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Infodengue

http://info.dengue.mat.br

This Jupyter notebook describes how to access and visualize Infodengue's datasets using the Python language. For more information about the data and how to cite them, visit here.

To execute this notebook, the following libraries are necessary:

import pandas as pd
import numpy as np

Data query

Infodengue's tables have data aggregated by week, provided by different sources. They could be consulted by a form, or directly from Python, via consult of an API. This functionality is available via the URL: https://info.dengue.mat.br/api/alertcity?params: must contain the following parameters:

• geocode: city's IBGE code
• disease: desease type to be consulted (str:dengue|chikungunya|zika)
• format: file extension/format (str:json|csv)
• ew_start: epidemiological week initial consultation (int:1-53)
• ew_end: epidemiological week final consultation (int:1-53)
• ey_start: year of initial consultation (int:0-9999)
• ey_end: year of final consultation (int:0-9999)

Every parameter mentioned so far is obligatory for the consultation. The following example shows the requisition of the register of dengue between 1 and 52 of the year 2020, in Rio de Janeiro (geocodigo = 3304557) on CSV: https://info.dengue.mat.br/api/alertcity?geocode=3304557&disease=dengue&format=csv&ew_start=1&ew_end=52&ey_start=2021&ey_end=2021

How to do it using Python?

1. Define the parameters and verify if the consult is correct:

url = "https://info.dengue.mat.br/api/alertcity"
geocode = 3304557
disease = "dengue"
format = "csv"
ew_start = 1
ew_end = 53
ey_start = 2021
ey_end = 2021

params =(
    "&disease="
    + f"{disease}"
    + "&geocode="
    + f"{geocode}"
    + "&disease="
    + f"{disease}"
    + "&format="
    + f"{format}"
    + "&ew_start="
    + f"{ew_start}"
    + "&ew_end="
    + f"{ew_end}"
    + "&ey_start=" 
    + f"{ey_start}"
    + "&ey_end="
    + f"{ey_end}"    
)

url_resp = "?".join([url, params])
url_resp

'https://info.dengue.mat.br/api/alertcity?&disease=dengue&geocode=3304557&disease=dengue&format=csv&ew_start=1&ew_end=53&ey_start=2021&ey_end=2021'

dados = pd.read_csv(url_resp, index_col='SE')
dados.T

SE	202101	202102	202103	202104	202105	202106	202107	202108	202109	202110	...	202131	202132	202133	202134	202135	202136	202137	202138	202139	202140
data_iniSE	2021-01-03	2021-01-10	2021-01-17	2021-01-24	2021-01-31	2021-02-07	2021-02-14	2021-02-21	2021-02-28	2021-03-07	...	2021-08-01	2021-08-08	2021-08-15	2021-08-22	2021-08-29	2021-09-05	2021-09-12	2021-09-19	2021-09-26	2021-10-03
casos_est	9	11	14	30	18	17	17	25	23	32	...	11	7	7	13	15	11	10	9	11	9
casos_est_min	9	11	14	30	18	17	17	25	23	32	...	11	7	7	12	14	9	7	5	6	2
casos_est_max	9	11	14	30	18	17	17	25	23	32	...	12	9	10	15	18	14	14	15	20	24
casos	9	11	14	30	18	17	17	25	23	32	...	11	8	7	12	14	9	7	5	5	1
p_rt1	0.642163	0.736372	0.825699	0.997716	0.567613	0.24269	0.293499	0.849171	0.717698	0.906233	...	0.135919	0.130337	0.280614	0.850713	0.891939	0.454617	0.278808	0.275069	0.562253	0.423368
p_inc100k	0.133377	0.163016	0.207475	0.444588	0.266753	0.251933	0.251933	0.37049	0.340851	0.474228	...	0.163016	0.103737	0.103737	0.192655	0.222294	0.163016	0.148196	0.133377	0.163016	0.133377
Localidade_id	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
nivel	2	2	2	2	2	2	2	2	2	2	...	1	1	1	1	1	1	1	1	1	1
id	330455720210118913	330455720210218913	330455720210318913	330455720210418913	330455720210518913	330455720210618913	330455720210718913	330455720210818913	330455720210918913	330455720211018913	...	330455720213118913	330455720213218913	330455720213318913	330455720213418913	330455720213518913	330455720213618913	330455720213718913	330455720213818913	330455720213918913	330455720214018913
versao_modelo	2021-10-13	2021-10-13	2021-10-13	2021-10-13	2021-10-13	2021-10-13	2021-10-13	2021-10-13	2021-10-13	2021-10-13	...	2021-10-13	2021-10-13	2021-10-13	2021-10-13	2021-10-13	2021-10-13	2021-10-13	2021-10-13	2021-10-13	2021-10-13
Rt	1	1	1	2	1	1	1	1	1	1	...	1	1	1	2	2	1	1	1	1	1
pop	6.74782e+06	6.74782e+06	6.74782e+06	6.74782e+06	6.74782e+06	6.74782e+06	6.74782e+06	6.74782e+06	6.74782e+06	6.74782e+06	...	6.74782e+06	6.74782e+06	6.74782e+06	6.74782e+06	6.74782e+06	6.74782e+06	6.74782e+06	6.74782e+06	6.74782e+06	6.74782e+06
tempmin	24	24	24	25	24	22	24	23	24	23	...	15	17	17	19	19	21	20	NaN	NaN	NaN
umidmax	90	89	84	80	85	90	95	95	90	96	...	93	95	95	92	93	92	94	NaN	NaN	NaN
receptivo	1	1	1	1	1	1	1	1	1	1	...	0	0	0	0	0	0	0	0	0	0
transmissao	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
nivel_inc	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
notif_accum_year	1032	1032	1032	1032	1032	1032	1032	1032	1032	1032	...	1032	1032	1032	1032	1032	1032	1032	1032	1032	1032

