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Simian varicella virus (SVV) infection of non-human primates models human varicella zoster virus (VZV) infection. Assessment of cell signaling immune responses in monkeys after primary SVV infection, after immunosuppression and during reactivation revealed strong pro-inflammatory responses and lesser anti-inflammatory components during varicella and reactivation. Pro-inflammatory mediators elevated during varicella included interferon-gamma (IFN-γ), interleukin (IL)-6, monocyte chemoattractant protein (MCP-1), interferon inducible T-cell α chemoattractant protein (I-TAC), interferon processing protein (IP-10), and anti-inflammatory interleukin-1 Receptor antagonist (IL-1Ra). After immunosuppression and at reactivation, levels of pro-inflammatory mediators MCP-1, eotaxin, IL-6, IL-8, MIF, RANTES (regulated-on-activation normal T-cell expressed and secreted), and HGF (hepatocyte growth factor) were elevated, as was the anti-inflammatory mediator IL-1Ra. Characterization of cytokine, chemokine and growth factor responses during different stages of varicella virus infection will facilitate immunotherapeutic and vaccine strategies.

Polyploidy, or whole genome duplication, has been an important feature of eukaryotic evolution. This is especially true in flowering plants, where all extant angiosperms have descended from polyploid species. Here we present a broad comparative analysis of the effect of polyploidy on flowering plant diversity. We examine the widely held hypothesis that polyploid flowering plants generate more diversity than their diploid counterparts, by fitting stochastic birth/death models to observed ploidal frequency data from 60 extant angiosperm genera. Our results suggest the opposite, that diploids speciate at higher rates than polyploids, through a combination of simple diploid speciation and tetraploidy. Importantly, the estimated diploid advantage stemmed primarily from a higher rate of polyploidization in diploids than polyploids. Our model is also able to account for the empirically observed correlation between polyploidy and species richness without assuming that polyploids have a speciation advantage over diploids.

Many endangered wildlife populations are vulnerable to infectious diseases for which vaccines exist; yet, pragmatic considerations often preclude large-scale vaccination efforts. These barriers could be reduced by focusing on individuals with the highest contact rates. However, the question then becomes whether targeted vaccination is sufficient to prevent large outbreaks. To evaluate the efficacy of targeted wildlife vaccinations, we simulate pathogen transmission and control on monthly association networks informed by behavioural data from a wild chimpanzee community (Kanyawara N = 37, Kibale National Park, Uganda). Despite considerable variation across monthly networks, our simulations indicate that targeting the most connected individuals can prevent large outbreaks with up to 35% fewer vaccines than random vaccination. Transmission heterogeneities might be attributed to biological differences among individuals (e.g. sex, age, dominance and family size). Thus, we also evaluate the effectiveness of a trait-based vaccination strategy, as trait data are often easier to collect than interaction data. Our simulations indicate that a trait-based strategy can prevent large outbreaks with up to 18% fewer vaccines than random vaccination, demonstrating that individual traits can serve as effective estimates of connectivity. Overall, these results suggest that fine-scale behavioural data can help optimize pathogen control efforts for endangered wildlife., (© 2014 The Author(s) Published by the Royal Society. All rights reserved.)

Mathematical modeling of disease transmission has provided quantitative predictions for health policy, facilitating the evaluation of epidemiological outcomes and the cost-effectiveness of interventions. However, typical sensitivity analyses of deterministic dynamic infectious disease models focus on model architecture and the relative importance of parameters but neglect parameter uncertainty when reporting model predictions. Consequently, model results that identify point estimates of intervention levels necessary to terminate transmission yield limited insight into the probability of success. We apply probabilistic uncertainty analysis to a dynamic model of influenza transmission and assess global uncertainty in outcome. We illustrate that when parameter uncertainty is not incorporated into outcome estimates, levels of vaccination and treatment predicted to prevent an influenza epidemic will only have an approximately 50% chance of terminating transmission and that sensitivity analysis alone is not sufficient to obtain this information. We demonstrate that accounting for parameter uncertainty yields probabilities of epidemiological outcomes based on the degree to which data support the range of model predictions. Unlike typical sensitivity analyses of dynamic models that only address variation in parameters, the probabilistic uncertainty analysis described here enables modelers to convey the robustness of their predictions to policy makers, extending the power of epidemiological modeling to improve public health., (Copyright © 2013 The Authors. Published by Elsevier B.V. All rights reserved.)

Background: The annual mortality rate of human rabies in rural Africa is 3.6 deaths per 100 000 persons. Rabies can be prevented with prompt postexposure prophylaxis, but this is costly and often inaccessible in rural Africa. Because 99% of human exposures occur through rabid dogs, canine vaccination also prevents transmission of rabies to humans., Objective: To evaluate the cost-effectiveness of rabies control through annual canine vaccination campaigns in rural sub-Saharan Africa., Design: We model transmission dynamics in dogs and wildlife and assess empirical uncertainty in the biological variables to make probability-based evaluations of cost-effectiveness., Data Sources: Epidemiologic variables from a contact-tracing study and literature and cost data from ongoing vaccination campaigns., Target Population: Two districts of rural Tanzania: Ngorongoro and Serengeti., Time Horizon: 10 years., Perspective: Health policymaker., Intervention: Vaccination coverage ranging from 0% to 95% in increments of 5%., Outcome Measures: Life-years for health outcomes and 2010 U.S. dollars for economic outcomes., Results of Base-Case Analysis: Annual canine vaccination campaigns were very cost-effective in both districts compared with no canine vaccination. In Serengeti, annual campaigns with as much as 70% coverage were cost-saving., Results of Sensitivity Analysis: Across a wide range of variable assumptions and levels of societal willingness to pay for life-years, the optimal vaccination coverage for Serengeti was 70%. In Ngorongoro, although optimal coverage depended on willingness to pay, vaccination campaigns were always cost-effective and lifesaving and therefore preferred., Limitation: Canine vaccination was very cost-effective in both districts, but there was greater uncertainty about the optimal coverage in Ngorongoro., Conclusion: Annual canine rabies vaccination campaigns conferred extraordinary value and dramatically reduced the health burden of rabies., Primary Funding Source: National Institutes of Health.

