The Shifting Demographic Landscape of Pandemic Influenza

Shweta Bansal is with Pennsylvania State University and NIH, Babak Pourbohloul is with British Columbia Centre for Disease Control and University of British Columbia, Nathaniel Hupert is with Weill Cornell Medical College and CDC, Bryan Grenfell is with Princeton University, Lauren Ancel Meyers is with UT Austin and Santa Fe Institute.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.This work was supported by the Research and Policy for Infectious Disease Dynamics (RAPIDD) program of the Science and Technology Directorate, Department of Homeland Security, and the Fogarty International Center, National Institutes of Health (NIH); grants from the James F. McDonnell Foundation, National Science Foundation (DEB-0749097), and NIH Models of Infectious Disease Agent Study (MIDAS) (U01-GM087719-01) to L.A.M.; and support from the Canadian Institutes of Health Research (PTL97125 and PAP93425) and the Michael Smith Foundation for Health Research to B.P. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Biological Sciences, School o

Time Evolution of Disease Spread on Networks with Degree Heterogeneity

Two crucial elements facilitate the understanding and control of communicable disease spread within a social setting. These components are, the underlying contact structure among individuals that determines the pattern of disease transmission; and the evolution of this pattern over time. Mathematical models of infectious diseases, which are in principle analytically tractable, use two general approaches to incorporate these elements. The first approach, generally known as compartmental modeling, addresses the time evolution of disease spread at the expense of simplifying the pattern of transmission. On the other hand, the second approach uses network theory to incorporate detailed information pertaining to the underlying contact structure among individuals. However, while providing accurate estimates on the final size of outbreaks/epidemics, this approach, in its current formalism, disregards the progression of time during outbreaks. So far, the only alternative that enables the integration of both aspects of disease spread simultaneously has been to abandon the analytical approach and rely on computer simulations. We offer a new analytical framework based on percolation theory, which incorporates both the complexity of contact network structure and the time progression of disease spread. Furthermore, we demonstrate that this framework is equally effective on finite- and "infinite"-size networks. Application of this formalism is not limited to disease spread; it can be equally applied to similar percolation phenomena on networks in other areas in science and technology.Comment: 20 pages, 6 figure

Modeling Control Strategies of Respiratory Pathogens

Contact network epidemiology can provide quantitative input even before pathogen is fully characterized

A Comparative Analysis of Influenza Vaccination Programs

The threat of avian influenza and the 2004-2005 influenza vaccine supply shortage in the United States has sparked a debate about optimal vaccination strategies to reduce the burden of morbidity and mortality caused by the influenza virus. We present a comparative analysis of two classes of suggested vaccination strategies: mortality-based strategies that target high risk populations and morbidity-based that target high prevalence populations. Applying the methods of contact network epidemiology to a model of disease transmission in a large urban population, we 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. 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

Heterogeneous Bond Percolation on Multitype Networks with an Application to Epidemic Dynamics

Considerable attention has been paid, in recent years, to the use of networks in modeling complex real-world systems. Among the many dynamical processes involving networks, propagation processes -- in which final state can be obtained by studying the underlying network percolation properties -- have raised formidable interest. In this paper, we present a bond percolation model of multitype networks with an arbitrary joint degree distribution that allows heterogeneity in the edge occupation probability. As previously demonstrated, the multitype approach allows many non-trivial mixing patterns such as assortativity and clustering between nodes. We derive a number of useful statistical properties of multitype networks as well as a general phase transition criterion. We also demonstrate that a number of previous models based on probability generating functions are special cases of the proposed formalism. We further show that the multitype approach, by naturally allowing heterogeneity in the bond occupation probability, overcomes some of the correlation issues encountered by previous models. We illustrate this point in the context of contact network epidemiology.Comment: 10 pages, 5 figures. Minor modifications were made in figures 3, 4 and 5 and in the text. Explanations and references were adde

Increasing health policy and systems research capacity in low- and middle-income countries: results from a bibliometric analysis

Background: For 20 years, substantial effort has been devoted to catalyse health policy and systems research (HPSR) to support vulnerable populations and resource-constrained regions through increased funding, institutional capacity-building and knowledge production; yet, participation from low- and middle-income countries (LMICs) is underrepresented in HPSR knowledge production. Methods: A bibliometric analysis of HPSR literature was conducted using a high-level keyword search. Health policy and/or health systems literature with a topic relevant to LMICs and whose lead author’s affiliation is in an LMIC were included for analysis. The trends in knowledge production from 1990 to 2015 were examined to understand how investment in HPSR benefits those it means to serve. Results: The total number of papers published in PubMed increases each year. HPSR publications represent approximately 10% of these publications, but this percentage is increasing at a greater rate than PubMed publications overall and the discipline is holding this momentum. HPSR publications with topics relevant to LMICs and an LMIC-affiliated lead authors (specifically from low-income countries) are increasing at a greater rate than any other category within the scope of this analysis. Conclusions: While the absolute number of publications remains low, lead authors from an LMIC have participated exponentially in the life and biomedical sciences (PubMed) since the early 2000s. HPSR publications with a topic relevant to LMICs and an LMIC lead author continue to increase at a greater rate than the life and biomedical science topics in general. This correlation is likely due to increased capacity for research within LMICs and the support for publications surrounding large HPSR initiatives. These findings provide strong evidence that continued support is key to the longevity and enhancement of HPSR toward its mandate.Medicine, Faculty ofScience, Faculty ofPopulation and Public Health (SPPH), School ofResources, Environment and Sustainability (IRES), Institute forReviewedFacult

Health policy and systems research collaboration pathways: lessons from a network science analysis

Background: The 2004 Mexico Declaration, and subsequent World Health Assembly resolutions, proposed a concerted support for the global development of health policy and systems research (HPSR). This included coordination across partners and advocates for the field of HPSR to monitor the development of the field, while promoting decision-making power and implementing responsibilities in low- and middle-income countries (LMICs). Methods: We used a network science approach to examine the structural properties of the HPSR co-authorship network across country economic groups in the PubMed citation database from 1990 to 2015. This analysis summarises the evolution of the publication, co-authorship and citation networks within HPSR. Results: This method allows identification of several features otherwise not apparent. The co-authorship network has evolved steadily from 1990 to 2015 in terms of number of publications, but more importantly, in terms of co-authorship network connectedness. Our analysis suggests that, despite growth in the contribution from low-income countries to HPSR literature, co-authorship remains highly localised. Lower middle-income countries have made progress toward global connectivity through diversified collaboration with various institutions and regions. Global connectivity of the upper middle-income countries (UpperMICs) are almost on par with high-income countries (HICs), indicating the transition of this group of countries toward becoming major contributors to the field. Conclusions: Network analysis allows examination of the connectedness among the HSPR community. Initially (early 1990s), research groups operated almost exclusively independently and, despite the topic being specifically on health policy in LMICs, HICs provided lead authorship. Since the early 1990s, the network has evolved significantly. In the full set analysis (1990–2015), for the first time in HPSR history, more than half of the authors are connected and lead authorship from UpperMICs is on par with that of HICs. This demonstrates the shift in participation and influence toward regions which HPSR primarily serves. Understanding these interactions can highlight the current strengths and future opportunities for identifying new strategies to enhance collaboration and support capacity-building efforts for HPSR.Medicine, Faculty ofScience, Faculty ofPopulation and Public Health (SPPH), School ofResources, Environment and Sustainability (IRES), Institute forReviewedFacult

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