1. Department of Environmental Science, Policy and Management, 140 Mulford Hall, University of California, Berkeley, California 94720-3114, USA
2. Biophysics Graduate Group, University of California, Berkeley, California 94720-3200, USA
3. Department of Mathematics, The College of William and Mary, Williamsburg, Virginia 23187-8975, USA
4. Centre for Mathematics, University of Hull, Hull HU6 7RX, UK

Correspondence to: J. O. Lloyd-Smith^1,2 Correspondence and requests for materials should be addressed to to J.O.L.-S. (Email: jls@nature.berkeley.edu).

Abstract

Population-level analyses often use average quantities to describe heterogeneous systems, particularly when variation does not arise from identifiable groups^{1, 2}. A prominent example, central to our current understanding of epidemic spread, is the basic reproductive number, R₀, which is defined as the mean number of infections caused by an infected individual in a susceptible population^{3, 4}. Population estimates of R₀ can obscure considerable individual variation in infectiousness, as highlighted during the global emergence of severe acute respiratory syndrome (SARS) by numerous 'superspreading events' in which certain individuals infected unusually large numbers of secondary cases^{5, 6, 7, 8, 9, 10}. For diseases transmitted by non-sexual direct contacts, such as SARS or smallpox, individual variation is difficult to measure empirically, and thus its importance for outbreak dynamics has been unclear^{2, 10, 11}. Here we present an integrated theoretical and statistical analysis of the influence of individual variation in infectiousness on disease emergence. Using contact tracing data from eight directly transmitted diseases, we show that the distribution of individual infectiousness around R₀ is often highly skewed. Model predictions accounting for this variation differ sharply from average-based approaches, with disease extinction more likely and outbreaks rarer but more explosive. Using these models, we explore implications for outbreak control, showing that individual-specific control measures outperform population-wide measures. Moreover, the dramatic improvements achieved through targeted control policies emphasize the need to identify predictive correlates of higher infectiousness. Our findings indicate that superspreading is a normal feature of disease spread, and to frame ongoing discussion we propose a rigorous definition for superspreading events and a method to predict their frequency.

1. Department of Environmental Science, Policy and Management, 140 Mulford Hall, University of California, Berkeley, California 94720-3114, USA
2. Biophysics Graduate Group, University of California, Berkeley, California 94720-3200, USA
3. Department of Mathematics, The College of William and Mary, Williamsburg, Virginia 23187-8975, USA
4. Centre for Mathematics, University of Hull, Hull HU6 7RX, UK

Correspondence to: J. O. Lloyd-Smith^1,2 Correspondence and requests for materials should be addressed to to J.O.L.-S. (Email: jls@nature.berkeley.edu).

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