How Modelling Can Enhance the Analysis of Imperfect Epidemic Data. Issue 5 (May 2019)

• Record Type: Journal Article
• Authors: Cauchemez, Simon; Hoze, Nathanaël; Cousien, Anthony; Nikolay, Birgit; ten bosch, Quirine
• Is Part Of: Trends in parasitology. Volume 35:Issue 5(2019)
• Page Start: 369
• Page End: 379
• Publication Date: 2019-05
• Subjects: mathematical modelling -- statistics -- epidemic dynamics -- transmission -- severity -- risk assessment; Parasitology -- Periodicals; Parasitology -- Periodicals; Biology -- Periodicals; Parasitology; Biology; Parasitologie -- Périodiques; Online resources; 571.999
• DOI: 10.1016/j.pt.2019.01.009
• Languages: English
• ISSNs: 1471-4922

Abstract

Abstract : Mathematical models play an increasingly important role in our understanding of the transmission and control of infectious diseases. Here, we present concrete examples illustrating how mathematical models, paired with rigorous statistical methods, are used to parse data of different levels of detail and breadth and estimate key epidemiological parameters (e.g., transmission and its determinants, severity, impact of interventions, drivers of epidemic dynamics) even when these parameters are not directly measurable, when data are limited, and when the epidemic process is only partially observed. Finally, we assess the hurdles to be taken to increase availability and applicability of these approaches in an effort to ultimately enhance their public health impact. Highlights: Many data can be used to estimate the transmission potential of a pathogen, including descriptions of the transmission chains, human cluster sizes, sources of infection, and epidemic curves. An important agenda in public health is understanding the impact of control methods. However, the dynamic nature of epidemics makes this task challenging. Models can disentangle the natural course of outbreaks from the effect of external factors. In the absence of reliable surveillance data, models can reconstruct epidemic history by combining age-specific seroprevalence data with an understanding of the natural history of infection. Mechanisms of immunity are hard to observe at an individual level, yet they affect population-level dynamics. Models can tease out such signatures. Morbidity and mortality can be difficult to estimate when many infections are unobserved and severe infections are reported more often. Models can be used to correct for under-reporting and selection bias.