The use of mathematical modeling studies for evidence synthesis and guideline development: A glossary

Abstract

Mathematical modeling studies are increasingly recognised as an important tool for evidence synthesis and to inform clinical and public health decision‐making, particularly when data from systematic reviews of primary studies do not adequately answer a research question. However, systematic reviewers and guideline developers may struggle with using the results of modeling studies, because, at least in part, of the lack of a common understanding of concepts and terminology between evidence synthesis experts and mathematical modellers. The use of a common terminology for modeling studies across different clinical and epidemiological research fields that span infectious and non‐communicable diseases will help systematic reviewers and guideline developers with the understanding, characterisation, comparison, and use of mathematical modeling studies. This glossary explains key terms used in mathematical modeling studies that are particularly salient to evidence synthesis and knowledge translation in clinical medicine and public health.

On the reliability of predictions on Covid-19 dynamics: A systematic and critical review of modelling techniques

“…for the modelling. Compartments are assumed to represent homogeneous sub-populations within which the entities being modelled–such as individuals or patients–have the same characteristics ( Porgo et al, 2019 ). Compartmental models were the most used (46.15%) regardless the topic addressed ( Fig.…”

“…One of the most common statistical modelling techniques used were the regression models. Statistical models, such as regression models, are typically phenomenological and describe the statistical relationship or association between different model variables ( Porgo et al, 2019 ). Less than four percent (3.5%; 8 out of 230) of the studies used a regression model (linear regression, polynomial regression etc.)…”

“…Deterministic model typically describes the average behavior of a system (e.g., populations or sub-populations) without taking into account stochastic processes or chance events in single entities (e.g., individuals). Hence, such models are typically applied to situations with a large number of individuals where stochastic variation becomes less important and het-erogeneity can be accounted for using various sub-populations ( Porgo et al, 2019 ). Stochastic models are models where the parameters, variables, and/or the change in variables can be described by probability distributions.…”

Strategies to exiting the COVID-19 lockdown for workplace and school: A scoping review

“…Overall, 23 mathematical models were assessed using the ‘QUAntitative-Deterministic models Risk of Infeasibility Assessment Checklist’ (QUADRIAC) ( Porgo et al, 2019 ). The risk of unfeasibility was low for 11 models (Araz OM, 2013; Brethouwer, 2020 , Giordano et al, 2020 , Hoertel et al, 2020 ; Kim et al, 2020; Lee et al, 2020 , Panovska-Griffiths et al, 2020 , Potter et al, 2012 , Prem et al, 2020 , Rawson et al, 2020 , Scala et al, 2020 ), medium for 10 models ( Aleta et al, 2020 ; D’Orazio M, 2020; Davey and Glass, 2008; Di Domenico et al, 2020a , Fokas et al, 2020 , German et al, 2020 , Karin et al, 2020 , Keeling et al, 2020 , Kraay et al, 2020 ), and high for two models (Gosce et al, 2020; McBryde et al, 2020 ) (Appendix Table 3 ).…”

The roadmap towards elimination of lymphatic filariasis by 2030: insights from quantitative and mathematical modelling

“…Outside of the modelling consortium, a geospatially-explicit model was recently developed for American Samoa, that captures the connectedness between sites via migrating humans 17 . For an explanation of modelling terminology, we refer to a recently published glossary 18 .…”