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app.py
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"""
Main application script.
"""
import base64
import io
# 3rd-party
import json
from datetime import date
import dash
import dash_bootstrap_components as dbc
import dash_core_components as dcc
import dash_html_components as html
import numpy as np
import pandas as pd
import plotly.graph_objs as go
from dash.dependencies import ALL, Input, Output, State
from plotly.subplots import make_subplots
from scipy.integrate import solve_ivp
# Local Library
import sample
# Header
external_stylesheets = [
"https://codepen.io/chriddyp/pen/bWLwgP.css",
dbc.themes.BOOTSTRAP,
]
app = dash.Dash(
__name__,
external_stylesheets=external_stylesheets,
title="COVID-19 Modelling with SEIQHCDRO",
suppress_callback_exceptions=True,
)
server = app.server
# Some frequently used CSS across different HTML elements
styles = {"pre": {"border": "thin lightgrey solid", "overflowX": "scroll"}}
colors = {"background": "#111111", "color": "#7FDBFF"}
tab = {"padding": "1%"}
PLOTLY_LOGO = "https://images.plot.ly/logo/new-branding/plotly-logomark.png"
# Overall page layout
app.layout = html.Div(
[
dcc.Location(id="url", refresh=False),
dbc.Navbar(
[
# Use row and col to control vertical alignment of logo / brand
dbc.Row(
[
dbc.Col(html.Img(src=PLOTLY_LOGO, height="30px")),
dbc.Col(
dbc.NavbarBrand(
"COVID-19 modelling with SEIQHCDRO",
className="ml-2",
style={"font-size": "20px", "vertical-align": "center"},
)
),
dbc.Col(
dbc.NavLink(
"Home",
href="/",
className="text-light font-weight-bold",
style={"font-size": "15px", "vertical-align": "center"},
)
),
dbc.Col(
dbc.NavLink(
"About",
href="/about",
className="text-light font-weight-bold",
style={"font-size": "15px", "vertical-align": "center"},
)
),
],
align="center",
no_gutters=True,
),
],
color="dark",
dark=True,
style={"text-decoration": "none", "color": "white"},
),
html.Div(id="page-content"),
html.Footer(
[
"Copyright \u00a92021 SEIQHCDRO COVID-19 Interactive Modelling Tool by Hoang Anh NGO, Tuan-Khoi NGUYEN and Thu-Anh NGUYEN",
html.Div(
[
"Visitors: ",
html.Img(
src="https://counter2.stat.ovh/private/freecounterstat.php?c=xj7lgw2xsbsyk917b5wrbr45ldmx56u6",
style={"width": "12%"},
),
],
style={
"width": "100%",
"text-align": "center",
"margin": "1% 0 0 0",
},
),
],
style={
"width": "100%",
"text-align": "center",
#'border-style':'outset',
"background-color": "#343a40",
"padding": "2%",
},
),
]
)
# About page
about_page = html.Div(
[
dcc.Markdown(
"""
#### SEIQHCDRO COVID-19 <br> INTERACTIVE MODELLING TOOL
# Introduction
In the past outbreaks, Vietnam has successfully controlled the COVID-19 pandemic by simultaneously applying numerous strategies,
including aggressive contact tracing, mandatory quarantine, routine testing, etc. To quantify the effectiveness of these measures,
we developed a multi-compartment model that integrates all of these practices to estimate impacts of possible mitigation scenarios
on the COVID-19 outbreak. To do that, we extended the traditional SEIR model into a 9-compartment model SEIQHCDRO with
S (Susceptible), E (Exposed), I (Infected), Q (Quarantined), H (Hospitalized), C (Critical), D (Death), R (Recovered)
and O (Other - Recovered).
Moreover, in order to turn our research
into an open-source project so that everyone can have access to, while creating a tool so that policy makers/public health specialists have a
tool to facilitate their policy advocate/policy making process, we decided to create this tool, alongside with publicizing all associated
source code.