Neutralizing antibodies may have critical importance in immunity against human immunodeficiency virus type 1 (HIV-1) infection. However, the amount of protective antibody needed at mucosal surfaces has not been fully established. Here, we evaluated systemic and mucosal pharmacokinetics (PK) and pharmacodynamics (PD) of 2F5 IgG and 2F5 Fab fragments with respect to protection against vaginal challenge with simian-human immunodeficiency virus-BaL in macaques. Antibody assessment demonstrated that 2F5 IgG was more potent than polymeric forms (IgM and IgA) across a range of cellular and tissue models. Vaginal challenge studies demonstrated a dose-dependent protection for 2F5 IgG and no protection with 2F5 Fab despite higher vaginal Fab levels at the time of challenge. Animals receiving 50 or 25 mg/kg of body weight 2F5 IgG were completely protected, while 3/5 animals receiving 5 mg/kg were protected. In the control animals, infection was established by a minimum of 1 to 4 transmitted/founder (T/F) variants, similar to natural human infection by this mucosal route; in the two infected animals that had received 5 mg 2F5 IgG, infection was established by a single T/F variant. Serum levels of 2F5 IgG were more predictive of sterilizing protection than measured vaginal levels. Fc-mediated antiviral activity did not appear to influence infection of primary target cells in cervical explants. However, PK studies highlighted the importance of the Fc portion in tissue biodistribution. Data presented in this study may be important in modeling serum levels of neutralizing antibodies that need to be achieved by either vaccination or passive infusion to prevent mucosal acquisition of HIV-1 infection in humans.

Recent Phase 2b dengue vaccine trials have demonstrated the safety of the vaccine and estimated the vaccine efficacy with further trials underway. In anticipation of vaccine roll-out, cost-effectiveness analysis of potential vaccination policies that quantify the dynamics of disease transmission are fundamental to the optimal allocation of available doses. We developed a dengue transmission and vaccination model and calculated, for a range of vaccination costs and willingness-to-pay thresholds, the level of vaccination coverage necessary to sustain herd-immunity, the price at which vaccination is cost-effective and is cost-saving, and the sensitivity of our results to parameter uncertainty. We compared two vaccine efficacy scenarios, one a more optimistic scenario and another based on the recent lower-than-expected efficacy from the latest clinical trials. We found that herd-immunity may be achieved by vaccinating 82% (95% CI 58-100%) of the population at a vaccine efficacy of 70%. At this efficacy, vaccination may be cost-effective for vaccination costs up to US$ 534 (95% CI $369-1008) per vaccinated individual and cost-saving up to $204 (95% CI $39-678). At the latest clinical trial estimates of an average of 30% vaccine efficacy, vaccination may be cost-effective and cost-saving at costs of up to $237 (95% CI $159-512) and $93 (95% CI $15-368), respectively. Our model provides an assessment of the cost-effectiveness of dengue vaccination in Brazil and incorporates the effect of herd immunity into dengue vaccination cost-effectiveness. Our results demonstrate that at the relatively low vaccine efficacy from the recent Phase 2b dengue vaccine trials, age-targeted vaccination may still be cost-effective provided the total vaccination cost is sufficiently low., (Copyright © 2013 Elsevier Ltd. All rights reserved.)

Background: Epidemiological data from Zimbabwe suggests that genital infection with Schistosoma haematobium may increase the risk of HIV infection in young women. Therefore, the treatment of Schistosoma haematobium with praziquantel could be a potential strategy for reducing HIV infection. Here we assess the potential cost-effectiveness of praziquantel as a novel intervention strategy against HIV infection., Methods: We developed a mathematical model of female genital schistosomiasis (FGS) and HIV infections in Zimbabwe that we fitted to cross-sectional data of FGS and HIV prevalence of 1999. We validated our epidemic projections using antenatal clinic data on HIV prevalence. We simulated annual praziquantel administration to school-age children. We then used these model predictions to perform a cost-effectiveness analysis of annual administration of praziquantel as a potential measure to reduce the burden of HIV in sub-Saharan Africa., Findings: We showed that for a variation of efficacy between 30-70% of mass praziquantel administration for reducing the enhanced risk of HIV transmission per sexual act due to FGS, annual administration of praziquantel to school-age children in Zimbabwe could result in net savings of US$16-101 million compared with no mass treatment of schistosomiasis over a ten-year period. For a variation in efficacy between 30-70% of mass praziquantel administration for reducing the acquisition of FGS, annual administration of praziquantel to school-age children could result in net savings of US$36-92 million over a ten-year period., Conclusions: In addition to reducing schistosomiasis burden, mass praziquantel administration may be a highly cost-effective way of reducing HIV infections in sub-Saharan Africa. Program costs per case of HIV averted are similar to, and under some conditions much better than, other interventions that are currently implemented in Africa to reduce HIV transmission. As a cost-saving strategy, mass praziquantel administration should be prioritized over other less cost-effective public health interventions.

Identification of the optimal vaccine allocation for the control of influenza requires consideration of uncertainty arising from numerous unpredictable factors, including viral evolution and diversity within the human population's immunity as well as variation in vaccine efficacy. The best policy must account for diverse potential outcomes based on these uncertainties. Here we used a mathematical model parametrized with survey-based contact data, demographic, and epidemiological data from seasonal influenza in the United States to determine the optimal vaccine allocation for five outcome measures: infections, hospitalizations, deaths, years of life loss, and contingent valuation. We incorporated uncertainty of epidemiological parameters and derive probability distributions of optimal age- and risk-specific allocation of vaccine. Our analysis demonstrated that previous recommendations of targeting schoolchildren (ages 5-17 years) and young adults (18-44 years) are generally robust in the face of uncertainty. However, when the outcome measure is to minimize deaths, years of life loss, or contingent valuation, uncertainty analysis identified scenarios under which it is optimal to target people at high risk for complications, even when vaccine are in abundance., (Copyright © 2013 Elsevier Ltd. All rights reserved.)

Epidemiological studies from sub-Saharan Africa show that genital infection with Schistosoma haematobium [corrected] may increase the risk for HIV infection in young women. Therefore, preventing schistosomiasis has the potential to reduce HIV transmission in sub-Saharan Africa. We developed a transmission model of female genital schistosomiasis and HIV infections that we fit to epidemiological data of HIV and female genital schistosomiasis prevalence and coinfection in rural Zimbabwe. We used the model to evaluate the cost-effectiveness of a multifaceted community-based intervention for preventing schistosomiasis and, consequently, HIV infections in rural Zimbabwe, from the perspective of a health payer. The community-based intervention combined provision of clean water, sanitation, and health education (WSH) with administration of praziquantel to school-aged children. Considering variation in efficacy between 10% and 70% of WSH for reducing S. haematobium [corrected] transmission, our model predicted that community-based intervention is likely to be cost-effective in Zimbabwe at an aggregated WSH cost corresponding to US $725-$1,000 per individual over a 20-y intervention period. These costs compare favorably with empirical measures of WSH provision in developing countries, indicating that integrated community-based intervention for reducing the transmission of S. haematobium [corrected] is an economically attractive strategy for reducing schistosomiasis and HIV transmission in sub-Saharan Africa that would have a powerful impact on averting infections and saving lives.

