Assessing vaccination sentiments with online social media: implications for infectious disease dynamics and control

There is great interest in the dynamics of health behaviors in social networks and how they affect collective public health outcomes, but measuring population health behaviors over time and space requires substantial resources. Here, we use publicly available data from 101,853 users of online social media collected over a time period of almost six months to measure the spatio-temporal sentiment towards a new vaccine. We validated our approach by identifying a strong correlation between sentiments expressed online and CDC-estimated vaccination rates by region. Analysis of the network of opinionated users showed that information flows more often between users who share the same sentiments - and less often between users who do not share the same sentiments - than expected by chance alone. We also found that most communities are dominated by either positive or negative sentiments towards the novel vaccine. Simulations of infectious disease transmission show that if clusters of negative vaccine sentiments lead to clusters of unprotected individuals, the likelihood of disease outbreaks is greatly increased. Online social media provide unprecedented access to data allowing for inexpensive and efficient tools to identify target areas for intervention efforts and to evaluate their effectiveness.     

Comparison of social networks derived from ecological data: implications for inferring infectious disease dynamics

1.  Social network analyses tend to focus on human interactions. However, there is a burgeoning interest in applying graph theory to ecological data from animal populations. Here we show how radio-tracking and capture–mark–recapture data collated from wild rodent populations can be used to generate contact networks.
2.  Both radio-tracking and capture–mark–recapture were undertaken simultaneously. Contact networks were derived and the following statistics estimated: mean-contact rate, edge distribution, connectance and centrality.
3.  Capture–mark–recapture networks produced more informative and complete networks when the rodent density was high and radio-tracking produced more informative networks when the density was low. Different data collection methods provide more data when certain ecological characteristics of the population prevail.
4.  Both sets of data produced networks with comparable edge (contact) distributions that were best described by a negative binomial distribution. Connectance and closeness were statistically different between the two data sets. Only betweenness was comparable. The differences between the networks have important consequences for the transmission of infectious diseases. Care should be taken when extrapolating social networks to transmission networks for inferring disease dynamics.     

Non-linear transmission rates and the dynamics of infectious disease.

This study considers how non-linearities in the transmission of microparasitic infections affect the population dynamics of host-parasite systems in which the disease is potentially lethal to the host. Non-linearities can either lead to a locally stable or unstable host-parasite equilibrium point, depending on the respective contributions of healthy and infected hosts to the functional form of the transmission rate. Analysis of the non-linear transmission model results in a revealing pair of local stability criteria. Specifically, stability requires sufficient total levels of intrinsic growth of the host population and total levels of density-dependent transmission. The most stable systems occur when increases in the density of healthy hosts result in increases in transmission efficiency, and increases in the number of infected hosts result in small decreases in transmission efficiency. These appear to be very reasonable relationships for directly transmitted microparasites.     

Challenges to global surveillance and response to infectious disease outbreaks of international importance

This article presents a notional scheme of global surveillance and response to infectious disease outbreaks and reviews 14 international surveillance and response programs. In combination, the scheme and the programs illustrate how, in an ideal world and in the real world, infectious disease outbreaks of public health significance could be detected and contained. Notable practices and achievements of the programs are cited; these may be useful when instituting new programs or redesigning existing ones. Insufficiencies are identified in four critical areas: health infrastructure; scientific methods and concepts of operation; essential human, technical, and financial resources; and international policies. These insufficiencies challenge global surveillance of and response to infectious disease outbreaks of international importance. This article is intended to help policymakers appreciate the complexity of the problem and assess the impact and cost-effectiveness of proposed solutions. An assessment of the potential contribution of appropriate diagnostic tests to surveillance and response is included.     

