Study population and setting
The model was of the general population, with no specific setting mentioned. The age distribution of the population was taken from different states in the United States.
Summary of Main Findings
The study used mathematical models to estimate the impact of identifying people who have recovered from COVID-19 and have protective antibodies to SARS-CoV-2 virus, and deploying them preferentially in roles requiring high levels of interaction with others (e.g. in healthcare, caring for the elderly, schools, food supply). The authors called this approach ‘shield immunity’. Their model findings suggested that the shield immunity strategy could substantially reduce the total number of COVID-19 infections and deaths, reduce the duration of the epidemic and reduce the burden on the healthcare system. Their results suggested that shield immunity could be effective in populations with different age profiles. As it was not known what proportion of COVID-19 infections are asymptomatic (have no symptoms), nor how long protective immunity might last for, the authors re-ran their model with different proportions of infections being asymptomatic, and with different durations of protective immunity, and found that the model still suggested that shield immunity would be effective in all cases, but particularly if fewer infections were asymptomatic, or if immunity lasted for at least 4 months. Their results also suggested that shield immunity provided additional benefits when combined with social distancing.
The model structure and assumptions were clear and transparent, and the authors checked whether their findings were affected by unknown quantities including the proportion of infections that do not have symptoms, and how long protective immunity lasts for.
The model was not fitted to data from a specific location, meaning it may not have represented a realistic epidemic. The shield immunity strategy was represented in the model in a non-intuitive way that makes it hard to understand exactly what the strategy would look like in real life, and hard to know whether the strategies they model could be realistically achieved. The processes of identifying people with immunity (e.g. through antibody testing) and of substituting them into high-interaction roles were not explicitly represented in the model so it is not clear what levels of testing or coverage of substitution would be needed to have the impact that they found. Current uncertainties around whether antibodies are good surrogates for protective immunity were not sufficiently stressed. Ethical and legal issues around employment differing by immune status and concerns that people may deliberately try to become infected so that they can gain immunity to improve their employment prospects were not addressed.
This study is one of the first to assess the potential impact on the COVID-19 epidemic of identifying people with immunity to the virus and preferentially placing them in roles with high levels of contact with others.