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. 2011 Dec 19;10(2):75–82. doi: 10.1890/110111

From superspreaders to disease hotspots: linking transmission across hosts and space

Sara H Paull 1,✉, Sejin Song 1, Katherine M McClure 1,2, Loren C Sackett 1, A Marm Kilpatrick 2, Pieter TJ Johnson 1
PMCID: PMC3589764  NIHMSID: NIHMS383617  PMID: 23482675

Abstract

Since the identification and imprisonment of “Typhoid Mary”, a woman who infected at least 47 people with typhoid in the early 1900s, epidemiologists have recognized that “superspreading” hosts play a key role in disease epidemics. Such variability in transmission also exists among species within a community and among habitat patches across a landscape, underscoring the need for an integrative framework for studying transmission heterogeneity, or the differences among hosts or locations in their contribution to pathogen spread. Here, we synthesize literature on human, plant, and animal diseases to evaluate the relative influence of host, pathogen, and environmental factors in producing highly infectious individuals, species, and landscapes. We show that host and spatial heterogeneity are closely linked and that quantitatively assessing the contribution of infectious individuals, species, or environmental patches to overall transmission can aid management strategies. We conclude by posing hypotheses regarding how pathogen natural history influences transmission variability and highlight emerging frontiers in this area of study.

In November of 2002, doctors in the Guangdong Province of China began noticing unusual pneumonia cases that were later identified as the beginning of an epidemic of severe acute respiratory syndrome (SARS). The virus that causes SARS, which ultimately infected over 8000 people and killed 774, spread from a Hong Kong hotel when infected hotel guests began traveling across the globe (WHO 2003). One of those infected was a flight attendant, who was ultimately linked to more than 100 SARS cases in Singapore (WHO 2003). Encapsulated within this account lies a key phenomenon that influences disease dynamics: heterogeneity in transmission among individuals and across space. The risk of infection varies by location (eg hotels), by behavior (eg air travel), and especially among individuals.

In a nutshell:

  • Diseases of plants, domestic animals, wildlife, and humans display transmission heterogeneity across individuals, species, and environments

  • Heterogeneity among hosts can be caused by variability in host behavior, environmental exposure, host and pathogen genetics, and patterns of co‐infection

  • Spatial heterogeneity can result from variable host densities and environmental characteristics

  • Quantitatively estimating host and spatial transmission heterogeneity and understanding their connections can aid in the development of cost‐effective, targeted management strategies

Individuals who are responsible for a disproportionate number of transmission events, such as Typhoid Mary and the flight attendant carrying the SARS virus (Leavitt 1996; WHO 2003), are characteristic of many infections in both humans and animals (Lloyd‐Smith et al. 2005; Hudson et al. 2008). This underscores the importance of understanding transmission heterogeneity, defined as variability in the contribution of specific hosts or locations to overall rates of pathogen spread. Failure to recognize and incorporate heterogeneity into epidemiological models can result in poor estimates of rates of disease spread and outbreak probabilities, and represent a missed opportunity for effective disease control strategies (Lloyd‐Smith et al. 2005). Transmission heterogeneity also occurs between different species and across landscapes, and host and spatial transmission heterogeneity can interact positively, with each facilitating heterogeneity in the other. A better understanding of such linkages may enhance our ability to identify the hosts and environments that contribute disproportionately to transmission. In this paper, we consider the causes of, consequences of, and linkages between transmission heterogeneity across individuals, species, and locations. We suggest mechanisms for how pathogen transmission mode may influence transmission heterogeneity across hosts and space, and we advocate a quantitative approach that will allow researchers and managers to determine the magnitude of transmission heterogeneity across hosts, species, and environments (Panel 1). Finally, we offer future research directions (Panel 2) that could provide a framework for understanding and managing transmission heterogeneity.

Panel 1. Quantifying transmission heterogeneity and its importance for management

Two key challenges for optimizing disease control are quantifying the magnitude of transmission heterogeneity across hosts and space and identifying key individuals or hotspots for targeted control efforts. Heterogeneity among individuals can be quantified by comparing the variability among individuals to that expected by chance. When transmission events can be observed directly (eg through contact tracing), one approach is to estimate the effective reproductive number R eff (the average number of new infections resulting from an original infection) for a population, and then to compare the number of secondary infections caused by individuals to a Poisson distribution with mean R eff (Lloyd‐Smith et al. 2005).

This conceptual approach can be extended to expected transmission contributions by subsets of hosts (identified by traits such as gender, age, and species). Prospective transmission among host subsets can be described by modified who‐acquires‐infection‐from‐whom (WAIFW) and R matrices (Anderson and May 1985; Diekmann et al. 1990; Dobson and Foufopoulos 2001). Each element of the R matrix describes transmission among one pair of host groups as the product of the mean infectious contact rate and the mean infectious period. The sum of each row quantifies the expected number of cases, summed across all groups, caused by an infectious individual in a given group, and thus determines the importance of that host group in pathogen invasion.

