Network based pandemic modeling

Beregningsbasert epidemiologi er et stort felt med mange tilnærminger og verktøy for modellering av pandemier. Disse tilnærmingene varierer fra statistisk analyse og differensialligninger, agentbasert modellering og til og med hybridmetoder som kombinerer de ovennevnte. Denne avhandlingen sikter mot å utvide verktøykassen med et nytt verktøysett som integrerer grafteori og agentbaserte metoder. Dette gjør at man kan utforske et bredt spekter av innstillinger og miljøer som endrer innvirkningen og utviklingen av patogen aktivitet i en gitt befolkning. Avhengig av det gitte miljøet, vil patogenet forsøke å optimalisere seg selv basert på ytelsesfunksjonsvekter som blir gitt som en input til systemet gjennom bruk av en variasjon av den genetiske algoritmen. Systemets fleksible natur gir opphav til mange forskjellige scenarier som kan testes. Systemvariablene varierer fra befolkningens sosiale struktur gjennom forskjellige nettverkstopologier, simulerte immunrespons med statiske og dynamiske befolkninger, sykdomsisolasjonspolitikk og mer.Computational epidemiology is a large field with many approaches and toolkits for modeling pandemics. These approaches ranges from statistical analysis and differential equations, agent- based modeling and even hybrid methods combining the aforementioned ones. This thesis aims to expand the toolkit with a novel toolset that integrates graph theory and agent-based methods. This allows one to explore a wide range of settings and environments that changes the impact and development of pathogen activity in a given population. Depending on the environment provided, the pathogen will attempt to optimize itself based performance function weights provided as an input to the system through the use of a variation of the genetic algorithm. The flexible nature of the system gives rise to numerous different scenarios that can be tested. The system variables range from the social structure of the population through different network topologies, simulated immune responses with static and dynamic populations, sickness isolation policy and more

Improving Safety through Leveraging Machine Learning and Safety-Related Data in the Construction Industry

This study presents a conceptual framework for integrating safety-related data with machine learning to improve its understanding of safety performance and construction safety management. Machine Learning techniques could discover latent hazards and risks by utilizing project-specific safety-related data and improve safety and decision-making processes. Findings suggest that machine learning can significantly improve safety performance by proactively identifying risks and measures from safety-related data rather than relying solely on historical safety outcomes and data. This could also provide a better understanding of the forthcoming construction projects' complex challenges and the impact of increasingly technical and organizational complexities on safety. However, challenges such as data compatibility, lack of standardization, misinformation risks, and ethical concerns (transparency, privacy, and fairness) necessitate a cautious approach to the use of machine learning. This proactive approach could lead to safer construction environments and continuous improvements in safety management. Future work will refine data collection and develop predictive models, with the current research in the 'DiSCo' project aiming for sustainable safety improvements in the construction industry.publishedVersio

Monitoring the Norwegian Atlantic slope current using a single moored current meter

Monitoring the Atlantic inflow (AI) of warm and saline water into the Nordic Seas (Norwegian, Greenland and Iceland Seas) is of great importance becauce of its impact on climate and ecology in Northern Europe and Arctic. In this study, an observation system for establishment of simple, robust and cost effective monitoring of the AI is validated in the Svinøy section, cutting through the AI just to the north of the Faroe-Shetland Channel. We concentrate on the eastern branch of the AI, the Norwegian Atlantic Slope Current (NwASC), an about 40km wide flow along the steep Norwegian slope. The database is an array of 15 current meters on 4 moorings covering the NwASC over a 2-year period 1998–2000. We test the hypothesis that long-term monitoring of the NwASC can be performed by using one single current meter suitable placed in the flow. The volume flux can then be estimated by construction of simple regression models using the single current meter record as the independent variable. For validation of statistical properties as stability, confidence and stationarity, the time series is split into two 1-year segments: a model period and a test period. Gridded correlation fields between currents and volume transport show correlation maxima in the core of the NwASC, ranging from 0.84 on a daily timescale to 0.97 on a monthly timescale. A more comprehensive correlation/ coherence analysis for each current meter record against volume transport on 7-day timescales, enable us to choose the optimal current meter for a linear regression model with (correlation, slope) coefficients of (0.87, 0.13) for the model period and (0.80, 0.13) for the test period. The similarity of the statistical properties for the model and test periods substantiates the stationarity, stability and robustness of the model. A linear regression model underestimates large fluxes and is thus extended to a second degree polynomia. This improves the curve fitting for strong currents with a minor increase in overall correlation, but is more sensistive and less stable. Overall, we find a linear regression model to be more robust and applicable for monitoring the NwASC. The applicability of a linear regression model as an estimator for volume flux of the NwASC is demonstrated using a 2-year time series, and validated against calculated transport. The calculated transport agrees with the statistical analysis and reveals a noisy fit on daily timescale, while the curves coincide well on both 7- and 30-day timescales with correlation coefficients of 0.84 and 0.86, respectively. On all timescales, the calculated and model transport give an overall mean flow of 4.4 Sv and show fluctuations on timescales of days to months, with the seasonal cycle being the most prominent

Anatomy of a Dansgaard-Oeschger warming transition: High-resolution analysis of the North Greenland Ice Core Project ice core

Large and abrupt temperature oscillations during the last glacial period, known as Dansgaard‐Oeschger (DO) events, are clearly observed in the Greenland ice core record. Here we present a new high‐resolution chemical (2 mm) and stable isotope (20 mm) record from the North Greenland Ice Core Project (NGRIP) ice core at the onset of one of the most prominent DO events of the last glacial, DO‐8, observed ∼38,000 years ago. The unique, subannual‐resolution NGRIP record provides a true sequence of change during a DO warming with detailed annual layer counting of very high depth resolution geochemical measurements used to determine the exact duration of the transition. The continental ions, indicative of long‐range atmospheric loading and dustiness from East Asia, are the first to change, followed by the snow accumulation, the moisture source conditions, and finally the atmospheric temperature in Greenland. The sequence of events shows that atmospheric and oceanic source and circulation changes preceded the DO warming by several years

The Return of Lake Agassiz: The University of North Dakota and the Flood of 1997

On April 19, 1997, Grand Forks and East Grand Forks were overtaken by the largest flood seen in the cities in modern times. Tens of thousands abandoned their homes. Scattered throughout the region, they could do little more than wait for news reports and worry about what they would find when the waters subsided. At the University of North Dakota, the academic year had come to an abrupt end. Students, faculty and staff were among the evacuated. A small group of UND personnel now had to decide how to protect, as much as possible, an institution worth hundreds of millions of dollars.https://commons.und.edu/und-books/1036/thumbnail.jp