DOCSLIB.ORG

Adaptive Human Behavior in Epidemiological Models

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Adaptive Human Behavior in Epidemiological Models Adaptive human behavior in epidemiological models Eli P. Fenichela,1, Carlos Castillo-Chavezb, M. G. Ceddiac, Gerardo Chowellb,d, Paula A. Gonzalez Parrae, Graham J. Hicklingf, Garth Hollowayc, Richard Horang, Benjamin Morinb, Charles Perringsa, Michael Springbornh, Leticia Velazqueze, and Cristina Villalobosi aSchool of Life Sciences and ecoSERVICES Group, Arizona State University, Tempe, AZ 85287-4501; bSchool of Human Evolution and Social Change, Arizona State University, Tempe, AZ 85287; cDepartment of Food Economics and Marketing, School of Agriculture Policy and Development, University of Reading, RG6 6AR Reading, United Kingdom; dDivision of Epidemiology and Population Studies, Fogarty International Center, National Institutes of Health, Bethesda, MD 20892-2220; eProgram in Computational Science, University of Texas at El Paso, El Paso, TX 79968-0514; fCenter for Wildlife Health, Department of Forestry, Wildlife, and Fisheries, and National Institute for Mathematical and Biological Synthesis, University of Tennessee, Knoxville, TN 37996-4563; gDepartment of Agricultural, Food, and Resource Economics, Michigan State University, East Lansing, MI 48824; hDepartment of Environmental Science and Policy, University of California, Davis, CA 95616; and iDepartment of Mathematics, University of Texas–Pan American, Edinburg, TX 78539 Edited by Partha Sarathi Dasgupta, University of Cambridge, Cambridge, United Kingdom, and approved February 23, 2011 (received for review July 30, 2010) The science and management of infectious disease are entering partments or agent-based models, and epidemiological–economic a new stage. Increasingly public policy to manage epidemics (epi-economic) models. Classical epidemiological models assume focuses on motivating people, through social distancing policies, contact rates are constant (frequency dependent) or proportional to alter their behavior to reduce contacts and reduce public disease to density (density dependent), although many extensions exist risk. Person-to-person contacts drive human disease dynamics. (6). A common extension is to specify a contact rate that is non- People value such contacts and are willing to accept some disease linear in the state variables—generally in the density of infected risk to gain contact-related benefits. The cost–benefit trade-offs individuals (e.g., refs. 6–8). Such extensions are a reduced-form that shape contact behavior, and hence the course of epidemics, approach to modeling behavioral responses to disease risks. This are often only implicitly incorporated in epidemiological models. This approach is limited in that it does not model the underlying de- approach creates difficulty in parsing out the effects of adaptive cision process and does not readily help decision makers design behavior. We use an epidemiological–economic model of disease incentives for socially desirable behaviors during an epidemic. dynamics to explicitly model the trade-offs that drive person-to- A second approach is to include behaviorally related compart- person contact decisions. Results indicate that including adaptive ments in addition to health status compartments. This approach human behavior significantly changes the predicted course of epi- involves developing behavioral rules for types of individuals in demics and that this inclusion has implications for parameter estima- different compartments, such as hospitalization and fear com- tion and interpretation and for the development of social distancing partments (9, 10) or spatial compartments joined as a network policies. Acknowledging adaptive behavior requires a shift in think- (11). Individuals in these compartments experience different ing about epidemiological processes and parameters. disease incidence. Extending this approach, so that all individuals have unique behavioral rules, yields an agent-based model (e.g., – – susceptible infected recovered model | R0 | reproductive number | ref. 12). This approach often requires the analyst to specify ex bioeconomics ante how changing incentives alters behavior and thus is restricted in its ability to aid in designing social distancing incentives. he science and management of infectious disease is entering Epi-economic models merge economics and epidemiology Ta new stage. The increasing focus on incentive structures to by explicitly analyzing individual