Hans Heesterbeek

Methicillin Resistant Staphylococcus aureus ST398 in Veal Calf Farming : Human MRSA Carriage Related with Animal Antimicrobial Usage and Farm Hygiene

Introduction Recently a specific MRSA sequence type, ST398, emerged in food production animals and farmers. Risk factors for carrying MRSA ST398 in both animals and humans have not been fully evaluated. In this cross-sectional study, we investigated factors associated with MRSA colonization in veal calves and humans working and living on these farms. Methods A sample of 102 veal calf farms were randomly selected and visited from March 2007–February 2008. Participating farmers were asked to fill in a questionnaire (n = 390) to identify potential risk factors. A nasal swab was taken from each participant. Furthermore, nasal swabs were taken from calves (n = 2151). Swabs were analysed for MRSA by selective enrichment and suspected colonies were confirmed as MRSA by using slide coagulase test and PCR for presence of the mecA-gene. Spa types were identified and a random selection of each spa type was tested with ST398 specific PCR. The Sequence Type of non ST398 strains was determined. Data were analyzed using logistic regression analysis. Results Human MRSA carriage was strongly associated with intensity of animal contact and with the number of MRSA positive animals on the farm. Calves were more often carrier when treated with antibiotics, while farm hygiene was associated with a lower prevalence of MRSA. Conclusion This is the first study showing direct associations between animal and human carriage of ST398. The direct associations between animal and human MRSA carriage and the association between MRSA and antimicrobial use in calves implicate prudent use of antibiotics in farm animals.

Persistence of Livestock Associated MRSA CC398 in Humans Is Dependent on Intensity of Animal Contact

Introduction The presence of Livestock Associated MRSA (LA-MRSA) in humans is associated with intensity of animal contact. It is unknown whether the presence of LA-MRSA is a result of carriage or retention of MRSA-contaminated dust. We conducted a longitudinal study among 155 veal farmers in which repeated nasal and throat swabs were taken for MRSA detection. Periods with and without animal exposure were covered. Methods Randomly, 51 veal calf farms were visited from June - December 2008. Participants were asked to fill in questionnaires (n = 155) to identify potential risk factors for MRSA colonisation. Nasal and throat swabs were repeatedly taken from each participant for approximately 2 months. Swabs were analysed for MRSA and MSSA by selective bacteriological culturing. Spa-types of the isolates were identified and a ST398 specific PCR was performed. Data were analyzed using generalized estimation equations (GEE) to allow for correlated observations within individuals. Results Mean MRSA prevalence was 38% in farmers and 16% in family members. Presence of MRSA in farmers was strongly related to duration of animal contact and was strongly reduced in periods with absence of animal contact (−58%). Family members, especially children, were more often carriers when the farmer was a carrier (OR = 2, P<0.05). Only 7% (n = 11) of the participants appeared to be persistent carriers. A large heterogeneity in spa-types was detected, however 92.7% belonged to LA-MRSA CC398. A surprisingly high fraction of the spa-types (7.3%) did not belong to CC398. Conclusion The presence of LA-MRSA in farmers is strongly animal-exposure related. The rapidly decreasing MRSA prevalence during absence of animal contact suggests that LA-MRSA is a poor persistent colonizer in most humans. These results are of relevance for MRSA control strategies.

