Stephen Weber, MD, MS Assistant Professor Section of Infectious Diseases Hospital Epidemiologist

1. The Anatomy of an Epidemic: A Rational Approach to Understanding, Preventing and Combating Infectious Diseases Stephen Weber, MD, MS Assistant Professor Section of Infectious Diseases Hospital Epidemiologist Director, Infection Control Program University of Chicago Hospitals

2. Overview • Introduction • Modeling and the Anatomy of Epidemics • Preventing and Controlling Epidemics • Epidemics and Luck

4. Smallpox SARS Anthrax Monkeypox Mumps Antibiotic-resistant Acinetobacter Community-associated MRSA Supertoxigenic Clostridium difficile Avian influenza Bordatella pertussis Measles West Nile Virus Highly-resistant Pseudomonas aeruginosa

6. Defining an epidemic • An outbreak of a contagious disease that spreads rapidly and widely. • An increased frequency of infection above the normal or usual level

7. Epidemic Surveillance World Health Organization (WHO) Centers for Disease Control and Prevention Illinois Department of Public Health Chicago Department of Public Health UCH Infection Control Program Individual Clinicians

9. Modeling and the Anatomy of Epidemics

10. Modeling Measles Keeling, et al. Proc R Soc Lond. 2002

11. Modeling Malaria dX/dt = A B Y (N - X) - r X dY/dt = A C X (M - Y) - m Y McKenzie and Samba, et al. Am J Trop Med Hyg. 2004

12. R0 = 1 R0 = 2 R0 = 3 Progression of an Epidemic • Basic reproductive number (R0) • Expected number of secondary cases on the introduction of one infected individual in a susceptible population R0 > 1 Epidemic disease R0 = 1 Endemic disease R0 < 1 Disease dies out

13. Generation # R0 1 2 3 …10 2 1 2 4 512 1 1 1 1 1 0.5 4 2 1 0

14. Basic Reproductive Numbers • SARS in general population: 0.49 • SARS (hospital transmission): 2.6 • Smallpox in a vulnerable population: 3.0-5.2 • Measles (pre-vaccine): 10-15 • Measles in Belgian schools (1996): 6.2-7.7 • 1918 pandemic influenza: 1.8-2.0 • Influenza on a commercial airliner: 10.4 Liao, et al. Risk Anal. 2005; Chowell, et al. Emerg Inf Dis. 2004; Mossong, et al. Epidemiol Infect. 2005; Meltzer, et al. Emerg Inf Dis. 2001.

15. R0 = p x k x d p = transmissibility k = contacts d = duration of contagiousness

16. Transmissibility (p) • Quantity of pathogen released • Mechanism of dissemination • Inherent infectiousness of the pathogen R0 = p x k x d

17. Quantity of pathogen released • Varies with state of disease • Early chickenpox • Herpes simplex • Cattarhal phase of viral infections • Varies with activity • Coughing vs. sneezing vs. talking R0 = p x k x d

18. Mechanism of dissemination • Respiratory • Influenza, tuberculosis • Contact • Seasonal viruses • Antibiotic-resistant bacteria • Fecal-oral • Salmonella, shigella, hepatitis A • Blood and body fluid • HIV, Hepatitis B and C R0 = p x k x d

19. Respiratory dissemination Droplet Droplet nuclei Pathogen BacteriaTB Size ≥ 5µ < 5µ Distance < 3 feet ? Persistence < 10 min. > 1 hr. Destination Upper airways Alveoli R0 = p x k x d

20. Inherent infectiousness • Biological differences between organisms • Adhesions, proteinases • Variation in host response • Expressed as the minimal infectious dose E. coli infecting bladder epithelium R0 = p x k x d

21. Contacts • Number of contacts • May be facilitated by environmental factors • Intensity of contacts R0 = p x k x d

22. R0 = p x k x d

23. Duration of Contagiousness (d) • Assuming a constant frequency of contacts and an unchanging degree of transmissibility, the longer the period of time that a patient is contagious the more likely he/she is to transmit the pathogen. • For some infections, the period of contagiousness may not always be associated with symptoms of illness. R0 = p x k x d

24. Duration of Contagiousness (d) • The Ebola paradox • Rapid mortality reduces period of contagiousness R0 = p x k x d

25. Preventing and Controlling Epidemics

26. Childbed fever: Vienna, 1847 Robert A. Thom (1966)

27. Cholera: London 1854

28. Modeling and Infection Control R0 = p x k x d Interventions to prevent the spread of epidemics target transmissibility (p), contacts (k) or duration of contagiousness (d).

29. Limiting transmissibility (p) • Reduce the quantity of pathogen released • Symptom control • Anti-tussives • Barrier precautions • Masks for patients

30. Limiting transmissibility Blood pressure cuffs: 14% • Act on the mechanism of dissemination • Environmental controls • Reduce inherent infectiousness • Difficult to reduce, but possible to increase Bedside Tables: 20% Bed rails: 26% Sheets: 40% Overall, 63% of VRE (+) patient rooms are contaminated

31. Preventing Contact

32. Quarantine and Isolation “une quarantaine de jours (a period of forty days)” Quarantine S M T W R F S Symptoms Begin Exposed Contagious Isolation

33. Social Controls • Restriction on public events and gatherings • Travel limitations • Building quarantines • Import/Export controls

34. Reducing duration of contagiousness • Antimicrobial therapy • Influenza control • Anti-HIV therapy • Enhanced case recognition • Syndromic surveillance • Limit contacts

35. Period of infectivity Ebola revisited Death 0 1 2 3 Days of illness Ebola: Natural History

36. Period of infectivity 3 3 4 Traditional funeral practices Ebola revisited Death 0 1 2 3 Days of illness Ebola: Current Practice

37. Period of infectivity 3 3 4 ICU Support Ebola revisited Death 0 1 2 3 Days of illness Ebola: USA

38. Epidemics and Luck

39. Epidemic Misfortune • Epidemics do not conform to the predictions of deterministic models. Stochastic phenomena prevail. • Monkeypox: Co-transport of Ghanan giant rat with prairie dogs • West Nile Virus: Survival of carrier mosquito through transatlantic flight • SARS: Co-mixing of viruses between humans, fowl and civets • HIV: Single African ancestral event

42. Improving the Odds • Understanding the role of chance in epidemics permits the deployment of manageable strategies to prevent spread. • Improved performance of day to day practices may be more important than an elaborate emergency response system.

43. Conclusions • Epidemics are driven by a relatively understandable interplay of pathogens, infected and susceptible hosts. • Understanding the mathematical as well as the biological underpinnings of epidemics is critical to prevention and control. • Sometimes, it really is better to be lucky than to be good.