behavioral choices in response motivate people to engage in social distancing—reducing in- to disease risk (13–18). People are assumed to make decisions terpersonal contacts and hence public disease risk (1)—changes to maximize utility, an index of well-being. People weigh the what health authorities need from epidemiological models. So- expected utility associated with decisions that include the pos- cial distancing is not new—for centuries humans quarantined sibility of future infection when choosing between behaviors such infected individuals and shunned the obviously ill, but new as vaccination choices (17) or different levels of interpersonal approaches are being used to deal with modern social inter- contact (12–15). Disease risks simultaneously affect and are af- actions. Scientific development of social distancing public poli- fected by agents’ decisions, creating a risk feedback—infection cies requires that epidemiological models explicitly address be- levels drive behaviors and contact rate decisions shape disease havioral responses to disease risk and other incentives affecting spread. The epi-economics literature is largely built on top of contact behavior. This paper models the role of adaptive be- classical epidemiology, so that the impact of economic behaviors havior in an epidemiological system. Recognizing adaptive be- on epidemiological processes and metrics generally is not ex- havior means explicitly incorporating behavioral responses to plored. In this paper, we explore how economic feedbacks alter disease risk and other incentives into epidemiological models (2, the underlying epidemiology and can fundamentally shift inter- 3). The workhorse of modern epidemiology, the compartmental pretation of epidemiological processes and metrics. epidemiological model (4, 5), does not explicitly include behav- The approach to modeling behavior has implications for ioral responses to disease risk. The transmission factors in these public health policy design. Nonlinear contact rate models and models combine and confound human behavior and biological models involving increasing compartmentalization generally fo- processes. We develop a simple compartmental model that ex- cus on estimating the basic reproductive number of the disease, plicitly incorporates adaptive behavior and show that this mod- ification alters understanding of standard epidemiological metrics. R Author contributions: E.P.F., C.C.-C., M.G.C., G.C., P.A.G.P., G.J.H., G.H., R.H., C.P., M.S., For example, the basic reproductive number, 0, is a function of L.V., and C.V. designed research; E.P.F. performed research; E.P.F. led modeling and led biological processes and human behavior, but R0 lacks a behav- the workshop where research was designed; M.S. contributed to modeling; and E.P.F., ioral interpretation in the existing literature. Biological and be- M.G.C., G.C., P.A.G.P., G.H., R.H., B.M., C.P., M.S., L.V., and C.V. wrote the paper. fl havioral feedbacks muddle R0’s biological interpretation and The authors declare no con ict of interest. confound its estimation. This article is a PNAS Direct Submission. Prior approaches that incorporate behavior into epidemiolog- 1To whom correspondence should be addressed. E-mail: [email protected]. fi ical models generally fall into three categories: speci cation of This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10. nonlinear contact rate functions, expanded epidemiological com- 1073/pnas.1011250108/-/DCSupplemental. 6306–6311 | PNAS | April 12, 2011 | vol. 108 | no. 15 www.pnas.org/cgi/doi/10.1073/pnas.1011250108 Downloaded by guest on September 27, 2021 R0,defined as the number of secondary infections in a naive management choices (e.g., vaccination and treatment). Ab- population that result from the initial introduction of a patho- stracting from these features facilitates clear illustration of how gen. Most of the literature recommends adopting public health adaptive contact behavior affects epidemiological dynamics policies to reduce R0. Roberts and Heesterbeek (ref. 19, p. 1359) and metrics. state that R0 is “the most pervasive and useful concept in System Eqs. 1–3 imply R0 = βC(·)N/v|lim S→N, lim I→0, lim Z→0 mathematical epidemiology” due to its perceived role in guiding (27); ergo this metric depends on contact behavior, as do system disease management. However, R0 implicitly includes disease- dynamics. In classical epidemiological models, C(·)isassumed free behavior and should be thought of as a reduced-form the same for all individuals regardless of health status, and mixing function. Estimates of R0 confound biological aspects of the is assumed to be homogeneous. It is common to assume either pathogen with social aspects of adaptive human responses to contacts are proportional to N,i.e.,C(·)=cN,sothatβC(·)I/N = disease risk. Moreover, R0 represents past events that may not be cβI, or contacts are constant, i.e., C(·)=c,soβC(·)I/N = cβI/N (6). informative of future events when behavior is adaptive. In each case, c is a fixed parameter, implying fixed (nonadaptive) The epi-economic literature on health policy focuses on the contact behavior and relegating behavior