For a vector‐borne pathogen, WAIFW and R matrices include one row for vector‐to‐host transmission and one column for host‐to‐vector transmission. If vertical transmission is absent, all other elements in the matrix are zero (Dobson and Foufopoulos 2001). As a result, the contribution of each host group j to initial amplification can be written as

graphic file with name FEE-10-75-e001.jpg

Here, fj is the fraction of vectors feeding on host species j, Nj is the fraction of the total host community represented by host species j, Cj is the competence (including susceptibility, infectiousness, and infectious period) of host group j, and R 0 is the sum across all j groups multiplied by the square of the vector biting rate and vector abundance. The importance in amplification, Aj, of each host group relative to its abundance is then Aj = (R 0,j/R 0)/Nj. To determine whether a host group is likely to be a superspreader at the nth percentile requires determining whether the observed value of Aj is unlikely given an expectation of Aj = 1 and the measurement error associated with estimating (R 0,j/R 0) and Nj. This entails determining the probability of observing the estimated value of Aj by comparing it to the ratio of two distributions that have identical means equal to Nj but possibly different variances as determined by the measurement error of (R 0,j/R 0) and Nj. The most appropriate distribution for Nj and (R 0,j/R 0) for this comparison will be an important problem for further study, but one possibility is to use beta distributions, which are flexible and vary between 0 and 1. For non‐vector‐borne pathogens, a priori quantification of importance is much more challenging because empirically measuring the infectious contact rate among hosts is difficult. If possible, identifying measurable surrogates for infectious contact rates would aid discovery of host traits that are correlated with contact rates.

Quantifying spatial heterogeneity and identifying the importance of hotspots relative to purely stochastic variation can follow the same methodology. Doing so requires defining the quantity of interest – the density of transmission events, the density of all infected hosts, or the density of infected hosts of one group or species. Subsequently, a hotspot at the nth percentile can be described if the quantity (eg number of transmission events) in that area divided by the fraction of the landscape of interest represented by that area (a number between 0 and 1) is greater than the nth percentile of the appropriate ratio of distributions as described above.

Managers can benefit substantially from understanding the relative contribution of host groups and hotspots to transmission because recognizing heterogeneity could enable highly targeted control. The cost‐effectiveness of targeted control, however, rests on the costs of identifying and controlling hotspots or key transmission individuals relative to the costs of broader, untargeted control.

Panel 2. Outstanding questions about host and spatial transmission heterogeneity

Several outstanding research questions remain before a theory can be developed that integrates our understanding of transmission heterogeneity across hosts and space. A combination of models for well‐parameterized systems, field surveys, and experimental tests of different management strategies (when ethical) will offer insight into these questions.

Causes of transmission heterogeneity:

  • To what extent is transmission heterogeneity caused by stochastic processes as opposed to inherent qualities of the individual, species, or environment?

  • Are the identities of individuals and locations that are responsible for large numbers of transmission events temporally stable?

Consequences of transmission heterogeneity:

  • Does the cause of transmission heterogeneity (eg high contact rates, shedding rates, long duration of infection) influence the strength of the heterogeneity or the co‐evolutionary implications?

  • How do the consequences of transmission heterogeneity for disease dynamics (epidemic growth rates and probability of pathogen extinction) vary across individuals, species, and space?

  • How frequently do hosts and environments with the highest contact rates form isolated sub‐networks that lead to reduced disease risk in those outside the network?

  • How does transmission heterogeneity influence co‐evolution between hosts and parasites?

Modeling and defining transmission heterogeneity and superspreading

Epidemiologists typically determine whether a pathogen can invade a host population by using the basic reproductive number (R 0). This value represents the average number of new infections caused by an infectious individual in an immunologically naïve host population (Anderson and May 1991). Subsequent work has shown that the intensity of macroparasitic infections (eg worms per individual) generally follows a negative binomial distribution with high skew (Shaw and Dobson 1995), with more highly infected individuals potentially being responsible for more transmission events. A later study proposed the “20/80” rule and showed that, in a variety of disease systems, 20% of hosts were responsible for at least 80% of transmission events (Woolhouse et al. 1997). Thus, models allowing for heterogeneity in the distribution of the expected number of new infections an infectious individual produces (rather than using a single mean value, R 0) have transformed the epidemiological modeling of diseases by taking into account changes in disease dynamics that result from such heterogeneities (Lloyd‐Smith et al. 2005). Importantly, heterogeneous transmission also occurs in cases where amplification host species or environmental “hotspots” contribute disproportionately to disease transmission within a community or landscape (Figure 1). Thus, one of the major goals of this paper is to extend the same principles that underlie host transmission heterogeneity to understand landscape variability in disease risk.

Figure 1.