Modeling infectious disease dynamics in the complex landscape of global health

Mathematical modeling of infectious diseases The spread of infectious diseases can be unpredictable. With the emergence of antibiotic resistance and worrying new viruses, and with ambitious plans for global eradication of polio and the elimination of malaria, the stakes have never been higher. Anticipation and measurement of the multiple factors involved in infectious disease can be greatly assisted by mathematical methods. In particular, modeling techniques can help to compensate for imperfect knowledge, gathered from large populations and under difficult prevailing circumstances. Heesterbeek et al. review the development of mathematical models used in epidemiology and how these can be harnessed to develop successful control strategies and inform public health policy. Science, this issue 10.1126/science.aaa4339 BACKGROUND Despite many notable successes in prevention and control, infectious diseases remain an enormous threat to human and animal health. The ecological and evolutionary dynamics of pathogens play out on a wide range of interconnected temporal, organizational, and spatial scales that span hours to months, cells to ecosystems, and local to global spread. Some pathogens are directly transmitted between individuals of a single species, whereas others circulate among multiple hosts, need arthropod vectors, or persist in environmental reservoirs. Many factors, including increasing antimicrobial resistance, human connectivity, population growth, urbanization, environmental and land-use change, as well as changing human behavior, present global challenges for prevention and control. Faced with this complexity, mathematical models offer valuable tools for understanding epidemiological patterns and for developing and evaluating evidence for decision-making in global health. ADVANCES During the past 50 years, the study of infectious disease dynamics has matured into a rich interdisciplinary field at the intersection of mathematics, epidemiology, ecology, evolutionary biology, immunology, sociology, and public health. The practical challenges range from establishing appropriate data collection to managing increasingly large volumes of information. The theoretical challenges require fundamental study of many-layered, nonlinear systems in which infections evolve and spread and where key events can be governed by unpredictable pathogen biology or human behavior. In this Review, we start with an examination of real-time outbreak response using the West African Ebola epidemic as an example. Here, the challenges range from underreporting of cases and deaths, and missing information on the impact of control measures to understanding human responses. The possibility of future zoonoses tests our ability to detect anomalous outbreaks and to estimate human-to-human transmissibility against a backdrop of ongoing zoonotic spillover while also assessing the risk of more dangerous strains evolving. Increased understanding of the dynamics of infections in food webs and ecosystems where host and nonhost species interact is key. Simultaneous multispecies infections are increasingly recognized as a notable public health burden, yet our understanding of how different species of pathogens interact within hosts is rudimentary. Pathogen genomics has become an essential tool for drawing inferences about evolution and transmission and, here but also in general, heterogeneity is the major challenge. Methods that depart from simplistic assumptions about random mixing are yielding new insights into the dynamics of transmission and control. There is rapid growth in estimation of model parameters from mismatched or incomplete data, and in contrasting model output with real-world observations. New data streams on social connectivity and behavior are being used, and combining data collected from very different sources and scales presents important challenges. All these mathematical endeavors have the potential to feed into public health policy and, indeed, an increasingly wide range of models is being used to support infectious disease control, elimination, and eradication efforts. OUTLOOK Mathematical modeling has the potential to probe the apparently intractable complexity of infectious disease dynamics. Coupled to continuous dialogue between decision-makers and the multidisciplinary infectious disease community, and by drawing on new data streams, mathematical models can lay bare mechanisms of transmission and indicate new approaches to prevention and control that help to shape national and international public health policy. Modeling for public health. Policy questions define the model’s purpose. Initial model design is based on current scientific understanding and the available relevant data. Model validation and fit to disease data may require further adaptation; sensitivity and uncertainty analysis can point to requirements for collection of additional specific data. Cycles of model testing and analysis thus lead to policy advice and improved scientific understanding. Despite some notable successes in the control of infectious diseases, transmissible pathogens still pose an enormous threat to human and animal health. The ecological and evolutionary dynamics of infections play out on a wide range of interconnected temporal, organizational, and spatial scales, which span hours to months, cells to ecosystems, and local to global spread. Moreover, some pathogens are directly transmitted between individuals of a single species, whereas others circulate among multiple hosts, need arthropod vectors, or can survive in environmental reservoirs. Many factors, including increasing antimicrobial resistance, increased human connectivity and changeable human behavior, elevate prevention and control from matters of national policy to international challenge. In the face of this complexity, mathematical models offer valuable tools for synthesizing information to understand epidemiological patterns, and for developing quantitative evidence for decision-making in global health.

Complexity theory and financial regulation

Economic policy needs interdisciplinary network analysis and behavioral modeling Traditional economic theory could not explain, much less predict, the near collapse of the financial system and its long-lasting effects on the global economy. Since the 2008 crisis, there has been increasing interest in using ideas from complexity theory to make sense of economic and financial markets. Concepts, such as tipping points, networks, contagion, feedback, and resilience have entered the financial and regulatory lexicon, but actual use of complexity models and results remains at an early stage. Recent insights and techniques offer potential for better monitoring and management of highly interconnected economic and financial systems and, thus, may help anticipate and manage future crises.