# Formation of the model
## Model Flowchart
As an multi-compartment epidemiological model, there must exist specific relations between each and every single compartment.
Such relations are expressed though the model flowchart below
""",
dangerously_allow_html=True,
),
html.Div(
[
html.Img(
src="https://drive.google.com/uc?export=view&id=1nb9DFzmOBdlbp8eSaMUsKA45owrYauf_",
style={"width": "50%", "fill": "#000"},
)
],
style={"width": "100%", "text-align": "center"},
),
dcc.Markdown(
"""
## Formula
From this, we develop a system of differential equations to simulate the relationship between these compartments. The system reads:
"""
),
html.Div(
[
html.Img(
src="https://drive.google.com/uc?export=view&id=1I6YFfapQGGgh7Cdq_AST_fmCWVaGWTZk",
style={"width": "40%", "fill": "#000"},
)
],
style={"width": "100%", "text-align": "center"},
),
dcc.Markdown(
"""
with two main types of hyper-parameters
* Proportion-related hyper-parameters `p`;
* Time interval related hyperparameters `T`.
To obtain the result, this system of ordinary differential equations (ODEs) will be solved using the `solve_ivp` command within the `SciPy` package
in Python. To prevent any stiffness of the system, the `Radau` method, i.e the implicit Runge-Kutta method of the Radau IIA family of order 5.
## Formulation of the basic reproduction number `R_0`
One of the most important aspects of this model is the ability to capture different levels of social distancing/lockdown to the spread
of the disease. As such, we have integrated these impacts onto the function representing the effective reproduction number `R_t` (i.e the basic reproduction
number `R_0` with respect to time).
"""
),
html.Div(
[
html.Img(
src="https://drive.google.com/uc?export=view&id=1r76mV3WO22H-Xm5wT_uDU4lHMpaKQxJh",
style={"width": "15%", "fill": "#000"},
)
],
style={"width": "100%", "text-align": "center"},
),
dcc.Markdown(
"""
Assume that there exists two consecutive time intervals separated by a policy scheme change at time `T`. Before time `T`, the population
inherits a scheme with change of the basic reproduction number `delta R_0`, contact rate reduction `p_cont` and contact rate reduction due
to journalism `p_jrnl`. After time `T`, the population now inherits a new scheme with a new set of parameters, `delta R'_0`, `p'_cont` and
`p'_jrnl`, respectively.
There are two cases that would happen:
* When `p'_cont >= p_cont` (i.e. the social distancing/lockdown measure tightens), the new function is:
"""
),
html.Div(
[
html.Img(
src="https://drive.google.com/uc?export=view&id=1PyMewJ3MVu30Ot5Qg-EvqmTm6GqtGLE9",
style={"width": "40%", "fill": "#000"},
)
],
style={"width": "100%", "text-align": "center"},
),
dcc.Markdown(
"""
* When `p'_cont < p_cont` (i.e. the social distancing/lockdown measure loosens), the function now becomes:
"""
),
html.Div(
[
html.Img(
src="https://drive.google.com/uc?export=view&id=1IBcmRAffnW26DYXZtCqby9U-w0qpn9eF",
style={"width": "40%", "fill": "#000"},
)
],
style={"width": "100%", "text-align": "center"},
),
dcc.Markdown(
"""
# Tool features
## Modelling features
In order to create the most interactive and convenient tool possible for COVID-19 modelling, we have decided to opt for
a number of features that makes it more comfortable for users to interact with this web application, including
* The ability to capture 30 different policy schemes in place, corresponding to 30 consecutive time intervals. This would
be helpful for policy advocates to simulate effects of different policy making processes.
* 3 pairs of plots representing different aspect of the outbreak with built-in gadgets of `Plotly` for generated plots, including:
- **Overall trend of infection**: Incidence and cumulative number of hospitalized/infected COVID-19 cases,
- **Critical and fatal cases**: Number of active critical cases daily and cumulative number of deceased cases,
- **Spread and containment**: Effective reproduction number (Basic reproduction number by day) and total number of quarantined individuals.
