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
scikit-learn is a python module for machine learning built on top of numpy / scipy

About scikit-learn is a python module for machine learning built on top of numpy / scipy. The purpose of the scikit-learn-tutorial subproject is to le

Gael Varoquaux 122 Dec 12, 2022
Implemented four supervised learning Machine Learning algorithms

Implemented four supervised learning Machine Learning algorithms from an algorithmic family called Classification and Regression Trees (CARTs), details see README_Report.

Teng (Elijah) Xue 0 Jan 31, 2022
Python package for stacking (machine learning technique)

vecstack Python package for stacking (stacked generalization) featuring lightweight functional API and fully compatible scikit-learn API Convenient wa

Igor Ivanov 671 Dec 25, 2022
Quantum Machine Learning

The Machine Learning package simply contains sample datasets at present. It has some classification algorithms such as QSVM and VQC (Variational Quantum Classifier), where this data can be used for e

Qiskit 364 Jan 08, 2023
fMRIprep Pipeline To Machine Learning

fMRIprep Pipeline To Machine Learning(Demo) 所有配置均在config.py文件下定义 前置环境(lilab) 各个节点均安装docker,并有fmripre的镜像 可以使用conda中的base环境(相应的第三份包之后更新) 1. fmriprep scr

Alien 3 Mar 08, 2022
MaD GUI is a basis for graphical annotation and computational analysis of time series data.

MaD GUI Machine Learning and Data Analytics Graphical User Interface MaD GUI is a basis for graphical annotation and computational analysis of time se

Machine Learning and Data Analytics Lab FAU 10 Dec 19, 2022
Decentralized deep learning in PyTorch. Built to train models on thousands of volunteers across the world.

Hivemind: decentralized deep learning in PyTorch Hivemind is a PyTorch library to train large neural networks across the Internet. Its intended usage

1.3k Jan 08, 2023
Deepchecks is a Python package for comprehensively validating your machine learning models and data with minimal effort

Deepchecks is a Python package for comprehensively validating your machine learning models and data with minimal effort

2.3k Jan 04, 2023
Turns your machine learning code into microservices with web API, interactive GUI, and more.

Turns your machine learning code into microservices with web API, interactive GUI, and more.

Machine Learning Tooling 2.8k Jan 02, 2023
Contains an implementation (sklearn API) of the algorithm proposed in "GENDIS: GEnetic DIscovery of Shapelets" and code to reproduce all experiments.

GENDIS GENetic DIscovery of Shapelets In the time series classification domain, shapelets are small subseries that are discriminative for a certain cl

IDLab Services 90 Oct 28, 2022
Predicting Keystrokes using an Audio Side-Channel Attack and Machine Learning

Predicting Keystrokes using an Audio Side-Channel Attack and Machine Learning My

3 Apr 10, 2022
Machine Learning approach for quantifying detector distortion fields

DistortionML Machine Learning approach for quantifying detector distortion fields. This project is a feasibility study for training a surrogate model

Joel Bernier 1 Nov 05, 2021
Time series changepoint detection

changepy Changepoint detection in time series in pure python Install pip install changepy Examples from changepy import pelt from cha

Rui Gil 92 Nov 08, 2022
Automated Time Series Forecasting

AutoTS AutoTS is a time series package for Python designed for rapidly deploying high-accuracy forecasts at scale. There are dozens of forecasting mod

Colin Catlin 652 Jan 03, 2023
Book Item Based Collaborative Filtering

Book-Item-Based-Collaborative-Filtering Collaborative filtering methods are used

Şebnem 3 Jan 06, 2022
A library to generate synthetic time series data by easy-to-use factors and generator

timeseries-generator This repository consists of a python packages that generates synthetic time series dataset in a generic way (under /timeseries_ge

Nike Inc. 87 Dec 20, 2022
Arquivos do curso online sobre a estatística voltada para ciência de dados e aprendizado de máquina.

Estatistica para Ciência de Dados e Machine Learning Arquivos do curso online sobre a estatística voltada para ciência de dados e aprendizado de máqui

Renan Barbosa 1 Jan 10, 2022
A simple and lightweight genetic algorithm for optimization of any machine learning model

geneticml This package contains a simple and lightweight genetic algorithm for optimization of any machine learning model. Installation Use pip to ins

Allan Barcelos 8 Aug 10, 2022
Test symmetries with sklearn decision tree models

Test symmetries with sklearn decision tree models Setup Begin from an environment with a recent version of python 3. source setup.sh Leave the enviro

Rupert Tombs 2 Jul 19, 2022
Machine Learning Model to predict the payment date of an invoice when it gets created in the system.

Payment-Date-Prediction Machine Learning Model to predict the payment date of an invoice when it gets created in the system.

15 Sep 09, 2022