Risk stratification in compartmental epidemic models: Where to draw the line?

J Theor Biol. 2017 Sep 7;428:1-17. doi: 10.1016/j.jtbi.2017.06.004. Epub 2017 Jun 9.

Abstract

Economic evaluations of infectious disease control interventions frequently use dynamic compartmental epidemic models. Such models capture heterogeneity in risk of infection by stratifying the population into discrete risk groups, thus approximating what is typically continuous variation in risk. An important open question is whether and how different risk stratification choices influence model predictions of intervention effects. We develop equivalent Susceptible-Infected-Susceptible (SIS) dynamic transmission models: an unstratified model, a model stratified into a high-risk and low-risk group, and a model with an arbitrary number of risk groups. Absent intervention, the models produce the same overall prevalence of infected individuals in steady state. We consider an intervention that either reduces the contact rate or increases the disease clearance rate. We develop analytical and numerical results characterizing the models and the effects of the intervention. We find that there exist multiple feasible choices of risk stratification, contact distribution, and within- and between-group contact rates for models that stratify risk. We show analytically and empirically that these choices can generate different estimates of intervention effectiveness, and that these differences can be significant enough to alter conclusions from cost-effectiveness analyses and change policy recommendations. We conclude that the choice of how to discretize risk in compartmental epidemic models can influence predicted effectiveness of interventions. Therefore, analysts should examine multiple alternatives and report the range of results.

Keywords: Compartmental epidemic models; Cost-effectiveness analysis; Infectious disease; Risk stratification.

Publication types

  • Research Support, U.S. Gov't, Non-P.H.S.
  • Research Support, N.I.H., Extramural

MeSH terms

  • Communicable Disease Control
  • Epidemics*
  • Gonorrhea / epidemiology
  • Gonorrhea / transmission
  • Homosexuality, Male / statistics & numerical data
  • Humans
  • Male
  • Models, Biological*
  • Numerical Analysis, Computer-Assisted
  • Prevalence
  • Risk Assessment*
  • Risk Factors