Population-level analyses often use average quantities to describe heterogeneous systems, particularly when variation does not arise from identifiable groups. A prominent example, central to our current understanding of epidemic spread, is the basic reproductive number, R(0), which is defined as the mean number of infections caused by an infected individual in a susceptible population. Population estimates of R(0) 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. 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. 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 R(0) 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.

Studies on the burden of human monkeypox in the Democratic Republic of the Congo (DRC) were last conducted from 1981 to 1986. Since then, the population that is immunologically naïve to orthopoxviruses has increased significantly due to cessation of mass smallpox vaccination campaigns. To assess the current risk of infection, we analyzed human monkeypox incidence trends in a monkeypox-enzootic region. Active, population-based surveillance was conducted in nine health zones in central DRC. Epidemiologic data and biological samples were obtained from suspected cases. Cumulative incidence (per 10,000 population) and major determinants of infection were compared with data from active surveillance in similar regions from 1981 to 1986. Between November 2005 and November 2007, 760 laboratory-confirmed human monkeypox cases were identified in participating health zones. The average annual cumulative incidence across zones was 5.53 per 10,000 (2.18–14.42). Factors associated with increased risk of infection included: living in forested areas, male gender, age < 15, and no prior smallpox vaccination. Vaccinated persons had a 5.2-fold lower risk of monkeypox than unvaccinated persons (0.78 vs. 4.05 per 10,000). Comparison of active surveillance data in the same health zone from the 1980s (0.72 per 10,000) and 2006–07 (14.42 per 10,000) suggests a 20-fold increase in human monkeypox incidence. Thirty years after mass smallpox vaccination campaigns ceased, human monkeypox incidence has dramatically increased in rural DRC. Improved surveillance and epidemiological analysis is needed to better assess the public health burden and develop strategies for reducing the risk of wider spread of infection.

Few infectious diseases are entirely human-specific: most human pathogens also circulate in animals, or else originated in non-human hosts. Influenza, plague, and trypanosomiasis are classic examples of zoonoses, or infections that transmit from animals to humans. The multi-host ecology of zoonoses leads to complex dynamics, and analytical tools such as mathematical modeling are vital to the development of effective control policies and research agendas. Much attention has focused on modeling pathogens with simpler life cycles and immediate global urgency, such as influenza and SARS, but vector-transmitted, chronic, and protozoan infections have been neglected, as have crucial processes such as cross-species transmission. Progress in understanding and combating zoonoses requires a new generation of models that addresses a broader set of pathogen life histories and integrates across host species and scientific disciplines.