The Info Dengue System
aims to generate a warning system in almost real-time for cases of
dengue transmission in the municipalities.
The main idea is the quick access to information!
The Departments of Health at all levels of government, through the use of information generated weekly by our
system, could become more agile
and effective in their decision-making about dengue control. The warning system
seeks to remove the distance
between the analysis of past information, and the need to generate information in
near-real-time for decision making.
For the system to work, several methods of analysis are used in an integrated manner, including
Bayesian modeling, and a pipeline for fast data gathering and processing. This automation, integrated with
statistical modeling, enables rapid
visualization of the distribution of cases of dengue in a municipality, over time and
space. The data that feeding the system
come from different sources, including climate, social networks and official disease
In addition, the site provides information about the history of dengue in the city in different formats
which can be accessed by all,
citizens, students, teachers, researchers, in short, anyone interested in understanding the current
situation of dengue transmission in their municipality.
The control of incidence and preparation for facing dengue epidemics depends on a
Effective monitoring of the signals that predict an
increase in cases and potential outbreaks. Therefore, the analysis of all information
about dengue generated during the process
of notification is of great importance, serving as the basis for the municipalities health policy decisions.
Dengue is difficult to predict, it turns. Some years it comes very strong, some years it barely appears.
The Info dengue is like a weather service, only instead of predicting sunshine and rain, it keeps an eye on dengue,
the mosquito, and what is being said about them on social networks. This information feeds statistical models
previously validated with historical data that indicate the status of dengue in the city.
When all is well, the map is GREEN
. The weather is not very good for the
mosquito and dengue happens here and there. However, when the temperature rises, mosquitoes begin to reproduce
faster. The YELLOW
indicates that it is a good temperature for
We need to pay attention to breeding. What can you do? Take 10 minutes of your day to combat dengue
Check out the tips on 10 minutes
When we put together lots of mosquitoes with lots of people who did not have dengue, we have the perfect situation for the virus
to spread. It passes from the mosquito to the person and then to another mosquito that passes to another person, and
then we have a sustained transmission of dengue! The color ORANGE
situation. It is time to redouble efforts to eliminate mosquito breeding sites. Also pay attention to symptoms
dengue in you, in your family, your neighbors and friends.
If one out of every 1,000 people in a municipality has dengue, the alert goes to red RED
Is a warning that if you did not have dengue yet, the risk is great. Great care must be taken. Pay attention to
symptoms and seek help.
DENGUE is CYCLICAL
, after the RED
alert, we usually go back to GREEN
and the whole cycle starts again. So do not forget to stay tuned to InfoDengue
. From it,
you can stay aware of the situation of
your neighborhood. Click the Map
and find out right now how dengue is behaving there, where you are.
A preliminary analysis of dengue case reporting data, shows that the time
delay between the notification and data entry has
a median of 14 days. To correct this delay, a lognormal survival model
(Carvalho et al, 2011)
was fitted to the data and used to infer the number of cases in the week, in the absence of delay.
With the corrected time series of cases for each health region, the effective reproductive number (Rt) is
calculated according to equation 4.2
Nishiura et al (2010)
and assuming a
Serial interval for dengue of 3 weeks.
Rt is basically a measure of the variation of the number of cases and is calculated as the ratio between the number of cases and is calculated as the ratio between the number of cases in a given time interval and the number of cases in the previous one.
If Rt> 1, there is expansion of the epidemic, if Rt %3C 1, there is a retraction. To account for the
uncertainty in estimates of this quantity,
we calculate the credibility interval (Coelho &
for Rt and define a period of expansion
when the probability that Rt> 1 is greater than 90%.
Regarding the tweet data, the same technique is used, generating a reproductive number of tweets (Rttw)
Significant positive changes indicate an increased risk.