Quality report of infectious disease modeling techniques for point-referenced spatial data: A Systematic review

Abstract

Spatial data modeling can provide significant value to healthcare organizations by improving decision support, resource management and distribution, and clinical outcomes. The aim of this study was to (i) summarize the trends of the modeling techniques used to analyze point-referenced spatial data in epidemiology and (ii) examine if all information required when applying these modeling techniques were properly reported in the published papers. A literature search was limited to journal papers published from January 2010 to June 2022 using PubMed, Scopus, Crossref, and Google Scholar. From 528 articles identified with the defined keywords, 351 were retained for the review. The results revealed that the use of modeling techniques in spatial data for infectious diseases increases exponentially over time. The most common spatial method was Empirical Bayesian Kriging [EBK] (52\% of the selected articles), followed by Spatial GLMMs (34\%) and Spatial smoothing Kernel Estimation (13\%).