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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

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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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.


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Learning/Performance Objectives 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.


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Features unique to infectious diseases: 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. 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.


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Outcomes of exposure 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. 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


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Definitions: 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. Incidence

2. Prevalence

3. Attack rate

4. Primary/secondary cases

5. Case fatality rate or ratio

6. Virulence


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Definitions continued: 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.

7. Mortality

8. Reproductive rate

9. Vector

10. Transmission routes

11. Reservoir vs source

12. Zoonosis


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Definitions continued: 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.

13. Incubation period

14. Serial interval

15. Infectious period

16. Latent period

17. Epidemic


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Mathematical Models for Epidemics 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.

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.


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Outbreak Analysis 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.

Early analysis:

Person: who is the case?

Place: where was the case infected?

Time: when was the case infected?


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Outbreak Analysis continued 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.

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.


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Outbreak Analysis continued 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.

Check the geography.

Check the age and sex.


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Factors Affecting Surveillance 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.

Outbreak discovery

Outbreak analysis

Validity of notification data

Notification delays

Information feedback

Sources of data


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Factors Affecting Infectivity 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.

Dose and route

Immunity

Co-factors

Subclinical infection


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Seroepidemiology 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.

Used for:

1. Description of seroprevalence in populations

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


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Seroepidemiology continued 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.

Importance of case and control classification:

Use of a gold standard reference.

Use of clinical diagnosis.


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Seroepidemiology continued 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.

Sensitivity

Specificity

Positive predictive value

Negative predictive value

Pre-test probability of disease


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Contact Patterns 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.

Use graphs or matrices to describe the network of contacts.

Study the networks by interviewing the cases about their contacts.

Study the contact structure.


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Transmission Probability Ratio 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.

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.


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TPR continued 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.

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

There are 4 types of transmission probabilities (tp).


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Binomial Transmission Probabilities 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.

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


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Study Designs 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.

Cross-sectional: risk or prevalence ratio

Case control: odds ratio

Cohort: relative risk

Survival analysis


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Study Issues 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.

Confounding

Bias

Misclassification

Interaction


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Epidemiology of vaccination 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.

Direct: immunity by infection or vaccination

Indirect: herd immunity

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


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Conclusion 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.

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.


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