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Aggregated distributions of factors influencing transmission potential across hosts and space. (a) A few yellow‐necked mouse (Apodemus flavicollis) individuals released most of the total eggs of the nematode Heligmosomoides polygyrus (modified with permission from Ferrari et al. 2004). (b) American robins (Turdus migratorius) infected a much greater percentage of mosquitoes with West Nile virus than did other bird species, and more than would be expected from their relative abundance (data from Kilpatrick et al. 2006). (c) A few ponds have very high percentages of snails infected with the trematode Ribeiroia ondatrae (Johnson unpublished data).

Extreme cases of transmission heterogeneity have been referred to as “superspreading events”. A useful definition for a superspreading event, proposed by Lloyd‐Smith et al. (2005), is when an infected host causes a greater number of secondary infections than the nth percentile of the expected distribution of new infections arising from a single infection, assuming homogeneous transmission potential across hosts. Lloyd‐Smith et al. (2005) used an example of 99th percentile superspreading events, but their definition allows for versatility across disease systems and contexts. The powerful influence of rare events (or “black swans”) drawn from the tail of probability distributions has similarly been recognized in other aspects of ecology (eg invasion, disturbance, dispersal) as well as in geology (eg meteorite impact) and the social sciences (eg war, financial market collapse; Mack et al. 2000; Taleb 2007; Whitten 2010).

Individual transmission heterogeneity

Transmission heterogeneity across individuals is often characterized by variability in contact rates with other potential hosts, duration of infectiousness, or parasite shedding rates (Figure 2; Lloyd‐Smith et al. 2005; Hudson et al. 2008; Hawley and Altizer 2011). Traits such as host age, sex, body condition, and genetics can influence all three of these characteristics by affecting host susceptibility, immune responses, or social behavior (Wilson et al. 2001; Altizer et al. 2003; Hudson et al. 2008; Beldomenico and Begon 2010). For instance, experimental manipulations in birds and rodents have shown that males can be important drivers of population‐level infection rates, and that elevated testosterone levels can suppress the immune response while increasing host contact rates (Ferrari et al. 2004; Mougeot et al. 2005; Grear et al. 2009).

Figure 2.

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Conceptual diagram illustrating causes (purple boxes) and consequences (blue box) of transmission heterogeneity (green boxes), as well as connections across hosts and space (large blue arrows). Here, “contact rate” refers to contacts between either two host species or a host and vector; “dispersal” refers to the movement of a pathogen from one environmental patch to another; and “host community” refers to the density of hosts and their collective reservoir potential. Many of the consequences of, and linkages between, heterogeneity at multiple scales remain to be discovered (Panel 2).

An emerging research frontier in disease ecology involves identifying the relative contributions of host, pathogen, and environmental characteristics in transmission heterogeneity among individuals. Factors such as genetic differences in host resistance or parasite virulence and genotype–environment interactions can lead to individual transmission heterogeneity. For example, experimental inoculations of bay laurel (Umbellularia californica) leaves with Phytophthora ramorum, the fungal pathogen that causes sudden oak death syndrome, showed that individual trees varied substantially in their genetic susceptibility to the pathogen (measured by mean lesion area; Anacker et al. 2008). However, field observations revealed that disease levels (measured as number of symptomatic leaves per tree) at the plot scale were primarily associated with local environmental differences in temperature and precipitation rather than with genetic differences among hosts (Anacker et al. 2008). The distribution of pathogens or vectors within the environment may also be a factor influencing host burdens and, by extension, transmission heterogeneity (Brunner and Ostfeld 2008). For instance, when parasites are clumped together rather than scattered evenly across the environment, individuals that happen to encounter the areas with higher parasite densities are likely to experience higher infection intensities as a result.

Intriguingly, other parasites and microorganisms co‐occurring within hosts can also influence the distribution of transmission rates across individuals (Hudson et al. 2008; Lloyd‐Smith et al. 2008). For instance, host differences in intestinal microbiota can determine whether individual mice shed high levels of pathogens in their feces (Lawley et al. 2009). Additionally, immune system trade‐offs in response to different types of pathogens (eg microparasites versus macroparasites) can increase the duration and intensity of infection in individuals harboring multiple pathogen types (Graham et al. 2007; Telfer et al. 2010; Hawley and Altizer 2011). Co‐infection may also reduce transmission potential as a result of cross‐immunity and the contact‐reducing behaviors of infected hosts (Rohani et al. 2003; Hawley and Altizer 2011). Determining the impact of co‐infections on transmission is an important direction for further research.

Quantifying individual transmission heterogeneity can help direct disease management strategies (Panel 1). When individuals that contribute disproportionately to transmission can be identified from infection levels or transmission studies such as contact tracing, targeted control efforts can be considerably more effective at preventing and containing outbreaks than random control (Woolhouse et al. 1997; Lloyd‐Smith et al. 2005). Even more effective control is possible when individuals with the potential for disproportionate transmission can be predicted before infecting others (Lloyd‐Smith et al. 2005; Hudson et al. 2008), for example based on host characteristics such as weight or sex (eg Perkins et al. 2003). However, identifying super‐spreaders may be costly in terms of time and effort, highlighting the need for methods to calculate the cost‐effectiveness of reducing R 0 via targeted versus untargeted population‐level controls.

