Modeling shield immunity to reduce COVID-19 transmission in long-term care facilities

Abstract

Nursing homes and other long-term care facilities in the United States have experienced severe COVID-19 outbreaks and elevated mortality rates, often following upon the inadvertent introduction of SARS-CoV-2. Following FDA emergency use approval, widespread distribution of vaccines has resulted in rapid reduction in COVID-19 cases in vulnerable, older populations. Yet, vaccination coverage remains incomplete amongst residents and healthcare workers. As such, mitigation and prevention strategies are needed to reduce the ongoing risk of transmission and mortality amongst vulnerable, nursing home populations. One such strategy is that of ′shield immunity′, in which recovered individuals increase their contact rates and therefore shield individuals who remain susceptible to infection. Here, we adapt recent population-scale shield immunity models to a network context. To do so, we evaluate network-based shield immunity by evaluating how restructured interactions in a bipartite network (e.g., between healthcare workers and long-term care residents) affects SARS-CoV-2 epidemic dynamics. First, we identify a series of rewiring principles that leverage viral testing, antibody testing, and vaccination information to reassign immunized healthcare workers to care for infected residents while retaining workload balance amidst an outbreak. We find a significant reduction in outbreak size when using infection and immune-based cohorting as a weekly intervention. Second, we also identify a preventative strategy using shield-immunity rewiring principles, by assigning susceptible healthcare workers to care for cohorts of immunized residents; this strategy reduces the risk that an inadvertent introduction of SARS-CoV-2 into the facility via a healthcare worker spreads to susceptible residents. Network-based epidemic modeling reveals that preventative rewiring can control the size of outbreaks at levels similar to that of isolation of infectious healthcare workers. Overall, this assessment of shield immunity provides further support for leveraging infection and immune status in network-based interventions to control and prevent the spread of COVID-19.

Modeling COVID-19 optimal testing strategies in long-term care facilities: An optimization-based approach

“…In Germany, [35] studied COVID-19 outbreaks in retirement homes and found that symptom control and testing, in addition to vaccination, are effective prevention strategies. [36] employed an SEIR network epidemic model based on disease status. They used the model to study a shield-immunity approach, considering a bipartite network between the staff and residents.…”

Optimal Workplace Occupancy Strategies during the COVID-19 Pandemic

“…The effect of a cyclic 4-day work and 10-day lockdown strategy in the workplace has been investigated by [27]. In nursing homes and long-term care facilities, [28] constructed a mathematical model based on bipartite networks consisting of health care workers and facility residents to investigate how restructuring interactions via the concept of shied immunity can change outbreak dynamics. Specifically, shield immunity refers to the practice of proportionally increasing the interactions between recovered and susceptible individuals to protect the susceptible from infection.…”

On the optimal presence strategies for workplace during pandemics: A COVID-19 inspired probabilistic model

“…The effect of cyclic 4-day work and 10-day lockdown strategy in workplace has been investigated by [26]. In nursing home and long-term care facilities, [27] constructed a mathematical model based on bipartite network consists of health care workers and the residents and investigated how the restructured interactions in the bipartite network can change the outcome of the epidemics in the facility by exploiting the strategy of shied immunity, where the recovered people increase their interactions with susceptible population to protect them from the infection. In order to support the decision-making process, [28] developed a strategy to predict the risk of infection among the health workers and other employees in the essential sectors by taking into account the working process, proximity between employees, type of activity, etc.…”