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Microbial Risk Assessment Part 2: Dynamic Epidemiology Models of Microbial Risk. Envr 133 Mark D. Sobsey Spring, 2006. Using Epidemiology for Microbial Risk Analysis.

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microbial risk assessment part 2 dynamic epidemiology models of microbial risk

Microbial Risk Assessment Part 2: Dynamic Epidemiology Models of Microbial Risk

Envr 133

Mark D. Sobsey

Spring, 2006

using epidemiology for microbial risk analysis
Using Epidemiology for Microbial Risk Analysis
  • Problem Formulation: What’s the problem? Determine what infectious disease is posing a risk, its clinical features, causative agent, routes of exposure/infection and health effects
  • Exposure Assessment: How, how much, when, where and why exposure occurs; vehicles, vectors, doses, loads, etc.
  • Health Effects Assessment:
    • Human clinical trials for dose-response
    • field studies of endemic and epidemic disease in populations
  • Risk characterization: Epidemiologic measurements and analyses of risk: relative risk, risk ratios, odds ratios; regression models of disease risk; dynamic model of disease risk
    • other disease burden characterizations: relative contribution to overall disease burdens; effects of prevention and control measures; economic considerations (monetary cost of the disease and cost effectiveness of prevention and control measures
epidemiology intervention study
Epidemiology Intervention Study

POPULATION

randomly select from population

CASE GROUP

(intervene to change level of exposure)

CONTROL GROUP

epidemiology cohort study
Epidemiology Cohort Study

POPULATION 2

(exposure 2)

POPULATION 1

(exposure 1)

randomly select from population

randomly select from population

COHORT 1

COHORT 2

epidemiology case control study
Epidemiology Case-Control Study

POPULATION 2

(NO exposure)

POPULATION 1

(exposure 1)

randomly select from population

randomly select from population

CASE GROUP

CONTROL GROUP

some more epidemiological terms and concepts
Some More Epidemiological Terms and Concepts

# cases

  • Outbreaks: two or more cases of disease associated with a specific agent, source, exposure and time period
  • Epidemic Curve (Epi-curve): Number of cases or other measure of the amount of illness in a population over time during an epidemic
    • Describes nature and time course of outbreak
    • Can estimate incubation time if exposure time is known
    • Can give clues to modes of transmission: point source, common source, and secondary transmission

Point Source

Time

# cases

Common Source

Time

databases for quantification and statistical assessment of disease
Databases for Quantification and Statistical Assessment of Disease
  • National Notifiable Disease Surveillance System
  • National Ambulatory Medical Care Survey
  • International Classification of Disease (ICD) Codes
  • Other Databases
    • Special surveys
    • Sentinel surveillance efforts
defined dynamic compartment epidemiology model of microbial risk
DEFINED: “DynamicCompartmentEpidemiologyModel” of Microbial Risk
  • DYNAMIC:a force that stimulates change or progress within a system
  • COMPARTMENT:a small space or subdivision for storage
  • EPIDEMIOLOGY:the statistical study of the distribution and determinants of disease in populations
  • MODEL:a hypothetical description of a complex entity or process
infectious disease transmission sir model host states in relation to pathogen transmission
Infectious Disease Transmission (SIR) Model:Host States in Relation to Pathogen Transmission

Pathogen Exposure

Susceptible

Infected

Resistant

 = the rate or probability of movement from one state to another

slide14
“Dynamic State” Epidemiological Model of Microbial Risk - Modeling Infectious Disease Dynamics and Transmission in Populations
  • Members of population move between states
    • States describe status with respect to a pathogen
  • Movement from state-to-state is modeled with ordinary differential equations;
    • define rates of movement between states: rate terms
  • Each transmission process is assumed to be independent
  • Change in fraction of population in any state from one time period to another can be described and quantified
  • Different sources of pathogen exposure can be identified and included in the model
slide15
“Dynamic State” Epidemiological Model of Microbial Risk - State Variables“SIR” Model of Infectious Disease

State Variables: track no. people in each state at a point in time

  • S = susceptible = not infectious; not symptomatic
  • I = Infected
    • C = carrier = infectious; not symptomatic
    • D = disease = infectious; symptomatic
  • R = Resistant; same as P = post infection (or) not infectious; not symptomatic; short-term or partial immunity
  • In epidemiology these states are called SIR
simple sir model
Simple SIR Model
  • dynamic in that the numbers in each compartment fluctuate over time
  • also dynamic in the sense that individuals are born susceptible, then may acquire the infection (move into the infectious compartment) and finally recover (move into the recovered compartment)
    • each member of the population typically progresses from susceptible to infectious to recovered
  • diseases tend to occur in cycles of outbreaks due to the variation in number of susceptibles (S(t)) over time
  • number of susceptibles falls rapidly as more of them are infected and thus enter the infectious and recovered compartments
  • disease cannot break out again until the number of susceptibles has built back up as a result of babies being born into the compartment
seir model
SEIR Model

