Governance structure affects transboundary disease management under alternative objectives

Abstract

Background The development of public health policy is inextricably linked with governance structure. In our increasingly globalized world, human migration and infectious diseases often span multiple administrative jurisdictions that might have different systems of government and divergent management objectives. However, few studies have considered how the allocation of regulatory authority among jurisdictions can affect disease management outcomes. Methods Here we evaluate the relative merits of decentralized and centralized management by developing and numerically analyzing a two-jurisdiction SIRS model that explicitly incorporates migration. In our model, managers choose between vaccination, isolation, medication, border closure, and a travel ban on infected individuals while aiming to minimize either the number of cases or the number of deaths. Results We consider a variety of scenarios and show how optimal strategies differ for decentralized and centralized management levels. We demonstrate that policies formed in the best interest of individual jurisdictions may not achieve global objectives, and identify situations where locally applied interventions can lead to an overall increase in the numbers of cases and deaths. Conclusions Our approach underscores the importance of tailoring disease management plans to existing regulatory structures as part of an evidence-based decision framework. Most importantly, we demonstrate that there needs to be a greater consideration of the degree to which governance structure impacts disease outcomes.

The effect of governance structures on optimal control of two-patch epidemic models

“…However, recent work suggests that this choice may be consequential for outbreak suppression (Blackwood et al. 2021 ). In this paper, we used two case studies to investigate the differences between two control policies of a central manager: “uniform”, where the level of control must be the same in both patches, and “non-uniform”, where the level of control is allowed to vary between the patches.…”

“…Recently, Blackwood et al. ( 2021 ) began to tackle the question of how governance structure impacts optimal control as a Structured Decision Making problem (Gregory et al. 2012 ; Shea et al.…”

“…The work of Blackwood et al. ( 2021 ) highlighted the potential trade-offs between the local and global management of a generic infectious disease, showing that policies which are best for one jurisdiction may lead to worse disease outcomes overall. We extend this work in three primary ways: (1) we consider the management of two specific pathogens with differing modes of transmission; (2) we optimize management strategies with time-varying functions using optimal control theory (with disease outcomes combined with cost of implementing the controls), and (3) we compare the optimal control decisions under both uniform and non-uniform governance structures following a method from Sanchirico et al.…”

“…We define a uniform policy as the implementation of exactly the same level of control in both patches (“centralized uniform management” in Blackwood et al. 2021 ), and a non-uniform policy as one in which the level of control for each patch is allowed to differ (“centralized jurisdiction-specific management” in Blackwood et al. ( 2021 )).…”

“… 2021 ), and a non-uniform policy as one in which the level of control for each patch is allowed to differ (“centralized jurisdiction-specific management” in Blackwood et al. ( 2021 )).…”