Original Title: Simulador de toma de decisiones en pandemias y otras catástrofes

Authors' objectives: The main objective of this report is to identify indicators and variables applicable to modelling and simulation methodologies for decision-making in disease outbreaks, epidemics and pandemics.

Authors' results and conclusions: Results As a result of searches of reference databases and other electronic resources, previously listed, and limited to SR and MA, a total of 1543 studies were identified, of which 1116 were not duplicated. A first independent pairwise selection was performed on the basis of title and abstract, initially discarding 1034 studies for not meeting the inclusion criteria or for meeting some of the exclusion criteria. Discrepancies were resolved by consensus. Of the 82 papers that were read in full text, 20 were finally selected by pairs for analysis. The qualities of the included studies ranged from moderate to critically low (13 studies obtained a critically low quality assessment, 1 study low quality and 6 studies moderate quality). None of the included SRs reached the maximum quality as defined by AMSTAR2. Regarding the scenario, 12 of the 20 SRs include only studies whose objective is the simulation or modelling of the progression of the COVID19 pandemic, as explicitly stated in their inclusion or exclusion criteria. Seven of the 20 SRs included in this report each address one pandemic. Only 1 of the 20 SRs includes studies whose object of modelling or simulation are different. Given the high number of SRs whose object is the analysis of models and simulators for the COVID19 pandemic, the disaggregated analysis of the rest of the results into COVID19 and non-COVID19 is proposed, to avoid possible bias by scenario. With respect to the perspective object of modelling or simulation, an attempt was made to collect data on: the scope of application within the health system, the type of health system financing contemplated, and the adaptability of the model or simulator to other health systems or levels of care. However, these results were only obtained in 4 of the SRs included and only for one of the sections (scope of application within the health system). Regarding intervention, only one of the included SRs included a small proportion of studies whose simulation included patients with pathologies not directly related to the disease (chronic cardiac pathology). Regarding the modelling and simulation methodologies applied by the studies included in the SRs, 11 of the 20 SRs include modelling of pandemic evolutions as primary studies while 9 SRs include simulations. The use of AI, ML or DL by the included primary studies is reported in 9 of the 20 SRs. Regarding the indicators to be evaluated and the variables, they were divided into clinical indicators, clinical management or hospital policy indicators and other types of indicators. In turn, a disaggregated analysis was performed according to the pandemics being simulated or modelled. The clinical indicators with the highest frequency in all types of simulators and models regardless of the causative disease are patient status, pandemic development and transmission. The management indicators with the highest frequency in all types of simulators and models regardless of the causative disease are control and restriction measures, vaccination plans and management of hospital needs. In the category of indicators that were neither clinical nor management indicators, defined as other indicators, they have a higher frequency in all types of SR simulators and models including climatological and population indicators. Conclusions Through the analysis of the 20 SRs included, despite their low quality, we have identified simulators and mathematical models for decision-making in epidemics or pandemics by recreating scenarios with clinical and management variables. Thanks to this evidence, we conclude that current computer resources and mathematical modelling and simulation methodologies allow the development and implementation of simulators for clinical or management decision-making in disease outbreaks, pandemics and epidemics. These types of simulators can contribute in a complementary way to clinical or management decision-making to obtain beneficial results for the population affected by the pandemic, allowing their measurement through these same indicators.

Authors' methods: Systematic literature review following the recommendations of the PRISMA statement and the content analysis methodology, to answer the research question in SPICE format (scenario, perspective, intervention, comparator and evaluation) without including a comparator. Data extraction was carried out following the content analysis methodology. This type of analysis is a research method for interpreting and quantifying textual data, its uniqueness lies mainly in its ability to convert qualitative textual data into quantitative data, which can then be systematically examined. To locate the evidence, we searched reference databases (Medline (Ovid), Embase (Elsevier), Cochrane Library (Wiley), The Web of Science (FECYT) Core Collection, International HTA database (InaHTA) and PsycInfo (EBSCO), Trip PRO Database and The Health Evidence Database) and specialised resources in COVID19 (The WHO COVID19 Research Database, The COVID19 Evidence Network to support Decision-making (COVID-END) and The Wales COVID19 Evidence Centre). The tool selected to assess the quality of the included studies was AMSTAR2.

Authors' identified further research: It is necessary to validate and evaluate their methodology in a standardized way, and to homogenize the Simulation and mathematical modelling methodologies, as well as the indicators and variables, although these may reflect singularities of the diseases under study

Project Status: Completed

URL for project: https://www.aetsa.org/publicacion/simulador-de-toma-de-decisiones-en-pandemias-u-otras-catastrofes/

Year Published: 2025

URL for published report: https://www.aetsa.org/download/publicaciones/25_2023_AETSA_Simulador_Pandemias_web.pdf

Requestor: Spanish Health Ministry

English language abstract: An English language summary is available

Publication Type: Full HTA

Country: Spain

DOI: 10.52766/WPLG6521

Organisation Name: Andalusian Health Technology Assessment Area

Contact Address: Area de Evaluacion de Tecnologias Sanitarias Sanitarias de Andalucia (AETSA) Avda. Innovación, s/n Edificio Arena 1. Sevilla (Spain) Tel. +34 955 006 309

Contact Name: aetsa.csalud@juntadeandalucia.es

Contact Email: aetsa.csalud@juntadeandalucia.es

Copyright: <p>Andalusian Agency for Health Technology Assessment (AETSA)</p>