Appropriate Models for the Management of Infectious Diseases

Background: Mathematical models have become invaluable management tools for epidemiologists, both shedding light on the mechanisms underlying observed dynamics as well as making quantitative predictions on the effectiveness of different control measures. Here, we explain how substantial biases are introduced by two important, yet largely ignored, assumptions at the core of the vast majority of such models. Methods and Findings: First, we use analytical methods to show that (i) ignoring the latent period or (ii) making the common assumption of exponentially distributed latent and infectious periods (when including the latent period) always results in underestimating the basic reproductive ratio of an infection from outbreak data. We then proceed to illustrate these points by fitting epidemic models to data from an influenza outbreak. Finally, we document how such unrealistic a priori assumptions concerning model structure give rise to systematically overoptimistic predictions on the outcome of potential management options. Conclusion: This work aims to highlight that, when developing models for public health use, we need to pay careful attention to the intrinsic assumptions embedded within classical frameworks. Two current biases in infectious disease models may substantially affect their usefulness in predicting disease oubreaks. Background: When a new infectious disease emerges, such as SARS, it is important to try to predict how the disease will behave, e.g., how infectious it is and what its latent period is, so that the spread of the disease through the population can be estimated and appropriate public health measures such as quarantining can be decided on. What Did the Authors Do?: They assessed different currently used mathematical models of disease outbreaks, including models that took no account of latent periods, and another that assumed that the latent and infectious periods had a particular pattern—called exponential. They showed that both of these assumptions could potentially lead to underestimating the way the disease spreads. They then tested their predictions on a known outbreak of influenza that occurred in a school. What Do These Findings Mean?: Public health officials may need to rethink the way that they try to predict outbreaks of infectious disease. Minimally, they need to be sure that they put into any model the most accurate predictions of the behavior of the disease. Where Can I Get More Information?: The Health Protection Agency in the United Kingdom has a Web site that explains its work on assessing infectious disease outbreaks: