An open-access database of infectious disease transmission trees to explore superspreader epidemiology.

Historically, emerging and reemerging infectious diseases have caused large, deadly, and expensive multinational outbreaks. Often outbreak investigations aim to identify who infected whom by reconstructing the outbreak transmission tree, which visualizes transmission between individuals as a network with nodes representing individuals and branches representing transmission from person to person. We compiled a database, called OutbreakTrees, of 382 published, standardized transmission trees consisting of 16 directly transmitted diseases ranging in size from 2 to 286 cases. For each tree and disease, we calculated several key statistics, such as tree size, average number of secondary infections, the dispersion parameter, and the proportion of cases considered superspreaders, and examined how these statistics varied over the course of each outbreak and under different assumptions about the completeness of outbreak investigations. We demonstrated the potential utility of the database through 2 short analyses addressing questions about superspreader epidemiology for a variety of diseases, including Coronavirus Disease 2019 (COVID-19). First, we found that our transmission trees were consistent with theory predicting that intermediate dispersion parameters give rise to the highest proportion of cases causing superspreading events. Additionally, we investigated patterns in how superspreaders are infected. Across trees with more than 1 superspreader, we found preliminary support for the theory that superspreaders generate other superspreaders. In sum, our findings put the role of superspreading in COVID-19 transmission in perspective with that of other diseases and suggest an approach to further research regarding the generation of superspreaders. These data have been made openly available to encourage reuse and further scientific inquiry.

Association between tuberculosis in men and social network structure in Kampala, Uganda.

BACKGROUND: Globally, tuberculosis disease (TB) is more common among males than females. Recent research proposes that differences in social mixing by sex could alter infection patterns in TB. We examine evidence for two mechanisms by which social-mixing could increase men's contact rates with TB cases. First, men could be positioned in social networks such that they contact more people or social groups. Second, preferential mixing by sex could prime men to have more exposure to TB cases. METHODS: We compared the networks of male and female TB cases and healthy matched controls living in Kampala, Uganda. Specifically, we estimated their positions in social networks (network distance to TB cases, degree, betweenness, and closeness) and assortativity patterns (mixing with adult men, women, and children inside and outside the household). RESULTS: The observed network consisted of 11,840 individuals. There were few differences in estimates of node position by sex. We found distinct mixing patterns by sex and TB disease status including that TB cases have proportionally more adult male contacts and fewer contacts with children. CONCLUSIONS: This analysis used a network approach to study how social mixing patterns are associated with TB disease. Understanding these mechanisms may have implications for designing targeted intervention strategies in high-burden populations.

The statistics of epidemic transitions.

Emerging and re-emerging pathogens exhibit very complex dynamics, are hard to model and difficult to predict. Their dynamics might appear intractable. However, new statistical approaches-rooted in dynamical systems and the theory of stochastic processes-have yielded insight into the dynamics of emerging and re-emerging pathogens. We argue that these approaches may lead to new methods for predicting epidemics. This perspective views pathogen emergence and re-emergence as a "critical transition," and uses the concept of noisy dynamic bifurcation to understand the relationship between the system observables and the distance to this transition. Because the system dynamics exhibit characteristic fluctuations in response to perturbations for a system in the vicinity of a critical point, we propose this information may be harnessed to develop early warning signals. Specifically, the motion of perturbations slows as the system approaches the transition.

Anticipating epidemic transitions with imperfect data.

Epidemic transitions are an important feature of infectious disease systems. As the transmissibility of a pathogen increases, the dynamics of disease spread shifts from limited stuttering chains of transmission to potentially large scale outbreaks. One proposed method to anticipate this transition are early-warning signals (EWS), summary statistics which undergo characteristic changes as the transition is approached. Although theoretically predicted, their mathematical basis does not take into account the nature of epidemiological data, which are typically aggregated into periodic case reports and subject to reporting error. The viability of EWS for epidemic transitions therefore remains uncertain. Here we demonstrate that most EWS can predict emergence even when calculated from imperfect data. We quantify performance using the area under the curve (AUC) statistic, a measure of how well an EWS distinguishes between numerical simulations of an emerging disease and one which is stationary. Values of the AUC statistic are compared across a range of different reporting scenarios. We find that different EWS respond to imperfect data differently. The mean, variance and first differenced variance all perform well unless reporting error is highly overdispersed. The autocorrelation, autocovariance and decay time perform well provided that the aggregation period of the data is larger than the serial interval and reporting error is not highly overdispersed. The coefficient of variation, skewness and kurtosis are found to be unreliable indicators of emergence. Overall, we find that seven of ten EWS considered perform well for most realistic reporting scenarios. We conclude that imperfect epidemiological data is not a barrier to using EWS for many potentially emerging diseases.

