Why Canadian fur trappers should stay in bed when they have the flu: modeling the geographic spread of infectious diseases Lisa Sattenspiel Department.

Slides:



Advertisements
Similar presentations
Program Evaluation: What is it?
Advertisements

DROUGHT MONITORING CENTRE - NAIROBI WHAT COULD BE DONE ON DROUGHT WITHIN ISDR PLATFORM?
An introduction Epidemiology matters: a new introduction to methodological foundations Chapter 1.
Paula Gonzalez 1, Leticia Velazquez 1,2, Miguel Argaez 1,2, Carlos Castillo-Chávez 3, Eli Fenichel 4 1 Computational Science Program, University of Texas.
Population dynamics of infectious diseases Arjan Stegeman.
 A public health science (foundation of public health)  Impacts personal decisions about our lifestyles  Affects government, public health agency and.
Presentation Topic : Modeling Human Vaccinating Behaviors On a Disease Diffusion Network PhD Student : Shang XIA Supervisor : Prof. Jiming LIU Department.
Welcome To Math 463: Introduction to Mathematical Biology
Community Level Interventions
Modeling the process of contact between subgroups in spatial epidemics Lisa Sattenspiel University of Missouri-Columbia.
The Effect of Climate on Infectious Disease
Alan F. Hamlet Dennis P. Lettenmaier Center for Science in the Earth System Climate Impacts Group and Department of Civil and Environmental Engineering.
Disease and Public Health Lecture 11 Medicine, Disease and Society in Britain,
Directions in national immigrant health National Symposium on Immigrant Health Citizenship and Immigration Canada Citoyenneté et Immigration Canada Medical.
Use of epidemiologic methods in disaster management Dr AA Abubakar Dept of Community Medicine Ahmadu Bello University Zaria Nigeria.
Fishing Effort: fishery patterns from individual actions Dr. Darren M. Gillis, Biological Sciences, University Of Manitoba, Winnipeg,
Epidemic Vs Pandemic 8.L.1.2.
Studying Geography The Big Idea
How does mass immunisation affect disease incidence? Niels G Becker (with help from Peter Caley ) National Centre for Epidemiology and Population Health.
An overview of a few of the methods used in landscape ecology studies.
Ethics Conference on Asian Flu Pandemic Ethical considerations among Response to H1N1 Pandemic in China China CDC, CFETP Huilai Ma, Guang Zeng.
1 AIDS 2010 Vienna, July 2010 HIV/AIDS and People from Countries where HIV is endemic – Black people of African and Caribbean descent living in Canada.
1. An Overview of the Data Analysis and Probability Standard for School Mathematics? 2.
Epidemiology. Comes from Greek words epi, meaning “on or upon” demos,meaning “people” logos, meaning “the study of” Study of distribution and determinants.
Motive Konza: understanding disease, since there is no apparent reason to manage native pathogens of native plants Also have background information in.
Dr. Engr. Sami ur Rahman Assistant Professor Department of Computer Science University of Malakand Research Methods in Computer Science Lecture: Research.
Simulacra & Simulation (& Health Care-Associated Infections) Michael Rubin, MD, PhD Section Chief, Epidemiology VA Salt Lake City Health Care System.
Health promotion and health education programs. Assumptions of Health Promotion Relationship between Health education& Promotion Definition of Program.
Epidemiology The Basics Only… Adapted with permission from a class presentation developed by Dr. Charles Lynch – University of Iowa, Iowa City.
Copyright © 2014 by The University of Kansas Using the Evaluation System to Answer Key Questions About Your Initiative.
Lecture 4 Transport Network and Flows. Mobility, Space and Place Transport is the vector by which movement and mobility is facilitated. It represents.
