DIMACS Workshop on The Epidemiology and Evolution of Influenza

January 25 - 27, 2006
DIMACS Center, Rutgers University, Piscataway, NJ

Catherine Macken, Los Alamos National Labs, cam@t10.lanl.gov
Alan Perelson, Los Alamos National Labs, asp@lanl.gov
Presented under the auspices of the Special Focus on Computational and Mathematical Epidemiology.


Viggo Andreasson, Roskilde University, Denmark

Title: The effect of cross-immunity on influenza drift-evolution

Influenza drift is characterized by a fairly constant rate of change and limited genetic variation in the genes coding for the key anti-genes.

Epidemic models incorporating cross-immunity among related strains can account for these phenomena. For most aspects of influenza drift-epidemiology the exact nature of the cross-immunity does not alter the qualitative results while quantitative results as well as model complexity may change significantly.

Maciej Boni, Standford University

Title: Antigenic Drift in a Single Season of Influenza

We use a mathematical model to study the evolution of influenza A during the epidemic dynamics of a single season. Classifying strains by their distance to the epidemic-originating strain, we show that neutral mutation yields a constant rate of antigenic evolution, even in the presence of epidemic dynamics. We introduce host immunity and viral immune escape to construct a non-neutral model. Our population dynamics can then be framed naturally in the context of population genetics, and we show that departure from neutrality is governed by the covariance between a strain's fitness and its distance to the original epidemic strain. We quantify the amount of antigenic evolution that takes place in excess of what is expected under neutrality and find that this excess amount is largest under strong host immunity and long epidemics.

Sara del Valle, LANL

Title: Planning for Pandemic Influenza: Impact of Intervention Strategies

We use a stochastic agent-based simulation model for the spread of pandemic influenza in Los Angeles and Portland. The model analyzes the simulated second-by-second movements of more than 1.6 million people in Portland and 16 million in the Los Angeles metropolitan area, matching boundary conditions to a global pandemic, and provides estimates of the effects of different intervention strategies. We evaluate the impact of partially effective antivirals becoming available part way into the epidemic and different quarantine strategies for a pandemic with a clinical attack rate of 25%. We show that delaying implementation of interventions will dramatically increase the total number of cases and deaths, and quarantining entering travelers can delay the start of the epidemic.

Peter S. Dodds, Duncan J. Watts, Roby Muhamad, Daniel Medina, Columbia University

Title: How big will an epidemic be?

Size distributions are considered fundamental to the description and understanding of many discrete phenomena, such as earthquakes, forest fires, organisms, and individual wealth. The form of a given size distribution evidently conveys much information about the predictability of the system in question. As one might expect, both simple and complex models of numerous phenomena are strongly geared towards reproducing observed size distributions. Little attention, however, has been paid to size distributions of epidemics, both empirically and in the construction of simple disease-spreading models. Most basic models (including network-based models) produce either unimodal or bimodal size distributions depending on whether or not stochasticity is included. In this talk, I'll discuss real data and a class of metapopulation models reveal that epidemic sizes may span many orders of magnitude. Data from Iceland on measles and other diseasese, for example, indicate surprisingly flat distributions of epidemic sizes which are poorly fit by power law distributions. In our model, we assume homogeneous mixing holds within local contexts, and that these contexts are embedded in a nested hierarchy of successively larger domains. We model the movement of individuals between contexts via simple transport parameters and allow diseases to spread stochastically. Our model exhibits some important stylized features of real epidemics, including extreme size variation and temporal heterogeneity. In particular, our results suggest that when epidemics do occur, the basic reproduction number R_0 may bear little relation to their final size. Finally, I'll put forward some measures for characterizing epidemic thresholds and discuss implications for the control of epidemics.

Lauren Meyers, University of Texas

Title: Using contact network models to compare 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. I will discuss 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. We have used the methods of contact network epidemiology to evaluate the efficacy of these strategies across a wide range of viral transmission rates and for two different age-specific mortality distributions. We have found 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.

Al Ozonoff and Paola Sebastiani, Boston University School of Public Health

Title: Modeling of seasonal baseline in respiratory syndrome data using Hidden Markov Models.

Syndromic surveillance is the developing public health practice of monitoring regional aggregate data. The typical approach is to capture electronic health records as patients first enter the health care system. This should lead to more timely signals of infectious disease outbreaks or other public health events. In order to maximize the benefit of this gain in timeliness, a baseline model for the expected case load is required. Case counts of respiratory illness such as influenza-like illness (ILI) exhibit a strong degree of seasonality, and typical approaches to modeling seasonal baseline use cyclic regression models (also known as Serfling's method) or variations on this approach. We propose an alternative approach to modeling seasonality of respiratory disease, based on Hidden Markov Models (HMMs). We demonstrate this approach on pneumonia and influenza (P+I) mortality data available from the Centers for Disease Control and Prevention (CDC), and compare the performance of HMMs to Serfling's method and related models.

David J. Topham, David H. Smith Center for Vaccine Biology & Immunology, Aab Institute for Biomedical Sciences, University of Rochester Medical Center

Title: Mechanisms of T cell mediated heterosubtype specific protection against influenza virus

Influenza virus is both a present and an emerging infectious disease. The specter of an influenza pandemic caused by avian influenza virus highlights the need to understand mechanisms of immune protection by emerging strains of the flu virus. Although we cannot accurately predict which influenza will emerge as the next pandemic, there is a possibility that humans possess or can be immunized to elicit cross-protective, heterosubtypic immune responses to the new strain. Understanding the mechanisms of heterosubtypic immunity is therefore paramount to the design of effective immunization strategies. Animal studies show that either T cells or B cells can mediate heterosubtypic immunity against flu. We have found that, for optimal protection, heterosubtype specific CD8 cytotoxic memory T cells must be retained in the lung tissue and airways. The collagen-binding integrin VLA-1 is essential for this to occur. On the other hand, our studies on CD4 T cells show that few are retained in the lung or airways, suggesting that they are regulated in a way that is distinct from the CD8 T cells. I will discuss the localization of influenza-specific CD4 and CD8 T cells in the extralymphoid tissue and lymphoid organs, and the relative contributions of the lymphoid and extralymphoid subsets to secondary influenza immunity.

Previous: Program
Workshop Index
DIMACS Homepage
Contacting the Center
Document last modified on January 23, 2006.