Contact patterns in group-structured populations determine the course of infectious disease outbreaks. Network-based models have revealed important connections between group-level contact patterns and the dynamics of epidemics, but these models typically ignore heterogeneities in within-group composition. Here, we analyse a flexible mathematical model of disease transmission in a hierarchically structured wildlife population, and find that increased variation in group size reduces the epidemic threshold, making social animal populations susceptible to a broader range of pathogens. Variation in group size also increases the likelihood of an epidemic for mildly transmissible diseases, but can reduce the likelihood and expected size of an epidemic for highly transmissible diseases. Further, we introduce the concept of epidemiological effective group size, which we define to be the group size of a hypothetical population containing groups of identical size that has the same epidemic threshold as an observed population. Using data from the Serengeti Lion Project, we find that pride-living Serengeti lions are epidemiologically comparable to a homogeneous population with up to 20 per cent larger prides.

Many pathogens spread primarily via direct contact between infected and susceptible hosts. Thus, the patterns of contacts or contact network of a population fundamentally shape the course of epidemics. While there is a robust and growing theory for the dynamics of single epidemics in networks, we know little about the impacts of network structure on long-term epidemic or endemic transmission. For seasonal diseases like influenza, pathogens repeatedly return to populations with complex and changing patterns of susceptibility and immunity acquired through prior infection. Here, we develop two mathematical approaches for modeling consecutive seasonal outbreaks of a partially-immunizing infection in a population with contact heterogeneity. Using methods from percolation theory we consider both leaky immunity, where all previously infected individuals gain partial immunity, and polarized immunity, where a fraction of previously infected individuals are fully immune. By restructuring the epidemiologically active portion of their host population, such diseases limit the potential of future outbreaks. We speculate that these dynamics can result in evolutionary pressure to increase infectiousness., (Copyright © 2012 Elsevier Ltd. All rights reserved.)

Pandemic influenza is an international public health concern. In light of the persistent threat of H5N1 avian influenza and the recent pandemic of A/H1N1swine influenza outbreak, public health agencies around the globe are continuously revising their preparedness plans. The A/H1N1 pandemic of 2009 demonstrated that influenza activity and severity might vary considerably among age groups and locations, and the distribution of an effective influenza vaccine may be significantly delayed and staggered. Thus, pandemic influenza vaccine distribution policies should be tailored to the demographic and spatial structures of communities. Here, we introduce a bi-criteria decision-making framework for vaccine distribution policies that is based on a geospatial and demographically-structured model of pandemic influenza transmission within and between counties of Arizona in the Unites States. Based on data from the 2009-2010 H1N1 pandemic, the policy predicted to reduce overall attack rate most effectively is prioritizing counties expected to experience the latest epidemic waves (a policy that may be politically untenable). However, when we consider reductions in both the attack rate and the waiting period for those seeking vaccines, the widely adopted pro rata policy (distributing according to population size) is also predicted to be an effective strategy.

The recent H1N1 pandemic influenza stimulated numerous studies into the attitudes and intentions about the H1N1 vaccine. However, no study has investigated prospective predictors of vaccination behaviour. We conducted a two-wave longitudinal study among residents in four U.S. cities during the course of the H1N1 outbreak, using Internet surveys to assess demographic, cognitive and emotional predictors of H1N1 vaccination behaviour. Surveys were conducted at two time points, before (Time 1) and after (Time 2) the H1N1 vaccine was widely available to the public. Results show that Time 2 vaccination rates, but not Time 1 vaccination intentions, tracked H1N1 prevalence across the four cities. Receipt of seasonal influenza vaccine in the previous year, worry, compliance with recommended interventions, household size and education assessed at Time 1 were significant prospective predictors of vaccination behaviour. Perception of the H1N1 vaccine, social influence and prioritised vaccine recipient status assessed at Time 2 also predicted vaccination behaviour. Critically, worry about H1N1 mediated the effects of both objective risk (prevalence at the city level) and perceived risk on vaccination behaviour. These results suggest that H1N1 vaccination behaviour appropriately reflected objective risk across regions, and worry acted as the mechanism by which vaccination behaviour followed objective risk.

Vaccination has proven effective in controlling many infectious diseases. However, differential effectiveness with regard to pathogen genotype is a frequent reason for failures in vaccine development. Often, insufficient immune response is induced to prevent infection by the diversity of existing serotypes present in pathogenic populations of bacteria. These vaccines that target a too narrow spectrum of serotypes do not offer sufficient prevention of infections, and can also lead to undesirable strain replacements. Here, we examine a novel idea to specifically exploit the narrow spectrum coverage of some vaccines to combat specific, emerging multi- and pan-resistant strains of pathogens. Application of a narrow-spectrum vaccine could serve to prevent infections by some strains that are hard to treat, rather than offer the vaccinated individual protection against infections by the pathogenic species as such. We suggest that vaccines targeted to resistant serotypes have the potential to become important public health tools, and would represent a new approach toward reducing the burden of particular multi-resistant strains occurring in hospitals. Vaccines targeting drug-resistant serotypes would also be the first clinical intervention with the potential to drive the evolution of pathogenic populations toward drug-sensitivity. We illustrate the feasibility of this approach by modeling a hypothetical vaccine that targets a subset of methicillin-resistant Staphylococcus aureus (MRSA) genotypes, in combination with drug treatment targeted at drug-sensitive genotypes. We find that a combined intervention strategy can limit nosocomial outbreaks, even when vaccine efficacy is imperfect. The broader utility of vaccine-based resistance control strategies should be further explored taking into account population structure, and the resistance and transmission patterns of the pathogen considered.

