The Effect of Mass Asymptomatic Testing in Controlling Infection Spread
Summary
As apparent from the recent global pandemic, understanding infectious diseases and developing effective mitigation strategies is a pressing public health issue. Mathematical models can be used to study and predict the effect of mitigation strategies in reducing the impact of an infection outbreak. One such mitigation strategy is the use of mass asymptomatic testing followed by isolation upon positive result. We use a time-since-infection framework to study the effect of testing at a constant rate on the final size and costs of an outbreak of an infection, assuming isolation upon testing positive. This deterministic approach allows for a mechanistic link between within-host dynamics and between-host transmission. The within-host dynamics are represented by an infectivity profile: the infectiousness, assuming a constant contact rate, of the average infected individual as a function of their infection age. Similarly, we use a testpositivity curve to define the probability of an infected individual testing positive if tested at any time during their infection. Assuming a constant rate of testing with instant test results, and immediate isolation upon a positive result, the two curves combine into a remaining infectivity, which can be used to calculate the full dynamics of an outbreak using a renewal equation for the incidence. The area under the remaining infectivity curve is defined as the control reproduction number, from which the final size of the outbreak can be derived. Using SARS-CoV-2 as a case study, the relationship between the distribution shapes of the infectivity and test-positivity curves and their influence on the required testing rate to suppress outbreaks can be explored. The combination of distribution shapes chosen for these curves can highly affect the resulting total remaining infectivity, in particular the early part of both curves that govern the outcome of the model. This result is significant, as possible effects of different distribution shapes are often disregarded in epidemiological models. This model shows the need for more careful consideration of the shape of such curves. Another application of the time-since-infection model is its usability to analyse the health economics involved in intervention strategy decisions. Notably, a simple example of a cost-benefit analysis shows total costs are not monotonically increasing with testing rate, but instead a possible optimal testing rate exists in terms of monetizable aspects of an outbreak. Generally, the model is well-suited to perform more elaborate cost-effectiveness analyses to inform policy decisions on outbreak mitigation strategies. Similarly to the adaptability of a health economics analysis, the model itself is highly suitable to be adapted to study various infections, and different aspects of mitigation strategies. For example, the effect of moving from a constant testing rate to regular interval testing could be explored, or delayed test results as with PCR testing. In conclusion, this time-since-infection model offers a widely applicable and easily adaptable tool to analyse the effect of various mitigation strategies on infection outbreaks, as well as study general assumptions made within the field of epidemiological modelling.