Cad Cam 3d Modelling
Henrietta Naughton
The Centre for Multilevel Modelling (CMM) is a research centre based at the University of Bristol. Our researchers are drawn from the School of Education. We collaborate with a range of researchers in the School of Geographical Sciences, Population Health Sciences and Bristol Veterinary School working with multilevel models.
Multilevel Modelling is one of the basic techniques used in quantitative social science research for modelling data with complex hierarchical structures. The Multilevel Modelling research theme focuses on producing new statistical methods for tackling research questions, developing new software for implementing this methodology and disseminating these techniques to the national and international social science community.
This project aims to refine and disseminate a recently proposed multilevel modelling approach to studying intersectionality of individual outcomes. That is, the notion that individuals' various social and political identities result in unique combinations of discrimination and privilege. More information
This project aims to better understand multilevel models for studying the stability of school value-added effects for school accountability and improvement. Particular focus will be on studying their sensitivity to sample size requirements, student mobility, and changing student intake sociodemographic composition. More information
This three-day course held in January and July every year provides an introduction to multilevel modelling and includes software practicals in your choice of software: MLwiN, R or Stata. We focus on multilevel modelling for continuous and binary responses (dependent or outcome variables) when the data are clustered (nested or hierarchical). More information
The Advanced Quantitative Methods (AQM) pathway of the SWDTP offers ESRC +3 and 1+3 postgraduate research training in the application of advanced quantitative methods in the social sciences and health. More information.
Proactive energy planners will address the challenges of variable renewable energy (VRE) integration directly, starting with long-term investment choices. Techno-economic assessments can help to inform policy development and set optimal targets for renewable power uptake. Scenario modelling, meanwhile, has become a critical planning tool for the power sector, with considerable knowledge being acquired in certain markets on how to represent VRE in long-term models.
Our comprehensive range of proprietary modelling software supports a wide variety of work including strategic research and development, environmental impact assessments, sediment and pollutant transport studies, storm surge forecasting, sea level rise and flood risk assessments, offshore engineering design, port and harbour design, maritime safety, coastal process simulation, and scheme design. With our team's expert knowledge of the complex ocean environment and the modelling methods that represent it, we provide efficient in-house solutions to help realise successful projects for our clients.
CHEMMAP is a chemical discharge modeling and response system that predicts the transport, fate, and biological impacts of a wide variety of chemical substances in the marine environment and atmosphere.
OILMAPLand is a land and surface water spill model system for simulating oil and chemical releases from pipelines and storage facilities. OILMAPLAND fills the need for a numerical modeling tool for oil and chemical spills that occur on land and then migrate to streams and lakes.
OILMAPWeb is a web based system for modelling oil releases. It provides rapid predictions of the movement of spilled oil. It includes simple procedures for simulating a spill using wind and hydrodynamic data and specifying spill parameters.
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Increasing interest in measuring, modelling and valuing ecosystem services (ES), the benefits that ecosystems provide to people, has resulted in the development of an array of ES assessment tools in recent years. Selecting an appropriate tool for measuring and modelling ES can be challenging. This document provides guidance for practitioners on existing tools that can be applied to measure or model ES provided by important sites for biodiversity and nature conservation, including Key Biodiversity Areas (KBAs), natural World Heritage sites (WHS), and protected areas (PAs). Selecting an appropriate tool requires identifying the specific question being addressed, what sorts of results or outputs are required, and consideration of practical factors such as the level of expertise, time and data required for applying any given tool. This guide builds on existing reviews of ES assessment tools, but has an explicit focus on assessing ES for sites of importance for biodiversity and nature conservation.
Mathematical Modelling of Natural Phenomena (MMNP) is an international research journal, which publishes top-level original papers, reviews and topical issues on mathematical modelling in biology, medicine, chemistry, physics, and other areas. Read more
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The coronavirus disease 2019 (COVID-19) pandemic is an ongoing crisis caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The first outbreak was detected in December 2019 in Wuhan, the capital of Hubei province, rapidly followed by the rest of Hubei and all other provinces in China. Within mainland China the epidemic was largely controlled by mid- to late March 2020, having generated >81,000 cases (cumulative incidence on 20 March 20201). This was primarily due to intense quarantine and social distancing (SD) measures, including: isolation of detected cases; tracing and management of their close contacts; closures of potential zoonotic sources of SARS-CoV-2; strict traffic restrictions and quarantine on the level of entire provinces (including suspension of public transportation, closures of airports, railway stations and highways within cities); cancellation of mass gathering activities; and other measures aimed to reduce transmission of the infection2,3,4.
Governments around the world are presently fighting the spread of COVID-19 within their jurisdictions by developing, applying and adjusting multiple variations on pandemic intervention strategies. While these strategies vary across nations, they share fundamental approaches that are adapted by national healthcare systems, aiming at a broad adoption within societies. In the absence of a COVID-19 vaccine, as pointed out by Ferguson et al.11, mitigation policies may include case isolation (CI) of patients and home quarantine (HQ) of their household (HH) members, SD of the individuals within specific age groups (e.g. the elderly, defined as >75 years), as well as people with compromised immune systems or other vulnerable groups. In addition, suppression policies may require an extension of CI and HQ with SD of the entire population. Often, such SD is supplemented by school and university closures.
Stochastic ABMs have been established as robust tools for tracing the fine-grained effect of heterogeneous intervention policies in diverse epidemic and pandemic settings7,8,12,13,14,15,16,17,18, including for policy advice currently in place in the USA and the UK11. In this study, we follow the ABM approach to quantitatively evaluate and compare several mitigation and suppression measures, using a high-resolution individual-based computational model calibrated to key characteristics of COVID-19 pandemics. The approach uses a modified and extended agent-based model, ACEMod (Australian Census-based Epidemic Model), previously developed and validated for simulations of pandemic influenza in Australia19,20,21,22. The epidemiological component, AMTraC-19, is developed and calibrated specifically to COVID-19 via reported invariants (outputs) such as the growth rate above. Importantly, our sensitivity analysis shows that key epidemiological outputs from our model (e.g. the growth rate, R0, generation time, etc.) are robust to uncertainty in the input parameters (e.g. the natural history of the disease, fraction of symptomatic cases, etc.).
The input parameters were calibrated to generate key characteristics in line with reported epidemiological data on COVID-19. We primarily calibrated by comparing these epidemiological characteristics to the mean of output variables, inferred from Monte Carlo simulations during non-intervention periods, with confidence intervals (CIs) constructed by bootstrapping (i.e. random sampling with replacement) with the bias-corrected percentile method23.
A combination of the case isolation (CI) and home quarantine (HQ) measures delays epidemic peaks and reduce their magnitude, in comparison to no interventions (NI), whereas school closures (SCs) have short-term effect. Several baseline and intervention scenarios, traced for a incidence, b prevalence, c cumulative incidence and d the daily growth rate of cumulative incidence \(\dotC\), shown as average (solid) and 95% confidence interval (shaded) profiles, over 20 runs. The 95% confidence intervals are constructed from the bias-corrected bootstrap distributions. The strategy with school closures combined with case isolation lasts 49 days (7 weeks), marked by a vertical dashed line. Restrictions on international arrivals are set to last until the end of each scenario. The alignment between simulated days and actual dates may slightly differ across separate runs.
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