Transient dynamics and early diagnostics in infectious disease

To date, mathematical models of the dynamics of infectious disease have consistently focused on understanding the long-term behavior of the interacting components, where the steady state solutions are paramount. However for most acute infections, the long-term behavior of the pathogen population is of little importance to the host and population health. We introduce the notion of transient pathology, where the short-term dynamics of interaction between the immune system and pathogens is the principal focus. We identify the amplifying effect of the absence of a fully operative immune system on the pathogenesis of the initial inoculum, and its implication for the acute severity of the infection. We then formalize the underlying dynamics, and derive two measures of transient pathogenicity: the peak of infection (maximum pathogenic load) and the time to peak of infection, both crucial to understanding the early dynamics of infection and its consequences for early intervention. Received: 25 January 2000 / Revised version: 30 November 2000 / Published online: 12 October 2001     

Using empirical social contact data to model person to person infectious disease transmission: An illustration for varicella

With the aim to improve dynamic models for infections transmitted predominantly through non-sexual social contacts, we compared three popular model estimation methods in how well they fitted seroprevalence data and produced estimates for the basic reproduction number R₀ and the effective vaccination level required for elimination of varicella. For two of these methods, interactions between age groups were parameterized using empirical social contact data whereas for the third method we used the current standard approach of imposing a simplifying structure on the ‘Who Acquires Infection From Whom’ (WAIFW) matrix. The first method was based on solving a set of differential equations to obtain an equilibrium value of the proportion of susceptibles. The second method was based on finding a solution for the age-specific force of infection using the formula of the mass action principle by means of iteration. Both solutions were contrasted with observed age-specific seroprevalence data. The best fit of the WAIFW matrix was obtained with contacts involving touching, and lasting longer than 15 min per day. Plausible values for R₀ for varicella in Belgium ranged from 7.66 to 13.44. Both approaches based on empirical social contact data provided a better fit to seroprevalence data than the current standard approach.     

Host resource supplies influence the dynamics and outcome of infectious disease

Pathogens and their host organisms share a wide range of resourceneeds that are required to support normal metabolism and growth.Because the development of infectious disease on or within thehost involves the processes of invasion and resource consumption,competition for growth-limiting resources potentially may occurbetween pathogens and cellular or sub-cellular components ofthe host ecosystem. Examples from the plant, animal, and microbiologicalliterature provide unambiguous evidence that external resourcesupplies to the host organism can have profound effects on theoutcome of infection by a broad diversity of bacterial, fungal,metazoan, protozoan, and viral pathogens.     

Revealing mechanisms of infectious disease spread through empirical contact networks

The spread of pathogens fundamentally depends on the underlying contacts between individuals. Modeling the dynamics of infectious disease spread through contact networks, however, can be challenging due to limited knowledge of how an infectious disease spreads and its transmission rate. We developed a novel statistical tool, INoDS (Identifying contact Networks of infectious Disease Spread) that estimates the transmission rate of an infectious disease outbreak, establishes epidemiological relevance of a contact network in explaining the observed pattern of infectious disease spread and enables model comparison between different contact network hypotheses. We show that our tool is robust to incomplete data and can be easily applied to datasets where infection timings of individuals are unknown. We tested the reliability of INoDS using simulation experiments of disease spread on a synthetic contact network and find that it is robust to incomplete data and is reliable under different settings of network dynamics and disease contagiousness compared with previous approaches. We demonstrate the applicability of our method in two host-pathogen systems: Crithidia bombi in bumblebee colonies and Salmonella in wild Australian sleepy lizard populations. INoDS thus provides a novel and reliable statistical tool for identifying transmission pathways of infectious disease spread. In addition, application of INoDS extends to understanding the spread of novel or emerging infectious disease, an alternative approach to laboratory transmission experiments, and overcoming common data-collection constraints.     

On the dynamics of predation risk perception for a vigilant forager

There has been much recent interest in modelling epidemics on networks, particularly in the presence of substantial clustering. Here, we develop pairwise methods to answer questions that are often addressed using epidemic models, in particular: on the basis of potential observations early in an outbreak, what can be predicted about the epidemic outcomes and the levels of intervention necessary to control the epidemic? We find that while some results are independent of the level of clustering (early growth predicts the level of ‘leaky’ vaccine needed for control and peak time, while the basic reproductive ratio predicts the random vaccination threshold) the relationship between other quantities is very sensitive to clustering.     