Previous game-theoretic studies of vaccination behavior typically have often assumed that populations are homogeneously mixed and that individuals are fully rational. In reality, there is heterogeneity in the number of contacts per individual, and individuals tend to imitate others who appear to have adopted successful strategies. Here, we use network-based mathematical models to study the effects of both imitation behavior and contact heterogeneity on vaccination coverage and disease dynamics. We integrate contact network epidemiological models with a framework for decision-making, within which individuals make their decisions either based purely on payoff maximization or by imitating the vaccination behavior of a social contact. Simulations suggest that when the cost of vaccination is high imitation behavior may decrease vaccination coverage. However, when the cost of vaccination is small relative to that of infection, imitation behavior increases vaccination coverage, but, surprisingly, also increases the magnitude of epidemics through the clustering of non-vaccinators within the network. Thus, imitation behavior may impede the eradication of infectious diseases. Calculations that ignore behavioral clustering caused by imitation may significantly underestimate the levels of vaccination coverage required to attain herd immunity.

Background: The spread of infectious diseases in wildlife populations is influenced by patterns of between-host contacts. Habitat "hotspots"--places attracting a large numbers of individuals or social groups--can significantly alter contact patterns and, hence, disease propagation. Research on the importance of habitat hotspots in wildlife epidemiology has primarily focused on how inter-individual contacts occurring at the hotspot itself increase disease transmission. However, in territorial animals, epidemiologically important contacts may primarily occur as animals cross through territories of conspecifics en route to habitat hotspots. So far, the phenomenon has received little attention. Here, we investigate the importance of these contacts in the case where infectious individuals keep visiting the hotspots and in the case where these individuals are not able to travel to the hotspot any more., Methodology and Principal Findings: We developed a simulation epidemiological model to investigate both cases in a scenario when transmission at the hotspot does not occur. We find that (i) hotspots still exacerbate epidemics, (ii) when infectious individuals do not travel to the hotspot, the most vulnerable individuals are those residing at intermediate distances from the hotspot rather than nearby, and (iii) the epidemiological vulnerability of a population is the highest when the number of hotspots is intermediate., Conclusions and Significance: By altering animal movements in their vicinity, habitat hotspots can thus strongly increase the spread of infectious diseases, even when disease transmission does not occur at the hotspot itself. Interestingly, when animals only visit the nearest hotspot, creating additional artificial hotspots, rather than reducing their number, may be an efficient disease control measure.

The increasingly complex and rapid transmission dynamics of many infectious diseases necessitates the use of new, more advanced methods for surveillance, early detection, and decision-making. Here, we demonstrate that a new method for optimizing surveillance networks can improve the quality of epidemiological information produced by typical provider-based networks. Using past surveillance and Internet search data, it determines the precise locations where providers should be enrolled. When applied to redesigning the provider-based, influenza-like-illness surveillance network (ILINet) for the state of Texas, the method identifies networks that are expected to significantly outperform the existing network with far fewer providers. This optimized network avoids informational redundancies and is thereby more effective than networks designed by conventional methods and a recently published algorithm based on maximizing population coverage. We show further that Google Flu Trends data, when incorporated into a network as a virtual provider, can enhance but not replace traditional surveillance methods.

Canine vaccination has been successful in controlling rabies in diverse settings worldwide. However, concerns remain that coverage levels which have previously been sufficient might be insufficient in systems where transmission occurs both between and within populations of domestic dogs and other carnivores. To evaluate the effectiveness of vaccination targeted at domestic dogs when wildlife also contributes to transmission, we applied a next-generation matrix model based on contract tracing data from the Ngorongoro and Serengeti Districts in northwest Tanzania. We calculated corresponding values of R(0), and determined, for policy purposes, the probabilities that various annual vaccination targets would control the disease, taking into account the empirical uncertainty in our field data. We found that transition rate estimates and corresponding probabilities of vaccination-based control indicate that rabies transmission in this region is driven by transmission within domestic dogs. Different patterns of rabies transmission between the two districts exist, with wildlife playing a more important part in Ngorongoro and leading to higher recommended coverage levels in that district. Nonetheless, our findings indicate that an annual dog vaccination campaign achieving the WHO-recommended target of 70% will control rabies in both districts with a high level of certainty. Our results support the feasibility of controlling rabies in Tanzania through dog vaccination.

Ring vaccination can be a highly effective control strategy for an emerging disease or in the final phase of disease eradication, as witnessed in the eradication of smallpox. However, the impact of behavioural dynamics on the effectiveness of ring vaccination has not been explored in mathematical models. Here, we analyze a series of stochastic models of voluntary ring vaccination. Contacts of an index case base vaccinating decisions on their own individual payoffs to vaccinate or not vaccinate, and they can also imitate the behaviour of other contacts of the index case. We find that including imitation changes the probability of containment through ring vaccination considerably. Imitation can cause a strong majority of contacts to choose vaccination in some cases, or to choose non-vaccination in other cases-even when the equivalent solution under perfectly rational (non-imitative) behaviour yields mixed choices. Moreover, imitation processes can result in very different outcomes in different stochastic realizations sampled from the same parameter distributions, by magnifying moderate tendencies toward one behaviour or the other: in some realizations, imitation causes a strong majority of contacts not to vaccinate, while in others, imitation promotes vaccination and reduces the number of secondary infections. Hence, the effectiveness of ring vaccination can depend significantly and unpredictably on imitation processes. Therefore, our results suggest that risk communication efforts should be initiated early in an outbreak when ring vaccination is to be applied, especially among subpopulations that are heavily influenced by peer opinions.

Territoriality in animal populations creates spatial structure that is thought to naturally buffer disease invasion. Often, however, territorial populations also include highly mobile, non-residential individuals that potentially serve as disease superspreaders. Using long-term data from the Serengeti Lion Project, we characterize the contact network structure of a territorial wildlife population and address the epidemiological impact of nomadic individuals. As expected, pride contacts are dominated by interactions with neighbouring prides and interspersed by encounters with nomads as they wander throughout the ecosystem. Yet the pride-pride network also includes occasional long-range contacts between prides, making it surprisingly small world and vulnerable to epidemics, even without nomads. While nomads increase both the local and global connectivity of the network, their epidemiological impact is marginal, particularly for diseases with short infectious periods like canine distemper virus. Thus, territoriality in Serengeti lions may be less protective and non-residents less important for disease transmission than previously considered., (© 2010 The Royal Society)

