Using&simulaon&to&evaluate& surveillance&and&control ...

Using simula+on to evaluate surveillance and control strategies for waterborne infec+ous disease outbreaks in Montreal

Workshop on Syndromic Surveillance of Health and Climate-‐Related Impacts, March 17, 2014

David Buckeridge, MD PhD FRCPC Canada Research Chair in Public Health Informa+cs

The Surveillance Lab, McGill Clinical and Health Informa+cs Department of Epidemiology and Biosta+s+cs, McGill University

Agence de la santé et des services sociaux de Montréal, Direc+on de santé publique

The Need to Evaluate Surveillance

•  Current surveillance systems have limita+ons –  Insensi+ve – Slow –  Inflexible

•  Many poten+al improvements – Automate laboratory-‐based surveillance – Point-‐of-‐care diagnos+cs – Surveillance of health-‐care u+liza+on

•  Evidence is need to priori+ze investment

Problem Formula+on

•  Detec+on is necessary to ini+ate interven+ons •  Benefits of detec+on are realized through effects of interven+ons

–  Reduced morbidity and mortality –  Decreased costs of other types

•  Interven+on strategies are outbreak-‐specific •  Outcomes depend not only on +meliness of detec+on •  Not a linear process as illustrated!

Detection Intervention Decision making

Outbreak

Mo+va+on for Simula+on

•  Significant morbidity and cost associated with waterborne disease outbreaks

•  Public health preven+on and control strategies must be evaluated

•  Limited historical data – Provide few insights on rela+ve benefits of alterna+ve strategies

– No control over outbreak condi+ons (and o\en li]le knowledge)

Model Descrip+on

Exposure

Infection infected or not

Census

Population Model

Water Drinking

Enquête OD

Mobility

Disease Progression

onset & duration of symptoms, mortality

Clinical Care and Reporting

Health-Care Seeking Behavior


Water Distribution

System Structure

Pathogen Dispersion

synthetic population

timing, amount,

treatment

agent location

over time

space-time distribution of pathogen

number of ingested

organisms

Exposure


Census

Population Model

Water Drinking

Enquête OD

Mobility

Disease Progression





Water Distribution

System Structure

Pathogen Dispersion


timing, amount,

treatment

agent location

over time


number of ingested

organisms

Exposure


Census

Population Model

Water Drinking

Enquête OD

Mobility

Disease Progression





Water Distribution

System Structure

Pathogen Dispersion


timing, amount,

treatment

agent location

over time


number of ingested

organisms

Okhmatovskaia A, Verma AD, Barbeau B, Carriere A, Pasquet R, Buckeridge DL (2010). A Simula+on Model of Waterborne Gastro-‐Intes+nal Disease Outbreaks: Descrip+on and Ini+al Evalua+on. AMIA Annual Symposium.

Model Descrip+on

Enquête OD

Mobility

timing, amount,

treatment

Water Drinking

agent location

over time

Census

Population Model


Water Distribution

System Structure

Pathogen Dispersion

space-time distribution of pathogen Water

Distribution System

Structure

Pathogen Dispersion

space-time distribution of pathogen Water

Distribution System

Structure

Pathogen Dispersion


Pathogen Dispersion

•  Team at Ecole Polytechnique (Benoit Barbeau) uses EPANET hydraulic model –  Describes 2,500 km of Montreal’s municipal water distribu+on system from Atwater plant

–  Simulates dissemina+on of pathogen through water system

–  Generates concentra+ons of C. parvum oocysts in tap water at different loca+ons every hour

Linking Water to Regions

Model Descrip+on

Exposure

Census

Population Model

Water Drinking

Enquête OD

Mobility

Water Distribution

System Structure

Pathogen Dispersion


timing, amount,

treatment

agent location

over time


Census

Population Model

Water Drinking

Enquête OD

Mobility

Water Distribution

System Structure

Pathogen Dispersion


timing, amount,

treatment

agent location

over time


Census

Population Model

Water Drinking

Enquête OD

Mobility

Water Distribution

System Structure

Pathogen Dispersion


timing, amount,

treatment

agent location

over time



number of ingested

organisms

Disease Progression


Disease

Symptomatic Illness

Complications

Infected

¬ Infected

Start: Exposed to C.parvum in drinking water

Health Care Utilization

Dose-response relation: (Teunis et. al, 1999)

pinf = 1 – e-rD

D – number of oocysts r – infectivity of oocysts

Disease Parameters

Name Unit Value Distribu1on Cita1on

Symptoms, General Prob 0.6112 U(0.5, 0.7) DuPont 1995

Symptoms, Immune compromised

Prob 0.7897 U(0.71, 0.85) Pozio 1997, …

Infec+vity per oocyst 0.1 U(0.05, 0.15) EPA 2003

Incuba+on, Mean Days 7 U(5,9) MacKenzie 1994, …

Incuba+on, Variance Days 6 U(2,10) MacKenzie 1994, …

Dura+on, Mean Days 5 Beta(1,7,4,2) MacKenzie 1994, …

Dura+on, Variance Days 6 U(4,8) Assump+on

Dura+on, Scale Immune compromised

None 6.5 U(3,10) Calibrated

Mortality, Complicated Prob 0.44 U(0.2,0.68) Pozio 1997

Single Parameter Sensi+vity Analysis

492.7

451.9

308.0

568.8

563.6

657.7

Households Drinking Tap Water

Water Treatment Probability

Infectivity of Oocysts

308.0 513.8 657.7Number of People

(in thousands)