* Ability to export all statistical data of a newly calibrated model in a personalized file name, including
- Information summary of the model (in either a .txt file or a .json file for further uploading and re-calibration);
- Total hospitalized/infected/critical/fatal cases in a single CSV file.
* Comparision with the current capacity for the number of quarantined/hospitalized cases
## Cross-checking and testing features
To facilitate the validation and cross-checking of models between users, we provide the following additional features:
* Samples .json files of the previous outbreaks in Vietnam, including the outbreaks in
- Danang (07-08/2020);
- Hai Duong (01-03/2021);
- Bac Giang (05-07/2021); and
- Ho Chi Minh (05/2021 onwards) with 3 scenarios: best, moderate and worst.
* Import (Upload) a .json file that includes all information of the previously calibrated model to continue further moedification
without having to restart the whole process from the beginning;
* Upload a csv file of the actual situation and illustrate it alongside with the prediction model. The uploaded csv file can include
up to 5 following fields:
- Number of new (incidence) cases per day (`daily_infected`);
- Total (cumulative) number of cases (`cumulative_infected`);
- Number of active critical cases (`active_critical`);
- Total (cumulative) number of deaths (`cumulative_deaths`);
- Number of active quarantined individuals (`active_quarantined`).
"""
),
dcc.Markdown(
"""
# Mentions
Up until now, our research project has been featured at two major conferences in lung health, public health and epidemiology, including:
- [The 52nd World Union Conference on Lung Health](https://theunion.org/our-work/conferences/52nd-union-world-conference-on-lung-health?fbclid=IwAR3DAw1R3eA8L0Jv0cr0aUtoqFJwESIHNvdGCyBKzkPF5KFbsUcXTOOK-ZM);
- [8th Vietnam Lung Association (VILA) Scientific Conference 2021](https://drive.google.com/file/d/10uBQJATgEIVgIAbQdp9n4oWGGYqOiKf-/view?fbclid=IwAR3bNTaz_4UKZxa2TaN5ge6FSJL88vURX7xzFURjkE0J-B4hxuKT_3ZZ3vQ).
Moreover, we also have a Markdown version ready for submission to Journal of Open Source Software. This can be found at the associated
[paper.md](https://github.com/tuankhoin/SEIQHCDRO-Interactive-Model/blob/main/paper.md) file within this repository.
Besides, our research has also been featured on a lot of renowned newspapers in Vietnam, including Tuoi tre, VNExpress, Soha, Viet Nam News, etc. as a major tool
for our policy advocates to fight against the COVID-19 pandemic in Vietnam.
# Citation
If SEIQHCDRO multi-compartment model in general or the interactive modelling website has been useful for your research and policy advocacy, and you would like to cite it in an scientific publication, please refer to our presentation at the **52nd Union World Conference on Lung Health** as follows:
"""
),
dcc.Markdown(
"""
```bibtex
@inproceedings{seiqhcdro-2021,
author = {Hoang Anh NGO and Tuan-Khoi NGUYEN and Thu-Anh NGUYEN},
title = {A novel compartment model for analyzing and predicting COVID-19 outbreaks in Vietnam},
booktitle = {Abstract Book, 52nd Union World Conference on Lung Health of the International Union Against Tuberculosis and Lung Disease (The Union)},
year = {2021},
month = {October},
date = {19--22},
pages = {S78--S78},
url = {https://theunion.org/sites/default/files/2021-10/UNION2021_Abstracts_High.pdf},
address = {Virtual}
}
```
""",
),
dcc.Markdown(
"""
# About the authors
* [**Hoang Anh NGO**](https://orcid.org/0000-0002-7583-753X) is the main author of the SEIQHCDRO model. He finished his undergraduate study at École Polytechnique with
a double major in Mathematics and Economics, minor in Computational Economics and is currently pursuing a Master of Science in Epidemiology at The University of Edinburgh.
