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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Sharyn Orton, Ph.D.
American Red Cross, Rockville, MD
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.
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.
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.
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
3. Attack rate
4. Primary/secondary cases
5. Case fatality rate or ratio
8. Reproductive rate
10. Transmission routes
11. Reservoir vs source
13. Incubation period
14. Serial interval
15. Infectious period
16. Latent period
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.
Person: who is the case?
Place: where was the case infected?
Time: when was the case infected?
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.
Check the geography.
Check the age and sex.
Validity of notification data
Sources of data
Dose and route
1. Description of seroprevalence in populations
2. Follow incidence by estimation from changes using multiple samples from a population
Importance of case and control classification:
Use of a gold standard reference.
Use of clinical diagnosis.
Positive predictive value
Negative predictive value
Pre-test probability of disease
Use graphs or matrices to describe the network of contacts.
Study the networks by interviewing the cases about their contacts.
Study the contact structure.
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 of differing types of contacts, infectious agents, infection routes or strains can be calculated.
There are 4 types of transmission probabilities (tp).
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
Cross-sectional: risk or prevalence ratio
Case control: odds ratio
Cohort: relative risk
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.