Separate roles of the latent and infectious periods in shaping the relation between the basic reproduction number and the intrinsic growth rate of infectious disease outbreaks

Two approximations are commonly used to describe the spread of an infectious disease at its early phase: (i) the branching processes based on the generation concept and (ii) the exponential growth over calendar time. The former is characterized by a mean parameter: the reproduction number R₀. The latter is characterized by a growth rate ρ, also known as the Malthusian number. It is common to use empirically observed ρ to assess R₀ using formulae derived either when both the latent and infectious periods follow exponential distributions or assuming both are fixed non-random quantities. This paper first points out that most of these formulae are special cases when the latent and infectious periods are gamma distributed, given by a closed-form solution in Anderson and Watson [1980. On the spread of a disease with gamma distributed latent and infectious periods. Biometrika 67 (1), 191-198]. A more general result will be then established which takes the result in Anderson and Watson [1980. On the spread of a disease with gamma distributed latent and infectious periods. Biometrika 67 (1), 191-198] as its special case. Three aspects separately shape the relationship between ρ and R₀. They are: (i) the intensity of infectious contacts as a counting process; (ii) the distribution of the latent period and (iii) the distribution of the infectious period. This article also distinguishes the generation time from the transmission interval. It shows that whereas the distribution of the generation time can be derived by the latent and infectious period distributions, the distribution of the transmission interval is also determined by the intensity of infectious contacts as a counting process and hence by R₀. Some syntheses among R₀, ρ and the average transmission interval are discussed. Numerical examples and simulation results are supplied to support the theoretical arguments.     

Individual differences in the perception of probability

In recent studies of humans estimating non-stationary probabilities, estimates appear to be unbiased on average, across the full range of probability values to be estimated. This finding is surprising given that experiments measuring probability estimation in other contexts have often identified conservatism: individuals tend to overestimate low probability events and underestimate high probability events. In other contexts, repulsive biases have also been documented, with individuals producing judgments that tend toward extreme values instead. Using extensive data from a probability estimation task that produces unbiased performance on average, we find substantial biases at the individual level; we document the coexistence of both conservative and repulsive biases in the same experimental context. Individual biases persist despite extensive experience with the task, and are also correlated with other behavioral differences, such as individual variation in response speed and adjustment rates. We conclude that the rich computational demands of our task give rise to a variety of behavioral patterns, and that the apparent unbiasedness of the pooled data is an artifact of the aggregation of heterogeneous biases.     

Effects of heterogeneous and clustered contact patterns on infectious disease dynamics

The spread of infectious diseases fundamentally depends on the pattern of contacts between individuals. Although studies of contact networks have shown that heterogeneity in the number of contacts and the duration of contacts can have far-reaching epidemiological consequences, models often assume that contacts are chosen at random and thereby ignore the sociological, temporal and/or spatial clustering of contacts. Here we investigate the simultaneous effects of heterogeneous and clustered contact patterns on epidemic dynamics. To model population structure, we generalize the configuration model which has a tunable degree distribution (number of contacts per node) and level of clustering (number of three cliques). To model epidemic dynamics for this class of random graph, we derive a tractable, low-dimensional system of ordinary differential equations that accounts for the effects of network structure on the course of the epidemic. We find that the interaction between clustering and the degree distribution is complex. Clustering always slows an epidemic, but simultaneously increasing clustering and the variance of the degree distribution can increase final epidemic size. We also show that bond percolation-based approximations can be highly biased if one incorrectly assumes that infectious periods are homogeneous, and the magnitude of this bias increases with the amount of clustering in the network. We apply this approach to model the high clustering of contacts within households, using contact parameters estimated from survey data of social interactions, and we identify conditions under which network models that do not account for household structure will be biased.     

Modeling immune response and its effect on infectious disease outbreak dynamics

Background

In recent epidemiological models, immunity is incorporated as a simplified value that determines the capacity of an individual to become infected or to transmit the disease. Moreover, the quality of the immune response determines the chances of infection and the length of time an individual is capable to infect others. We present a model that incorporates individuals’ immune responses to, further, examine the role of the collective immune response of individuals in a population during an infectious outbreak.

Methods

We constructed a contagion model that incorporates the collective immune response of individuals represented by the superposition of individual immune responses (PIR). Multiple probability distributions are used to represent the immunocompetence of different age groups, thereby modeling the concept of Population Immune Response (PIR). Multiple experiments were conducted in which the population is divided in different age groups for which each group has a unique immune response quality and thus a different length for its immune periods. Finally, we explored the effects of implementing different vaccination strategies in the population.

Results

The experiments displayed important variations in the outbreak dynamics as a consequence of incorporating PIR in homogeneous and mixed populations. The experiments showed that individuals with weak immune responses and those who are immune to the pathogen play a significant role in shaping the outbreak dynamics. Finally, after implementing different vaccination strategies, the results suggest that if vaccination resources are limited, the vaccination should be targeted towards individuals that spread the disease for a longer period of time.