He is also currently a Research Contractor at Woolcock Institute of Medical Research Vietnam and Department of Information and Network (INFRES, Télécom Paris). His research
interests focus on Epidemiology, (Online) Machine Learning and its applications in Medicine.
* [**Tuan-Khoi Nguyen**](https://tkhoinguyen.netlify.app/) is the data engineer and tool developer of this project. He is currently in his first year of Masters of Science
in Mechatronics at The University of Melbourne with a Bachelor of Science in Mechatronics, after finishing the BSc. within the same field. His research interest focuses on
Machine Learning and its autonomous applications in Robotics and real life problems.
* Dr [**Nguyen Thu Anh**](https://www.researchgate.net/profile/Nguyen-Anh-50) is an epidemiologist and a social scientist by training, with more than 20 years of experience.
She holds an honorary position as Senior Clinical Lecturer at University of Sydney, and the head of the
Woolcock Institute of Medical Research in Vietnam.
"""
),
dcc.Markdown(
"""
# Acknowledgement
We would like to send our sincerest gratitude towards all team members of [5F Team](https://5fteam.com/) for contributing
valuable insights and data to help us complete out model:
* BPharm. Duyen T. Duong, Woolcock Institute of Medical Research Vietnam
* MS. Thao Huong Nguyen, Independent Social Researcher
* Kim Anh Le, MD PhD, Hanoi University of Public Health
* Cuong Quoc Nguyen, MD PhD, Independent Epidemiologist
* Phuc Phan, MD PhD, Vietnam National Hospital of Pediatrics
* Nguyen Huyen Nguyen, MD, National Hospital of Tropical Diseases (NHTD)
Moreover, we also want to send our warmest thanks to our fellow
colleagues and readers for their thoughtful and scholarly evaluation of the model.
All comments are hugely appreciated.
"""
),
dcc.Markdown(
"""
# License
SEIQHCDRO COVID-19 Interactive Modelling Tool is a free and open-source web application/software licensed under the
[3-clause BSD license](https://github.com/tuankhoin/SEIQHCDRO-Interactive-Model/blob/main/LICENSE).
# Authorship & Contribution
* Based on the license, this is a free software/web application released by the authors. There is no relation between the application and authors' affiliations.
* Source code is stored as open source on [GitHub](https://github.com/tuankhoin/SEIQHCDRO-Interactive-Model) and [Zenodo](https://doi.org/10.5281/zenodo.5136441).
* For support, please write an issue in the [issue tracker of this project](https://github.com/tuankhoin/SEIQHCDRO-Interactive-Model/issues).
* For contribution, please fork and make a pull request.
# Website legal disclaimer
The information contained in this website is for convenience or reference only. The content cannot be considered to be medical advice and is not intended
to be a substitute for professional medical counselling, diagnosis or treatment. For any concern please consult a trusted specialist in the field.
Whilst we endeavor to keep the information up to date and correct, we make no representations or warranties of any kind, express or implied,
about the completeness, accuracy, timeliness, reliability, suitability or availability with respect to the website or the information,
products, services, or related graphics, images, text and all other materials contained on the website for any purpose.
It is not meant to be applicable to any specific individual’s medical condition and any reliance you place on such information is therefore
strictly at your own risk.
In no event will we be liable for any loss or damage including without limitation, indirect or consequential loss or damage, or any loss or damage whatsoever
arising from loss of data or profits arising out of, or in connection with, the use of this website.
"""
),
],
style={"margin": "5%"},
)
def generate_inputs():
r"""
Generate the basic input slots and stages for the main page.
Returns
-------
widgets : :class:`list`
A list of HTML Elements for the basic stage inputs.
