Outreach Strategies for Vaccine Distribution: A Multi-period Stochastic Modeling Approach

Abstract

Vaccination has been proven to be the most effective method to prevent infectious diseases.However, in many low and middle-income countries with geographically dispersed and nomadic populations, last-mile vaccine delivery can be extremely complex. Because newborns in remote locations within these countries often do not have direct access to clinics and hospitals, they face significant risk from diseases and infections. An approach known as outreach is typically utilized to raise immunization rates in these situations. A set of these remote population centers is chosen, and over an appropriate time horizon, teams of clinicians and support personnel are sent from a depot to set up mobile clinics at these locations to vaccinate people there and in the immediate surrounding area. In this paper, we model the problem of optimally designing outreach efforts as a mixed integer program that is a combination of a set covering problem and a vehicle routing problem. In addition, because elements relevant to outreach (such as populations and road conditions) are often unstable and unpredictable, we incorporate uncertainty to study the robustness of the worst-case solutions and the related issue of the value of information.

Vaccine network design to maximize immunization coverage

“…We aimed to support a government's strategic decisions in terms of budget for vaccination campaigns as well as a structured and optimized way to determine the location of the vaccination points. It was not the objective of this model to provide a daily schedule of how to operate the network, and for this reason, we did not follow the location routing approach applied by Mofrad (2016) and Yang and Rajgopal (2019).…”

“…The formulation included stochastic demand but did not consider causal demand based on population distance. The modeling approach in the third paper (Yang and Rajgopal, 2019) was very similar to Mofrad (2016), with the addition of a time aspect into the outreach trip planning. In this sense, the work by Yang and Rajgopal (2019) can be seen as a vehicle routing problem with time windows (VRPTW) combined with a set-covering problem (SCP).…”

A robust optimization problem for drone-based equitable pandemic vaccine distribution with uncertain supply

“… [5] derived a CCP for a stochastic distribution problem in low-and middle-income countries. In addition to unstable demand, Yang and Rajgopal [61] also dealt with the uncertainty of travel time, which optimizes the location and routing decisions of mobile clinics. A multi-period modeling method was employed to consider the worst-case scenario and update the decision.…”

Multi-period vaccine allocation model in a pandemic: A case study of COVID-19 in Australia

“…The first is the geographical dispersion of the population to be vaccinated. Using mobile vaccination units play a vital role in geographically dispersed populations and especially in rural and remote areas ( Muckstadt et al, 2021 , Yang and Rajgopal, 2021 ). The second reason is the resource and capacity limitation (i.e., number of medical centers, vaccine supply) in vaccine allocation and distribution.…”

“…Conversely, in a pandemic situation when the lives of people are at stake and all economic factors are linked to vaccinating the populace at early as possible, the objective function is formulated to minimize the risk of having an unvaccinated population. For example, Yang and Rajgopal (2021) proposed a multi-period allocation model within the context of a non-pandemic vaccine supply chain. They use an approach known as outreach to increase the immunization rates among geographically dispersed and nomadic populations in low- and middle-income countries.…”