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
Traingenerator 🧙 A web app to generate template code for machine learning ✨

Traingenerator 🧙 A web app to generate template code for machine learning ✨ 🎉 Traingenerator is now live! 🎉

Johannes Rieke 1.2k Jan 07, 2023
WAGMA-SGD is a decentralized asynchronous SGD for distributed deep learning training based on model averaging.

WAGMA-SGD is a decentralized asynchronous SGD based on wait-avoiding group model averaging. The synchronization is relaxed by making the collectives externally-triggerable, namely, a collective can b

Shigang Li 6 Jun 18, 2022
ThunderGBM: Fast GBDTs and Random Forests on GPUs

Documentations | Installation | Parameters | Python (scikit-learn) interface What's new? ThunderGBM won 2019 Best Paper Award from IEEE Transactions o

Xtra Computing Group 648 Dec 16, 2022
Used Logistic Regression, Random Forest, and XGBoost to predict the outcome of Search & Destroy games from the Call of Duty World League for the 2018 and 2019 seasons.

Call of Duty World League: Search & Destroy Outcome Predictions Growing up as an avid Call of Duty player, I was always curious about what factors led

Brett Vogelsang 2 Jan 18, 2022
CD) in machine learning projectsImplementing continuous integration & delivery (CI/CD) in machine learning projects

CML with cloud compute This repository contains a sample project using CML with Terraform (via the cml-runner function) to launch an AWS EC2 instance

Iterative 19 Oct 03, 2022
LightGBM + Optuna: no brainer

AutoLGBM LightGBM + Optuna: no brainer auto train lightgbm directly from CSV files auto tune lightgbm using optuna auto serve best lightgbm model usin

Rishiraj Acharya 22 Dec 15, 2022
Using Logistic Regression and classifiers of the dataset to produce an accurate recall, f-1 and precision score

Using Logistic Regression and classifiers of the dataset to produce an accurate recall, f-1 and precision score

Thines Kumar 1 Jan 31, 2022
PennyLane is a cross-platform Python library for differentiable programming of quantum computers

PennyLane is a cross-platform Python library for differentiable programming of quantum computers. Train a quantum computer the same way as a neural ne

PennyLaneAI 1.6k Jan 01, 2023
A simple machine learning package to cluster keywords in higher-level groups.

Simple Keyword Clusterer A simple machine learning package to cluster keywords in higher-level groups. Example: "Senior Frontend Engineer" -- "Fronte

Andrea D'Agostino 10 Dec 18, 2022
Data Version Control or DVC is an open-source tool for data science and machine learning projects

Continuous Machine Learning project integration with DVC Data Version Control or DVC is an open-source tool for data science and machine learning proj

Azaria Gebremichael 2 Jul 29, 2021
虚拟货币(BTC、ETH)炒币量化系统项目。在一版本的基础上加入了趋势判断

🎉 第二版本 🎉 (现货趋势网格) 介绍 在第一版本的基础上 趋势判断,不在固定点位开单,选择更优的开仓点位 优势: 🎉 简单易上手 安全(不用将api_secret告诉他人) 如何启动 修改app目录下的authorization文件

幸福村的码农 250 Jan 07, 2023
Microsoft contributing libraries, tools, recipes, sample codes and workshop contents for machine learning & deep learning.

Microsoft contributing libraries, tools, recipes, sample codes and workshop contents for machine learning & deep learning.

Microsoft 366 Jan 03, 2023
🔬 A curated list of awesome machine learning strategies & tools in financial market.

🔬 A curated list of awesome machine learning strategies & tools in financial market.

GeorgeZou 1.6k Dec 30, 2022
Houseprices - Predict sales prices and practice feature engineering, RFs, and gradient boosting

House Prices - Advanced Regression Techniques Predicting House Prices with Machine Learning This project is build to enhance my knowledge about machin

1 Jan 01, 2022
ETNA – time series forecasting framework

ETNA Time Series Library Predict your time series the easiest way Homepage | Documentation | Tutorials | Contribution Guide | Release Notes ETNA is an

Tinkoff.AI 675 Jan 08, 2023
Machine learning algorithms implementation

Machine learning algorithms implementation This repository consisits of implementation of various machine learning algorithms. The algorithms implemen

Karun Dawadi 1 Jan 03, 2022
Iris-Heroku - Putting a Machine Learning Model into Production with Flask and Heroku

Puesta en Producción de un modelo de aprendizaje automático con Flask y Heroku L

Jesùs Guillen 1 Jun 03, 2022
Penguins species predictor app is used to classify penguins species created using python's scikit-learn, fastapi, numpy and joblib packages.

Penguins Classification App Penguins species predictor app is used to classify penguins species using their island, sex, bill length (mm), bill depth

Siva Prakash 3 Apr 05, 2022
Scikit-learn compatible wrapper of the Random Bits Forest program written by (Wang et al., 2016)

sklearn-compatible Random Bits Forest Scikit-learn compatible wrapper of the Random Bits Forest program written by Wang et al., 2016, available as a b

Tamas Madl 8 Jul 24, 2021