Load more

Recommended publications
  • Continuous Time Individual-Level Models of Infectious Disease: Epiilmct
    Continuous Time Individual-Level Models of Infectious Disease: EpiILMCT Waleed Almutiry Vineetha Warriyar K V Rob Deardon Qassim University Uinversity of Calgary University of Calgary Abstract This paper describes the R package EpiILMCT, which allows users to study the spread of infectious disease using continuous time individual level models (ILMs). The package provides tools for simulation from continuous time ILMs that are based on either spatial demographic, contact network, or a combination of both of them, and for the graphical summarization of epidemics. Model fitting is carried out within a Bayesian Markov Chain Monte Carlo (MCMC) framework. The continuous time ILMs can be implemented within either susceptible-infected-removed (SIR) or susceptible- infected-notified-removed (SINR) compartmental frameworks. As infectious disease data is often partially observed, data uncertainties in the form of missing infection times - and in some situations missing removal times - are accounted for using data augmentation techniques. The package is illustrated using both simulated and an experimental data set on the spread of the tomato spotted wilt virus (TSWV) disease. Keywords: EpiILMCT, infectious disease, individual level modelling, spatial models, con- tact networks, R. 1. Introduction Innovative mathematical and mechanistic approaches to the modelling of infectious dis- eases are continuing to emerge in the literature. These can be used to understand the spread of disease through a population - whether homogeneous or heterogeneous - and enable researchers to construct predictive models to develop control strategies to disrupt arXiv:2006.00135v1 [stat.AP] 30 May 2020 disease transmission. For example, Deardon et al. (2010) introduced a class of discrete time individual-level models (ILMs) which incorporate population heterogeneities by modelling the transmission of disease given various individual-level risk factors.
    [Show full text]
  • The Basic Reproduction Number As a Predictor for Epidemic Outbreaks in Temporal Networks
    The basic reproduction number as a predictor for epidemic outbreaks in temporal networks Petter Holme1,2,3 and Naoki Masuda4 1Department of Energy Science, Sungkyunkwan University, 440-746 Suwon, Korea 2IceLab, Department of Physics, Umeå University, 90187 Umeå, Sweden 3Department of Sociology, Stockholm University, 10961 Stockholm, Sweden 4Department of Engineering Mathematics, University of Bristol, BS8 1UB, Bristol, UK E-mail address: [email protected] Abstract The basic reproduction number R₀—the number of individuals directly infected by an infectious person in an otherwise susceptible population—is arguably the most widely used estimator of how severe an epidemic outbreak can be. This severity can be more directly measured as the fraction people infected once the outbreak is over, Ω. In traditional mathematical epidemiology and common formulations of static network epidemiology, there is a deterministic relationship between R₀ and Ω. However, if one considers disease spreading on a temporal contact network—where one knows when contacts happen, not only between whom—then larger R₀ does not necessarily imply larger Ω. In this paper, we numerically investigate the relationship between R₀ and Ω for a set of empirical temporal networks of human contacts. Among 31 explanatory descriptors of temporal network structure, we identify those that make R₀ an imperfect predictor of Ω. We find that descriptors related to both temporal and topological aspects affect the relationship between R₀ and Ω, but in different ways. Introduction The interaction between medical and theoretical epidemiology of infectious diseases is probably not as strong as it should. Many results in the respective fields fail to migrate to the other.
