Scaling properties of childhood infectious diseases epidemics before and after mass vaccination in Canada

Measles and Rubella: Scale Free Distribution of Local Infection Clusters

This study examined the size distribution of local infection clusters (referred to as clusters hereafter) of measles and rubella from 2008-2013 in Japan. When the logarithm of the cluster sizes were plotted on the x-axis and the logarithm of their frequencies were plotted on the y-axis, the plots fell on a rightward descending straight line. The size distribution was observed to follow a power law. As the size distribution of the clusters could be equated with that of local secondary infections initiated by 1 patient, the size distribution of the clusters, in fact, represented the effective reproduction numbers at the local level. As the power law distribution has no typical sizes, it was suggested that measles or rubella epidemics in Japan had no typical reproduction number. Higher the population size and higher the total number of patients, flatter was the slope of the plots, thus larger was the proportion of larger clusters. An epidemic of measles or rubella in Japan could be represented more appropriately by the cluster size frequency distribution rather than by the reproduction number.

Quantifying seasonal population fluxes driving rubella transmission dynamics using mobile phone data

Changing patterns of human aggregation are thought to drive annual and multiannual outbreaks of infectious diseases, but the paucity of data about travel behavior and population flux over time has made this idea difficult to test quantitatively. Current measures of human mobility, especially in low-income settings, are often static, relying on approximate travel times, road networks, or cross-sectional surveys. Mobile phone data provide a unique source of information about human travel, but the power of these data to describe epidemiologically relevant changes in population density remains unclear. Here we quantify seasonal travel patterns using mobile phone data from nearly 15 million anonymous subscribers in Kenya. Using a rich data source of rubella incidence, we show that patterns of population travel (fluxes) inferred from mobile phone data are predictive of disease transmission and improve significantly on standard school term time and weather covariates. Further, combining seasonal and spatial data on travel from mobile phone data allows us to characterize seasonal fluctuations in risk across Kenya and produce dynamic importation risk maps for rubella. Mobile phone data therefore offer a valuable previously unidentified source of data for measuring key drivers of seasonal epidemics.

Quasi-neutral theory of epidemic outbreaks

Some epidemics have been empirically observed to exhibit outbreaks of all possible sizes, i.e., to be scale-free or scale-invariant. Different explanations for this finding have been put forward; among them there is a model for "accidental pathogens" which leads to power-law distributed outbreaks without apparent need of parameter fine tuning. This model has been claimed to be related to self-organized criticality, and its critical properties have been conjectured to be related to directed percolation. Instead, we show that this is a (quasi) neutral model, analogous to those used in Population Genetics and Ecology, with the same critical behavior as the voter-model, i.e. the theory of accidental pathogens is a (quasi)-neutral theory. This analogy allows us to explain all the system phenomenology, including generic scale invariance and the associated scaling exponents, in a parsimonious and simple way.

The effect of superspreading on epidemic outbreak size distributions

Recently, evidence has been presented to suggest that there are significant heterogeneities in the transmission of communicable diseases. Here, a stochastic simulation model of an epidemic process that allows for these heterogeneities is used to demonstrate the potentially considerable effect that heterogeneity of transmission will have on epidemic outbreak size distributions. Our simulation results agree well with approximations gained from the theory of branching processes. Outbreak size distributions have previously been used to infer basic epidemiological parameters. We show that if superspreading does occur then such distributions must be interpreted with care. The simulation results are discussed in relation to measles epidemics in isolated populations and in predominantly urban scenarios. The effect of three different disease control policies on outbreak size distributions are shown for varying levels of heterogeneity and disease control effort.

A simple guide to chaos and complexity

The concepts of complexity and chaos are being invoked with increasing frequency in the health sciences literature. However, the concepts underpinning these concepts are foreign to many health scientists and there is some looseness in how they have been translated from their origins in mathematics and physics, which is leading to confusion and error in their application. Nonetheless, used carefully, "complexity science" has the potential to invigorate many areas of health science and may lead to important practical outcomes; but if it is to do so, we need the discipline that comes from a proper and responsible usage of its concepts. Hopefully, this glossary will go some way towards achieving that objective.

Modelling disease spread and control in networks: implications for plant sciences

Networks are ubiquitous in natural, technological and social systems. They are of increasing relevance for improved understanding and control of infectious diseases of plants, animals and humans, given the interconnectedness of today's world. Recent modelling work on disease development in complex networks shows: the relative rapidity of pathogen spread in scale-free compared with random networks, unless there is high local clustering; the theoretical absence of an epidemic threshold in scale-free networks of infinite size, which implies that diseases with low infection rates can spread in them, but the emergence of a threshold when realistic features are added to networks (e.g. finite size, household structure or deactivation of links); and the influence on epidemic dynamics of asymmetrical interactions. Models suggest that control of pathogens spreading in scale-free networks should focus on highly connected individuals rather than on mass random immunization. A growing number of empirical applications of network theory in human medicine and animal disease ecology confirm the potential of the approach, and suggest that network thinking could also benefit plant epidemiology and forest pathology, particularly in human-modified pathosystems linked by commercial transport of plant and disease propagules. Potential consequences for the study and management of plant and tree diseases are discussed.

Analysis of the effect of immunization in rabies time series

The authors analysed time series of rabies cases diagnosed in Hungary between 1967 and 2001. In Transdanubia (West Hungary), an oral immunization program started in 1992 and in East Hungary in 2001. Both long term and seasonal trends were identified in the time series of rabies cases. In order to characterize the underlying processes governing the behaviour of the epidemic, the fluctuations around the trend were analysed before and after immunization separately. It turned out that the tail of the complementary cumulative distribution functions differ. The tail of the distribution follows an inverse power law (IPL) function and describes the distribution of extreme events. The significant difference in the IPL exponents before and after immunization can be explained by the theory of Highly Optimized Tolerance (HOT).