Efficient vaccination strategies to reduce the epidemic mortality in population with heterogeneous fatality rate

Abstract

An insufficient supply of effective SARS-CoV-2 vaccine in most countries demands an effective vaccination strategy to minimize the damage caused by the disease. Currently, many countries vaccinate their population in descending order of age to minimize the deaths caused by the disease; however, the effectiveness of this strategy needs to be quantitatively assessed. We employed the susceptible-infected-recovered-dead (SIRD) model to investigate various vaccination strategies. In complex network, the case fatality rate (CFR)-based method was shown to be more effective than the load-based strategy when there is a low supply of vaccine; however, when there is a sufficient quantity of vaccine, the load-based strategy is more effective than the CFR-based strategy. We also constructed a metapopulation model with empirical human contact and CFR data for SARS-CoV-2 and investigated vaccination strategies. We found that the age-based strategy, which is currently employed in many countries, is more effective when the basic reproduction number is high and vaccination supply is low, but the rate-based method outperforms the age-based strategy when there is sufficiently high supply of vaccine. Simulated annealing is performed to find the optimal vaccination strategy. We identified a first-order phase transition in the vaccination strategies which is characterized by discontinuous transition in the optimal strategy and the hysteresis. This phase transition implies that mixing the age-based and rate-based strategy is ineffective in reducing the number of deaths. These conclusions are valid even when the heterogeneous degree distribution of human contact is considered.

Effective vaccination strategy using graph neural network ansatz

“…2(f). Similar phenomena in the metapopulation model have been reported, and the first-order phase transition has been identified [49]; however, such research has not been extended to networks due to the lack of an appropriate method to study the optimal vaccination strategy in complex networks. By considering the node features and tailoring the vaccination strategy to specific levels of vaccine supply, GNNA enables us to observe a new phenomenon in complex networks that could not be observed by the existing vaccination strategies.…”

“…This transition is of theoretical interest also with real-world implications. For instance, the hysteresis of the optimal vaccination strategy implies that mixing the fatality-and centralitybased strategy is ineffective [49]. Our framework can be implemented in future research to find other new phenomena in the optimal vaccination strategies that couldn't be observed in the current centrality-based vaccination paradigm.…”

“…Such a situation wherein the nodes of a network possess distinctive characteristics is relevant in real-world epidemics. For instance, the case fatality rate of COVID-19 varies significantly according to age [42][43][44][45][46][47][48]; hence, it is not trivial to determine whether senior population with high fatality rate or young population with high contact rate should be vaccinated first to minimize the epidemic mortality [49]. The age dependent efficacy and probability of severe reaction to vaccines further complicates the issue [50,51].…”