Description:This repository contains the software code and the documentation of a model that sets the frequencies of public transport lines and derives the Pareto optimal demand management strategies considering the pandemic-imposed vehicle capacities due to COVID-19.Referencing:In case you use this code for scientific purposes, you can cite the paper "A demand management and frequency settings model for complying to the COVID-19 capacity limits in public transport operations" once it is publicly available.License:MIT LicenseDependencies:Note that the script is written in Python. Running or modifying this script requires an installed version of Python 3.6. In addition, the mathematical model is solved with the use of Gurobi 9.0.3. You would need a Gurobi license to obtain an optimal solution.To implement the model, you would need to create text files with your dataset following the provided documentation.
The dataset contains results of experiments for the study of our research https://arxiv.org/abs/2011.12770. Data is used to generate results with this reproducible notebook: https://github.com/RafalKucharskiPK/ExMAS/blob/master/ExMAS/spinoffs/corona/02_plots.ipynbFiles:- png with maps- .csv with epidemic modelling results (For any given day, the model outputs information about the number of travellers in each state (S-I-Q-R) and newly infected travellers, based on which we can reproduce epidemic spreading profiles.)Abstract of the study:Urban mobility needs alternative sustainable travel modes to keep our pandemic cities in motion. Ride-pooling, where a single vehicle is shared by more than one traveller, is not only appealing for mobility platforms and their travellers, but also for promoting the sustainability of urban mobility systems. Yet, the potential of ride-pooling rides to serve as a safe and effective alternative given the personal and public health risks considerations associated with the COVID-19 pandemic is hitherto unknown. To answer this, we combine epidemiological and behavioural shareability models to examine spreading among ride-pooling travellers, with an application for Amsterdam. Findings are at first sight devastating, with only few initially infected travellers needed to spread the virus to hundreds of ride-pooling users. Without intervention, ride-pooling system may substantially contribute to virus spreading. Notwithstanding, we identify an effective control measure allowing to halt the spreading before the outbreaks (at 50 instead of 800 infections) without sacrificing the efficiency achieved by pooling. Fixed matches among co-travellers disconnect the otherwise dense contact network, encapsulating the virus in small communities and preventing the outbreaks.
OperationAIR is a student team of Delft University of Technology that developed a simplified ventilator: the AIRone. It was developed as a non-certified, advanced emergency ventilator at times of a national shortage due to the covid-19 pandemic, in close contact with experts from Leiden University Medical Center and Erasmus Medical Center. This repository contains the hardware and software design data that can be used to recreate the project. Further more, the various documents developed for the ventilator including the user manual, technical and design specifications and lessons learnt are also archived herewith. This resource intends to give other researchers a head start on their design process. The files give an insight in the process and can prevent you from repeating the same mistakes.
Code used together with its results for the paper as part of my Bachelor's Thesis project. The research consists of optimizing the kallisto algorithm for predicting the abundances of SARS-CoV-2 variants in wastewater samples. Specifically, I look at how only sequencing certain regions of the genome influences the prediction accuracy of this pipeline.
Description:This repository contains the software code related to deriving dynamic service patterns in order to comply with the pandemic-imposed vehicle capacity limits in public transport operations.Currently, this repository contains:The model_case_study.py script which is the source code of the devised dynamic service pattern model introduced in the paper "A model for modifying the public transport service patterns to account for the imposed COVID-19 capacity", which is currently under scientific review. This script contains all necessary functions to calculate the solution of the mathematical program for the scenario described in the case study of the scientific paper.The model_demonstration.py script that contains the implementation of the demonstration scenario in the aforementioned scientific paper.Referencing:In case you use this code for scientific purposes, you can cite the paper "A model for modifying the public transport service patterns to account for the imposed COVID-19 capacity" once it is publicly available.License:MIT LicenseDependencies:Note that the script model_case_study.py is written in Python. Running or modifying this script requires an installed version of Python 3.6. In addition, the mathematical model is solved with the use of Gurobi 9.0.3. You would need a Gurobi license to obtain an optimal solution.Research Project:This software code is developed in the research programme 'COVID 19 Wetenschap voor de Praktijk', project number:10430042010018. The project is funded by the Dutch Research Organization for Health Research and Development (ZonMw)