Species‐level transmission heterogeneity

In addition to intraspecific heterogeneity described above, many pathogens infect multiple host species, in which contact rates and pathogen transmissibility vary, leading to differences between species in their contribution to disease transmission (Woolhouse et al. 2001; Haydon et al. 2002). Interspecific variability in transmission is a function of host susceptibility, contact rates with vectors or other hosts, and host competence, which is the efficiency with which a host produces and transmits a pathogen (Figure 2; Kilpatrick et al. 2006; Brisson et al. 2008; Cronin et al. 2010). For instance, American robins (Turdus migratorius) are competent hosts for West Nile virus (WNV), and are more frequently fed upon by WNV‐transmitting mosquitoes relative to their abundance. As a result, robins may infect 24–71% of WNV‐infected vectors at a site, despite representing only 1–8% of the avian hosts (Kilpatrick et al. 2006).

Identifying hosts that amplify pathogen transmission (“amplification hosts”) is a key priority for effective management of multi‐host diseases. For vector‐borne diseases, it has been hypothesized that hosts that infect more vectors are associated with an “r‐selected” (eg rapid turnover) life‐history strategy (Cronin et al. 2010). For example, experimental exposure of six grass species to barley yellow dwarf virus suggested that plants with a “quick return” phenotype (eg high levels of nitrogen and phosphorus, as well as high metabolic rates in leaves) had increased host susceptibility, competence, and vector population sizes, most likely because their leaves offered more resources for vector and pathogen growth and generally had fewer defenses (Cronin et al. 2010). Such hosts enhance the population of infected vectors, increasing infection risk for co‐occurring host species. Species that tend to have high population densities or show highly social behavior may also be at increased risk of contracting pathogens for which transmission is dependent on host density, resulting from higher contact rates (Altizer et al. 2003). By elevating overall disease prevalence, such hosts can amplify transmission throughout multiple species' communities.

In extreme cases, amplification hosts that make disproportionate contributions to community‐wide disease transmission relative to their abundance may be considered “superspreading species”. With a definition analogous to that of superspreading individuals (Lloyd‐Smith et al. 2005), a superspreading species can be identified by determining the expected distribution of community‐wide transmission events assuming homogeneous transmission potential across host species (Panel 1). A species responsible for more infections than the nth percentile of the expected distribution can be classified as a community superspreader (Panel 1; eg Kilpatrick et al. 2006). When identifying superspreading species, the transmission event of interest should be defined explicitly. This could include transmission that is either intra‐ or interspecific, or to a host species of particular interest (eg humans or domestic animals), depending on the research or management objective. For WNV, Kilpatrick et al. (2006) demonstrated that robins are an example of a superspreading species because the proportion of mosquitoes potentially infected by robins relative to the proportion of the host community that they represent is far greater than would be expected from chance alone if the host community were homogeneous (Panel 1).

Environmental transmission heterogeneity

Pathogens are also characterized by spatial heterogeneity in transmission, and regions with particularly high pathogen prevalence (percentage of infected hosts), intensity (pathogens per infected host), or transmission rates are frequently referred to as “hotspots” or transmission foci. Hotspots may also be “source areas”, from which pathogens disperse to less infected areas across a landscape. Environmental hotspots are often characterized by conditions that facilitate either elevated pathogen survival or greater densities of amplification hosts (for diseases with density‐dependent transmission; Figure 2). For instance, Vibrio cholerae, the bacteria that causes cholera, can become concentrated on water hyacinths (Eichornia crassipes), which enhance the pathogen's survival (Spira et al. 1981), potentially leading to transmission hotspots in areas that have been heavily invaded by water hyacinth. Similarly, Farnsworth et al. (2005) found that chronic wasting disease prevalence in Colorado mule deer (Odocoileus hemionus) populations was twice as high near human developments as compared with that in more remote areas. This may have been caused by elevated deer densities that can result from artificial feeding stations, small home ranges, and lack of predators in developed areas.

Changes in host susceptibility, community structure, and contact rates that generate disease hotspots in humans, plants, and wildlife are frequently associated with anthropogenic environmental changes, such as fragmentation and eutrophication, that alter habitat quality and territory size (Patz et al. 2004; Johnson et al. 2010). For instance, poor habitat quality and populations isolated in fragmented habitats can cause stress and reduced genetic diversity in host populations, thereby reducing host immunity and resulting in spatial variability in transmission potential across the landscape (Pearman and Garner 2005; Bradley and Altizer 2007). Nutrient enrichment can also elevate disease risk through changes to pathogen abundance and virulence, host and vector densities, or host susceptibility (Johnson et al. 2010). For example, a study in Belize showed that nutrient enrichment of water bodies associated with agricultural areas supports different aquatic plant communities that promote breeding by an efficient malaria vector, Anopheles vestitipennis (Grieco et al. 2006). Alternatively, fragmentation and nutrient enrichment can impede the spread of a disease by reducing host density and contact rates (Bradley and Altizer 2007; Johnson et al. 2010).

