Abstract
We study the effects of two mechanisms which increase the efficacy of contact-tracing applications (CTAs) such as the mobile phone contact-tracing applications that have been used during the COVID-19 epidemic. The first mechanism is the introduction of user referrals. We compare four scenarios for the uptake of CTAs—(1) the p% of individuals that use the CTA are chosen randomly, (2) a smaller initial set of randomly-chosen users each refer a contact to use the CTA, achieving p% in total, (3) a small initial set of randomly-chosen users each refer around half of their contacts to use the CTA, achieving p% in total, and (4) for comparison, an idealised scenario in which the p% of the population that uses the CTA is the p% with the most contacts. Using agent-based epidemiological models incorporating a geometric space, we find that, even when the uptake percentage p% is small, CTAs are an effective tool for mitigating the spread of the epidemic in all scenarios. Moreover, user referrals significantly improve efficacy. In addition, it turns out that user referrals reduce the quarantine load. The second mechanism for increasing the efficacy of CTAs is tuning the severity of quarantine measures. Our modelling shows that using CTAs with mild quarantine measures is effective in reducing the maximum hospital load and the number of people who become ill, but leads to a relatively high quarantine load, which may cause economic disruption. Fortunately, under stricter quarantine measures, the advantages are maintained but the quarantine load is reduced. Our models incorporate geometric inhomogeneous random graphs to study the effects of the presence of super-spreaders and of the absence of long-distant contacts (e.g., through travel restrictions) on our conclusions.
Highlights
Agent-based epidemiological models allow a population to be embedded in a geometric space to capture the effect of the “local” and “long-distance” contacts that arise in real populations
We find that contact-tracing applications (CTAs) are effective in decreasing the size of an epidemic and in decreasing the maximum number of people who are simultaneously hospitalised, and typically this effectiveness increases linearly or super-linearly with the percentage of the population that use CTA
The novelty of our study is to compare recommendation-based uptake with random uptake, where we find that recommending can significantly improve the efficacy of the CTA
Summary
Agent-based epidemiological models allow a population to be embedded in a geometric space to capture the effect of the “local” and “long-distance” contacts that arise in real populations. We find that in scenarios where the CTA is recommended to acquaintances the epidemic size and maximum hospital load decrease at a much higher rate than the rate that would be achieved by randomly selecting the same number of CTA users One might expect these advantages of recommendation-based uptake to come at the cost of increased quarantine. The critical point on the ‘quarantine-strictness scale’ above which the social disruption starts decreasing comes earlier in recommender scenarios and degree-targetted uptake than in random uptake, Increasing efficacy of contact-tracing applications by user referrals and stricter quarantining and even earlier if individuals are more restricted in their movements, i.e., the underlying contact network does not have many long-range connection, ranging over large spatial distances.
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