Trends in the Mechanistic and Dynamic Modeling of Infectious Diseases

Abstract

The dynamics of infectious disease epidemics are driven by interactions between individuals with differing disease status (e.g., susceptible, infected, immune). Mechanistic models that capture the dynamics of such Bdependent happenings^are a fundamental tool of infectious disease epidemiology. Recent methodological advances combined with access to new data sources and computational power have resulted in an explosion in the use of dynamic models in the analysis of emerging and established infectious diseases. Increasing use of models to inform practical public health decision making has challenged the field to develop new methods to exploit available data and appropriately characterize the uncertainty in the results. Here, we discuss recent advances and areas of active research in the mechanistic and dynamic modeling of infectious disease. We highlight how a growing emphasis on data and inference, novel forecasting methods, and increasing access to Bbig data^are changing the field of infectious disease dynamics. We showcase the application of these methods in phylodynamic research, which combines mechanistic models with rich sources of molecular data to tie genetic data to population-level disease dynamics. As dynamics and mechanistic modeling methods mature and are increasingly tied to principled statistical approaches, the historic separation between the infectious disease dynamics and Btraditional^epidemiologic methods is beginning to erode; this presents new opportunities for cross pollination between fields and novel applications.

A general framework for combining ecosystem models

“…The framework presented here is not exclusive to ecosystem simulators in fisheries, but can be used to combine any mechanistic simulators in many areas of ecology (e.g. individual‐based models, Railsback & Grimm, ) or even other areas of research such as systems biology (Kuepfer, Peter, Sauer, & Stelling, ) and epidemiology (Lessler, Azman, Grabowski, Salje, & Rodriguez‐Barraquer, ).…”

Mechanistic Modelling of Coronavirus Infections and the Impact of Confined Neighbourhoods on a Short Time Scale

“…Traditional models of infectious diseases usually assume that susceptible and host populations mix readily, and fail to capture the interactions between individuals [60,61,62,63], whereas mechanistic models account for the spatial proximity between individuals and explicitly model the contact process -which is directly related to the disease transmission process [20,11]. Also, much focus in the literature is on the long term effects of epidemic spread, which is important to address questions related to disease spread/control and the resulting socio-economic impacts etc.…”

Mechanistic modelling of coronavirus infections and the impact of confined neighbourhoods on a short time scale

“…Traditional models of infectious diseases usually assume that susceptible and host populations mix readily, and fail to capture the interactions between individuals (Hethcote, 2000), whereas mechanistic models account for the spatial proximity between individuals and explicitly model the contact process -which is directly related to the disease transmission process (Lessler et al, 2016;Fofana and Hurford, 2017). Also, much focus in the literature is on the long term effects of epidemic spread, which is important to address questions related to disease spread/control and the resulting socio-economic impacts etc.…”

“…Mechanistic movement models provide an alternative modelling approach to conventional epidemiological models (SIR, SEIR), as a means to better understand the dynamics of disease spread (Lessler et al, 2016;Frasca et al, 2006;Buscarino et al, 2008;Fofana and Hurford, 2017). One advantage is that the spatial proximity between individuals is explicitly accounted for through individual movement rules, where susceptible individuals come into close contact with infectious hosts, and are possibly infected.…”