A systematic review to identify areas of enhancements of pandemic simulation models for operational use at provincial and local levels

Abstract

BackgroundIn recent years, computer simulation models have supported development of pandemic influenza preparedness policies. However, U.S. policymakers have raised several concerns about the practical use of these models. In this review paper, we examine the extent to which the current literature already addresses these concerns and identify means of enhancing the current models for higher operational use.MethodsWe surveyed PubMed and other sources for published research literature on simulation models for influenza pandemic preparedness. We identified 23 models published between 1990 and 2010 that consider single-region (e.g., country, province, city) outbreaks and multi-pronged mitigation strategies. We developed a plan for examination of the literature based on the concerns raised by the policymakers.ResultsWhile examining the concerns about the adequacy and validity of data, we found that though the epidemiological data supporting the models appears to be adequate, it should be validated through as many updates as possible during an outbreak. Demographical data must improve its interfaces for access, retrieval, and translation into model parameters. Regarding the concern about credibility and validity of modeling assumptions, we found that the models often simplify reality to reduce computational burden. Such simplifications may be permissible if they do not interfere with the performance assessment of the mitigation strategies. We also agreed with the concern that social behavior is inadequately represented in pandemic influenza models. Our review showed that the models consider only a few social-behavioral aspects including contact rates, withdrawal from work or school due to symptoms appearance or to care for sick relatives, and compliance to social distancing, vaccination, and antiviral prophylaxis. The concern about the degree of accessibility of the models is palpable, since we found three models that are currently accessible by the public while other models are seeking public accessibility. Policymakers would prefer models scalable to any population size that can be downloadable and operable in personal computers. But scaling models to larger populations would often require computational needs that cannot be handled with personal computers and laptops. As a limitation, we state that some existing models could not be included in our review due to their limited available documentation discussing the choice of relevant parameter values.ConclusionsTo adequately address the concerns of the policymakers, we need continuing model enhancements in critical areas including: updating of epidemiological data during a pandemic, smooth handling of large demographical databases, incorporation of a broader spectrum of social-behavioral aspects, updating information for contact patterns, adaptation of recent methodologies for collecting human mobility data, and improvement of computational efficiency and accessibility.

Investigating the Influence of Spatial and Temporal Granularities on Agent-Based Modeling

“…Our study assesses changes in speed, intensity, and spatial spread of a synthetic disease to investigate the influence of STGs on model processes in an ABM. Following the methodologies used in the network-based studies, we sought parsimony in the design of a synthetic model, rather than focusing on matching population distributions, disease characteristics, and movement patterns (see, e.g., Grassly andFraser 2008 andPrieto et al 2012). Conceptually, a simulation of our synthetic model may represent disease spread at various spatial or temporal scales ranging from a national, state, county, city, or building scale spanning years, months, days, or hours based on the parameters used.…”

“…Our future studies will also investigate the sensitivity of intervention strategies to STGs, elucidating any relationships between them, which Prieto et al. () discusses with respect to simplifying assumptions and we briefly discussed in Realistic Community section. Generally, intervention strategies whether medical or mobility (McLafferty ) limit the potential distance of infection (e.g., travel restrictions) or the number of potential susceptibles (e.g., mass or targeted immunizations).…”

Themes and correlations of participant experience and evaluation of an interactive bioterrorism release exercise – a mixed methods study

“…Scenario simulations commonly use diseases, such as influenza, that reflect the most concern regarding social and economic impact, but relatively modest mortality and morbidity compared to diseases such as security sensitive bioterrorism agents (7)(8). Despite the generalisable nature of most pandemic responses, these often fail to take account of the broader impacts of a pandemic scenario (7,9) or the potential for an event involving a pathogen of greater transmissibility and severity. Additionally, the considerable logistical challenges likely to be faced in a pandemic, (10) such as impacts on civil infrastructure and society, are often considered superficially (11).…”

GPGPU parallelization of self-calibrating agent-based influenza outbreak simulation

“…Diffusion-based models offer rapid processing times by coarsening the granularity of the simulation. These models assign individuals into compartments, where in each compartment every individual makes the same number of contacts and a contact can be any individual in the compartment [2]. These simulations may be run quickly, but are less informative because they do not model individual behavior.…”

“…Due to their computation cost, agent-based models are yet to be adapted to support operational decisions that generally occur in cycles of less than 4 to 6 hours [2], [3]. This implies several design considerations: 1) Models should be calibrated as fast as possible.…”