Exploring a micro-simulation study on shielding individuals at high risk of COVID-19

The coronavirus disease 2019 (COVID-19) pandemic, caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus, has ravaged lives and livelihoods, worldwide. It has been particularly difficult to contain the pandemic in poorer and densely populated regions.

A mitigating option, known as “shielding”, consists of physically isolating individuals known to be at high risk of developing severe disease and dying from COVID-19.

A new study, published on the medRxiv* preprint server, used an individual-based mathematical model to simulate the evolution of COVID-19 in a population, where a fraction of high-risk individuals is relocated to shielding residences having varying degrees of contact with others.

What is Shielding?

Shielding is a mitigating measure that could reduce the risk of severe disease and mortality rates while herd immunity builds up in the low-risk population groups. Like other containment measures, it could also lessen the burden on health services and allow societies and economies to remain open and functional.

It has been stated that shielding is a community-led and -designed intervention with no pre-set modalities. Shielding high-risk individuals in “shielding residences” could be a likely arrangement where isolation within households is not possible. However, how effective shielding is could depend on a number of factors, such as the number of people shielded together, the timing of its introduction, arrangements for infection prevention, etc.

A new study

Existing compartmental dynamic models do not fully capture the individual-level dynamics of shielding. What remains unknown is the possible harm of inadvertently introducing infection into shielded residences, which might or might not outweigh the benefits of the intervention.

In the current study, scientists constructed an individual-based mathematical model with the objective to simulate the COVID-19 epidemic in a population where a certain proportion of individuals above a given age cut-off are relocated to shielding residences. In these residences, individuals were modeled to have varying levels of contact with their respective households, the outside world, and fellow shielding residents. Scientists collected data on household demographics and social mixing patterns from an internally displaced persons’ camp in Somaliland and set the simulation in that context. They contrasted an unmitigated epidemic with a shielding intervention accompanied by various measures to contain the spread of the disease. Sensitivity analyses were conducted to evaluate parameters such as residence size, the reproduction number, reduction in the number of contacts, and prior immunity in the population.

Main findings

Scientists found that the impact of shielding largely depends on how effectively it is implemented and the amount of coverage of the population. The total number of individuals shielded together and the reduction in contact between shielded and unshielded individuals are essential factors determining the effectiveness of the policy.

The model predicted that smaller shielding residences would be considerably more effective than larger ones as they are less likely to be breached and once breached, there is limited opportunity for further transmission. Where it is not possible to implement small residences, medium-scale establishments, with additional mitigation measures, could be an alternative. It was further observed that shielding near the old household may result in greater care by low-risk individuals, where contact rates with unshielded individuals cannot be reduced by much. It was also observed that contact intensity could be reduced by grouping individuals from different households, as opposed to the same households. 

At the other end of the spectrum, the model predicted that large residences should be avoided unless a very stringent form of shielding (little or no contact between shielded and unshielded individuals ) is implemented. However, this would be quite difficult to implement in reality. If self-isolation in a residence were to be unfeasible, screening could be implemented, whereby, where symptomatic individuals exit the shielding residence as soon as possible after symptom onset. 

The model predicted marginal effects of other mitigation measures, such as testing individuals before shielding and dismantling the shielding residence once a case is detected. This is so because these measures would fail to detect individuals already infected but not yet infectious. Further, in situations with a low reproduction number, shielding individuals in medium- to large groups could increase their infection risk and result in harm. 

Limitations

Scientists only considered Digaale IDP camp, which is a relatively small-scale settlement and may not be representative of other low resource or crisis-affected regions. Further, the number of cases and deaths that would be expected after infection were not estimated. The analysis assumed that only individuals aged 60+ years old were high-risk but more definitions of at-risk populations should be considered in future research. Lastly, simultaneous implementations of other measures were also not considered, but typically shielding measures are accompanied by other non-pharmaceutical interventions. Future research could address some of these concerns to provide a holistic understanding of shielding measures to contain a pandemic.

*Important notice

medRxiv publishes preliminary scientific reports that are not peer-reviewed and, therefore, should not be regarded as conclusive, guide clinical practice/health-related behavior, or treated as established information.

Written by

Dr. Priyom Bose

Priyom holds a Ph.D. in Plant Biology and Biotechnology from the University of Madras, India. She is an active researcher and an experienced science writer. Priyom has also co-authored several original research articles that have been published in reputed peer-reviewed journals. She is also an avid reader and an amateur photographer.