Background: Influenza vaccination is vital for reducing H1N1 infection-mediated morbidity and mortality. To reduce transmission and achieve herd immunity during the initial 2009-2010 pandemic season, the US Centers for Disease Control and Prevention (CDC) recommended that initial priority for H1N1 vaccines be given to individuals under age 25, as these individuals are more likely to spread influenza than older adults. However, due to significant delay in vaccine delivery for the H1N1 influenza pandemic, a large fraction of population was exposed to the H1N1 virus and thereby obtained immunity prior to the wide availability of vaccines. This exposure affects the spread of the disease and needs to be considered when prioritizing vaccine distribution., Methods: To determine optimal H1N1 vaccine distributions based on individual self-interest versus population interest, we constructed a game theoretical age-structured model of influenza transmission and considered the impact of delayed vaccination., Results: Our results indicate that if individuals decide to vaccinate according to self-interest, the resulting optimal vaccination strategy would prioritize adults of age 25 to 49 followed by either preschool-age children before the pandemic peak or older adults (age 50-64) at the pandemic peak. In contrast, the vaccine allocation strategy that is optimal for the population as a whole would prioritize individuals of ages 5 to 64 to curb a growing pandemic regardless of the timing of the vaccination program., Conclusions: Our results indicate that for a delayed vaccine distribution, the priorities that are optimal at a population level do not align with those that are optimal according to individual self-interest. Moreover, the discordance between the optimal vaccine distributions based on individual self-interest and those based on population interest is even more pronounced when vaccine availability is delayed. To determine optimal vaccine allocation for pandemic influenza, public health agencies need to consider both the changes in infection risks among age groups and expected patterns of adherence.

The effectiveness of seasonal influenza vaccination programs depends on individual-level compliance. Perceptions about risks associated with infection and vaccination can strongly influence vaccination decisions and thus the ultimate course of an epidemic. Here we investigate the interplay between contact patterns, influenza-related behavior, and disease dynamics by incorporating game theory into network models. When individuals make decisions based on past epidemics, we find that individuals with many contacts vaccinate, whereas individuals with few contacts do not. However, the threshold number of contacts above which to vaccinate is highly dependent on the overall network structure of the population and has the potential to oscillate more wildly than has been observed empirically. When we increase the number of prior seasons that individuals recall when making vaccination decisions, behavior and thus disease dynamics become less variable. For some networks, we also find that higher flu transmission rates may, counterintuitively, lead to lower (vaccine-mediated) disease prevalence. Our work demonstrates that rich and complex dynamics can result from the interaction between infectious diseases, human contact patterns, and behavior.

In 2009, public health agencies across the globe worked to mitigate the impact of the swine-origin influenza A (pH1N1) virus. These efforts included intensified surveillance, social distancing, hygiene measures, and the targeted use of antiviral medications to prevent infection (prophylaxis). In addition, aggressive antiviral treatment was recommended for certain patient subgroups to reduce the severity and duration of symptoms. To assist States and other localities meet these needs, the U.S. Government distributed a quarter of the antiviral medications in the Strategic National Stockpile within weeks of the pandemic's start. However, there are no quantitative models guiding the geo-temporal distribution of the remainder of the Stockpile in relation to pandemic spread or severity. We present a tactical optimization model for distributing this stockpile for treatment of infected cases during the early stages of a pandemic like 2009 pH1N1, prior to the wide availability of a strain-specific vaccine. Our optimization method efficiently searches large sets of intervention strategies applied to a stochastic network model of pandemic influenza transmission within and among U.S. cities. The resulting optimized strategies depend on the transmissability of the virus and postulated rates of antiviral uptake and wastage (through misallocation or loss). Our results suggest that an aggressive community-based antiviral treatment strategy involving early, widespread, pro-rata distribution of antivirals to States can contribute to slowing the transmission of mildly transmissible strains, like pH1N1. For more highly transmissible strains, outcomes of antiviral use are more heavily impacted by choice of distribution intervals, quantities per shipment, and timing of shipments in relation to pandemic spread. This study supports previous modeling results suggesting that appropriate antiviral treatment may be an effective mitigation strategy during the early stages of future influenza pandemics, increasing the need for systematic efforts to optimize distribution strategies and provide tactical guidance for public health policy-makers.

Background: The trajectory of an infectious disease outbreak is affected by the behavior of individuals, and the behavior is often related to individuals' risk perception. We assessed temporal changes and geographical differences in risk perceptions and precautionary behaviors in response to H1N1 influenza., Methods: 1,290 US adults completed an online survey on risk perceptions, interests in pharmaceutical interventions (preventive intervention and curative intervention), and engagement in precautionary activities (information seeking activities and taking quarantine measures) in response to H1N1 influenza between April 28 and May 27 2009. Associations of risk perceptions and precautionary behaviors with respondents' sex, age, and household size were analyzed. Linear and quadratic time trends were assessed by regression analyses. Geographic differences in risk perception and precautionary behaviors were evaluated. Predictors of willingness to take pharmaceutical intervention were analyzed., Results: Respondents from larger households reported stronger interest in taking medications and engaged in more precautionary activities, as would be normatively predicted. Perceived risk increased over time, whereas interest in pharmaceutical preventive interventions and the engagement in some precautionary activities decreased over time. Respondents who live in states with higher H1N1 incidence per population perceived a higher likelihood of influenza infection, but did not express greater interests in pharmaceutical interventions, nor did they engage in a higher degree of precautionary activities. Perceived likelihood of influenza infection, willingness to take medications and engagement in information seeking activities were higher for women than men., Conclusions: Perceived risk of infection and precautionary behavior can be dynamic in time, and differ by demographic characteristics and geographical locations. These patterns will likely influence the effectiveness of disease control measures.

Although contact network models have yielded important insights into infectious disease transmission and control throughout the last decade, researchers have just begun to explore the dynamic nature of contact patterns and their epidemiological significance. Most network models have assumed that contacts are static through time. Developing more realistic models of the social interactions that underlie the spread of infectious diseases thus remains an important challenge for both data gatherers and modelers. In this article, we review some recent data-driven and process-driven approaches that capture the dynamics of human contact, and discuss future challenges for the field.

The risk of acquiring sexually transmitted infections (STIs) depends on individual behavior and the network of risky partnerships in which an individual participates. STI epidemics often spread rapidly and primarily among individuals central to transmission networks; and thus they often defy the mass-action principle since incidence is not proportional to the infectious fraction of the population. Here, we estimate the contact network structure for an Atlanta, Georgia community with heterogeneous sexual and drug-related risk behaviors and build a detailed transmission model for HIV through this population. We show that accurate estimation of epidemic incidence requires careful measurement and inclusion of diverse factors including concurrency (having multiple partners), the duration of partnerships, serosorting (preference for partners with matching disease state), and heterogeneity in the number and kinds of partners. In the focal population, we find that injection drug users (IDUs) do not directly cause many secondary infections; yet they bridge the heterosexual and men-who-have-sex-with-men (MSM) populations and are thereby indirectly responsible for extensive transmission., (Copyright © 2010 Elsevier B.V. All rights reserved.)

