Epidemic control with endogenous treatment capability under popular discontent and social fatigue

Abstract

This paper investigates how the optimization of policymaker interventions against an epidemic disease is affected by contextual factors related to (i) a cost‐centered approach to countering the epidemic, (ii) eventual correlative popular discontent, and (iii) growing social fatigue engendered by nontherapeutic interventions. Three nontherapeutic policies—mobility restrictions (MR), securing social interactions (SSI), and a combination of both (MR + SSI)—are compared in terms of effectiveness and efficiency in preventing infections and saving lives. The costs associated with these policies are economic as well as operational. The objective is to select the lowest cost policy that is the least sensitive to the cited contextual factors. In many ways, our model applies to the Covid‐19 pandemic in its prevaccine phase. Our study shows the significant influence of popular discontent on epidemic control policies. We show that in contrast to the standard delayed peak strategy, which seeks to delay and flatten the epidemic peak, there is an alternative strategy, counter to conventional wisdom, which consists of accelerating the onset of the epidemic peak. Although the delayed peak strategy is contingent on popular neutrality, the advanced peak strategy results from popular discontent as a corollary of a cost‐centered approach. We also find that social fatigue does not affect the social cost much but, in contrast to the controlled scenarios without social fatigue, it amplifies the peak of infections. In this context, the inclusive policy that incorporates MR + SSI mitigates the negative impact of social fatigue, although it comes at the cost of tighter restrictions than either of the exclusive policies. We conclude that, as opposed to popular discontent, social fatigue should be disregarded for the sake of greater effectiveness and efficiency in infection and death prevention. Our results can be used by policymakers to allocate resources appropriately in MR, SSI, or both and to build treatment capability.

A queueing‐theoretic framework for evaluating transmission risks in service facilities during a pandemic

“…(2020), and El Ouardighi et al. (2021). Unlike these models and their focus on population‐ and community‐level disease spread, our focus is on small‐scale settings like a service facility that is prone to congestion driven by idiosyncratic stochasticity.…”

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“…Similar to Ferguson et al (2020), their findings reveal that multiple intervention policies, such as widespread testing, contact tracing, and social distancing, should be integrated to end the outbreak. The literature also considers the impact of mobility patterns (Delen et al, 2020), age-specific contact settings (Kyrychko et al, 2020), as well as popular discontent and social fatigue on pandemic control policies (Ouardighi et al, 2021).…”

Chasing up and locking down the virus: Optimal pandemic interventions within a network

“…Among other things, they emphasize the importance of testing which they find to enable the almost entire elimination of mortality at low economic costs in a one-region/group model. El Ouardighi et al (2021) take account of social fatigue and popular discontent when determining the optimal application of nonpharmaceutical interventions (mobility restrictions, isolation, and securing social interactions) over time. Acemoglu et al (2021) emphasize the importance of distinguishing between different risk groups when designing policy measures.…”