"""
num_slider = [
html.Div(
[
html.H3("Number of Stages:"),
dbc.Tooltip(
"Number of main representative stages during the pandemic",
target="div-num",
placement="right",
),
dcc.Input(
id="num",
min=0,
max=30,
value=3,
step=1,
type="number", # tooltip={'always_visible': True}
),
],
id="div-num",
)
]
text_boxes = [
html.H3("Stage Inputs"),
html.Div(id="in-r0"),
dbc.Tooltip(
html.Ul(
[
html.H6(
"Excluding the default first stage with zero reduction, each stage contains the following:"
),
html.Li("Starting date"),
html.Li(
"Assumed reduction rate within 15 days when 100% containment scheme is in place "
),
html.Li("Contained proportion due to new policy scheme"),
],
style={"text-align": "left"},
),
target="in-r0",
placement="right",
),
]
input_list = [num_slider, text_boxes]
widgets = list()
for sublist in input_list:
sublist.append(html.H1(""))
for item in sublist:
widgets.append(item)
return widgets
# Main page
main_page = html.Div(
[
html.Div(
[
html.Div(
[html.H1("COVID-19 Multi-compartment Modelling Result".upper())],
style={"width": "100%", "text-align": "center", "padding": "1%"},
),
# Inputs
html.Div(
[
# Basic inputs
dbc.Button(
html.H2("Basic Inputs"),
id="collapse-button",
className="mb-3",
color="primary",
style={"width": "100%"},
),
dbc.Collapse(
[
html.Div(
[
html.H3("Population"),
dbc.Tooltip(
"Population taken into account",
target="div-N",
placement="right",
),
dcc.Slider(
id="slider-N",
min=100000,
max=100000000,
value=11000000,
step=1000,
tooltip={
"always_visible": True,
"placement": "top",
},
marks={
i: str(i)
for i in [100000, 50000000, 100000000]
},
),
],
id="div-N",
),
html.Div(
[
html.H3("Outbreak Date"),
dbc.Tooltip(
"Assumed first day of outbreak",
target="div-date",
placement="right",
),
dcc.DatePickerSingle(
id="date",
min_date_allowed=date(2020, 1, 1),
max_date_allowed=date(2030, 12, 31),
initial_visible_month=date(2021, 5, 1),
date=date(2021, 5, 1),
display_format="DD/MM/YYYY",
),
],
id="div-date",
),
html.Div(
[
html.H3("Number of Days"),
dbc.Tooltip(
"Length of outbreak",
target="div-ndate",
placement="right",
),
dcc.Input(
id="ndate",
value=300,
min=10,
max=1000,
type="number",
),
],
id="div-ndate",
),
html.Div(
[
html.H3("Hospital Capacity: "),
dbc.Tooltip(
"Hospital Capacity",
target="div-hcap",
placement="right",
),
dcc.Input(
id="hcap", value=100000, type="number"
),
],
style={"margin": "4% 0%"},
id="div-hcap",
),
html.Div(
[
html.H3("Quarantine Capacity: "),
dbc.Tooltip(
"Quarantine Capacity",
target="div-qar",
placement="right",
),
dcc.Input(
id="hqar", value=10000, type="number"
),
],
style={"margin": "4% 0%"},
id="div-qar",
),
html.Div(
[
html.H3(
"Initial basic reproduction number (R0):"
),
dbc.Tooltip(
"Initial basic reproduction number at first day of outbreak",
target="div-r0",
placement="right",
),
dcc.Input(
id="slider-r0",
min=0,
max=20,
value=4.1,
step=0.001,
type="number", # tooltip={'always_visible': True}
),
],
id="div-r0",
),
html.Div(generate_inputs()),
],
id="collapse",
style=tab,
),
# Proportional inputs
dbc.Button(
html.H2("Proportion Inputs"),
id="collapse-button-p",
className="mb-3",
color="info",
style={"width": "100%"},
),
dbc.Collapse(
[
html.Div(
[