Identifying transmission cycles at the human-animal interface: the role of animal reservoirs in maintaining gambiense human african trypanosomiasis.

Many infections can be transmitted between animals and humans. The epidemiological roles of different species can vary from important reservoirs to dead-end hosts. Here, we present a method to identify transmission cycles in different combinations of species from field data. We used this method to synthesise epidemiological and ecological data from Bipindi, Cameroon, a historical focus of gambiense Human African Trypanosomiasis (HAT, sleeping sickness), a disease that has often been considered to be maintained mainly by humans. We estimated the basic reproduction number of gambiense HAT in Bipindi and evaluated the potential for transmission in the absence of human cases. We found that under the assumption of random mixing between vectors and hosts, gambiense HAT could not be maintained in this focus without the contribution of animals. This result remains robust under extensive sensitivity analysis. When using the distributions of species among habitats to estimate the amount of mixing between those species, we found indications for an independent transmission cycle in wild animals. Stochastic simulation of the system confirmed that unless vectors moved between species very rarely, reintroduction would usually occur shortly after elimination of the infection from human populations. This suggests that elimination strategies may have to be reconsidered as targeting human cases alone would be insufficient for control, and reintroduction from animal reservoirs would remain a threat. Our approach is broadly applicable and could reveal animal reservoirs critical to the control of other infectious diseases.

Integrated Mapping of Establishment Risk for Emerging Vector-Borne Infections: A Case Study of Canine Leishmaniasis in Southwest France

Background Zoonotic visceral leishmaniasis is endemic in the Mediterranean Basin, where the dog is the main reservoir host. The diseases causative agent, Leishmania infantum, is transmitted by blood-feeding female sandflies. This paper reports an integrative study of canine leishmaniasis in a region of France spanning the southwest Massif Central and the northeast Pyrenees, where the vectors are the sandflies Phlebotomus ariasi and P. perniciosus. Methods Sandflies were sampled in 2005 using sticky traps placed uniformly over an area of approximately 100 by 150 km. High- and low-resolution satellite data for the area were combined to construct a model of the sandfly data, which was then used to predict sandfly abundance throughout the area on a pixel by pixel basis (resolution of c. 1 km). Using literature- and expert-derived estimates of other variables and parameters, a spatially explicit R 0 map for leishmaniasis was constructed within a Geographical Information System. R 0 is a measure of the risk of establishment of a disease in an area, and it also correlates with the amount of control needed to stop transmission. Conclusions To our knowledge, this is the first analysis that combines a vector abundance prediction model, based on remotely-sensed variables measured at different levels of spatial resolution, with a fully mechanistic process-based temperature-dependent R 0 model. The resulting maps should be considered as proofs-of-principle rather than as ready-to-use risk maps, since validation is currently not possible. The described approach, based on integrating several modeling methods, provides a useful new set of tools for the study of the risk of outbreaks of vector-borne diseases.

The effectiveness of contact tracing in emerging epidemics.

Background Contact tracing plays an important role in the control of emerging infectious diseases, but little is known yet about its effectiveness. Here we deduce from a generic mathematical model how effectiveness of tracing relates to various aspects of time, such as the course of individual infectivity, the (variability in) time between infection and symptom-based detection, and delays in the tracing process. In addition, the possibility of iteratively tracing of yet asymptomatic infecteds is considered. With these insights we explain why contact tracing was and will be effective for control of smallpox and SARS, only partially effective for foot-and-mouth disease, and likely not effective for influenza. Methods and Findings We investigate contact tracing in a model of an emerging epidemic that is flexible enough to use for most infections. We consider isolation of symptomatic infecteds as the basic scenario, and express effectiveness as the proportion of contacts that need to be traced for a reproduction ratio smaller than 1. We obtain general results for special cases, which are interpreted with respect to the likely success of tracing for influenza, smallpox, SARS, and foot-and-mouth disease epidemics. Conclusions We conclude that (1) there is no general predictive formula for the proportion to be traced as there is for the proportion to be vaccinated; (2) variability in time to detection is favourable for effective tracing; (3) tracing effectiveness need not be sensitive to the duration of the latent period and tracing delays; (4) iterative tracing primarily improves effectiveness when single-step tracing is on the brink of being effective.