Background: As Pandemic (H1N1) 2009 influenza spreads around the globe, it strikes school-age children more often than adults. Although there is some evidence of pre-existing immunity among older adults, this alone may not explain the significant gap in age-specific infection rates., Methods and Findings: Based on a retrospective analysis of pandemic strains of influenza from the last century, we show that school-age children typically experience the highest attack rates in primarily naive populations, with the burden shifting to adults during the subsequent season. Using a parsimonious network-based mathematical model which incorporates the changing distribution of contacts in the susceptible population, we demonstrate that new pandemic strains of influenza are expected to shift the epidemiological landscape in exactly this way., Conclusions: Our analysis provides a simple demographic explanation for the age bias observed for H1N1/09 attack rates, and suggests that this bias may shift in coming months. These results have significant implications for the allocation of public health resources for H1N1/09 and future influenza pandemics.

During unexpected infectious disease outbreaks, public health agencies must make effective use of limited resources. Vaccine distribution may be delayed and staggered through time, as underscored by the 2009 H1N1 influenza pandemic. Using a mathematical model parametrized with data from the 2009 H1N1 pandemic, we found that optimal allocations of vaccine among people in different age groups and people with high-risk conditions depends on the schedule of vaccine availability relative to the progress of the epidemic. For the projected schedule of H1N1 vaccine availability, the optimal strategy to reduce influenza-related deaths is to initial target high-risk people, followed by school-aged children (5-17) and then young adults (18-44). The optimal strategy to minimize hospitalizations, however, is to target ages 5-44 throughout the vaccination campaign, with only a tiny amount of vaccine used on high-risk people. We find that optimizing at each vaccine release time independently does not give the overall optimal strategy. In this manuscript, we derive policy recommendations for 2009 H1N1 vaccine allocation using a mathematical model. In addition, our optimization procedures, which consider staggered releases over the entire epidemic altogether, are applicable to other outbreaks where not all supplies are available initially.

Public health agencies across the globe are working to mitigate the impact of the 2009 pandemic caused by swine-origin influenza A (H1N1) virus. Prior to the large-scale distribution of an effective vaccine, the primary modes of control have included careful surveillance, social distancing and hygiene measures, strategic school closures, other community measures, and the prudent use of antiviral medications to prevent infection (prophylaxis) or reduce the severity and duration of symptoms (treatment). Here, we use mathematical models to determine the optimal geo-temporal tactics for distributing the U.S. strategic national stockpile of antivirals for treatment of infected cases during the early stages of a pandemic, prior to the wide availability of vaccines.We present a versatile optimization method for efficiently searching large sets of public health intervention strategies, and apply it to evaluating tactics for distributing antiviral medications from the U.S. Strategic National Stockpile (SNS). We implemented the algorithm on a network model of H1N1 transmission within and among U.S. cities to project the epidemiological impacts of antiviral stockpile distribution schedules and priorities. The resulting optimized strategies critically depend on the rates of antiviral uptake and wastage (through misallocation or loss). And while a surprisingly simple pro rata distribution schedule is competitive with the optimized strategies across a wide range of uptake and wastage, other equally simple policies perform poorly.Even as vaccination campaigns get underway worldwide, antiviral medications continue to play a critical in reducing H1N1-associated morbidity and mortality. If efforts are made to increase the fraction of cases treated promptly with antivirals above current levels, our model suggests that optimal use of the antiviral component of the Strategic National Stockpile may appreciably slow the transmission of H1N1 during fall 2009, thereby improving the impact of targeted vaccination. A more aggressive optimized antiviral strategy of this type may prove critical to mitigating future flu pandemics, but may increase the risk of antiviral resistance.

Background: As Pandemic (H1N1) 2009 influenza spreads around the globe, it strikes school-age children more often than adults. Although there is some evidence of pre-existing immunity among older adults, this alone may not explain the significant gap in age-specific infection rates., Methods & Findings: Based on a retrospective analysis of pandemic strains of influenza from the last century, we show that school-age children typically experience the highest attack rates in primarily naive populations, with the burden shifting to adults during the subsequent season. Using a parsimonious network-based mathematical model which incorporates the changing distribution of contacts in the susceptible population, we demonstrate that new pandemic strains of influenza are expected to shift the epidemiological landscape in exactly this way., Conclusions: Our results provide a simple demographic explanation for the age bias observed for H1N1/09 attack rates, and a prediction that this bias will shift in coming months. These results also have significant implications for the allocation of public health resources including vaccine distribution policies.

Background: Between 5 and 25 April 2009, pandemic (H1N1) 2009 caused a substantial, severe outbreak in Mexico, and subsequently developed into the first global pandemic in 41 years. We determined the reproduction number of pandemic (H1N1) 2009 by analyzing the dynamics of the complete case series in Mexico City during this early period., Methods: We analyzed three mutually exclusive datasets from Mexico City Distrito Federal which constituted all suspect cases from 15 March to 25 April: confirmed pandemic (H1N1) 2009 infections, non-pandemic influenza A infections and patients who tested negative for influenza. We estimated the initial reproduction number from 497 suspect cases identified prior to 20 April, using a novel contact network methodology incorporating dates of symptom onset and hospitalization, variation in contact rates, extrinsic sociological factors, and uncertainties in underreporting and disease progression. We tested the robustness of this estimate using both the subset of laboratory-confirmed pandemic (H1N1) 2009 infections and an extended case series through 25 April, adjusted for suspected ascertainment bias., Results: The initial reproduction number (95% confidence interval range) for this novel virus is 1.51 (1.32-1.71) based on suspected cases and 1.43 (1.29-1.57) based on confirmed cases before 20 April. The longer time series (through 25 April) yielded a higher estimate of 2.04 (1.84-2.25), which reduced to 1.44 (1.38-1.51) after correction for ascertainment bias., Conclusions: The estimated transmission characteristics of pandemic (H1N1) 2009 suggest that pharmaceutical and non-pharmaceutical mitigation measures may appreciably limit its spread prior the development of an effective vaccine.