    [Show full text]
  • Epidemic Models
    Chapter 9 Epidemic Models 9.1 Introduction to Epidemic Models Communicable diseases such as measles, influenza, and tuberculosis are a fact of life. We will be concerned with both epidemics, which are sudden outbreaks of a disease, and endemic situations, in which a disease is always present. The AIDS epidemic, the recent SARS epidemic, recurring influenza pandemics, and outbursts of diseases such as the Ebola virus are events of concern and interest to many peo- ple. The prevalence and effects of many diseases in less-developed countries are probably not as well known but may be of even more importance. Every year mil- lions, of people die of measles, respiratory infections, diarrhea, and other diseases that are easily treated and not considered dangerous in the Western world. Diseases such as malaria, typhus, cholera, schistosomiasis, and sleeping sickness are endemic in many parts of the world. The effects of high disease mortality on mean life span and of disease debilitation and mortality on the economy in afflicted countries are considerable. We give a brief introduction to the modeling of epidemics; more thorough de- scriptions may be found in such references as [Anderson & May (1991), Diekmann & Heesterbeek (2000)]. This chapter will describe models for epidemics, and the next chapter will deal with models for endemic situations, but we begin with some general ideas about disease transmission. The idea of invisible living creatures as agents of disease goes back at least to the writings of Aristotle (384 BC–322 BC). It developed as a theory in the sixteenth century. The existence of microorganisms was demonstrated by van Leeuwenhoek (1632–1723) with the aid of the first microscopes.
    [Show full text]
  • Commentary Inducing Autoimmune Disease to Treat Cancer
    Proc. Natl. Acad. Sci. USA Vol. 96, pp. 5340–5342, May 1999 Commentary Inducing autoimmune disease to treat cancer Drew M. Pardoll Department of Oncology, Johns Hopkins University School of Medicine, Baltimore, MD 21205-2196 For many years, visions for development of successful immu- colleagues (6) discovered that the target for a melanoma- notherapy of cancer revolved around the induction of immune specific CD81 T cell clone grown from a melanoma patient was responses against tumor-specific ‘‘neoantigens.’’ However, as wild-type tyrosinase, a melanosomal enzyme selectively ex- demonstrated in a recent paper in the Proceedings by Overwijk pressed in melanocytes and responsible for one of the steps in et al. (1), the generation of tissue-specific autoimmune re- melanin biosynthesis. Subsequently, a number of investigators sponses represents an approach to cancer immunotherapy that found that their melanoma-specific CD81 T cells indeed is gaining momentum. Thus, a new principle in cancer therapy recognized melanocyte-specific antigens rather than melano- states that the ability to induce tissue-specific autoimmunity ma-specific antigens (7–10). Most of these antigens appear to will allow for the treatment of many important cancers. be melanosomal proteins, and a number of them, including The original focus on tumor-specific neoantigens derived tyrosinase, TRP-1, TRP-2, and gp100, are involved in melanin from a number of findings. Vaccination-challenge experiments biosynthesis. Other melanosomal proteins such as MART1y performed between carcinogen-induced murine tumor models Melan A have no known function but are nonetheless mela- typically demonstrated that autologous tumors vaccinated nocyte-specific tissue differentiation antigens.
    [Show full text]
  • Clustering of Susceptible Individuals Within Households Can Drive an Outbreak: an Individual-Based Model Exploration
    medRxiv preprint doi: https://doi.org/10.1101/2019.12.10.19014282; this version posted December 14, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license . Clustering of susceptible individuals within households can drive an outbreak: an individual-based model exploration Elise Kuylen1,2,*, Lander Willem1, Jan Broeckhove3, Philippe Beutels1, and Niel Hens1,4 1Centre for Health Economics Research and Modelling Infectious Diseases (CHERMID), Vaccine and Infectious Disease Institute, University of Antwerp, Antwerp, Belgium 2Discipline Group Computer Sciences, Hasselt University, Hasselt, Belgium 3IDLab, Department of Mathematics and Computer Science, University of Antwerp, Antwerp, Belgium 4I-BioStat, Data Science Institute, Hasselt University, Hasselt, Belgium *[email protected] ABSTRACT When estimating important measures such as the herd immunity threshold, and the corresponding efforts required to eliminate measles, it is often assumed that susceptible individuals are uniformly distributed throughout populations. However, unvaccinated individuals may be clustered in a variety of ways, including by geographic location, by age, in schools, or in households. Here, we investigate to which extent different levels of within-household clustering of susceptible individuals may impact the risk and persistence of measles outbreaks. To this end, we apply an individual-based model, Stride, to a population of 600,000 individuals, using data from Flanders, Belgium. We compare realistic scenarios regarding the distribution of susceptible individuals within households in terms of their impact on epidemiological measures for outbreak risk and persistence.
