Optimality in COVID-19 vaccination strategies determined by heterogeneity in human-human interaction networks

Abstract

Interactions between humans cause transmission of SARS-CoV-2. We demonstrate that heterogeneity in human-human interactions give rise to non-linear infection networks that gain complexity with time. Consequently, targeted vaccination strategies are challenged as such effects are not accurately captured by epidemiological models assuming homogeneous mixing. With vaccines being prepared for global deployment determining optimality for swiftly reaching population level immunity in heterogeneous local communities world-wide is critical. We introduce a model that predicts the effect of vaccination into an ongoing COVID-19 outbreak using precision simulation of human-human interaction and infection networks. We show that simulations incorporating non-linear network complexity and local heterogeneity can enable governance with performance-quantified vaccination strategies. Vaccinating highly interactive people diminishes the risk for an infection wave, while vaccinating the elderly reduces fatalities at low population level immunity. Interestingly, a combined strategy is not better due to non-linear effects. While risk groups should be vaccinated first to minimize fatalities, significant optimality branching is observed with increasing population level immunity. Importantly, we demonstrate that regardless of immunization strategy non-pharmaceutical interventions are required to prevent ICU overload and breakdown of healthcare systems. The approach, adaptable in real-time and applicable to other viruses, provides a highly valuable platform for the current and future pandemics.

Models of COVID-19 vaccine prioritisation: a systematic literature search and narrative review

“…Six studies reported that the trade-off between direct and indirect protection is sensitive to the proportion of people vaccinated. [12] [9] [21] [22] [15] [32] These papers stated that when vaccine supply is very low vaccination has a minimal impact on interrupting transmission, so more deaths can be prevented to vaccinate groups at risk of severe disease (e.g., key workers, seniors and clinical risk groups). However, as supply increases, this opens up the possibility of interrupting transmission, which can change prioritisation to the young or those with many contacts.…”

“…Seven studies recommended vaccination prioritisation based on social or occupational interactions compared to age group prioritisation. [28] [27] [31][32] [36][14] [37] Of these, three studies recommended prioritising essential workers to minimise cases[28] [27][14] and four studies recommended prioritising high social contact adults compared to other age groups. [31] [32] [37] [36]…”

A Vaccination Simulator for COVID-19: Effective and Sterilizing Immunization Cases

“…administer the vaccinations, and whether there is a strong healthcare infrastructure, sufficient funding and a system for vaccination monitoring [8]. The common strategies are aimed to reduce the mortality, decrease the pressure on the healthcare, and minimize the infection spread over a specific period of time [5].…”

Estimating the impact of interventions against COVID-19: From lockdown to vaccination

“…Various levels of pre-existing immunity were also assumed, ranging from 5% to 20%, depending on the region. In [ 75 ], the authors studied the optimal choice of vaccination strategy under a partial or complete lockdown. Each of the individuals appearing in their model had a pre-assigned daily routine, specified on the resolution of 1 hour, with the routine determining the order in which the individuals move between different locations, such as workplaces, schools, public places, hospitals and homes.…”