Conclusions

Our results suggest that it is essential for the public health establishment to increase their understanding of the characteristics of regional demographics that could impact the quality of the immune response of the individuals. The results indicate that it is necessary to further investigate mitigation strategies to limit the capacity to transmit the disease by individuals that spread the pathogen for extended periods of time. Ultimately, this study suggests that it is crucial for public health researchers to identify appropriate targeted vaccination regimes and to explore the link between PIR and outbreak dynamics to improve the monitoring and mitigating efforts of ongoing and future epidemics.

     

Seasonality and the dynamics of infectious diseases

Seasonal variations in temperature, rainfall and resource availability are ubiquitous and can exert strong pressures on population dynamics. Infectious diseases provide some of the best-studied examples of the role of seasonality in shaping population fluctuations. In this paper, we review examples from human and wildlife disease systems to illustrate the challenges inherent in understanding the mechanisms and impacts of seasonal environmental drivers. Empirical evidence points to several biologically distinct mechanisms by which seasonality can impact host–pathogen interactions, including seasonal changes in host social behaviour and contact rates, variation in encounters with infective stages in the environment, annual pulses of host births and deaths and changes in host immune defences. Mathematical models and field observations show that the strength and mechanisms of seasonality can alter the spread and persistence of infectious diseases, and that population-level responses can range from simple annual cycles to more complex multiyear fluctuations. From an applied perspective, understanding the timing and causes of seasonality offers important insights into how parasite–host systems operate, how and when parasite control measures should be applied, and how disease risks will respond to anthropogenic climate change and altered patterns of seasonality. Finally, by focusing on well-studied examples of infectious diseases, we hope to highlight general insights that are relevant to other ecological interactions.     

The metapopulation dynamics of an infectious disease: tuberculosis in possums

An SEI metapopulation model is developed for the spread of an infectious agent by migration. The model portrays two age classes on a number of patches connected by migration routes which are used as host animals mature. A feature of this model is that the basic reproduction ratio may be computed directly, using a scheme that separates topography, demography, and epidemiology. We also provide formulas for individual patch basic reproduction numbers and discuss their connection with the basic reproduction ratio for the system. The model is applied to the problem of spatial spread of bovine tuberculosis in a possum population. The temporal dynamics of infection are investigated for some generic networks of migration links, and the basic reproduction ratio is computed-its value is not greatly different from that for a homogeneous model. Three scenarios are considered for the control of bovine tuberculosis in possums where the spatial aspect is shown to be crucial for the design of disease management operations.     

Impact of disease characteristics and knowledge on public risk perception of zoonoses

Zoonoses represent a global public health threat. Understanding lay perceptions of risk associated with these diseases can better inform proportionate policy interventions that mitigate their current and future impacts. While individual zoonoses (e.g. bovine spongiform encephalopathy) have received scientific and public attention, we know little about how multiple zoonotic diseases vary relative to each other in lay risk perceptions. To this end, we examined public perceptions of 11 zoonoses across 12 qualitative attributes of risk among the UK public (n = 727, volunteer sample), using an online survey. We found that attribute ratings were predominantly explained via two basic dimensions of risk related to public knowledge and dread. We also show that, despite participants reporting low familiarity with most of the diseases presented, zoonoses were perceived as essentially avoidable. These findings imply that infection is viewed as dependent upon actions under personal control which has significant implications for policy development.     

Spatial heterogeneity, social structure and disease dynamics of animal populations

Social groupings, population dynamics and population movements of animals all give rise to spatio-temporal variations in population levels. These variations may be of crucial importance when considering the spread of infectious diseases since infection levels do not increase unless there is a sufficient pool of susceptible individuals. This paper explores the impact of social groupings on the potential for an endemic disease to develop in a spatially explicit model system. Analysis of the model demonstrates that the explicit inclusion of space allows asymmetry between groups to arise when this was not possible in the equivalent spatially homogeneous system. Moreover, differences in movement behaviours for susceptible and infected individuals gives rise to different spatial profiles for the populations. These profiles were not observed in previous work on an epidemic system. The results are discussed in an ecological context with reference to furious and dumb strains of infectious diseases.