15 skills found
ryansmcgee / SeirsplusModels of SEIRS epidemic dynamics with extensions, including network-structured populations, testing, contact tracing, and social distancing.
abhijitmjj / Prediction Of Epidemic Disease Dynamics Using Machine Learning ModelPrediction of epidemic disease spread using Machine Learning.
EpiModel / EpiModelCOVIDNetwork-Based Epidemic Modeling of Transmission Dynamics for SARS-CoV-2
juniper-consortium / Growth Rate EstimReproducible model for: "Bayesian Estimation of real-time Epidemic Growth Rates using Gaussian Processes: local dynamics of SARS-CoV-2 in England” (2022)
epiverse-trace / EpichainsMethods for simulating and analysing the sizes and lengths of infectious disease transmission chains from branching process models
Bahloulm / Fractional Order SEIQRDP Model For Simulating The Dynamics Of COVID 19 EpidemicFractional-order SEIQRDP Model for Simulating the Dynamics of COVID-19 Epidemic
1057499672 / Globalized Stochastic Meta Population SEIR ModelThe COVID-19 (COVID-19) epidemic has entered the era of globalization. As of September in 2020, 25.84 million people have been diagnosed globally. In order to help public health decision-makers improve their decision-making effectiveness, timeliness, and accuracy. Epidemic trajectory prediction and policy intervention simulation are useful tools, especially when vaccines are not yet available globally. SEIR model is a mainstream and developing dynamics model of infectious diseases, embodying the idea of differential, able to predict the future outbreak scenario based on initial data. This study further develops the traditional SEIR model by integrating human mobility and non-pharmaceutical interventions into the model. This model has four objectives: 1. Evaluate the effect of Wuhan shutdown policy (such as how much R0 has been reduced and how many cases have been avoided) 2. Assess the effectiveness of epidemic intervention policies in Wuhan, China, and western countries after the Wuhan shutdown 3. Evaluate the effects of non-drug interventions (such as inter-city travel restrictions, international travel ban, suspected-cases isolation and social-distance control) 4. Forecast the future trajectory of the epidemic in each region This model is significantly different from the traditional SEIR model in the following three aspects: 1. Allow people infected in the incubation period and those with symptoms to spread across regions (the scale of population migration is based on baidu migration platform and national transport database). In terms of regional scope, this paper covers 31 provinces in China and 13 western countries with severe epidemics. 2. Allow different levels of government policy intervention, including isolation, social distance control, and border closure. 3. Parameters such as basic infection number R0 are allowed to change with time. Therefore, the "global SEIR model", to some extent, avoids the staticity of the traditional SEIR model, simulating the real social environment better. This model can help public health event decision makers to make decisions, including the following three points: 1. In the early stage of the outbreak, it can assist decision makers to quickly make the most economic and effective policy intervention decisions, so as to control the development of the epidemic as soon as possible. 2. In the middle stage of epidemic development, decision-makers can be assisted to evaluate the effectiveness of initial intervention policies, so as to dynamically optimize and adjust policies based on feedback. 3. In the later stage of the epidemic, it can assist decision makers to assess the possibility of imported cases from abroad. Given the high variability of COVID-19 virus, the fact that vaccines are not yet globally available, and global medical resources are far away from adequate, it is necessary for policy makers in all countries to build a globalized SEIR model that integrated as many nations and regions as possible.
epiforecasts / Bpmodels[RETIRED. Use the epichains package instead]. Methods for simulating and analysing the sizes and lengths of chains from branching process models
tjxie / KUX PandemicMacroMacroeconomic dynamics and reallocation in an epidemic
jameshay218 / VirosolverR package to infer epidemic dynamics from virological data
SamuelBrand1 / Kenya Covid Three WavesModel supporting "COVID-19 Transmission Dynamics Underlying Epidemic Waves in Kenya"
LISA-ITMO / Epidemiological XAIResearch and analysis of interpretable AI and ML methods for modeling disease dynamics during an epidemic
Venn1998 / ComplexNetworksSimple projects to understand concepts from the Complex Network course at UOC: Structural Descriptors, Models of Complex Networks, Community Detection, Dynamics in CN (Epidemic simulation)
COG-UK / UK Lineage Dynamics AnalysisLarge-scale virus genome sequencing reveals the genetic structure and importation dynamics of a national COVID-19 epidemic.
StefanoMagni / Model COVID19 Dynamics Luxembourg Austria SwedenThis repository contains the codes associated with the study "Modelling COVID-19 dynamics and potential for herd immunity by vaccination in Austria, Luxembourg and Sweden", published in 2021 in Journal of Theoretical Biology: https://doi.org/10.1016/j.jtbi.2021.110874. The main authors of this code are Françoise Kemp (https://www.researchgate.net/profile/Francoise-Kemp) and Stefano Magni (https://www.researchgate.net/profile/Stefano-Magni-3). If you use our work please cite our journal publication, e.g. as "Françoise Kemp, Daniele Proverbio, Atte Aalto, Laurent Mombaerts, Aymeric Fouquier d’Hérouël, Andreas Husch, Christophe Ley, Jorge Gonçalves, Alexander Skupin, Stefano Magni, Modelling COVID-19 dynamics and potential for herd immunity by vaccination in Austria, Luxembourg and Sweden, Journal of Theoretical Biology, Volume 530, 2021, 110874, ISSN 0022-5193, https://doi.org/10.1016/j.jtbi.2021.110874". This model represents an extension of the epidemiological SEIR model to represent the spread of COVID-19 within a population. It has been extended to include 1) social interaction, 2) undetected cases, 3) disease progression through hospitalization, ICU and death and 4) vaccination. It is fit to time series public data of detected cases, hospital and ICU occupations and death, trough a literature-based parameter set cross validated by means of Bayesian inference and Markov Chain Monte Carlo methods. The model is separately fit to three countries: Luxembourg, Austria and Sweden, thus the repository contains 3 independent python codes, in the form of Jupyther notebooks. Also the time series data to which the model is fit are provided as 3 separate files in the repository, one for each country. The model is employed to investigate a number of topics through simulations, including the impact of social interaction on the epidemic dynamics, estimate of the effective reproduction number Reff(t), the impact of vaccination and the interplay of vaccines rollout speeds and social interaction in the pursuit of herd immunity. The first version of this work appeared as a preprint and is available at https://doi.org/10.1101/2020.12.31.20249088).