    [Show full text]
  • Lesson 1 Introduction to Epidemiology
    Lesson 1 Introduction to Epidemiology Epidemiology is considered the basic science of public health, and with good reason. Epidemiology is: a) a quantitative basic science built on a working knowledge of probability, statistics, and sound research methods; b) a method of causal reasoning based on developing and testing hypotheses pertaining to occurrence and prevention of morbidity and mortality; and c) a tool for public health action to promote and protect the public’s health based on science, causal reasoning, and a dose of practical common sense (2). As a public health discipline, epidemiology is instilled with the spirit that epidemiologic information should be used to promote and protect the public’s health. Hence, epidemiology involves both science and public health practice. The term applied epidemiology is sometimes used to describe the application or practice of epidemiology to address public health issues. Examples of applied epidemiology include the following: • the monitoring of reports of communicable diseases in the community • the study of whether a particular dietary component influences your risk of developing cancer • evaluation of the effectiveness and impact of a cholesterol awareness program • analysis of historical trends and current data to project future public health resource needs Objectives After studying this lesson and answering the questions in the exercises, a student will be able to do the following: • Define epidemiology • Summarize the historical evolution of epidemiology • Describe the elements of a case
    [Show full text]
  • Download This Issue
    COVID ECONOMICS VETTED AND REAL-TIME PAPERS ECONOMIC EPIDEMIOLOGY: ISSUE 48 A REVIEW 10 SEPTEMBER 2020 David McAdams INDIVIDUALISM Bo Bian, Jingjing Li, Ting Xu FORECASTING THE SHOCK and Natasha Z. Foutz Felipe Meza ENTREPRENEUR DEBT AVERSION IS WHO TRUSTED? Mikael Paaso, Vesa Pursiainen Nirosha Elsem Varghese, Iryna and Sami Torstila Sabat, Sebastian Neuman‑Böhme, SUPPLY CHAIN DISRUPTION Jonas Schreyögg, Tom Stargardt, Matthias Meier and Eugenio Pinto Aleksandra Torbica, Job van Exel, PANDEMICS, POVERTY, AND Pedro Pita Barros and Werner Brouwer SOCIAL COHESION ECONOMISTS: FROM VILLAINS Remi Jedwab, Amjad M. Khan, Richard TO HEROES? Damania, Jason Russ and Esha D. Zaver Diane Coyle Covid Economics Vetted and Real-Time Papers Covid Economics, Vetted and Real-Time Papers, from CEPR, brings together formal investigations on the economic issues emanating from the Covid outbreak, based on explicit theory and/or empirical evidence, to improve the knowledge base. Founder: Beatrice Weder di Mauro, President of CEPR Editor: Charles Wyplosz, Graduate Institute Geneva and CEPR Contact: Submissions should be made at https://portal.cepr.org/call-papers- covid-economics. Other queries should be sent to [email protected]. Copyright for the papers appearing in this issue of Covid Economics: Vetted and Real-Time Papers is held by the individual authors. The Centre for Economic Policy Research (CEPR) The Centre for Economic Policy Research (CEPR) is a network of over 1,500 research economists based mostly in European universities. The Centre’s goal is twofold: to promote world-class research, and to get the policy-relevant results into the hands of key decision-makers. CEPR’s guiding principle is ‘Research excellence with policy relevance’.
