Modelling the Epidemiology of Infectious Diseases for Decision Analysis

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Abstract

The number of economic evaluations related to infectious disease topics has increased over the last 2 decades. However, many such evaluations rely on models that do not take into account unique features of infectious diseases that can affect the estimated value of interventions against them. These include their transmissibility from infected to susceptible individuals, the possibility of acquiring natural immunity following recovery from infection and the uncertainties that arise as a result of their complex natural history and epidemiology. Modellers conducting economic evaluations of infectious disease interventions need to know the main features of different types of infectious disease models, the situations in which they should be applied and the effects of model choices on the cost effectiveness of interventions.

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Most cited references 52

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Mathematical models of infectious disease transmission

Nicholas C. Grassly, Christophe Fraser (2008)

Key Points Mathematical analysis and modelling is an important part of infectious disease epidemiology. Application of mathematical models to disease surveillance data can be used to address both scientific hypotheses and disease-control policy questions. The link between the biology of an infectious disease, the process of transmission and the mathematics that are used to describe them is not always clear in published research. An understanding of this link is needed to critically interpret these publications and the policy recommendations and scientific conclusions that are contained within them. This Review describes the biology of the transmission process and how it can be represented mathematically. It shows how this representation leads to a mathematical model of infectious disease epidemics as a function of underlying disease natural history and ecology. The mathematical description of disease epidemics immediately leads to several useful results, including the expected size of an epidemic and the critical level that is needed for an intervention to achieve effective disease control. Statistical methods to fit mathematical models of disease surveillance data are outlined and the fundamental importance of the concept of likelihood is highlighted. The fit of mathematical models to surveillance data can provide estimates of key model parameters that determine a disease's natural history or the impact of an intervention, and are crucially dependent on the appropriate choice of mathematical model. The Review ends with four outstanding challenges in mathematical infectious disease epidemiology that are essential for progress in our understanding of the ecology and evolution of infectious diseases. This understanding could lead to improvements in disease control.

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Vaccines: past, present and future

Stanley Plotkin (2005)

The vaccines developed over the first two hundred years since Jenner's lifetime have accomplished striking reductions of infection and disease wherever applied. Pasteur's early approaches to vaccine development, attenuation and inactivation, are even now the two poles of vaccine technology. Today, purification of microbial elements, genetic engineering and improved knowledge of immune protection allow direct creation of attenuated mutants, expression of vaccine proteins in live vectors, purification and even synthesis of microbial antigens, and induction of a variety of immune responses through manipulation of DNA, RNA, proteins and polysaccharides. Both noninfectious and infectious diseases are now within the realm of vaccinology. The profusion of new vaccines enables new populations to be targeted for vaccination, and requires the development of routes of administration additional to injection. With all this come new problems in the production, regulation and distribution of vaccines.

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Cost-effectiveness of screening for HIV in the era of highly active antiretroviral therapy.

Gillian D Sanders, Ahmed M Bayoumi, Vandana Sundaram … (2005)

The costs, benefits, and cost-effectiveness of screening for human immunodeficiency virus (HIV) in health care settings during the era of highly active antiretroviral therapy (HAART) have not been determined. We developed a Markov model of costs, quality of life, and survival associated with an HIV-screening program as compared with current practice. In both strategies, symptomatic patients were identified through symptom-based case finding. Identified patients started treatment when their CD4 count dropped to 350 cells per cubic millimeter. Disease progression was defined on the basis of CD4 levels and viral load. The likelihood of sexual transmission was based on viral load, knowledge of HIV status, and efficacy of counseling. Given a 1 percent prevalence of unidentified HIV infection, screening increased life expectancy by 5.48 days, or 4.70 quality-adjusted days, at an estimated cost of 194 dollars per screened patient, for a cost-effectiveness ratio of 15,078 dollars per quality-adjusted life-year. Screening cost less than 50,000 dollars per quality-adjusted life-year if the prevalence of unidentified HIV infection exceeded 0.05 percent. Excluding HIV transmission, the cost-effectiveness of screening was 41,736 dollars per quality-adjusted life-year. Screening every five years, as compared with a one-time screening program, cost 57,138 dollars per quality-adjusted life-year, but was more attractive in settings with a high incidence of infection. Our results were sensitive to the efficacy of behavior modification, the benefit of early identification and therapy, and the prevalence and incidence of HIV infection. The cost-effectiveness of routine HIV screening in health care settings, even in relatively low-prevalence populations, is similar to that of commonly accepted interventions, and such programs should be expanded. Copyright 2005 Massachusetts Medical Society.

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Author and article information

Contributors

Mark Jit: mark.jit@hpa.org.uk

Journal

Journal ID (nlm-ta): Pharmacoeconomics

Journal ID (iso-abbrev): Pharmacoeconomics

Title: Pharmacoeconomics

Publisher: Springer International Publishing (Cham )

ISSN (Print): 1170-7690

ISSN (Electronic): 1179-2027

Publication date (Electronic): 23 December 2012

Publication date (Print): 2011

Volume: 29

Issue: 5

Pages: 371-386

Affiliations

[1 ]GRID grid.9004.d, ISNI 0000000121968713, Modelling and Economics Unit, Centre for Infections, Health Protection Agency, ; 61 Colindale Avenue, London, NW9 6BT UK

[2 ]GRID grid.23856.3a, ISNI 0000000419368390, Department of Preventive and Social Medicine, , Laval University, ; Quebec City, Quebec Canada

[3 ]URESP, Centre de recherche FRSQ du CHA universitaire de Québec, Québec, Canada

Article

Publisher ID: 29050371

DOI: 10.2165/11539960-000000000-00000

PMC ID: 7100690

PubMed ID: 21504239

SO-VID: 594dccc5-f2fa-4c4c-a182-0b450772fef0

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This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.

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ScienceOpen disciplines: Economics of health & social care

Keywords: influenza,economic evaluation,natural immunity,west nile virus,severe acute respiratory syndrome

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ScienceOpen disciplines: Economics of health & social care

Keywords: influenza, economic evaluation, natural immunity, west nile virus, severe acute respiratory syndrome

Modelling the Epidemiology of Infectious Diseases for Decision Analysis : A Primer

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Microbiology Society: Coronaviruses

Most cited references 52

Mathematical models of infectious disease transmission

Vaccines: past, present and future

Cost-effectiveness of screening for HIV in the era of highly active antiretroviral therapy.

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