Number Infected

301.2

276.3

257.0

188.2

347.6

344.5

359.6

402.0



Symptoms Probability



(in thousands)

Number Symptomatic (Incident)

Model Descrip+on

Exposure

Census

Population Model

Water Drinking

Enquête OD

Mobility

Water Distribution

System Structure

Pathogen Dispersion


timing, amount,

treatment

agent location

over time


Census

Population Model

Water Drinking

Enquête OD

Mobility

Water Distribution

System Structure

Pathogen Dispersion


timing, amount,

treatment

agent location

over time


Census

Population Model

Water Drinking

Enquête OD

Mobility

Water Distribution

System Structure

Pathogen Dispersion


timing, amount,

treatment

agent location

over time



number of ingested

organisms

Disease Progression





Single Parameter Sensi+vity Analysis

22.2

22.2

21.5

21.3

19.6

18.2

13.3

12.1

16.0

10.2

17.5

22.8

23.5

23.0

24.7

24.4

25.5

28.5

32.3

42.8

41.8

58.0

Immunocompromised Population

Seek Care Probability Weekend Coefficient

Seek ED Probability (Weekend)



Symptoms Probability


Seek ED Probability (Weekday)

Seek Care Probability Slope

Seek Care Probability (Day 1)

Illness Duration Mean


(in thousands)

Number Visiting Emergency Department

Model Descrip+on

Exposure

Census

Population Model

Water Drinking

Enquête OD

Mobility

Water Distribution

System Structure

Pathogen Dispersion


timing, amount,

treatment

agent location

over time


Census

Population Model

Water Drinking

Enquête OD

Mobility

Water Distribution

System Structure

Pathogen Dispersion


timing, amount,

treatment

agent location

over time


Census

Population Model

Water Drinking

Enquête OD

Mobility

Water Distribution

System Structure

Pathogen Dispersion


timing, amount,

treatment

agent location

over time



number of ingested

organisms

Disease Progression





Boil Water Advisory Study

•  Geographical seing: Island of Montreal •  Specific pathogen: Cryptosporidium parvum •  Scenario: Failure of Atwater treatment plan •  Research ques+ons:

– Primary: How is the effec+veness of a boil-‐water advisory influenced by the dura+on of the contamina+on and the +ming of the advisory?

– Secondary: What surveillance methods are likely to be associated with effec+ve early detec+on?

Scenarios and Outcomes

•  Atwater Treatment Plant – Main plant in Montreal –  Capacity 680,000 m3 d-‐1

•  Scenarios (165) –  Failure (5): 3 to 20 days –  Oocyst (3): 0.01 to 1 per L –  BWA (11): 0 to 28 days

•  Outcomes – Morbidity, Mortality –  Healthcare U+liza+on –  Cost

Simula+on, Uncertainty & Computa+on

•  Simula+on model is rela+vely complex –  Use many (~30) parameters

–  Computa+onally expensive

•  Many simula+on runs –  Required to evaluate sensi+vity of findings –  Cumbersome and +me consuming to perform

3 Experimental parameters

[165]

30 Uncertain parameters

[1000] 165,000 Runs

Applica+on of SnAP

•  Model – Stochas+c – 2 million agents

•  Colosse (U Laval) – 1K nodes, 8K cores – 24 TB memory

– 1 PB data storage

Randomness and Uncertainty

X ~ N(0, 1)

X ~ N(μ, σ)

μ ~ U(-‐1, 1)

σ ~ U(0, 2)

X

Frequency

-6 -4 -2 0 2 4 6

050

100

150

200

X

Frequency

-6 -4 -2 0 2 4 6

050

100

150

200

250

300

LHS Samples Efficiently

Hoare et al. Theore+cal Biology and Medical Modelling 2008 5:4

Random Full Factorial

La+n Hypercube •  Define N-‐dimensional parameter space

•  Par++on each parameter distribu+on into equal probability intervals

•  Uniformly draw from each bin so that each bin is sampled exactly once

•  Efficient, unbiased coverage of space

Mul+ple Parameter Sensi+vity Analysis

Effec+veness of Boil Water Advisory

Propor+on of Infec+ons

Averted

Ini+al Results and Next Steps

•  So far – Poten+al benefit of surveillance increases as dura+on of exposure increases

– Recognizing space—+me ‘signature’ of plant failure may allow early detec+on

•  Next Steps – Apply detec+on methods to simulated laboratory and healthcare u+liza+on data

– Analyze results in terms of morbidity and cost

surveillance.mcgill.ca

[email protected]

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