PubMed · 14416721
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I M LIBRACH. 1960. Infectious diseases.. https://pubmed.ncbi.nlm.nih.gov/14416721/
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In recent years, researchers have made substantial progress in the development of methods to measure the burden of resistance and the application of those methods to the limited data available. Our understanding of the costs incurred by patients infected with resistant strains in hospital settings is much better than it was 10, or even 5, years ago. Research on the impact of resistance in the community is more limited. When multiple treatment options are available and prescribed treatment is empirical, resistance will lead to higher expenditures on drugs but not necessarily to increased patient morbidity and mortality. Understanding to what degree prescribing patterns are driven by real versus perceived limitations of first-line drugs is important for assessing the ability of public health campaigns to change the behavior of patients and providers.
Although population structure has long been recognized as relevant to the spread of infectious disease, traditional mathematical models have understated the role of nonhomogenous mixing in populations with geographical and social structure. Recently, a wide variety of spatial and network models have been proposed that incorporate various aspects of interaction structure among individuals. However, these more complex models necessarily suffer from limited tractability, rendering general conclusions difficult to draw. In seeking a compromise between parsimony and realism, we introduce a class of metapopulation models in which we assume homogeneous mixing holds within local contexts, and that these contexts are embedded in a nested hierarchy of successively larger domains. We model the movement of individuals between contexts via simple transport parameters and allow diseases to spread stochastically. Our model exhibits some important stylized features of real epidemics, including extreme size variation and temporal heterogeneity, that are difficult to characterize with traditional measures. In particular, our results suggest that when epidemics do occur the basic reproduction number R(0) may bear little relation to their final size. Informed by our model's behavior, we suggest measures for characterizing epidemic thresholds and discuss implications for the control of epidemics.
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.