    [Show full text]
  • Economic Activity and the Spread of Viral Diseases: Evidence from High Frequency Data
    IZA DP No. 9326 Economic Activity and the Spread of Viral Diseases: Evidence from High Frequency Data Jérôme Adda September 2015 DISCUSSION PAPER SERIES Forschungsinstitut zur Zukunft der Arbeit Institute for the Study of Labor Economic Activity and the Spread of Viral Diseases: Evidence from High Frequency Data Jérôme Adda Bocconi University, IGIER and IZA Discussion Paper No. 9326 September 2015 IZA P.O. Box 7240 53072 Bonn Germany Phone: +49-228-3894-0 Fax: +49-228-3894-180 E-mail: [email protected] Any opinions expressed here are those of the author(s) and not those of IZA. Research published in this series may include views on policy, but the institute itself takes no institutional policy positions. The IZA research network is committed to the IZA Guiding Principles of Research Integrity. The Institute for the Study of Labor (IZA) in Bonn is a local and virtual international research center and a place of communication between science, politics and business. IZA is an independent nonprofit organization supported by Deutsche Post Foundation. The center is associated with the University of Bonn and offers a stimulating research environment through its international network, workshops and conferences, data service, project support, research visits and doctoral program. IZA engages in (i) original and internationally competitive research in all fields of labor economics, (ii) development of policy concepts, and (iii) dissemination of research results and concepts to the interested public. IZA Discussion Papers often represent preliminary work and are circulated to encourage discussion. Citation of such a paper should account for its provisional character. A revised version may be available directly from the author.
    [Show full text]
  • Some Mathematical Models in Epidemiology
    Preliminary Definitions and Assumptions Mathematical Models and their analysis Some Mathematical Models in Epidemiology by Peeyush Chandra Department of Mathematics and Statistics Indian Institute of Technology Kanpur, 208016 Email: [email protected] Peeyush Chandra Some Mathematical Models in Epidemiology Preliminary Definitions and Assumptions Mathematical Models and their analysis Definition (Epidemiology) It is a discipline, which deals with the study of infectious diseases in a population. It is concerned with all aspects of epidemic, e.g. spread, control, vaccination strategy etc. WHY Mathematical Models ? The aim of epidemic modeling is to understand and if possible control the spread of the disease. In this context following questions may arise: • How fast the disease spreads ? • How much of the total population is infected or will be infected ? • Control measures ! • Effects of Migration/ Environment/ Ecology, etc. • Persistence of the disease. Peeyush Chandra Some Mathematical Models in Epidemiology Preliminary Definitions and Assumptions Mathematical Models and their analysis Infectious diseases are basically of two types: Acute (Fast Infectious): • Stay for a short period (days/weeks) e.g. Influenza, Chickenpox etc. Chronic Infectious Disease: • Stay for larger period (month/year) e.g. hepatitis. In general the spread of an infectious disease depends upon: • Susceptible population, • Infective population, • The immune class, and • The mode of transmission. Peeyush Chandra Some Mathematical Models in Epidemiology Preliminary Definitions and Assumptions Mathematical Models and their analysis Assumptions We shall make some general assumptions, which are common to all the models and then look at some simple models before taking specific problems. • The disease is transmitted by contact (direct or indirect) between an infected individual and a susceptible individual.
    [Show full text]
  • Economic Epidemiology in the Wake of Covid-19∗
    Economic epidemiology in the wake of Covid-19∗ David McAdamsy August 27, 2020 Abstract Infectious diseases, ideas, new products, and other \infectants" spread in epidemic fashion through social contact. The Covid-19 pandemic, the prolifer- ation of \fake news," and the rise of antibiotic resistance have thrust economic epidemiology into the forefront of public-policy debate and re-invigorated the field. Focusing for concreteness on disease-causing pathogens, this paper pro- vides a taxonomy of economic-epidemic models, emphasizing both the biology / immunology of the disease and the economics of the social context. An economic epidemic is one whose diffusion through the agent population is generated by agents' endogenous behavior. I highlight properties of the Nash-equilibrium epi- demic trajectory and discuss ways in which public-health authorities can change the game for the better, (i) by imposing restrictions on agent activity to reduce the harm done during a viral outbreak and (ii) by enabling diagnostic-informed interventions to slow or even reverse the rise of antibiotic resistance. Keywords: economic epidemic, epidemic limbo, Covid-19, lockdown, antibiotic resistance, diagnostics ∗When citing this paper, please use the following: McAdams D. 2021. Economic epidemiology in the wake of Covid-19. Annual Review of Economics 13: Submitted. https://doi.org/10.1146/annurev- economics-082120-122900 yFuqua School of Business and Economics Department, Duke University, Durham, North Carolina, USA. Email: [email protected]. I thank David Argente, Chris Avery, Troy Day, Mike Hoy, Gregor Jarosch, Philipp Kircher, Anton Korinek, Ramanan Laxminarayan, Tomas Philipson, Elena Quercioli, Steve Redding, Tim Reluga, Bob Rowthorn, Yangbo Song, Arjun Srinivasan, and Flavio Toxvaerd for helpful comments and encouragement.
    [Show full text]
  • Foundations of Epidemiology
    66221_CH01_5398.qxd 6/19/09 11:16 AM Page 1 © Jones and Bartlett Publishers, LLC. NOT FOR SALE OR DISTRIBUTION CHAPTER 1 Foundations of Epidemiology OBJECTIVES After completing this chapter, you will be able to: ■ Define epidemiology. ■ Define descriptive epidemiology. ■ Define analytic epidemiology. ■ Identify some activities performed in epidemiology. ■ Explain the role of epidemiology in public health practice and individual decision making. ■ Define epidemic, endemic, and pandemic. ■ Describe common source, propagated, and mixed epidemics. ■ Describe why a standard case definition and adequate levels of reporting are important in epidemiologic investigations. ■ Describe the epidemiology triangle for infectious disease. ■ Describe the advanced epidemiology triangle for chronic diseases and behavioral disorders. ■ Define the three levels of prevention used in public health and epidemiology. ■ Understand the basic vocabulary used in epidemiology. 66221_CH01_5398.qxd 6/19/09 11:16 AM Page 2 © Jones and Bartlett Publishers, LLC. NOT FOR SALE OR DISTRIBUTION 2 CHAPTER 1 ■ Foundations of Epidemiology In recent years, the important role of epidemiology has become increasingly recognized. Epidemiology is a core subject required in public health and health education programs; it is a study that provides information about public health problems and the causes of those problems. This information is then used to improve the health and social conditions of people. Epidemiology has a population focus in that epidemiologic investigations are concerned with the collective health of the people in a community or population under study. In contrast, a clinician is concerned for the health of an individual. The clinician focuses on treating and caring for the patient, whereas the epidemiologist focuses on iden- tifying the source or exposure of disease, disability or death, the number of persons exposed, and the potential for further spread.
    [Show full text]
  • Population Dynamics of Infectious Diseases: a Discrete Time Model
    ecological modelling 198 (2006) 183–194 available at www.sciencedirect.com journal homepage: www.elsevier.com/locate/ecolmodel Population dynamics of infectious diseases: A discrete time model Madan K. Oli a,∗, Meenakshi Venkataraman a, Paul A. Klein b, Lori D. Wendland c, Mary B. Brown c a Department of Wildlife Ecology and Conservation, 110 Newins-Zeigler Hall, University of Florida, Gainesville, FL 32611-0430, United States b Department of Pathology, Immunology, and Laboratory Medicine, College of Medicine, University of Florida, Gainesville, FL 32610-0275 c Department of Infectious Diseases and Pathology, College of Veterinary Medicine, University of Florida, Gainesville, FL 32610-0880, United States article info abstract Article history: Mathematical models of infectious diseases can provide important insight into our under- Received 23 June 2005 standing of epidemiological processes, the course of infection within a host, the transmis- Received in revised form 31 March sion dynamics in a host population, and formulation or implementation of infection control 2006 programs. We present a framework for modeling the dynamics of infectious diseases in dis- Accepted 18 April 2006 crete time, based on the theory of matrix population models. The modeling framework Published on line 14 June 2006 presented here can be used to model any infectious disease of humans or wildlife with dis- crete disease states, irrespective of the number of disease states. The model allows rigorous Keywords: estimation of important quantities, including the basic reproduction ratio of the disease Basic reproduction ratio R0 (R0) and growth rate of the population ( ), and permits quantification of the sensitivity of R0 Epidemiological model and to model parameters.
    [Show full text]