This article describes a multi-dimensional approach to the classification of the research literature on simulation and modelling in health care. The aim of the study was to analyse the relative frequency of use of a range of operational research modelling approaches in health care, along with the specific domains of application and the level of implementation. Given the vast scale of the health care modelling literature, a novel review methodology was adopted, similar in concept to the approach of stratified sampling. The results provide new insights into the level of activity across many areas of application, highlighting important relationships and pointing to key areas of omission and neglect in the literature. In addition, the approach presented in this article provides a systematic and generic methodology that can be extended to other application domains as well as other types of information source in health-care modelling.

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BackgroundThere is an increasing recognition that modelling and simulation can assist in the process of designing health care policies, strategies and operations. However, the current use is limited and answers to questions such as what methods to use and when remain somewhat underdeveloped.AimThe aim of this study is to provide a mechanism for decision makers in health services planning and management to compare a broad range of modelling and simulation methods so that they can better select and use them or better commission relevant modelling and simulation work.MethodsThis paper proposes a modelling and simulation method comparison and selection tool developed from a comprehensive literature review, the research team's extensive expertise and inputs from potential users. Twenty-eight different methods were identified, characterised by their relevance to different application areas, project life cycle stages, types of output and levels of insight, and four input resources required (time, money, knowledge and data).ResultsThe characterisation is presented in matrix forms to allow quick comparison and selection. This paper also highlights significant knowledge gaps in the existing literature when assessing the applicability of particular approaches to health services management, where modelling and simulation skills are scarce let alone money and time.ConclusionsA modelling and simulation method comparison and selection tool is developed to assist with the selection of methods appropriate to supporting specific decision making processes. In particular it addresses the issue of which method is most appropriate to which specific health services management problem, what the user might expect to be obtained from the method, and what is required to use the method. In summary, we believe the tool adds value to the scarce existing literature on methods comparison and selection.

a b s t r a c tWhile literature reviews with a large-scale scope are nowadays becoming a staple element of modern research practice, there are many challenges in taking on such an endeavour, yet little evidence of previous studies addressing these challenges exists. This paper introduces a practical and efficient review framework for extremely large corpora of literature, refined by five parallel implementations within a multi-disciplinary project aiming to map out the research and practice landscape of modelling, simulation, and management methods, spanning a variety of sectors of application where such methods have made a significant impact. Centred on searching and screening techniques along with the use of some emerging IT-assisted analytic and visualisation tools, the proposed framework consists of four key methodological elements to deal with the scale of the reviews, namely: (a) an incremental and iterative review structure, (b) a 3-stage screening phase including filtering, sampling and sifting, (c) use of visualisation tools, and (d) reference chasing (both forward and backward). Five parallel implementations of systematically conducted literature search and screening yielded a total initial search result of 146 087 papers, ultimately narrowed down to a final set of 1383 papers which was manageable within the limited time and other constraints of this research work.

Background: WHO has declared COVID-19 infection a health emergency of international concern on 11th March, 2020. It is not clear whether clinical characteristics of pregnant women with COVID-19 differ from those of nonpregnant women and whether it aggravates COVID-19 symptoms and whether antiviral therapy is necessary for COVID-19 infected pregnant women.Methods: This is prospective study of 125 cases based on the compiled clinical data for pregnant women with COVID-19 between 15th April 2020 and 10th June 2020. A laboratory confirmed positive case of COVID-19 infection in pregnant women were included.Results: The most common symptoms at presentation were cough in 61.6% (77/125) and fever in 46.4% (58/125). Other reported symptoms were sore throat in 13.6% (17/125), myalgia in 10.4% (13/125) while 38.4% (48/125) were asymptomatic. There were total 97 deliveries (including 2 twins’ deliveries) among which 3 cases had IUD. Present study reported 96 live births. The incidence of missed abortion was 2.4% (3/125). The incidence of preterm birth before 37 weeks was 8.2% (8/97). Ninety-six (96.9%) of neonates were tested for SARS-CoV-2 viral nucleic acid on nasopharyngeal and pharyngeal samples and 16.67% (16/96) were resulted positive.Conclusions: At present, there is no evidence regarding the greater risk of pregnant women to succumb to COVID-19 infection and experience severe pneumonia. The risks of spontaneous abortion and preterm birth are not increased as reported in this study but shows possibility of vertical transmission when it manifests during the third trimester of pregnancy.