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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 Overview 1. Introduction 2. Modeling and the Anatomy of Epidemics 3. Preventing and Controlling Epidemics 4. Epidemics and Luck Smallpox SARS Anthrax Monkeypox Mumps Antibiotic-resistant Acinetobacter Communityassociated MRSA Supertoxigenic Clostridium difficile Avian influenza Bordatella pertussis Measles West Nile Virus Highly-resistant Pseudomonas aeruginosa 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 1. 2. Smallpox 100 100 75 75 No. of cases No. of cases Seasonal viruses 50 25 50 25 0 0 2004 2005 2006 2007 2004 2005 2006 2007 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 Modeling and the Anatomy of Epidemics Modeling Measles Keeling, et al. Proc R Soc Lond. 2002 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 Progression of an Epidemic Basic reproductive number (R0) R0 = 1 Expected number of secondary cases on the introduction of one infected individual in a susceptible population R0 = 2 R0 > 1 Epidemic disease R0 = 1 Endemic disease R0 < 1 Disease dies out R0 = 3 R0 Generation # 1 2 3 …10 2 1 2 4 512 1 1 1 1 1 0.5 4 2 1 0 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. R0 = p x k x d p = transmissibility k = contacts d = duration of contagiousness Transmissibility (p) 1. Quantity of pathogen released 2. Mechanism of dissemination 3. Inherent infectiousness of the pathogen R0 = p x k x d Quantity of pathogen released Varies with state of disease Varies with activity R0 = p x k x d Early chickenpox Herpes simplex Cattarhal phase of viral infections Coughing vs. sneezing vs. talking Mechanism of dissemination Respiratory Contact Seasonal viruses Antibiotic-resistant bacteria Fecal-oral Influenza, tuberculosis Salmonella, shigella, hepatitis A Blood and body fluid HIV, Hepatitis B and C R0 = p x k x d Respiratory dissemination Pathogen Size Distance Persistence Droplet Droplet nuclei Bacteria TB ≥ 5µ < 5µ < 3 feet ? < 10 min. > 1 hr. Destination Upper airways R0 = p x k x d Alveoli Inherent infectiousness Biological differences between organisms Adhesions, proteinases Variation in host response Expressed as the minimal infectious dose R0 = p x k x d E. coli infecting bladder epithelium Contacts Number of contacts R0 = p x k x d May be facilitated by environmental factors Intensity of contacts R0 = p x k x d 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 Duration of Contagiousness (d) The Ebola paradox Rapid mortality reduces period of contagiousness R0 = p x k x d Preventing and Controlling Epidemics Childbed fever: Vienna, 1847 Robert A. Thom (1966) Cholera: London 1854 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). Limiting transmissibility (p) Reduce the quantity of pathogen released Symptom control Anti-tussives Barrier precautions Masks for patients Limiting transmissibility Act on the mechanism of dissemination Environmental controls Reduce inherent infectiousness Difficult to reduce, but possible to increase Blood pressure cuffs: 14% Bedside Tables: 20% Bed rails: 26% Sheets: 40% Overall, 63% of VRE (+) patient rooms are contaminated Preventing Contact Quarantine and Isolation “une quarantaine de jours (a period of forty days)” Quarantine S Exposed M T Contagious W R F Symptoms Begin Isolation S Social Controls Restriction on public events and gatherings Travel limitations Building quarantines Import/Export controls Reducing duration of contagiousness Antimicrobial therapy Influenza control Anti-HIV therapy Enhanced case recognition Syndromic surveillance Limit contacts Ebola revisited Period of infectivity 0 1 2 Days of illness Ebola: Natural History Death 3 Ebola revisited Period of infectivity Death 0 1 2 Days of illness Ebola: Current Practice 3 4 Traditional funeral practices Ebola revisited Period of infectivity 0 1 2 Days of illness Ebola: USA Death 3 4 ICU Support Epidemics and Luck 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 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. Conclusions 1. Epidemics are driven by a relatively understandable interplay of pathogens, infected and susceptible hosts. 2. Understanding the mathematical as well as the biological underpinnings of epidemics is critical to prevention and control. 3. Sometimes, it really is better to be lucky than to be good.