Prioritizing vaccination based on analysis of community networks

Abstract

Many countries that had early access to COVID-19 vaccines implemented vaccination strategies that prioritized health care workers and the elderly. As barriers to access eased, vaccine prioritization strategies have been relaxed. However, these strategies are still an important tool for decision makers to manage new variants, plan for future booster shots, or stage mass vaccinations. This paper explores the impact of vaccine prioritization strategies using networks that represent communities with different demographics and connectivity. The impact of vaccination is compared to non-medical intervention to reduce transmission. Several sources of uncertainty are considered, including vaccine willingness and mask effectiveness. This paper finds that while prioritization strategies can have a large impact on reducing deaths and peak hospitalization, selecting the best strategy depends on community characteristics and the desired objective. Additionally, in some cases random vaccination performs as well as more targeted prioritization strategies. Understanding these trade-offs is important when planning vaccine distribution.

Transparent transmission models for informing public health policy: the role of trust and generalizability

“…Optimal control strategies can depend on whether transmission, healthcare capacity or mortality is being targeted (e.g. [ 9 , 10 ]). Where some models are more equipped to answer certain questions than others, having a clearly defined target is crucial to being able to adequately capture relevant disease dynamics and convey results to policymakers.…”

“…Providing projections solely for synthetic communities may limit translatability into policy and decrease relatability for the public. The local demographic structure of a community may be highly influential on optimal vaccine prioritization, as has been previously demonstrated within the context of the USA [ 9 ]. For these reasons, it is exciting that Conway et al .…”

A Multi-SCALE Community Network-Based SEIQR Model to Evaluate the Dynamic NPIs of COVID-19

“…FARZ has three input parameters, FARZ (n, m, k), which determine the population, average number of contacts, and number of communities, respectively. It also has four control parameters that control the strength of the community structure, clustering coefficient, degree correlation, and distribution of the community sizes, respectively, with values taken from [35]. Specifically, we divided the contact network into three layers and used three sizes of areas to perform the division of close contacts: the number of administrative districts (large range), number of townships (medium range), and number of residential areas (small range) in Shanghai, respectively.…”

“…We compared our model (red line) and the model using the population age distribution (blue line) with the real data (yellow line), and the difference between the two models and the real data can be seen in Figure 4. The population age distribution model [35] was used to construct community networks with different age distributions and household sizes. We collected data on the age distribution [26] and the number of contacts in each age group [31] in Shanghai and simulated the infection results from March to July in Shanghai.…”