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# DARt | ||
[![DOI](https://zenodo.org/badge/306890959.svg)](https://zenodo.org/badge/latestdoi/306890959) | ||
[![GitHub license](https://img.shields.io/github/license/Naereen/StrapDown.js.svg)](https://github.com/Kerr93/DARt/blob/master/LICENSE) | ||
![Safe](https://img.shields.io/badge/Stay-Safe-red?logo=data:image/svg%2bxml;base64,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) | ||
[![GitHub stars](https://img.shields.io/github/stars/Kerr93/DARt.svg?style=social&label=Star&maxAge=2592000)](https://github.com/Kerr93/DARt/stargazers) | ||
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### A tool to estimate Instantaneous Reproduction Number(Rt) | ||
cal_r()provide real-time estimation of time-varying distribution of | ||
Rt and infected numbers from a range of epidemic observations (e.g., number of onsets and confirmed cases). | ||
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test DARtTool via https://dsi-dart.shinyapps.io/covidrt/. | ||
**DARt: Estimate Real-time Infection and the Time-varying Epidemiological Parameter R<sub>t** | ||
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Online platform: https://dsi-dart.shinyapps.io/covidrt/. | ||
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## Installation | ||
```{r, eval = FALSE} | ||
pip install DARtTool | ||
``` | ||
## Usage | ||
dart = DARt(GT,D_s,Filename) | ||
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## Quick Start | ||
`DARtTool` is designed to be used with simple function calls, the core | ||
functions of `DARtTool`are `DARt()` and `car_r()`. In the following section we give an overview of the simple use case for`DARt()`and`cal_r()`. | ||
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cal_r() | ||
`DARt()` and `cal_r()` are the two single-call functions can be used on its own to infer the underlying infection case curve from reported cases and estimate Rt. | ||
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## Arguments | ||
GT: generation time distribution | ||
Firstly we need to define a DARt class, and initialize with three parameters: gt (generation time), ds(report of incubation delay) and an inputfile in csv format. | ||
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D_s: delay time distribution | ||
``` | ||
import DARtTool | ||
dart = DARtTool.DARt(filename='./inputfile.csv') | ||
``` | ||
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Filename:input file | ||
The function cal_r() represents the core functionality of the package aiming to: | ||
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## References: | ||
This tool is described in the following paper: | ||
1. infer the underlying infection case curve from observations and estimate Rt. | ||
2. provides visualisations of results. | ||
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## Examples | ||
``` | ||
dart.cal_r() | ||
``` | ||
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Estimating the underlying infection cases and Rt curve via smoothing is substantially computationally demanding than using filter but can provide reliable estimate. | ||
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## Citation | ||
If you use this tool in your research, please cite our paper. | ||
```bibtex | ||
@misc{yang2020revealing, | ||
title={Revealing the Transmission Dynamics of COVID-19: A Bayesian Framework for $R_t$ Estimation}, | ||
author={Xian Yang and Shuo Wang and Yuting Xing and Ling Li and Richard Yi Da Xu and Karl J. Friston and Yike Guo}, | ||
year={2020}, | ||
eprint={2101.01532}, | ||
archivePrefix={arXiv}, | ||
primaryClass={stat.AP} | ||
} | ||
``` | ||
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GT = [0, 0, 0.165720874545241, 0.226350757019051, 0.245007574714227, 0.213515210247327, | ||
0.149405583474155] # 1:7; non-zero index 3:7 | ||
D_s = [0, 0, 0, 0, 0, 0, 0.0996906, 0.1130266, 0.1143032, 0.1069238, 0.0937167999999999] | ||
dart = DARt(GT=GT, D_s=D_s, filename='uk_report_1011.csv') | ||
dart.cal_r() | ||
## License | ||
This source code is licensed under the [MIT](https://github.com/Kerr93/DARt/blob/master/LICENSE) license. | ||
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