SCIENTIFIC PROGRAMS AND ACTIVITIES
|September 19, 2014|
|Apr. 15, 2011
Jonathan Wang (University of Toronto)
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.
May 13, 2011
|David Earn (McMaster
Mar. 25, 2011
|Feb. 25, 2011
David Fisman (University of Toronto)
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 person-to-person and water-borne transmission of cholera. We modeled within- and between-region epidemic spread with between-region transmission dependent on population sizes and distance between regional centroids (i.e., a so-called gravity model).
Setting: Haiti, 2010-2011.
Data Sources: Haitian hospitalization data, 2009 census data, literature-derived parameter values, and through model calibration.
Measurements: Dates of epidemic onset, hospitalizations.
Results: The plausible range for choleras 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.
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 between-region disease transmission with the size of and
distance between populations (analogous to
Jan. 21, 2011
Jane Heffernan (York University)
The immune system, how it affects disease progression in-host
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
Dec. 2, 2010
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
Using Mathematical Models for Public Health Decision-Making: 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 non-pharmaceutical
intervention measures. We will discuss two examples of the
use of mathematical models for public health decision-making.
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 agent-based 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.