Optimal Time-Varying Vaccine Allocation Amid Pandemics With Uncertain Immunity Ratios

Abstract

There is a concerted effort to develop vaccines to combat the public health crisis caused by COVID-19. Health experts are raising questions concerning the clinical trials, price and distribution of the vaccines among the public. Policymakers must design regulations to allocate vaccines equitably based on epidemiological as well as economic considerations. Allocation strategies must factor in the extent and duration of immunity the vaccines yield from future infections. We present a time-varying linear optimization-based approach, which incorporates epidemiological factors, such as population density, susceptible count and infected ratio as well as transportation costs, to disseminate vaccines among zones. Our approach also employs an update rule on the epidemiological statistics spawning from the SEIRD model to learn the extent of immunity provided by the vaccines. Our experiments are performed on the map of New York State with realistic mobility using spatial and ordinary differential equation-based SEIRD models. We show that the proposed approach allocates vaccines optimally while preventing zones from resource starvation. It adapts its vaccine recommendation to meet the prespecified epidemiological and economic policy requirements of zones, and is highly amenable to accommodate multiple immunity ratios, vaccine types and the time-span of acquired vaccine immunity. Moreover, we demonstrate that one can learn the latent immunity ratios of the vaccines to make more informed recommendations with time. Our ordinary differential equation-based analysis on the real demographic and infection data of New York State show that a small fraction of zones tends to exhibit a high resource demand due to their vulnerability to the pandemic spread. Our vaccine allocation strategy incorporates infection ratio as one of the criteria and is applicable to conceive a drug allocation strategy. The results suggest that incorporating the vulnerability score of zones to the epidemic spread into vaccine allocation may enhance recommendations and aid policy making.

Effectiveness of Network Interdiction Strategies to Limit Contagion During a Pandemic

“…Matrajit et al presented an age-stratified optimization approach based on deaths, symptomatic infections, and ICU/non-ICU hospitalizations [33]. Roy et al presented a time-varying vaccination distribution strategy that incorporates socioeconomic and demographic factors to disseminate vaccines among zones [34].…”

Two-Strain COVID-19 Model Using Delayed Dynamic System and Big Data

“…One of the most effective prophylactic mean for COVID-19 is vaccination. In [35] the authors proposed the vaccination strategy using time-varying linear optimization-based approach to disseminate vaccines among zones. The solution is based on SEIRD model to learn the extent of immunity provided by the vaccines.…”

Simulating Immunization Campaigns and Vaccine Protection Against COVID-19 Pandemic in Brazil

“…Most recently, modified SIR-type models with vaccination compartments have been employed for dealing with the COVID-19 pandemic. The so-called SEIRD model was employed by Roy et al [20] to investigate the best vaccine allocation strategy in the New York State, while Fudolig and Howard [21] introduced a multi-strain version of the SIR model to study some variations in the reproduction number of the disease. Based on the advance of the SARS-CoV-2 vaccines, Saad-Roy et al [22] presented a very robust investigation concerning the single or double doses of immunizers, including the analysis of different scenarios in terms of trans-mission and vaccine immunity.…”