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Infectious Disease Epidemiology. Sharyn Orton, Ph.D. American Red Cross, Rockville, MD Suggested reading: Modern Infectious Disease Epidemiology (1994) by Johan Giesecke Modern Epidemiology (1998) by Kenneth Rothman and Sander Greenland.

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infectious disease epidemiology

Infectious Disease Epidemiology

Sharyn Orton, Ph.D.

American Red Cross, Rockville, MD

Suggested reading:

Modern Infectious Disease Epidemiology (1994) by Johan Giesecke

Modern Epidemiology (1998) by Kenneth Rothman and Sander Greenland


My interest in infectious disease epidemiology stems from my 20+ years as a Medical Technologist. An advanced degree in Epidemiology and Biostatistics has enabled me to better understand the dynamics and power of infectious disease epidemics, as well as the important differences from diseases caused by “non” infectious agents.


Learning/Performance Objectives

1. Understand the unique differences between infectious and “non”- infectious disease epidemiology.

2. Understand the terminology.

3. Be able to calculate sensitivity, specificity, predictive values and transmission probabilities.


Features unique to infectious diseases:

1. A case may also be a source.

2. People may be immune.

3. A case may be a source without being recognized.

4. There is often a need for urgency.

5. Preventive measures often have good scientific basis.


Outcomes of exposure

1. No infection

2. Clinical infection resulting in death, immunity, carrier or non-immunity

3. Sub-clinical infection resulting in immunity, carrier or non-immunity

4. Carrier



1. Incidence

2. Prevalence

3. Attack rate

4. Primary/secondary cases

5. Case fatality rate or ratio

6. Virulence


Definitions continued:

7. Mortality

8. Reproductive rate

9. Vector

10. Transmission routes

11. Reservoir vs source

12. Zoonosis


Definitions continued:

13. Incubation period

14. Serial interval

15. Infectious period

16. Latent period

17. Epidemic


Mathematical Models for Epidemics

Person to person spread relies on the reproduction rate, which is the average number of people infected by one case.

This is influenced by the attack rate of disease, the frequency of contact, the duration of infectivity and the immune status of the population.


Outbreak Analysis

Early analysis:

Person: who is the case?

Place: where was the case infected?

Time: when was the case infected?


Outbreak Analysis continued

Epidemic Curve

1. Plot the date on the horizontal axis.

2. Plot the number of cases on the vertical axis.

3. Determine if the outbreak is point source, continuous or person to person.


Outbreak Analysis continued

Check the geography.

Check the age and sex.


Factors Affecting Surveillance

Outbreak discovery

Outbreak analysis

Validity of notification data

Notification delays

Information feedback

Sources of data


Factors Affecting Infectivity

Dose and route



Subclinical infection



Used for:

1. Description of seroprevalence in populations

2. Follow incidence by estimation from changes using multiple samples from a population


Seroepidemiology continued

Importance of case and control classification:

Use of a gold standard reference.

Use of clinical diagnosis.


Seroepidemiology continued



Positive predictive value

Negative predictive value

Pre-test probability of disease


Contact Patterns

Use graphs or matrices to describe the network of contacts.

Study the networks by interviewing the cases about their contacts.

Study the contact structure.


Transmission Probability Ratio

TPR is a measure of risk of transmission from infected to susceptible individuals during a contact.

For any given type of contact or agent, an estimate of the effect of a covariate on susceptibility, infectiousness or both can be made.


TPR continued

TPR of differing types of contacts, infectious agents, infection routes or strains can be calculated.

There are 4 types of transmission probabilities (tp).


Binomial Transmission Probabilities

Used when susceptibles make more than one potentially infectious contact.

The maximum likelihood estimate of the tp under the binomial model=

# of susceptibles who become infected 

total number of contacts with infectives


Study Designs

Cross-sectional: risk or prevalence ratio

Case control: odds ratio

Cohort: relative risk

Survival analysis


Study Issues






Epidemiology of vaccination

Direct: immunity by infection or vaccination

Indirect: herd immunity

Vaccine efficacy (%) =Iu-Iv/Iu x 100



Infectious and “non”-infectious disease epidemiology have important differences due to the inherently different nature of the risk factors (biological agent i.e. virus, bacteria vs chemical, environmental or genetic).

It is important to understand and consider these differences when conducting infectious disease research.