Regional variability in host contact rates in settings such as daycare facilities, hospitals, markets, and farms, as well as regional differences in behavior, can also create hotspots of disease. Marine salmon farms, in which high densities of captive salmon are maintained in coastal pens, are examples of transmission foci that can enhance sea lice infection pressure for nearby wild salmon by as much as four orders of magnitude (Krkošek et al. 2005). Similarly, aggregation of children in schools enhances transmission of childhood diseases, such as measles (eg Bjørnstad et al. 2002). Social animal behaviors that tend to aggregate and increase contacts between hosts, such as those that occur at collective mating grounds (eg leks), overwintering dens, watering holes, and feeding sites, can also elevate disease transmission at those sites (Altizer et al. 2003, 2006). Cultural differences in human behavior can also influence disease transmission by shifting host–pathogen contact rates (Alexander and McNutt 2010). For instance, regional differences in caregiving and nursing techniques may have contributed in part to differences in the rates of human‐to‐human transmission and mortality from Nipah virus outbreaks in Bangladesh as compared with those in Malaysia (Chong et al. 2008), creating regional heterogeneity in disease transmission.

Quantitative methods for estimating the net contribution of hotspots to landscape‐level transmission may facilitate control measures. Extreme levels of spatial transmission heterogeneity might lead to environmental hotspots that are analogous to superspreaders (Lloyd‐Smith et al. 2005), in that such patches are responsible for greater than the nth percentile of the expected fraction of regional transmission events (Panel 1). It is particularly important with spatial transmission heterogeneity to explicitly identify the transmission measure of interest before assessing the importance of transmission heterogeneity to disease dynamics. The relevant measure of transmission for habitat patches will depend on the management scenario and could include, for example, the number of farms infected by a single farm (Keeling et al. 2001) or the number of host infections that occur in spatially defined areas.

Integrating management of host and spatial transmission heterogeneity

Treating, vaccinating, or altering the contact patterns of potential individual superspreaders before an outbreak begins could reduce the chances of a major outbreak and slow initial epidemic growth rates, thereby increasing the effectiveness of applied control measures (Lloyd‐Smith et al. 2005). Nonetheless, several issues surrounding the consequences of interspecific and spatial transmission heterogeneity for outbreak probabilities and epidemic trajectories are important avenues for future research (Panel 2). In addition, new theory for understanding when to expect transmission heterogeneity will improve disease mitigation strategies. Management could also be enhanced by identifying and addressing connections between host and spatial heterogeneity. Finally, it will be important to develop new strategies for identifying the most infectious hosts and landscapes, and new management tools to reduce their contribution to transmission.

Integration of previous research suggests several hypotheses regarding the causes of heterogeneous transmission patterns and the pathogen transmission modes most likely involved. For instance, we hypothesize that heterogeneity in contact rates is more important than heterogeneity in infectiousness for creating superspreaders, because in an age of globalized travel and practices that increase host density, such as industrialized farming and habitat fragmentation, contact rates may vary more than infectivity. Another testable hypothesis is that specific pathogen transmission modes are more commonly associated with certain types of transmission heterogeneity. For instance, transmission variability across hosts may be more common among infections transmitted through vectors or direct host‐to‐host contact; this is because there are no intermediate transmission steps (eg to other obligate host species) that would dilute the influence of variable contact rates, competence, or infectivity on overall transmission. Environmental transmission heterogeneity may be more prevalent among pathogens with density‐dependent transmission or environmentally transmitted stages, because transmission foci are often characterized by high host or pathogen densities. Empirical testing of these hypotheses will require quantitative approaches for identifying transmission heterogeneity (Panel 1) in addition to meta‐analyses that integrate the results from multiple studies.

Connections between host and spatial transmission heterogeneity are likely common and could have implications for disease management. Logically, superspreaders create hotspots of transmission around them, and hosts in a disease hotspot, by definition, experience increased infection pressure as compared with others in the population. For example, interspecific transmission heterogeneities in Lyme disease can theoretically lead to environmental hotspots because fragmented habitats often support a higher fraction of competent amplification hosts, which could increase disease risk (eg measured as nymph infection prevalence) in disturbed landscapes (LoGiudice et al. 2003). Environmental hotspots that occur as a result of temporary aggregation of hosts – for example, at a watering hole – could also facilitate individual superspreading by serving as a source location for infection among highly social individuals that disperse from the location and subsequently infect many others. Finally, heterogeneity occurs both among and within species, and amplification species may contain one or more superspreading individuals. For example, domestic dogs account for the majority of rabies cases in parts of rural Tanzania; infected dogs vary substantially in their biting rates, such that in addition to being the dominant amplification host in the system, some domestic dogs account for a disproportionate number of canine rabies transmission events (Hampson et al. 2009). These patterns suggest that a priority for future research in disease ecology should be the development of a framework integrating transmission heterogeneity across hosts and space (Panel 2).