Serengeti lions frequently experience viral outbreaks. In 1994, one-third of Serengeti lions died from canine distemper virus (CDV). Based on the limited epidemiological data available from this period, it has been unclear whether the 1994 outbreak was propagated by lion-to-lion transmission alone or involved multiple introductions from other sympatric carnivore species. More broadly, we do not know whether contacts between lions allow any pathogen with a relatively short infectious period to percolate through the population (i.e. reach epidemic proportions). We built one of the most realistic contact network models for a wildlife population to date, based on detailed behavioural and movement data from a long-term lion study population. The model allowed us to identify previously unrecognized biases in the sparse data from the 1994 outbreak and develop methods for judiciously inferring disease dynamics from typical wildlife samples. Our analysis of the model in light of the 1994 outbreak data strongly suggest that, although lions are sufficiently well connected to sustain epidemics of CDV-like diseases, the 1994 epidemic was fuelled by multiple spillovers from other carnivore species, such as jackals and hyenas.

The reproductive ratio, R0, is a fundamental quantity in epidemiology, which determines the initial increase in an infectious disease in a susceptible host population. In most epidemic models, there is a specific value of R0, the epidemic threshold, above which epidemics are possible, but below which epidemics cannot occur. As the complexity of an epidemic model increases, so too does the difficulty of calculating epidemic thresholds. Here we derive the reproductive ratio and epidemic thresholds for susceptible-infected-recovered (SIR) epidemics in a simple class of dynamic random networks. As in most epidemiological models, R0 depends on two basic epidemic parameters, the transmission and recovery rates. We find that R0 also depends on social parameters, namely the degree distribution that describes heterogeneity in the numbers of concurrent contacts and the mixing parameter that gives the rate at which contacts are initiated and terminated. We show that social mixing fundamentally changes the epidemiological landscape and, consequently, that static network approximations of dynamic networks can be inadequate.

Non-invasive blood perfusion measurement systems have been developed and tested in a phantom tissue and an animal model. The probes use a small sensor with a laminated flat thermocouple to measure the heat transfer and temperature response to an arbitrary thermal event (convective or conductive) imposed on the tissue surface. Blood perfusion and thermal contact resistance are estimated by comparing heat flux data with a mathematical model of the tissue. The perfusion probes were evaluated for repeatability and sensitivity using both a phantom tissue test stand and exposed rat liver tests. Perfusion in the phantom tissue tests was varied by controlling the flow of water into the phantom tissue test section, and the perfusion in the exposed liver tests was varied by temporarily occluding blood flow through the portal vein. The phantom tissue tests indicated that the probes can be used to detect small changes in perfusion (0.005 ml/ml/s). The probes qualitatively tracked the changes in the perfusion of the liver model due to occlusion of the portal vein.

Evolution by natural selection is fundamentally shaped by the fitness landscapes in which it occurs. Yet fitness landscapes are vast and complex, and thus we know relatively little about the long-range constraints they impose on evolutionary dynamics. Here, we exhaustively survey the structural landscapes of RNA molecules of lengths 12 to 18 nucleotides, and develop a network model to describe the relationship between sequence and structure. We find that phenotype abundance--the number of genotypes producing a particular phenotype--varies in a predictable manner and critically influences evolutionary dynamics. A study of naturally occurring functional RNA molecules using a new structural statistic suggests that these molecules are biased toward abundant phenotypes. This supports an "ascent of the abundant" hypothesis, in which evolution yields abundant phenotypes even when they are not the most fit.

Contact patterns in populations fundamentally influence the spread of infectious diseases. Current mathematical methods for epidemiological forecasting on networks largely assume that contacts between individuals are fixed, at least for the duration of an outbreak. In reality, contact patterns may be quite fluid, with individuals frequently making and breaking social or sexual relationships. Here, we develop a mathematical approach to predicting disease transmission on dynamic networks in which each individual has a characteristic behaviour (typical contact number), but the identities of their contacts change in time. We show that dynamic contact patterns shape epidemiological dynamics in ways that cannot be adequately captured in static network models or mass-action models. Our new model interpolates smoothly between static network models and mass-action models using a mixing parameter, thereby providing a bridge between disparate classes of epidemiological models. Using epidemiological and sexual contact data from an Atlanta high school, we demonstrate the application of this method for forecasting and controlling sexually transmitted disease outbreaks.

Heterogeneity in host contact patterns profoundly shapes population-level disease dynamics. Many epidemiological models make simplifying assumptions about the patterns of disease-causing interactions among hosts. In particular, homogeneous-mixing models assume that all hosts have identical rates of disease-causing contacts. In recent years, several network-based approaches have been developed to explicitly model heterogeneity in host contact patterns. Here, we use a network perspective to quantify the extent to which real populations depart from the homogeneous-mixing assumption, in terms of both the underlying network structure and the resulting epidemiological dynamics. We find that human contact patterns are indeed more heterogeneous than assumed by homogeneous-mixing models, but are not as variable as some have speculated. We then evaluate a variety of methodologies for incorporating contact heterogeneity, including network-based models and several modifications to the simple SIR compartmental model. We conclude that the homogeneous-mixing compartmental model is appropriate when host populations are nearly homogeneous, and can be modified effectively for a few classes of non-homogeneous networks. In general, however, network models are more intuitive and accurate for predicting disease spread through heterogeneous host populations.

The spread of infectious disease through communities depends fundamentally on the underlying patterns of contacts between individuals. Generally, the more contacts one individual has, the more vulnerable they are to infection during an epidemic. Thus, outbreaks disproportionately impact the most highly connected demographics. Epidemics can then lead, through immunization or removal of individuals, to sparser networks that are more resistant to future transmission of a given disease. Using several classes of contact networks-Poisson, scale-free and small-world-we characterize the structural evolution of a network due to an epidemic in terms of frailty (the degree to which highly connected individuals are more vulnerable to infection) and interference (the extent to which the epidemic cuts off connectivity among the susceptible population that remains following an epidemic). The evolution of the susceptible network over the course of an epidemic differs among the classes of networks; frailty, relative to interference, accounts for an increasing component of network evolution on networks with greater variance in contacts. The result is that immunization due to prior epidemics can provide greater community protection than random vaccination on networks with heterogeneous contact patterns, while the reverse is true for highly structured populations.

Deleterious mutations are considered a major impediment to adaptation, and there are straightforward expectations for the rate at which they accumulate as a function of population size and mutation rate. In a simulation model of an evolving population of asexually replicating RNA molecules, initially deleterious mutations accumulated at rates nearly equal to that of initially beneficial mutations, without impeding evolutionary progress. As the mutation rate was increased within a moderate range, deleterious mutation accumulation and mean fitness improvement both increased. The fixation rates were higher than predicted by many population-genetic models. This seemingly paradoxical result was resolved in part by the observation that, during the time to fixation, the selection coefficient (s) of initially deleterious mutations reversed to confer a selective advantage. Significantly, more than half of the fixations of initially deleterious mutations involved fitness reversals. These fitness reversals had a substantial effect on the total fitness of the genome and thus contributed to its success in the population. Despite the relative importance of fitness reversals, however, the probabilities of fixation for both initially beneficial and initially deleterious mutations were exceedingly small (on the order of 10(-5) of all mutations).

