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A practical introduction to modeling complex systems. A primer for thinking about the introduction and spread of infectious diseases along the farm-to-fork continuum. Amy L. Greer, BSc, MSc, PhD Tier 2 Canada Research Chair in Population Disease

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A practical introduction to modeling complex

  • systems. A primer for thinking about the

introduction and spread of infectious diseases along the farm-to-fork continuum.

Amy L. Greer, BSc, MSc, PhD Tier 2 Canada Research Chair in Population Disease Modeling Department of Population Medicine, Ontario Veterinary College, University of Guelph

agreer@uoguelph.ca

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Outline

  • Food-borne disease risk in Canada as a “One Health” case

study.

  • Using statistical models to identify acute environmental

effects.

  • Pre-harvest interventions to prevent and control the spread of

food-borne pathogens in animal products and produce.

  • The challenging health economics of pre-harvest

interventions.

  • Conclusions and ideas for moving forward.
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SLIDE 3

Thomas et al. 2013

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SLIDE 4

Thomas et al. 2013

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SLIDE 5

Improving food safety through a One Health approach

Chofnes et al. 2012

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Improving food safety through a One Health approach

Chofnes et al. 2012

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SLIDE 7
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SLIDE 8

www.cdc.gov

Post-harvest interventions Pre-harvest interventions

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SLIDE 9

www.cdc.gov

Post-harvest interventions Pre-harvest interventions Focus on environmental exposures

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Seasonally oscillating environmental exposures

0! 10! 20! 30! 40!

01/1994! 01/1996! 01/1998! 01/2000! 01/2002! 01/2004! 01/2006! 01/2008!

Date!

TMAX (C)! MAXCIE/10! Delaware River dissolved O2 (*2)!

Philadelphia,*PA,*USA*

Figure courtesy of Dr. David Fisman, DLSPH

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SLIDE 11

Seasonally oscillating environmental exposures

0! 10! 20! 30! 40!

01/1994! 01/1996! 01/1998! 01/2000! 01/2002! 01/2004! 01/2006! 01/2008!

Date!

TMAX (C)! MAXCIE/10! Delaware River dissolved O2 (*2)!

Philadelphia,*PA,*USA*

Figure courtesy of Dr. David Fisman, DLSPH

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Seasonally oscillating environmental exposures

0! 10! 20! 30! 40!

01/1994! 01/1996! 01/1998! 01/2000! 01/2002! 01/2004! 01/2006! 01/2008!

Date!

TMAX (C)! MAXCIE/10! Delaware River dissolved O2 (*2)!

Philadelphia,*PA,*USA*

Figure courtesy of Dr. David Fisman, DLSPH

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Seasonally oscillating environmental exposures

0! 10! 20! 30! 40!

01/1994! 01/1996! 01/1998! 01/2000! 01/2002! 01/2004! 01/2006! 01/2008!

Date!

TMAX (C)! MAXCIE/10! Delaware River dissolved O2 (*2)!

Philadelphia,*PA,*USA*

Figure courtesy of Dr. David Fisman, DLSPH

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SLIDE 14

Seasonally oscillating environmental exposures

0! 10! 20! 30! 40!

01/1994! 01/1996! 01/1998! 01/2000! 01/2002! 01/2004! 01/2006! 01/2008!

Date!

TMAX (C)! MAXCIE/10! Delaware River dissolved O2 (*2)!

Philadelphia,*PA,*USA*

Figure courtesy of Dr. David Fisman, DLSPH

Need methods that account for predicted seasonal relationships between environmental conditions and incidence of seasonal infectious diseases

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A methodological caveat

  • Establishing causal links between environmental factors

and disease occurrence is difficult when the disease is seasonal.

  • Relationships may be confounded with underlying

factors.

  • Strong correlation is necessary but not necessarily

sufficient.

  • Aggregation of exposures may lead to “ecological fallacy”
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Is it really the season?

R²#=#0.93894# 0# 10# 0# 2# 4# 6# 8# 10# 12#

Cases#per#week#

Figure courtesy of L. Kinlin & A. White

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Is it really the season?

R²#=#0.93894# 0# 10# 0# 2# 4# 6# 8# 10# 12#

Cases#per#week#

Figure courtesy of L. Kinlin & A. White

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SLIDE 18

Is it really the season?

R²#=#0.93894# 0# 10# 0# 2# 4# 6# 8# 10# 12#

Cases#per#week#

Figure courtesy of L. Kinlin & A. White

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Is it really the season?