Perhaps the most practical information about transmission heterogeneity from a management perspective is determining what measures may be useful for identifying the most infectious individuals, species, and locations so that they can be targeted before infecting others. If, as hypothesized, contact rates play the greatest role in transmission heterogeneity, then quantifying contact networks representing the set of interactions between nodes (eg individuals, species, or environments) could provide a surrogate measure for identifying potential superspreaders (Figure 3; eg Keeling and Eames 2005), particularly if traits characteristic of the highest contact nodes can be identified. Importantly, specific characteristics, such as the duration of infectiousness and contact, must be considered to determine the role of high contact nodes (eg Craft et al. 2011). Additionally, because infectiousness frequently co‐varies with susceptibility (Hudson et al. 2008; Lloyd‐Smith et al. 2008), another potential indicator for identifying possible superspreaders and amplification hosts could involve measures of susceptibility such as body condition, testosterone level, or immunocompetence (Beldomenico and Begon 2010). Identifying the most infectious hosts before they infect others offers a superior management strategy to post hoc analyses that detect such hosts from infection levels or transmission studies; however, recognizing these individuals and determining the probability that they will become infected can be challenging. Intriguingly, advanced tools, such as therapeutic interfering particles (particles engineered to replicate along with the pathogen, but that inhibit pathogen growth), can eliminate the challenges of identifying superspreaders a priori because they are transmitted among hosts along with the pathogen, naturally making their way to the most high‐risk populations (Metzger et al. 2011). Further research that identifies the conditions under which easily observable host or landscape traits are associated with high transmission rates will aid targeted disease control efforts.

Figure 3.

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Hypothetical contact network showing potential super‐spreaders (red circles) with higher than average connections to the rest of the network. The human, robin, and farm icons represent some of the possible identities of these potential super‐spreaders. Blue circles represent nodes (eg individuals, species, or locations) with lower than average connections (red lines) to the rest of the network.

Conclusions

Transmission heterogeneity occurs among hosts and across space (Figure 1); more formal quantification of these heterogeneities, along with the costs associated with their identification and targeted control, could help to make targeted management more cost‐effective (Panel 1). Importantly, such heterogeneities in transmission are intimately linked (Figure 2), so that a better understanding of these linkages will lead to important advances. Addressing critical questions about when host and spatial transmission heterogeneities are stochastically generated or temporally variable and what the implications are for disease dynamics and host–pathogen evolution will require the integration of modeling and empirical approaches (Panel 2). Given the growing influence of anthropogenic environmental change on disease risk for humans, plants, and animals, focusing research efforts toward developing an integrative theory for understanding transmission heterogeneity across scales will be useful for ecologists, public health officials, and wildlife managers.

Acknowledgments

We thank J Kish and Z McGregor‐Dorsey for early help in the formulation of our ideas, as well as S Collinge, V McKenzie, B Melbourne, S Orlofske, K Dosch, D Preston, M Joseph, J Mihaljevic, and M Redmond for comments on earlier versions of the manuscript. SHP was supported by the US Environmental Protection Agency (EPA) under the Science to Achieve Results (STAR) Graduate Fellowship Program). EPA has not officially endorsed this publication and the views expressed herein may not reflect the views of the EPA. KMM was partly supported by a Cota‐Robles Fellowship from UCSC. AMK was supported by a National Science Foundation (NSF) grant from the NSF–National Institutes of Health (NIH) Ecology of Infectious Disease Program (EF‐0914866) and NIH grant 1R01AI090159‐01. PTJJ was supported by a fellowship from the David and Lucile Packard Foundation and a grant from NSF (DEB‐0841758) and the Morris Animal Foundation.

Footnotes

Beyond the Frontier: Listen to Sara Paull discussing this research on Frontiers' monthly podcast, at http://www.frontiersinecology.org.