Forecasting infectious disease emergence subject to seasonal forcing.

BACKGROUND: Despite high vaccination coverage, many childhood infections pose a growing threat to human populations. Accurate disease forecasting would be of tremendous value to public health. Forecasting disease emergence using early warning signals (EWS) is possible in non-seasonal models of infectious diseases. Here, we assessed whether EWS also anticipate disease emergence in seasonal models. METHODS: We simulated the dynamics of an immunizing infectious pathogen approaching the tipping point to disease endemicity. To explore the effect of seasonality on the reliability of early warning statistics, we varied the amplitude of fluctuations around the average transmission. We proposed and analyzed two new early warning signals based on the wavelet spectrum. We measured the reliability of the early warning signals depending on the strength of their trend preceding the tipping point and then calculated the Area Under the Curve (AUC) statistic. RESULTS: Early warning signals were reliable when disease transmission was subject to seasonal forcing. Wavelet-based early warning signals were as reliable as other conventional early warning signals. We found that removing seasonal trends, prior to analysis, did not improve early warning statistics uniformly. CONCLUSIONS: Early warning signals anticipate the onset of critical transitions for infectious diseases which are subject to seasonal forcing. Wavelet-based early warning statistics can also be used to forecast infectious disease.

Transgenerational effects of maternal and grandmaternal age on offspring viability and performance in Drosophila melanogaster.

In non-social insects, fitness is determined by relative lifetime fertility. Fertility generally declines with age as a part of senescence. For females, senescence has profound effects on fitness by decreasing viability and fertility as well as those of her offspring. However, important aspects of these maternal effects, including the cause(s) of reduced offspring performance and carry-over effects of maternal age, are poorly understood. Drosophila melanogaster is a useful system for examining potential transgenerational effects of increasing maternal age, because of their use as a model system for studying the physiology and genetic architecture of both reproduction and senescence. To test the hypothesis that female senescence has transgenerational effects on offspring viability and development, we measured the effects of maternal age on offspring survival over two generations and under two larval densities in two laboratory strains of flies (Oregon-R and Canton-S). Transgenerational effects of maternal age influence embryonic viability and embryonic to adult viability in both strains. However, the generation causing the effects, and the magnitude and direction of those effects differed by genotype. The effects of maternal age on embryonic to adult viability when larvae are stressed was also genotype-specific. Maternal effects involve provisioning: older females produced smaller eggs and larger offspring. These results show that maternal age has profound, complex, and multigenerational consequences on several components of offspring fitness and traits. This study contributes to a body of work demonstrating that female age is an important condition affecting phenotypic variation and viability across multiple generations.

The song of the old mother: reproductive senescence in female drosophila.

Among animals with multiple reproductive episodes, changes in adult condition over time can have profound effects on lifetime reproductive fitness and offspring performance. The changes in condition associated with senescence can be particularly acute for females who support reproductive processes from oogenesis through fertilization. The pomace fly Drosophila melanogaster is a well-established model system for exploring the physiology of reproduction and senescence. In this review, we describe how increasing maternal age in Drosophila affects reproductive fitness and offspring performance as well as the genetic foundation of these effects. Describing the processes underlying female reproductive senescence helps us understand diverse phenomena including population demographics, condition-dependent selection, sexual conflict, and transgenerational effects of maternal condition on offspring fitness. Understanding the genetic basis of reproductive senescence clarifies the nature of life-history trade-offs as well as potential ways to augment and/or limit female fertility in a variety of organisms.