
201011
Infectious Disease Epidemiology Afficionados (IDEA) Journal Club
Organizing Committee:
Dr. David Fisman (University of Toronto)
Dr. Sivaloganathan (Centre for Mathematical Medicine)
Dr. Jianhong Wu (York/MITACS)
Dr. Amy Greer (Public Health Agency of Canada and Dalla Lana School
of Public Health, University of Toronto),
Dr. Chris Bauch (Guelph
Infectious Disease Epidemiology Afficionados (IDEA) Journal Club
and seminar series as an activity that will provide the opportunity
to link mathematicians, epidemiologists, mathematical modelers,
and public health professionals interested in infectious disease
dynamics.
Meetings are held once a month on Fridays in the Stewart Library
at Fields.
UPCOMING SEMINARS 
Apr. 15, 2011
11:30 am
Stewart Library 
Jonathan Wang (University of Toronto)
A realtime prediction model for aid in hospitallevel
pandemic influenza planning
In light of the recent H1N1 pandemic in 2009, there has been
an increased interest in using mathematical modelling for
public health decision making. However, there is very little
research into using mathematical modelling to estimate the
impact that the pandemic will have on hospital resources.
One such model in the literature is FluSurge (http://www.cdc.gov/flu/tools/flusurge).
A model developed by the CDC in 2006, it allows for an estimation
of the number of incoming patients into a hospital based on
historical data and from that estimation, assesses the adequacy
of the existing resources in the hospital to meet the demand.
I will be addressing some of the shortcomings of FluSurge
as well as proposing a novel model that improves upon the
aforementioned shortcomings. Some of these improvements include:
integrating realtime hospitalization data to generate a prediction
of pandemic characteristics, changing the FluSurge algorithm
to utilize epidemiological theory to model hospitalization
data, and developing the granularity of the model parameters
for the user to manipulate.
The goal of this modelling exercise is to develop a model
that balances the accuracy of the algorithm with the simplicity
of the design to facilitate integration and use in a realtime
hospital setting.

POSTPONED
May 13, 2011
11:30 am
Stewart Library

David Earn (McMaster
University)
TBA 
PAST SEMINARS 
POSTPONED
Mar. 25, 2011
11:30 am
Stewart Library

Sharmisthra Mishra
(Imperial College)
TBA 
Feb. 25, 2011
12:00 pm
Stewart Library 
David Fisman (University of Toronto)
Cholera in Haiti: Insights from a Simple Gravity Model
Background: Haiti is in the midst of a cholera epidemic.
Surveillance data for formulation of models of this epidemic
are limited, but such models can aid understanding of epidemic
processes and help define control strategies.
Objective: We used a mathematical model to predict the sequence
and timing of regional cholera epidemics in Haiti and explore
the potential impacts of control strategies.
Design: Compartmental mathematical model allowing persontoperson
and waterborne transmission of cholera. We modeled within
and betweenregion epidemic spread with betweenregion transmission
dependent on population sizes and distance between regional
centroids (i.e., a socalled “gravity” model).
Setting: Haiti, 20102011.
Data Sources: Haitian hospitalization data, 2009 census data,
literaturederived parameter values, and through model calibration.
Measurements: Dates of epidemic onset, hospitalizations.
Results: The plausible range for cholera’s basic reproductive
number (R0 , defined as the number of secondary cases per
primary case in a totally susceptible population without intervention)
was 2.06 to 2.78. Order and timing of regional cholera outbreaks
was predicted by our “gravity” model.
Analyses incorporating changes in disease dynamics over time
suggest that public health interventions have made a substantial
impact on the course of
this epidemic. A limited vaccine supply provided late in the
epidemic was
projected to have a modest impact.
Limitations: Simplifying assumptions necessary for modeling;
projections based on initial epidemic dynamics inferred from
available data.
Conclusions: Notwithstanding limited surveillance from the
Haitian cholera epidemic, a model that associates the strength
of betweenregion disease transmission with the size of and
distance between populations (analogous to
gravity) closely reproduces reported disease patterns. This
model is a tool that planners, policy makers, and medical
personnel seeking to manage Haiti’s cholera epidemic
could begin to use immediately.

Jan. 21, 2011
12 pm
Room 210

Jane Heffernan (York University)
Mathematical immunology: The effects of the immune system
and of immunity on disease progression inhost and in a population
The immune system, how it affects disease progression inhost
and how immunity is developed and is used to prevent future
infections are not well understood. I will discuss different
aspects of these topics, informed by mathematical models,
of four different viral infections: HIV, Herpes, viral hepatitis
and influenza.

Dec. 2, 2010
12pm
Stewart Library

Eva Wong, MPH (c) 1,2, and Amy Greer, MSc,
PhD 1,2
1 Division of Epidemiology, Dalla Lana School of Public Health,
University of Toronto
2 Surveillance and Risk Assessment Division, Public Health
Agency of Canada
Using Mathematical Models for Public Health DecisionMaking:
How Models are Contributing to Public Health Planning for
Remote and Isolated Communities in Canada and the Renewal
of the National Antiviral Stockpile
The 2009 Influenza A (H1N1) pandemic had a relatively mild
effect on the general Canadian population. However, a disproportionate
burden of illness was observed among vulnerable groups including
Aboriginal populations and individuals living in remote and
isolated communities. Dynamic models for infectious disease
have the ability to reshape the strategic thinking for pandemic
planning in the future from the development of planning scenarios
to providing scientific advise on specific public health measures
such as strategies for antiviral drug stockpiling, vaccine
development and prioritisation, as well as nonpharmaceutical
intervention measures. We will discuss two examples of the
use of mathematical models for public health decisionmaking.
First, we will describe ways that models are contributing
to decisions regarding the renewal of the National Antiviral
Stockpile. Second, we will discuss ways that agentbased models
can help us to better understand the transmission of respiratory
infections in remote and isolated communities in Canada and
how we can use these models to explore optimal intervention
strategies. The research discussed will play an important
role in informing policy decisions to mitigate disease outcomes
and protect the health of vulnerable populations in the future.