References

  1. Alexander KA and McNutt JW. 2010. Human behavior influences infectious disease emergence at the human–animal interface. Front Ecol Environ 8: 522–26. [Google Scholar]
  2. Altizer S, Nunn CL, Thrall PH, et al 2003. Social organization and parasite risk in mammals: integrating theory and empirical studies. Annu Rev Ecol Evol S 34: 517–47. [Google Scholar]
  3. Altizer S, Dobson A, Hosseini P, et al 2006. Seasonality and the dynamics of infectious diseases. Ecol Lett 9: 467–84. [DOI] [PubMed] [Google Scholar]
  4. Anacker BL, Rank NE, Huberli D, et al 2008. Susceptibility to Phytophthora ramorum in a key infectious host: landscape variation in host genotype, host phenotype, and environmental factors. New Phytol 177: 756–66. [DOI] [PubMed] [Google Scholar]
  5. Anderson RM and May RM. 1985. Age-related changes in the rate of disease transmissions: implications for the design of vaccination programs. J Hyg (Lond) 94: 365–436. [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Anderson RM and May RM. 1991. Infectious diseases of humans: dynamics and control. Oxford, UK: Oxford University Press. [Google Scholar]
  7. Beldomenico PM and Begon M. 2010. Disease spread, susceptibility and infection intensity: vicious circles? Trends Ecol Evol 25: 21–27. [DOI] [PubMed] [Google Scholar]
  8. Bjørnstad ON, Finkenstadt BF, and Grenfell BT. 2002. Dynamics of measles epidemics: estimating scaling of transmission rates using a time series SIR model. Ecol Monogr 72: 169–84. [Google Scholar]
  9. Bradley CA and Altizer S. 2007. Urbanization and the ecology of wildlife diseases. Trends Ecol Evol 22: 95–102. [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Brisson D, Dykhuizen DE, and Ostfeld RS. 2008. Conspicuous impacts of inconspicuous hosts on the Lyme disease epidemic. Proc Biol Sci 275: 227–35. [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Brunner JL and Ostfeld RS. 2008. Multiple causes of variable tick burdens on small-mammal hosts. Ecology 89: 2259–72. [DOI] [PubMed] [Google Scholar]
  12. Chong HT, Hossain MJ, and Tan CT. 2008. Differences in epidemiologic and clinical features of Nipah virus encephalitis between the Malaysian and Bangladesh outbreaks. Neurol Asia 13: 23–26. [Google Scholar]
  13. Craft ME, Volz E, Packer C, and Meyers LA. 2011. Disease transmission in territorial populations: the small-world network of Serengeti lions. J R Soc Interface 8: 776–86. [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Cronin JP, Welsh ME, Dekkers MG, et al 2010. Host physiological phenotype explains pathogen reservoir potential. Ecol Lett 13: 1221–32. [DOI] [PubMed] [Google Scholar]
  15. Diekmann O, Heesterbeek JAP, and Metz JAJ. 1990. On the definition and the computation of the basic reproduction ratio R 0 in models for infectious-diseases in heterogeneous populations. J Math Biol 28: 365–82. [DOI] [PubMed] [Google Scholar]
  16. Dobson A and Foufopoulos J. 2001. Emerging infectious pathogens of wildlife. Philos T Roy Soc B 356: 1001–12. [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Farnsworth ML, Wolfe LL, Hobbs NT, et al 2005. Human land use influences chronic wasting disease prevalence in mule deer. Ecol Appl 15: 119–26. [Google Scholar]
  18. Ferrari N, Cattadori IM, Nespereira J, et al 2004. The role of host sex in parasite dynamics: field experiments on the yellow-necked mouse Apodemus flavicollis . Ecol Lett 7: 88–94. [Google Scholar]
  19. Graham AL, Cattadori IM, Lloyd-Smith JO, et al 2007. Transmission consequences of coinfection: cytokines writ large? Trends Parasitol 23: 284–91. [DOI] [PubMed] [Google Scholar]
  20. Grear DA, Perkins SE, and Hudson PJ. 2009. Does elevated testosterone result in increased exposure and transmission of parasites? Ecol Lett 12: 528–37. [DOI] [PubMed] [Google Scholar]
  21. Grieco JP, Johnson S, Achee NL, et al 2006. Distribution of Anopheles albimanus, Anopheles vestitipennis, and Anopheles crucians associated with land use in northern Belize. J Med Entomol 4: 614–22. [DOI] [PubMed] [Google Scholar]
  22. Hampson K, Dushoff J, Cleaveland S, et al 2009. Transmission dynamics and prospects for the elimination of canine rabies. PLoS Biol 7: 462–71. [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Hawley DM and Altizer SM. 2011. Disease ecology meets ecological immunology: understanding the links between organismal immunity and infection dynamics in natural populations. Funct Ecol 25: 48–60. [Google Scholar]
  24. Haydon DT, Cleaveland S, Taylor LH, and Laurenson MK. 2002. Identifying reservoirs of infection: a conceptual and practical challenge. Emerg Infect Dis 8: 1468–73. [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Hudson PJ, Perkins SE, and Cattadori IM. 2008. The emergence of wildlife disease and the application of ecology. In: Ostfeld RS, Keesing F, and Eviner VT . (Eds). Infectious disease ecology: effects of ecosystems on disease and of disease on ecosystems. Princeton, NJ: Princeton University Press. [Google Scholar]
