Simulation of early COVID-19 using SIR model and variants (SEIR ...).

Overview

COVID-19-simulation

Simulation of early COVID-19 using SIR model and variants (SEIR ...). Made by the Laboratory of Sustainable Life Assessment (GYRO) of the Federal Technologycal University - Parana (UTFPR-ct) in the scope of the project GYRO4Life

Running the simulation

The code runs based on a csv with the same structure of nc85.csv or oa85.csv files which has a time series of confirmed cases and deaths and metadata information about the region being characterized on the line. Both cases and deaths have to be given for the simulation.

The main code is simulação.py, which receives a couple of arguments:

  • 1: region code (for the csv being used). In case the argument is empty ("-"), it will run for all lines of the csv [ex: -28]
  • 2: Name of the csv file with confirmed cases (omit the '.csv') [ex: nc85.csv -> -nc85]
  • 2: Name of the csv file with confirmed deaths (omit the '.csv') [ex: oa85.csv -> -oa85]
  • 3: Fitting method [-0: basinhopp, -1: differential evolution [default], -2: powell, -3: cobyla] [ex: -1]
  • 4: Boolean and quantity of opening and closure regimes for the simulation for confirmed cases (works as a contingency method reducing the probability of infection). '-0-0' ignores this factor for a simulation without contingency methods. If a quantity is given on the second argument, the boolean argument must be 1 [ex: '-1-1']
  • 5: Boolean and quantity of opening and closure regimes for the simulation for confirmed deaths (works as a contingency method reducing the probability of infection). '-0-0' ignores this factor for a simulation without contingency methods. If a quantity is given on the second argument, the boolean argument must be 1 [ex: '-1-1']
  • 6: Type of simulation [-n: simulation of one location (one csv line), -s: simulation of all csv locations, -b: bootstrap of one location [has uncertainty], -sl: simulation of a location with sensibility analysis] [ex: -n]
  • 7: Simulation period in days [ex: -200]
  • 8: number of days for validation [ex: -5]
  • 9: Subtype of simulation [-mod: hospitalization simulation, -std: SEIR simulation with asymptomatic and deaths]
  • 10: Run tests and additional graphics [-0: no, -1: yes]

Example call for a SEIR simulation with bootstrap using cases and deaths in Brazil. The simulation is done for 200 days and with a validation of 5 days.

python simulacao.py -28 -nc85 -oa85 -1 -1-2-0-0 -b -200 -5 -str -0
Owner
José Paulo Pereira das Dores Savioli
José Paulo Pereira das Dores Savioli
A repository to work on Machine Learning course. Select an algorithm to classify writer's gender, of Hebrew texts.

MachineLearning A repository to work on Machine Learning course. Select an algorithm to classify writer's gender, of Hebrew texts. Tested algorithms:

Haim Adrian 1 Feb 01, 2022
Evaluate on three different ML model for feature selection using Breast cancer data.

Anomaly-detection-Feature-Selection Evaluate on three different ML model for feature selection using Breast cancer data. ML models: SVM, KNN and MLP.

Tarek idrees 1 Mar 17, 2022
An MLOps framework to package, deploy, monitor and manage thousands of production machine learning models

Seldon Core: Blazing Fast, Industry-Ready ML An open source platform to deploy your machine learning models on Kubernetes at massive scale. Overview S

Seldon 3.5k Jan 01, 2023
Real-time domain adaptation for semantic segmentation

Advanced-Machine-Learning This repository contains the code for the project Real

Andrea Cavallo 1 Jan 30, 2022
Required for a machine learning pipeline data preprocessing and variable engineering script needs to be prepared

Feature-Engineering Required for a machine learning pipeline data preprocessing and variable engineering script needs to be prepared. When the dataset

kemalgunay 5 Apr 21, 2022
Pandas DataFrames and Series as Interactive Tables in Jupyter

Pandas DataFrames and Series as Interactive Tables in Jupyter Star Turn pandas DataFrames and Series into interactive datatables in both your notebook

Marc Wouts 364 Jan 04, 2023
ClearML - Auto-Magical Suite of tools to streamline your ML workflow. Experiment Manager, MLOps and Data-Management

ClearML - Auto-Magical Suite of tools to streamline your ML workflow Experiment Manager, MLOps and Data-Management ClearML Formerly known as Allegro T

ClearML 4k Jan 09, 2023
Simple Machine Learning Tool Kit

Getting started smltk (Simple Machine Learning Tool Kit) package is implemented for helping your work during data preparation testing your model The g

Alessandra Bilardi 1 Dec 30, 2021
Combines Bayesian analyses from many datasets.

PosteriorStacker Combines Bayesian analyses from many datasets. Introduction Method Tutorial Output plot and files Introduction Fitting a model to a d

Johannes Buchner 19 Feb 13, 2022
ETNA is an easy-to-use time series forecasting framework.

ETNA is an easy-to-use time series forecasting framework. It includes built in toolkits for time series preprocessing, feature generation, a variety of predictive models with unified interface - from

Tinkoff.AI 674 Jan 07, 2023
Kalman filter library

The kalman filter framework described here is an incredibly powerful tool for any optimization problem, but particularly for visual odometry, sensor fusion localization or SLAM.

comma.ai 276 Jan 01, 2023
Machine Learning toolbox for Humans

Reproducible Experiment Platform (REP) REP is ipython-based environment for conducting data-driven research in a consistent and reproducible way. Main

Yandex 663 Dec 31, 2022
Binary Classification Problem with Machine Learning

Binary Classification Problem with Machine Learning Solving Approach: 1) Ultimate Goal of the Assignment: This assignment is about solving a binary cl

Dinesh Mali 0 Jan 20, 2022
A machine learning project that predicts the price of used cars in the UK

Car Price Prediction Image Credit: AA Cars Project Overview Scraped 3000 used cars data from AA Cars website using Python and BeautifulSoup. Cleaned t

Victor Umunna 7 Oct 13, 2022
Self Organising Map (SOM) for clustering of atomistic samples through unsupervised learning.

Self Organising Map for Clustering of Atomistic Samples - V2 Description Self Organising Map (also known as Kohonen Network) implemented in Python for

Franco Aquistapace 0 Nov 16, 2021
Data Efficient Decision Making

Data Efficient Decision Making

Microsoft 197 Jan 06, 2023
This is a curated list of medical data for machine learning

Medical Data for Machine Learning This is a curated list of medical data for machine learning. This list is provided for informational purposes only,

Andrew L. Beam 5.4k Dec 26, 2022
Dual Adaptive Sampling for Machine Learning Interatomic potential.

DAS Dual Adaptive Sampling for Machine Learning Interatomic potential. How to cite If you use this code in your research, please cite this using: Hong

6 Jul 06, 2022
A Pythonic framework for threat modeling

pytm: A Pythonic framework for threat modeling Introduction Traditional threat modeling too often comes late to the party, or sometimes not at all. In

Izar Tarandach 644 Dec 20, 2022