Paper Title
A COMPARATIVE STUDY OF MATHEMATICAL MODELS FOR RANDOM DATA ANALYSIS: INSIGHTS FROM CASE STUDIESAbstract
Mathematical modeling studies are increasingly recognized 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. 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, characterization, comparison, and use of mathematical modeling studies. Scientific data is often analyzed in the context of domain-specific problems, for example, failure diagnostics, predictive analysis, and computational estimation. These problems can be solved using approaches such as mathematical models or heuristic methods. In this study we compare a heuristic approach based on mining stored data with a mathematical approach based on applying state-of-the-art formulae to solve an estimation problem. 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 goal is to estimate results of scientific experiments given their input conditions.
KEYWORDS : Mathematical modeling, scientific data, failure diagnostics, predictive analysis, mathematical modellers, input conditions