R²#=#0.93894# 0# 10# 0# 2# 4# 6# 8# 10# 12#

Cases#per#week#

Figure courtesy of L. Kinlin & A. White

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Is it really the season?

R²#=#0.93894# 0# 10# 0# 2# 4# 6# 8# 10# 12#

Cases#per#week#

Figure courtesy of L. Kinlin & A. White

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Environment and disease

What environmental factors are associated with an increased occurrence of disease? Hypothesis Environmental factors that increase pathogen survival, persistence, or proliferation in the environment will be related temporally and spatially to human and/or animal

  • utbreaks or case occurrence.
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Poisson regression

Environmental Exposure Seasonal smoothers

sin(2π/52) cos(2π/52)

Poisson Regression Analysis

Expected cases

Figure courtesy of L. Kinlin & A. White

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Case-crossover analysis

  • Evaluate acute associations between environmental

exposures and cases

  • 2:1 matched design
  • Random directionality of control selection

M Tu W Th F Sa Su M Tu W Th F Sa Su M Tu Th F Sa Su W

case%onset % hazard% period% control% period% control% period%

Figure courtesy of L. Kinlin & A. White Fisman et al. 2005 Greer et al. 2009

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Figure courtesy of L. Kinlin & A. White

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Environmental forcing in dynamic models

Eisenberg et al. 2013 Tuite et al. 2011 Tien and Earn, 2010

Susceptible

Infected Recovered

βISI

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Environmental forcing in dynamic models

Eisenberg et al. 2013 Tuite et al. 2011 Tien and Earn, 2010

Water

βWSW

Susceptible

Infected Recovered

βISI

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Environmental forcing in dynamic models

Eisenberg et al. 2013 Tuite et al. 2011 Tien and Earn, 2010

Water

βWSW

  • 1. Statistical models to look at

relationships between pathogen and rainfall

  • 2. Dynamic models “forced” by the

rainfall time series

Susceptible

Infected Recovered

βISI

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Environmental forcing in dynamic models

Eisenberg et al. 2013 Tuite et al. 2011 Tien and Earn, 2010

Rainfall Water

βWSW

  • 1. Statistical models to look at

relationships between pathogen and rainfall

  • 2. Dynamic models “forced” by the

rainfall time series

Susceptible

Infected Recovered

βISI

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Environmental forcing in dynamic models

Eisenberg et al. 2013 Tuite et al. 2011 Tien and Earn, 2010

Rainfall Water

βWSW

e.g. Flooding leading to raw sewage contamination

  • f water sources

e.g. Low water levels leading to increased usage

  • f existing water sources.
  • 1. Statistical models to look at

relationships between pathogen and rainfall

  • 2. Dynamic models “forced” by the

rainfall time series

Susceptible

Infected Recovered

βISI

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Using a “Cholera” model to think about leafy greens

Water

βW

Uncolonized plants Colonized plants

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Water

βW

Uncolonized plants Colonized plants Rainfall temperature, UV, humidity etc. spray vs. flood irrigation environmental conditions, plant lifecycle

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Manure

βM

Uncolonized plants Colonized plants temperature, UV, humidity etc. mechanism of application environmental conditions, plant lifecycle

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Pre-harvest interventions for animal products

  • 1. management practices to

decrease animal exposure to pathogens in the farm environment

  • 2. reducing contacts between

different species

  • 3. prevent contamination of

feed and water sources

  • 4. surveillance for “super-

shedders”

  • 5. vaccination
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Farm environment

βE

Uncolonized cattle (S) Colonized cattle (I) temperature, UV, humidity etc.

βCI

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Farm environment

βE

Uncolonized cattle (S) Colonized cattle (I) temperature, UV, humidity etc.

βCI

Super-shedding cattle (IS)

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Vaccinated cattle (V) Farm environment

βE

Uncolonized cattle (S) Colonized cattle (I) temperature, UV, humidity etc.

βCI

Super-shedding cattle (IS) surveillance prevent contamination

  • f feed and water or
  • ther environmental

reservoirs

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Health economic challenges for One Health

  • Is the intervention good value for money?
  • Societal and governmental perspectives consider all direct and

indirect costs regardless of to whom the costs are accrued. An example There are no direct economic implications for farmers with VTEC colonized cattle. Farmers pay out of pocket for vaccine (economic loss for farmers) Healthcare system benefits as a result of farmers out of pocket expenses with no benefit being seen by the farmers.

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Conclusions

  • Mathematical models provide

us with a unique framework within which to examine the complex biological dynamics at the human-animal- environment interface.

Colon et al. 2008