VECMAtk: a scalable verification, validation and uncertainty quantification toolkit for scientific simulations.

We present the VECMA toolkit (VECMAtk), a flexible software environment for single and multiscale simulations that introduces directly applicable and reusable procedures for verification, validation (V&V), sensitivity analysis (SA) and uncertainty quantication (UQ). It enables users to verify key aspects of their applications, systematically compare and validate the simulation outputs against observational or benchmark data, and run simulations conveniently on any platform from the desktop to current multi-petascale computers. In this sequel to our paper on VECMAtk which we presented last year [1] we focus on a range of functional and performance improvements that we have introduced, cover newly introduced components, and applications examples from seven different domains such as conflict modelling and environmental sciences. We also present several implemented patterns for UQ/SA and V&V, and guide the reader through one example concerning COVID-19 modelling in detail. This article is part of the theme issue 'Reliability and reproducibility in computational science: implementing verification, validation and uncertainty quantification in silico'.

Sensitivity-driven simulation development: a case study in forced migration.

This paper presents an approach named sensitivity-driven simulation development (SDSD), where the use of sensitivity analysis (SA) guides the focus of further simulation development and refinement efforts, avoiding direct calibration to validation data. SA identifies assumptions that are particularly pivotal to the validation result, and in response model ruleset refinement resolves those assumptions in greater detail, balancing the sensitivity more evenly across the different assumptions and parameters. We implement and demonstrate our approach to refine agent-based models of forcibly displaced people in neighbouring countries. Over 70.8 million people are forcibly displaced worldwide, of which 26 million are refugees fleeing from armed conflicts, violence, natural disaster or famine. Predicting forced migration movements is important today, as it can help governments and NGOs to effectively assist vulnerable migrants and efficiently allocate humanitarian resources. We use an initial SA iteration to steer the simulation development process and identify several pivotal parameters. We then show that we are able to reduce the relative sensitivity of these parameters in a secondary SA iteration by approximately 54% on average. This article is part of the theme issue 'Reliability and reproducibility in computational science: implementing verification, validation and uncertainty quantification in silico'.

Detection of breast cancer presenting as a mass in women with dense breasts - digital breast tomosynthesis versus full-field digital mammography.

AIM: To evaluate the sensitivity and specificity of digital breast tomosynthesis compared with full-field digital mammography in detection of breast cancer presenting as a mass in women with dense breasts. Маterials and Methods: This study included 347 asymptomatic and symptomatic patients with dense breasts who underwent full-field digital mammography, digital breast tomosynthesis and hand-held full breast ultrasound. 57 core-needle biopsies were performed. Pathology included 31 invasive cancers and 26 non-cancerous lesions. RESULTS: Sensitivity of full-field digital mammography was 61.3% [0.422-0.789] and sensitivity of digital breast tomosynthesis was 77.4% [0.589-0.904]. Specificity of full-field digital mammography was 92.7% [0.893-0.953] that was 2.2% lower than the specificity of digital breast tomosynthesis - 94.9% [0.919-0.971]. CONCLUSION: Results of our study showed superior sensitivity and specificity of digital breast tomosynthesis compared to full-field digital mammography for detection of malignant masses in women with dense breasts.