Detecting emerging transmissibility of avian influenza virus in human households

Accumulating infections of highly pathogenic H5N1 avian influenza in humans underlines the need to track the ability of these viruses to spread among humans. A human-transmissible avian influenza virus is expected to cause clusters of infections in humans living in close contact. Therefore, epidemiological analysis of infection clusters in human households is of key importance. Infection clusters may arise from transmission events from (i) the animal reservoir, (ii) humans who were infected by animals (primary human-to-human transmission), or (iii) humans who were infected by humans (secondary human-to-human transmission). Here we propose a method of analysing household infection data to detect changes in the transmissibility of avian influenza viruses in humans at an early stage. The method is applied to an outbreak of H7N7 avian influenza virus in The Netherlands that was the cause of more than 30 human-to-human transmission events. The analyses indicate that secondary human-to-human transmission is plausible for the Dutch household infection data. Based on the estimates of the within-household transmission parameters, we evaluate the effectiveness of antiviral prophylaxis, and conclude that it is unlikely that all household infections can be prevented with current antiviral drugs. We discuss the applicability of our method for the detection of emerging human-to-human transmission of avian influenza viruses in particular, and for the analysis of within-household infection data in general.

The ideal reporting interval for an epidemic to objectively interpret the epidemiological time course.

The reporting interval of infectious diseases is often determined as a time unit in the calendar regardless of the epidemiological characteristics of the disease. No guidelines have been proposed to choose the reporting interval of infectious diseases. The present study aims at translating coarsely reported epidemic data into the reproduction number and clarifying the ideal reporting interval to offer detailed insights into the time course of an epidemic. We briefly revisit the dispersibility ratio, i.e. ratio of cases in successive reporting intervals, proposed by Clare Oswald Stallybrass, detecting technical flaws in the historical studies. We derive a corrected expression for this quantity and propose simple algorithms to estimate the effective reproduction number as a function of time, adjusting the reporting interval to the generation time of a disease and demonstrating a clear relationship among the generation-time distribution, reporting interval and growth rate of an epidemic. Our exercise suggests that an ideal reporting interval is the mean generation time, so that the ratio of cases in successive intervals can yield the reproduction number. When it is impractical to report observations every mean generation time, we also present an alternative method that enables us to obtain straightforward estimates of the reproduction number for any reporting interval that suits the practical purpose of infection control.

How to find natural reservoir hosts from endemic prevalence in a multi-host population: a case study of influenza in waterfowl.

The transmission dynamics of infectious diseases critically depend on reservoir hosts, which can sustain the pathogen (or maintain the transmission) in the population even in the absence of other hosts. Although a theoretical foundation of the transmission dynamics in a multi-host population has been established, no quantitative methods exist for the identification of natural reservoir hosts. For a host to maintain the transmission alone, the host-specific reproduction number (U), interpreted as the average number of secondary transmissions caused by a single primary case in the host(s) of interest in the absence of all other hosts, must be greater than unity. If the host-excluded reproduction number (Q), representing the average number of secondary transmissions per single primary case in other hosts in the absence of the host(s) of interest, is below unity, transmission cannot be maintained in the multi-host population in the absence of the focal host(s). The present study proposes a simple method for the identification of reservoir host(s) from observed endemic prevalence data across a range of host species. As an example, we analyze an aggregated surveillance dataset of influenza A virus in wild birds among which dabbling ducks exhibit higher prevalence compared to other bird species. Since the heterogeneous contact patterns between different host species are not directly observable, we test four different contact structures to account for the uncertainty. Meeting the requirements of U>1 and Q<1 for all four different contact structures, mallards and other dabbling ducks most likely constitute the reservoir community which plays a predominant role in maintaining the transmission of influenza A virus in the water bird population. We further discuss epidemiological issues which are concerned with the interpretation of influenza prevalence data, identifying key features to be fully clarified in the future.