20 rows × 40 columns

1. Consulting:

• data_iniSE: First day of the epidemiological week (Sunday);
• SE: epidemiological week;
• casos_est: Estimated number of cases per week using the nowcasting model (note: Values ​​are updated retrospectively every week);
• casos_est_min e casos_est_max: 95% credibility interval of the estimated number of cases;
• casos: Number of reported cases per week (Values ​​are updated retrospectively every week);
• p_rt1: Probability of (Rt> 1). For issuing the orange alert, we use criterion p_rt1 > 0.95 for 3 weeks or more;
• p_inc100k: Estimated incidence rate per 100,000;
• Localidade_id: Sub-municipality division (currently implemented only in Rio de Janeiro);
• nivel: Alert level (1 = green, 2 = yellow, 3 = orange, 4 = red);
• id: Numerical index;
• versao_modelo: Model version (internal use);
• Rt: Point estimate of the reproductive number of cases, see (Know more);
• pop: estimated population (IBGE);
• tempmin: average of the daily minimum temperatures along the week;
• umidmax: average of the daily maximum relative air humidity along the week;
• receptivo: indicates climate receptivity, i.e., conditions for high vectorial capacity. 0 = unfavorable, 1 = favorable, 2 = favorable this week and last week, 3 = favorable for at least three weeks (sufficient for completing a transmission cycle);
• transmissao: evidence of sustained transmission: 0 = no evidence, 1 = possible, 2 = likely, 3 = highly likely;
• nivel_inc: Estimated incidence below the pre-epidemic threshold, 1 = above the pre-epidemic threshold, but below the epidemic threshold, 2 = above the epidemic threshold;
• notif_accum_year: accumulated number of cases per year;

1. Plotting examples

# Filter Indicators
# Slicing data by epidemiological week
this_week = dados.index.max()
last_week = this_week - 1
cases_this_week = dados.loc[this_week]['casos_est']
cases_last_week = dados.loc[last_week]['casos_est']

print(dados.loc[last_week:].T)

SE                            202139              202140
data_iniSE                2021-09-26          2021-10-03
casos_est                         11                   9
casos_est_min                      6                   2
casos_est_max                     20                  24
casos                              5                   1
p_rt1                       0.562253            0.423368
p_inc100k                   0.163016            0.133377
Localidade_id                      0                   0
nivel                              1                   1
id                330455720213918913  330455720214018913
versao_modelo             2021-10-13          2021-10-13
Rt                                 1                   1
pop                      6.74782e+06         6.74782e+06
tempmin                          NaN                 NaN
umidmax                          NaN                 NaN
receptivo                          0                   0
transmissao                        0                   0
nivel_inc                          0                   0
notif_accum_year                1032                1032

import plotly.graph_objects as go
from plotly.offline import iplot


trace1  = go.Scatter(
        mode='lines+markers',
        x=dados.index.map(
            lambda v: '%s' % (str(v)[-6:])
        ),
        y=dados['casos_est'],
        name="Estimated cases",
        marker_color='#1A5CF4',
        hovertemplate=(
            '%{text}' +
            '<br>' +
            '%{y:1f} Estimated cases' +
            '<extra></extra>'
        ),
        text="SE: " + dados.index.map(lambda v: '{}'.format(str(v)[-2:]))  + 
             "<br>" + 
             "Date: " + dados.data_iniSE.loc[dados.index],  
        )
  

trace2 = go.Bar(
        x=dados.index.map(
            lambda v: '%s' % (str(v)[-6:])
        ),
        y=dados['casos'],
        name="Reported cases",
        marker_color ='#BAF3FE',
        marker_line_color='rgb(8,48,107)',
        opacity=0.5,
        hovertemplate=(
            '%{text}' +
            '<br>' +
            '%{y:1f} Reported cases' +
            '<extra></extra>'
        ),
        text="SE: " + dados.index.map(lambda v: '{}'.format(str(v)[-2:]))  + 
             "<br>" + 
             "Date: " + dados.data_iniSE.loc[dados.index],  
    )

trace3 = go.Indicator(
    mode = "number+delta",
    value = cases_this_week,
    delta={
        'reference': cases_last_week,
        'increasing': {'color': "#e60000"},
        'decreasing': {'color': "#1e824c"},
        "font": {"size": 36},
    },
    title = {
        "text": "SE:{} <br> Estimated cases".format(
            str(this_week)[-2:]
        )
    },
    domain = {'y': [0, 1], 'x': [0.25, 0.75]}
)

data = [trace1, trace2, trace3]

layout = go.Layout(
    title_text='Total reported and estimated cases for Rio de Janeiro',
    yaxis=dict(
        title_text='Number of cases',
        side = 'left',
        showline=False,
        showgrid=True,
        showticklabels=True,
        linecolor='rgb(204, 204, 204)',
        linewidth=0,
        gridcolor='rgb(176, 196, 222)',
    ),
    xaxis=dict(
        title_text='Epidemiological Week',
        showline=False,
        showgrid=True,
        showticklabels=True,
        tickangle=-60,
        linecolor='rgb(204, 204, 204)',
        linewidth=0,
        gridcolor='rgb(176, 196, 222)',

    ),
    showlegend=False,
    plot_bgcolor='rgb(255, 255, 255)',
    paper_bgcolor='rgb(245, 246, 249)',
    autosize=True,
)


fig = go.Figure(data=data, layout=layout)
fig.show()