Background: The threat of avian influenza and the 2004-2005 influenza vaccine supply shortage in the United States have sparked a debate about optimal vaccination strategies to reduce the burden of morbidity and mortality caused by the influenza virus., Methods and Findings: We present a comparative analysis of two classes of suggested vaccination strategies: mortality-based strategies that target high-risk populations and morbidity-based strategies that target high-prevalence populations. Applying the methods of contact network epidemiology to a model of disease transmission in a large urban population, we assume that vaccine supplies are limited and then evaluate the efficacy of these strategies across a wide range of viral transmission rates and for two different age-specific mortality distributions. We find that the optimal strategy depends critically on the viral transmission level (reproductive rate) of the virus: morbidity-based strategies outperform mortality-based strategies for moderately transmissible strains, while the reverse is true for highly transmissible strains. These results hold for a range of mortality rates reported for prior influenza epidemics and pandemics. Furthermore, we show that vaccination delays and multiple introductions of disease into the community have a more detrimental impact on morbidity-based strategies than mortality-based strategies., Conclusions: If public health officials have reasonable estimates of the viral transmission rate and the frequency of new introductions into the community prior to an outbreak, then these methods can guide the design of optimal vaccination priorities. When such information is unreliable or not available, as is often the case, this study recommends mortality-based vaccination priorities.

Contact network epidemiology is an approach to modeling the spread of infectious diseases that explicitly considers patterns of person-to-person contacts within a community. Contacts can be asymmetric, with a person more likely to infect one of their contacts than to become infected by that contact. This is true for some sexually transmitted diseases that are more easily caught by women than men during heterosexual encounters; and for severe infectious diseases that cause an average person to seek medical attention and thereby potentially infect health care workers (HCWs) who would not, in turn, have an opportunity to infect that average person. Here we use methods from percolation theory to develop a mathematical framework for predicting disease transmission through semi-directed contact networks in which some contacts are undirected-the probability of transmission is symmetric between individuals-and others are directed-transmission is possible only in one direction. We find that the probability of an epidemic and the expected fraction of a population infected during an epidemic can be different in semi-directed networks, in contrast to the routine assumption that these two quantities are equal. We furthermore demonstrate that these methods more accurately predict the vulnerability of HCWs and the efficacy of various hospital-based containment strategies during outbreaks of severe respiratory diseases.

The wide distribution of polyploidy among plants has led to a variety of theories for the evolutionary advantages of polyploidy. Here we claim that the abundance of polyploidy may be the result of a simple ratcheting process that does not require evolutionary advantages due to the biological properties of organisms. The evolution of polyploidy is a one-way process in which chromosome number can increase but not decrease. Using a simple mathematical model, we show that average ploidal level within a plant lineage can continually increase to the levels observed today, even if there are ecological or physiological disadvantages to higher ploidy. The model allowed us to estimate the average net speciation and polyploidy rates for ten angiosperm genera. Based on these estimates, the model predicts distributions of ploidal levels statistically similar to those observed in nine of the 10 genera.

Quasispecies are clouds of genotypes that appear in a population at mutation-selection balance. This concept has recently attracted the attention of virologists, because many RNA viruses appear to generate high levels of genetic variation that may enhance the evolution of drug resistance and immune escape. The literature on these important evolutionary processes is, however, quite challenging. Here we use simple models to link mutation-selection balance theory to the most novel property of quasispecies: the error threshold-a mutation rate below which populations equilibrate in a traditional mutation-selection balance and above which the population experiences an error catastrophe, that is, the loss of the favored genotype through frequent deleterious mutations. These models show that a single fitness landscape may contain multiple, hierarchically organized error thresholds and that an error threshold is affected by the extent of back mutation and redundancy in the genotype-to-phenotype map. Importantly, an error threshold is distinct from an extinction threshold, which is the complete loss of the population through lethal mutations. Based on this framework, we argue that the lethal mutagenesis of a viral infection by mutation-inducing drugs is not a true error catastophe, but is an extinction catastrophe.

Beneficial mutations are the driving force of evolution by natural selection. Yet, relatively little is known about the distribution of the fitness effects of beneficial mutations in populations. Recent work of Gillespie and Orr suggested some of the first generalizations for the distributions of beneficial fitness effects and, surprisingly, they depend only weakly on biological details. In particular, the theory suggests that beneficial mutations obey an exponential distribution of fitness effects, with the same exponential parameter across different regions of genotype space, provided only that few possible beneficial mutations are available to that genotype. Here we tested this hypothesis with a quasi-empirical model of RNA evolution in which fitness is based on the secondary structures of molecules and their thermodynamic stabilities. The fitnesses of randomly selected genotypes appeared to follow a Gumbel-type distribution and thus conform to a basic assumption of adaptation theory. However, the observed distributions of beneficial fitness effects conflict with specific predictions of the theory. In particular, the distributions of beneficial fitness effects appeared exponential only when the vast majority of small-effect beneficial mutations were ignored. Additionally, the distribution of beneficial fitness effects varied with the fitness of the parent genotype. We believe that correlation of the fitness values among similar genotypes is likely the cause of the departure from the predictions of recent adaptation theory. Although in conflict with the current theory, these results suggest that more complex statistical generalizations about beneficial mutations may be possible.

Organisms employ a multitude of strategies to cope with the dynamical environments in which they live. Homeostasis and physiological plasticity buffer changes within the lifetime of an organism, while stochastic developmental programs and hypermutability track changes on longer time-scales. An alternative long-term mechanism is "genetic potential"--a heightened sensitivity to the effects of mutation that facilitates rapid evolution to novel states. Using a transparent mathematical model, we illustrate the concept of genetic potential and show that as environmental variability decreases, the evolving population reaches three distinct steady state conditions: (1) organismal flexibility, (2) genetic potential, and (3) genetic robustness. As a specific example of this concept we examine fluctuating selection for hydrophobicity in a single amino acid. We see the same three stages, suggesting that environmental fluctuations can produce allele distributions that are distinct not only from those found under constant conditions, but also from the transient allele distributions that arise under isolated selective sweeps.