  26. Johnson PTJ, Townsend AR, Cleveland CC, et al 2010. Linking environmental nutrient enrichment and disease emergence in humans and wildlife. Ecol Appl 20: 16–29. [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Keeling MJ and Eames KTD. 2005. Networks and epidemic models. J R Soc Interface 2: 295–307. [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Keeling MJ, Woolhouse MEJ, Shaw DJ, et al 2001. Dynamics of the 2001 UK foot and mouth epidemic: stochastic dispersal in a heterogeneous landscape. Science 294: 813–17. [DOI] [PubMed] [Google Scholar]
  29. Kilpatrick AM, Daszak P, Jones MJ, et al 2006. Host heterogeneity dominates West Nile virus transmission. P R Soc Lond B 273: 2327–33. [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Krkošek M, Lewis MA, and Volpe JP. 2005. Transmission dynamics of parasitic sea lice from farm to wild salmon. P R Soc Lond B 272: 689–96. [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Lawley TD, Clare S, Walker AW, et al 2009. Antibiotic treatment of Clostridium difficile carrier mice triggers a supershedder state, spore-mediated transmission, and severe disease in immuno-compromised hosts. Infect Immun 77: 3661–69. [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Leavitt JW. 1996. Typhoid Mary: captive to the public's health. Boston, MA: Beacon Press. [Google Scholar]
  33. Lloyd-Smith JO, Schreiber SJ, Kopp PE, and Getz WM. 2005. Superspreading and the effect of individual variation on disease emergence. Nature 438: 355–59. [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Lloyd-Smith JO, Poss M, and Grenfell BT. 2008. HIV-1/parasite co-infection and the emergence of new parasite strains. Parasitology 135: 795–806. [DOI] [PubMed] [Google Scholar]
  35. LoGiudice K, Ostfeld RS, Schmidt KA, and Keesing F. 2003. The ecology of infectious disease: effects of host diversity and community composition on Lyme disease risk. P Natl Acad Sci USA 100: 567–71. [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Mack RN, Simberloff D, Lonsdale WM, et al 2000. Biotic invasions: causes, epidemiology, global consequences, and control. Ecol Appl 10: 689–710. [Google Scholar]
  37. Metzger VT, Lloyd-Smith JO, and Weinberger LS. 2011. Autonomous targeting of infectious superspreaders using engineered transmissible therapies. PLoS Comput Biol 7: e1002015. [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Mougeot F, Redpath SM, Piertney SB, and Hudson PJ. 2005. Separating behavioral and physiological mechanisms in testosterone-mediated trade-offs. Am Nat 166: 158–68. [DOI] [PubMed] [Google Scholar]
  39. Patz JA, Daszak P, Tabor GM, et al 2004. Unhealthy landscapes: policy recommendations on land use change and infectious disease emergence. Environ Health Persp 112: 1092–98. [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. Pearman PB and Garner TWJ. 2005. Susceptibility of Italian agile frog populations to an emerging strain of Ranavirus parallels population genetic diversity. Ecol Lett 8: 401–08. [Google Scholar]
  41. Perkins SE, Cattadori IM, Tagliapietra V, et al 2003. Empirical evidence for key hosts in persistence of a tick-borne disease. Int J Parasitol 33: 909–17. [DOI] [PubMed] [Google Scholar]
  42. Rohani P, Green CJ, Mantilla-Beniers NB, and Grenfell BT. 2003. Ecological interference between fatal diseases. Nature 422: 885–88. [DOI] [PubMed] [Google Scholar]
  43. Shaw DJ and Dobson AP. 1995. Patterns of macroparasite abundance and aggregation in wildlife populations: a quantitative review. Parasitology 111: S111–33. [DOI] [PubMed] [Google Scholar]
  44. Spira WM, Huq A, Ahmed QS, and Saeed YA. 1981. Uptake of Vibrio cholerae biotype eltor from contaminated water by water hyacinth (Eichornia crassipes). Appl Environ Microbiol 42: 550–53. [DOI] [PMC free article] [PubMed] [Google Scholar]
  45. Taleb NN. 2007. The black swan: the impact of the highly improbable. New York, NY: Random House. [Google Scholar]
  46. Telfer S, Lambin X, Birtles R, et al 2010. Species interactions in a parasite community drive infection risk in a wildlife population. Science 330: 243–46. [DOI] [PMC free article] [PubMed] [Google Scholar]
  47. Whitten EHT. 2010. Influence of the improbable in earth sciences. P Geologist Assoc 121: 249–51. [Google Scholar]
  48. WHO (World Health Organization) . 2003. Severe acute respiratory syndrome (SARS): status of the outbreak and lessons for the immediate future. Geneva, Switzerland: WHO. [Google Scholar]
  49. Wilson K, Bjørnstad ON, Dobson AP, et al 2001. Heterogeneities in macroparasite infections: patterns and processes. In: Hudson PJ, Rizzoli A, Grenfell BT, et al (Eds). The ecology of wildlife diseases. Oxford, UK: Oxford University Press. [Google Scholar]
  50. Woolhouse MEJ, Dye C, Etard JF, et al 1997. Heterogeneities in the transmission of infectious agents: implications for the design of control programs. P Natl Acad Sci USA 94: 338–42. [DOI] [PMC free article] [PubMed] [Google Scholar]
  51. Woolhouse M, Chase-Topping M, Haydon D, et al 2001. Epidemiology: foot-and-mouth disease under control in the UK. Nature 411: 258–59. [DOI] [PubMed] [Google Scholar]

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