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# DARt
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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).

test DARtTool via https://dsi-dart.shinyapps.io/covidrt/.
**DARt: Estimate Real-time Infection and the Time-varying Epidemiological Parameter R<sub>t**

Online platform: https://dsi-dart.shinyapps.io/covidrt/.

## Installation
```{r, eval = FALSE}
pip install DARtTool
```
## Usage
dart = DARt(GT,D_s,Filename)

## 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()`.

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.

## 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.

D_s: delay time distribution
```
import DARtTool
dart = DARtTool.DARt(filename='./inputfile.csv')
```

Filename:input file
The function cal_r() represents the core functionality of the package aiming to:

## 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.

## Examples
```
dart.cal_r()
```

Estimating the underlying infection cases and Rt curve via smoothing is substantially computationally demanding than using filter but can provide reliable estimate.

## 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}
}
```

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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