Positive feedback loops exacerbate the influence of superspreaders in disease transmission

Abstract Superspreaders are recognised as being important drivers of disease spread, most recently having played a major role in the ongoing COVID-19 pandemic. However, models to date have assumed random occurrence of superspreaders, irrespective of whom they were infected by. Evidence suggests though that this may not be the case, and that those individuals infected by superspreaders may be more likely to become superspreaders themselves, hence creating a positive feedback loop. This could happen, for example, through superspreaders releasing high infective doses that cause high infection intensities in newly-infected individuals, which increases the likelihood that they, in turn, will become superspreaders. Here, we examine the effects of such a positive feedback loop on (1) the final epidemic size, (2) the basic reproductive number, R0, and (3) the peak prevalence of superspreaders. We show that positive feedback loops can have a profound effect on the outcome of an epidemic, even when the transmission advantage of superspreaders is moderate, and despite peak prevalence of superspreaders remaining low. We argue that positive superspreader feedback loops in different infectious diseases, including SARS-CoV-2, should be investigated further.