series (ΔDIC > 70 for all three states). Age characteristics of each state epidemic are shown in Figure 4 (top rows), and the inferred contact parameters are shown in Figure 4 (bottom rows); inference of contact rates is influenced by the model assumption that the 0-19 age group is 60% as susceptible to infection as the other age groups [24].…”

SARS-CoV-2 attack rate and population immunity in southern New England, March 2020 - May 2021

“…As in our previous analyses on population-level signals showing evidence of different patterns of clinical progression during different epidemic phases [ 1 , 25 ], we include changepoints in the ICU admission fraction in our model to allow for changes in clinical management for hospitalized patients; a lower ICU admission fraction suggests that hospitalized patients have improved chances of recovery and a lower chance of death. The first changepoint was inferred as Jun 2 for RI, May 26 for MA, and Jun 5 for CT (medians from posteriors), and the second changepoints were inferred as Dec 10 for RI, Sep 12 for MA, and Nov 6 for CT (see Supplementary Materials for all posteriors).…”

“…Daily time points from March 1 2020 to June 6 2021 were included in this analysis. Cumulative hospitalization data were included for MA even though these data were not available for our previous two analyses [ 1 , 25 ]. Details on CT data sources are in the Supplementary Methods ; RI and MA data sources are described in Wikle et al [ 1 ].…”

“…A validated Bayesian inferential framework based on a dynamical epidemic model (compartmental diagram in Supplementary Figure 1) was used to fit case, hospitalization, and death data from Massachusetts, Connecticut, and Rhode Island [1,25]. Eleven daily data streams were collected from each state: (1) cumulative confirmed cases, (2) cumulative confirmed cases by age, (3) cumulative hospitalized cases, (4) cumulative hospitalized cases by age, (5) number of patients currently hospitalized, (6) number of patients currently in ICU, (7) number of patients currently on mechanical ventilation, (8) cumulative deaths, (9) cumulative deaths by age, (10) cumulative hospital deaths, (11) cumulative hospital discharges.…”