Similar to the simple SIR model with the following exception:

  • For many infections, there is a period of time during which the individual has been infected but is not yet infectious himself. During this latent period the individual is in compartment E (for exposed).
msir model
MSIR Model

Similar to the simple SIR model with the following exception:

  • For many infections, babies are not born into the susceptible compartment but are immune to the disease for the first few months of life due to protection from maternal antibodies.
simple sir model19
Simple SIR Model

Similar to the simple SIR model with the following exception:

  • With certain infectious diseases, some people who have been infected never completely recover and continue to carry the infection, while not suffering the disease themselves. They may then move back into the infectious compartment and suffer symptoms (as in tuberculosis) or they may continue to infect others in their carrier state, while not suffering symptoms. (Ex. Typhoid Fever)
simple sir model20
Simple SIR Model
  • Similar to the simple SIR model with the following exception:
  • Some infections, such as influenza, do not confer long lasting immunity. Such infections do not have a recovered state and individuals become susceptible again after infection.
slide21

Infectious Disease Transmission Model at the Population Level: Dynamic Model

  • Risk estimation depends on transmission dynamics and exposure pathways. Example: Water
dynamic state epidemiological model of microbial transmission and disease risk
“Dynamic State” Epidemiological Model of Microbial Transmission and Disease Risk

Susceptible

Carrier I

Diseased I

Post-infection

dynamic state epidemiological model of microbial transmission and disease risk24
“Dynamic State” Epidemiological Model of Microbial Transmission and Disease Risk

Susceptible

Carrier I

Diseased I

Post-infection

additional analyses of health effects health effects assessments previous lecture
Additional Analyses of Health Effects:Health Effects Assessments(previous lecture)
  • Health Outcomes of Microbial Infection
  • Identification and diagnosis of disease caused by the microbe
    • disease (symptom complex and signs)
    • Acute and chronic disease outcomes
    • mortality
    • diagnostic tests
  • Sensitive populations and effects on them
  • Disease Databases and Epidemiological Data
methods to diagnose infectious disease previous lecture
Methods to Diagnose Infectious Disease(previous lecture)
  • Symptoms (subjective: headache, pain) and Signs (objective: fever, rash, diarrhea)
  • Clinical diagnosis: lab tests
    • Detect causative organism in clinical specimens
    • Detect other specific factors associated with infection
  • Immune response
    • Detect and assay antibodies
    • Detect and assay other specific immune responses
health outcomes of microbial infection previous lecture
Health Outcomes of Microbial Infection(previous lecture)
  • Acute Outcomes
    • Diarrhea, vomiting, rash, fever, etc.
  • Chronic Outcomes
    • Paralysis, hemorrhagic uremia, reactive arthritis, etc.
  • Hospitalizations
  • Deaths
slide29
Impacts of Household Water Quality on Gastrointestinal Illness - Payment Study #1 (An Intervention Study)
outcomes of infection process to be quantified previous lecture
Outcomes of Infection Process to be Quantified(previous lecture)

Exposure

Infection

Asymptomatic Infection

Advanced Illness, Chronic Infections and Sequelae

Disease

Acute Symptomatic Illness:

Severity and Debilitation

Sensitive Populations

Mortality

Hospitalization

sensitive populations previous lecture
Sensitive Populations(previous lecture)
  • Infants and young children
  • Elderly
  • Immunocompromized
    • Persons with AIDs
    • Cancer patients
    • Transplant patients
  • Pregnant
  • Malnourished
mortality ratios for enteric pathogens in nursing homes versus general population previous lecture
Mortality Ratios for Enteric Pathogens in Nursing Homes Versus General Population(previous lecture)
slide38
Mortality Ratios Among Specific Immunocompromised Patient Groups with Adenovirus Infection(previous lecture)
databases for quantification and statistical assessment of disease39
Databases for Quantification and Statistical Assessment of Disease
  • National Notifiable Disease Surveillance System
  • National Ambulatory Medical Care Survey
  • International Classification of Disease (ICD) Codes
  • Other Databases
    • Special surveys
    • Sentinel surveillance efforts
elements that may be considered in risk characterization
Elements That May Be Considered inRisk Characterization
  • Evaluate health consequences of exposure scenario
    • Risk description (event)
    • Risk estimation (magnitude, probability)
  • Characterize uncertainty/variability/confidence in estimates
  • Conduct sensitivity analysis
    • evaluate most important variables and information needs
  • Address items in problem formulation (reality check)
  • Evaluate various control measures and their effects on risk magnitude and profile
  • Conduct decision analysis
    • evaluate alternative risk management strategies