html.H6("Hospitalisation"),
dbc.Tooltip(
"Proportion of infected people getting hospitalized (For example, if 100 positive cases exist in the community, this proportion of them would be directly sent to a hospital).",
target="div-ph",
placement="right",
),
dcc.Slider(
id="slider-ph",
min=0,
max=1,
value=0.85,
step=0.01,
tooltip={"always_visible": True},
),
],
id="div-ph",
),
html.Div(
[
html.H6("Critical"),
dbc.Tooltip(
"Proportion of COVID-19 positive hospitalized patients turning critical.",
target="div-pc",
placement="right",
),
dcc.Slider(
id="slider-pc",
min=0,
max=1,
value=0.04,
step=0.01,
tooltip={"always_visible": True},
),
],
id="div-pc",
),
html.Div(
[
html.H6("Deceased"),
dbc.Tooltip(
"Proportion of COVID-19 positive critical patients deceased.",
target="div-pf",
placement="right",
),
dcc.Slider(
id="slider-pf",
min=0,
max=1,
value=0.25,
step=0.01,
tooltip={"always_visible": True},
),
],
id="div-pf",
),
html.Div(
[
html.H6("Media Impact"),
dbc.Tooltip(
"Media Impact as a contact reduction rate on the reproduction number.",
target="div-pj",
placement="right",
),
dcc.Slider(
id="slider-pj",
min=0,
max=1,
value=0.12,
step=0.01,
tooltip={"always_visible": True},
),
],
id="div-pj",
),
html.Div(
[
html.H6("Quarantined"),
dbc.Tooltip(
"Proportion of contact cases who requires quarantined actually get quarantined upon contact tracing (For example, if 100 contact cases need quarantine, this proportion of them would get quarantined immediately).",
target="div-pquar",
placement="right",
),
dcc.Slider(
id="slider-pquar",
min=0,
max=1,
value=0.8,
step=0.01,
tooltip={"always_visible": True},
),
],
id="div-pquar",
),
html.Div(
[
html.H6("Quarantined, then Hospitalised"),
dbc.Tooltip(
"Proportion of individuals quarantined for a long duration of time before getting positive result and hospitalized.",
target="div-pqhsp",
placement="right",
),
dcc.Slider(
id="slider-pqhsp",
min=0,
max=1,
value=0.1,
step=0.01,
tooltip={"always_visible": True},
),
],
id="div-pqhsp",
),
html.Div(
[
html.H6("Cross-Contamination"),
dbc.Tooltip(
"Cross-contamination rate in quarantine facilities.",
target="div-pcross",
placement="right",
),
dcc.Slider(
id="slider-pcross",
min=0,
max=1,
value=0.01,
step=0.01,
tooltip={"always_visible": True},
),
],
id="div-pcross",
),
],
id="collapse-p",
style=tab,
),
# Timing inputs
dbc.Button(
html.H2("Time Inputs"),
id="collapse-button-t",
className="mb-3",
color="danger",
style={"width": "100%"},
),
dbc.Collapse(
[
html.Div(
[
html.H6("Incubation"),
dbc.Tooltip(
"Incubation period.",
target="div-tinc",
placement="right",
),
dcc.Slider(
id="slider-tinc",
min=2.5,
max=7,
value=4.5,
step=0.1,
tooltip={"always_visible": True},
),
],
id="div-tinc",
),
html.Div(
[
html.H6("Infectious"),
dbc.Tooltip(
"Infectious period.",
target="div-tinf",
placement="right",
),
dcc.Slider(
id="slider-tinf",
min=1.0,
max=7,
value=2.9,
step=0.1,
tooltip={"always_visible": True},
),
],
id="div-tinf",
),
html.Div(
[
html.H6("Intensive Care"),
dbc.Tooltip(
"Time spent within the ICU.",
target="div-ticu",
placement="right",
),
dcc.Slider(
id="slider-ticu",
min=10.0,
max=14,
value=11,
step=0.1,
tooltip={"always_visible": True},
),
],
id="div-ticu",
),