Best Practice Guideline for the Workplace During Pandemic Influenza Occupational Health and Safety Employment Standards.
Lisa Pion-Berlin, PhD President and Chief Executive Officer Parents Anonymous ® Inc. Leah Davis, California State Parent Team Achieving Shared Leadership®
Study Designs Afshin Ostovar Bushehr University of Medical Sciences Bushehr, /4/20151.
Epidemiology. Classically speaking Epi = upon (among) Demos = people Ology = science Epidemiology = the science which deals with what falls upon people…..
Lecture 1 Introduction- Manifestations of Transport and Tourism.
V5 Epidemics on networks
A Data Intensive High Performance Simulation & Visualization Framework for Disease Surveillance Arif Ghafoor, David Ebert, Madiha Sahar Ross Maciejewski,
December 2002 Section 6b Canadian Impacts of Climate Change (2)
Chapter 1 – A Geographer’s World
Introduction to Epidemiology Instructor: Guan-Hua Huang, Ph.D. Class meetings: Wednesday 1:30-4:30.
Louis Gross, Ecology and Evolutionary Biology and Mathematics, University of Tennessee Thoughts on Raccoon Rabies Models.
Copyright © 2012 Wolters Kluwer Health | Lippincott Williams & Wilkins Chapter 19: Community Preparedness: Disaster and Terrorism.
Biological Attack Model (BAM) Formal Progress Report April 5, 2007 Sponsor: Dr. Yifan Liu Team Members: Richard Bornhorst Robert Grillo Deepak Janardhanan.
Diseases Unit 3. Disease Outbreak  A disease outbreak happens when a disease occurs in greater numbers than expected in a community, region or during.
Developing a Framework for Modeling and Simulating Aedes aegypti and Dengue Fever Dynamics Tiago Lima (UFOP), Tiago Carneiro (UFOP), Raquel Lana (Fiocruz),
The management of small pelagics. Comprise the 1/3 of the total world landings Comprise more than 50% of the total Mediterranean landings, while Two species,
Copyright © 2014 by The University of Kansas Using the Evaluation System to Answer Key Questions About Your Initiative.
Community Planning 101 Disability Preparedness Summit Nebraska Volunteer Service Commission Laurie Barger Sutter November 5, 2007.
Statistical Analysis Quantitative research is first and foremost a logical rather than a mathematical (i.e., statistical) operation Statistics represent.
Landscape ecology methods
Responding To An Infection Transmission Emergency Jim Koopman MD MPH University of Michigan Center for the Study of Complex Systems & Dept. of Epidemiology.
WHAT CONTRIBUTES TO THE BUILDING OF RESILIENT COMMUNITIES?: INTEGRATION OF KNOWLEDGE, RISK PERCEPTION, AND AWARENESS OF SOCIAL VULNERABILITY Pamela McMullin-Messier.
Brad Greening Rutgers University Duration of Infectivity and Disease in Dynamic Networks Bobby Zandstra Florida Gulf Coast University Long- vs. Short-term.
Chapter 3.  By ecology, we mean the body of knowledge concerning the economy of nature -- the investigation of the total relations of the animal both.
Copyright © 2010, 2006, 2002 by Mosby, Inc., an affiliate of Elsevier Inc. Chapter 1 Community-Oriented Nursing and Community-Based Nursing Carolyn A.
Chapter 9 Introduction to the t Statistic
Chapter 1 – A Geographer’s World
Ch 1 A Geographer’s World
Epidemiology.
Fundamentals of Epidemiology
Review of Probability Theory
Pandemic Flu & General Disaster Preparedness
Interpretation of large-scale stochastic epidemic models
Tabulations and Statistics
Travel Patterns and Disease Transmission
Epidemiological Modeling to Guide Efficacy Study Design Evaluating Vaccines to Prevent Emerging Diseases An Vandebosch, PhD Joint Statistical meetings,
Geographic Concepts These are the ideas that link the studies in Geography together and give a focus for our investigations.
Susceptible, Infected, Recovered: the SIR Model of an Epidemic
Landscape ecology methods
Presentation transcript:

Why Canadian fur trappers should stay in bed when they have the flu: modeling the geographic spread of infectious diseases Lisa Sattenspiel Department of Anthropology University of Missouri-Columbia

Major approaches to modeling the transmission of infectious diseases Deterministic compartmental models (systems of differential equations) Statistical approaches (e.g., regression analysis, time series analysis, generalized linear models, spatial statistics) Stochastic compartmental models (e.g. chain binomial model) Individual-based mathematical models Computer-based models –Microsimulations –Agent-based models

a mobility process  the distribution of destinations  the rate of leaving a community  the rate of return Community 1 Community 2 Community 3 + a disease process SusceptibleInfectiousRecovered infectionrecovery The chance of infection is a function of both contact (a social process) and transmission (a biological process) General structure of a deterministic epidemic model with three linked communities

Community composition as a result of the mobility process B B C C A A A B B B C C B C C A A C C A A B B C A B A C mobility process Transmission of infection occurs only between an infectious person and a susceptible person who happen to be in the same region at time t. The risk of infection is a function not only of the personal characteristics of the susceptible and infectious individuals, but also of the place where they come into contact with one another.

The Keewatin District

The environment at and near Norway House

Mortality before, during, and after the flu epidemic

Mortality among communities within Manitoba

Distribution of deaths by family Norway House families G-value = 17.33, p < 0.05, df = 2 significantly fewer observed

Families with three to five deaths Norway House 1919

Initial questions a)How do changes in the rates and patterns of mobility affect epidemic spread? b)How do changes in rates of contact within communities affect epidemic spread?

Changing social organization by varying the contact rate within communities DOES lead to significant changes in the size and timing of epidemic peaks

Some of the questions addressed in the project 1)How do changes in frequency and direction of travel among socially linked communities influence patterns of disease spread within and among those communities? 2)How do differences in rates of contact and other aspects of social structure within communities affect epidemic transmission within and among communities? 3)What is the effect of different types of settlement structures and economic relationships among communities on patterns of epidemic spread? 4)What was the impact of quarantine policies on the spread of the flu through the study communities? 5)Do we see the same kinds of results with other diseases and in other locations and time periods?

Solution: Develop an individual-based epidemic model that can deal with the variability of individual behaviors and the stochasticity that results when populations are small BUT the real study populations are so small that the deterministic models presented so far are not really the best ones to use.

Seasonal differences in social organization in the northern fur trade Social group size and composition Dispersal on the land Resource availability Modes of travel Travel routes Numbers traveling Time to complete a journey

Stage 1 Develop a single-post agent- based model that captures significant aspects of the community structure at the main post, Norway House

Summer Epidemics: short duration, high peak, peak quickly Winter Epidemics: long duration, low peak, peak slowly

Stage 2 Extend the Stage 1 model to three posts so that results can be compared directly to those from the deterministic model

NHOHGL ModelNHODE Model Predicted Number Infected at NH Predicted Number Infected at OH77 Predicted Number Infected at GL07 Predicted Extent of Epidemic Spread Rarely reaches OH, never reaches GL Epidemic routinely reaches both OH and GL Shape of the Epidemic Curvean initial case building up to a rather short and defined epidemic peak. Timing of the epidemic peaksFirst at NH Predicted Impact of SeasonalitySummer epidemic has earlier and more severe peak Result of the Introduction of the flu at OH or GL instead of NH Epidemic fails to spread; nearly all epidemic totals are impacted Epidemic spreads more readily; timing of the epidemic is affected but not the severity Parameters that influence the timing of the epidemic mobility, travel patterns, contact rates, and population parameters Mobility, travel patterns, and contact rates Parameters that Influence the spread of the epidemic mobility, travel patterns, contact rates, and population parameters Mobility and travel patterns Parameters that Influence Epidemic severity mobility, travel patterns, contact rates, and population parameters Contact rates

Major potential contributions of mathematical models to human disease research Focus research efforts on factors most likely to have a significant impact on patterns of epidemic spread. –Simulation results illuminated relative roles of population mobility and social contact within communities on infectious disease spread and shifted focus to factors influencing social contact. Identify critical areas with insufficient data. –Results stimulated new archival searches to find data on settlement structure and seasonal activities. Help to understand conditions under which infectious diseases emerge and spread across a landscape. –Simulations showed, for example, that patterns of mobility influence the timing of epidemic peaks and the patterns of an epidemic’s spread across space.

Major potential contributions of mathematical models to human disease research (cont.) May help to identify potential hot spots for the evolution of new diseases. –Simulations indicated the importance of communities taking a central role in a region, suggesting that these communities are potential hot spots in their regions. Allow for “experimentation” on human populations that would be impossible or unethical in the real world. –Infectious disease simulations follow the progress of potential epidemics within communities. Well-structured models that are grounded in high quality data provide valuable inferences with which to predict the impact of future epidemics within communities. Can be used to evaluate the efficacy of potential control strategies before attempting costly and/or risky field trials. –Simulations pointed out the difficulty of achieving success with quarantine measures alone.

Acknowledgements Collaborators and colleagues: McMaster University — Ann Herring, Dick Preston University of Manitoba — Rob Hoppa University of Missouri — Carrie Ahillen, Connie Carpenter, Nate Green, Suman Kanuganti, Melissa Stoops, Emily Williams Funding: The National Science Foundation The Canadian Social Sciences and Humanities Research Council Special thanks to the Norway House First Cree Nation who generously gave permission to work with their historical documents. Leonard McKay, in particular, was a constant source of encouragement and assistance.