html.Div(
[
html.H6("Hospitalised"),
dbc.Tooltip(
"Time spent hospitalised for non-critical patients.",
target="div-thsp",
placement="right",
),
dcc.Slider(
id="slider-thsp",
min=7,
max=21,
value=21,
step=0.1,
tooltip={"always_visible": True},
),
],
id="div-thsp",
),
html.Div(
[
html.H6("Critical"),
dbc.Tooltip(
"Time spent hospitalized before turning critical.",
target="div-tcrt",
placement="right",
),
dcc.Slider(
id="slider-tcrt",
min=1,
max=14,
value=7,
step=0.1,
tooltip={"always_visible": True},
),
],
id="div-tcrt",
),
html.Div(
[
html.H6("Self-Recovery"),
dbc.Tooltip(
"Self-Recovery time for non-disclosed cases.",
target="div-trec",
placement="right",
),
dcc.Slider(
id="slider-trec",
min=7,
max=21,
value=21,
step=0.1,
tooltip={"always_visible": True},
),
],
id="div-trec",
),
html.Div(
[
html.H6("Quarantine"),
dbc.Tooltip(
"Quarantine time under regulation.",
target="div-tqar",
placement="right",
),
dcc.Slider(
id="slider-tqar",
min=4,
max=21,
value=21,
step=0.1,
tooltip={"always_visible": True},
),
],
id="div-tqar",
),
html.Div(
[
html.H6("Quarantined, then Hospitalised"),
dbc.Tooltip(
"Time interval between the last COVID-19 positive result until getting hospitalized.",
target="div-tqah",
placement="right",
),
dcc.Slider(
id="slider-tqah",
min=0,
max=5,
value=2,
step=0.1,
tooltip={"always_visible": True},
),
],
id="div-tqah",
),
],
id="collapse-t",
style=tab,
),
# Input uploader
html.Div(
[
dcc.Upload(
html.Button(
["\f Upload custom .json input file"],
style={
"color": "white",
"margin": "2% 0",
"width": "100%",
},
),
id="up",
style={"padding": "2% 0", "font-style": "bold"},
),
dbc.Tooltip(
"You can import your json file that you have exported previously, rather than having to readjust inputs all over again",
target="div-up",
placement="right",
),
],
id="div-up",
),
html.Div(
[
dcc.Upload(
html.Button(
["\f Upload .csv stats for comparing"],
style={
"color": "white",
"margin": "2% 0",
"width": "100%",
},
),
id="up_stat",
style={"padding": "0% 0", "font-style": "bold"},
),
dbc.Tooltip(
html.Ul(
[
html.H6(
"You can upload a csv file of real statistics to compare. The device will find any matching columns and add them to the plot for comparison (assuming 1st row is 1st day of outbreak). The following column names can be selected:"
),
html.Li("infected"),
html.Li("daily_infected"),
html.Li("active_critical"),
html.Li("active_quarantined"),
html.Li("deaths"),
],
style={"text-align": "left"},
),
target="div-up-stat",
placement="right",
),
html.P(id="err", style={"color": "red"}),
],
id="div-up-stat",
),
],
style={
"width": "33%",
"display": "inline-block",
"vertical-align": "top",
"padding": "2%",
},
),
# Output
html.Div(
[
# Modes and sample picker
html.Div(
[
dcc.Checklist(
options=[
{"label": "Show Hospital Capacity", "value": 1},
{
"label": "Show Quarantine Capacity",
"value": 3,
},
{"label": "Show by Date", "value": 2},
],
value=[],
labelStyle={"display": "block"},
id="mods",
)
],
style={
"padding": "0% 3%",
"display": "inline-block",
"width": "35%",
},
),
html.Div(
[
dcc.Dropdown(
options=[
{"label": sample.name[n], "value": n}
for n in sample.name.keys()
],
placeholder="Select an example region",
id="init",
)
],