About InfoDengue

InfoDengue is an early-warning system for arbovirosis in Brazil. It relies on the continuous analysis of online, climate and epidemiological data.

In 2021, the system reached the national level with the support of the Brazilian Health Ministry. As a result, all municipalities began to receive weekly InfoDengue bulletins. Reports at the state level can be requested by the state health secretariats. Access the tab “how to participate” to know more. Implemented in 2015, the system was developed by researchers from the Scientific Computing Program (Fundação Oswaldo Cruz, RJ) and the School of Applied Mathematics (Fundação Getúlio Vargas) with the strong collaboration of the Rio de Janeiro Municipal Health Secretariat, the Observatory of Dengue (UFMG) and researchers from the Federal University of Paraná and the State University of Western Paraná.

The Infodengue website is accessed by many people and it is common for us to receive news that this information is used in the definition of travel and other activities.

Our network of collaborators has grown and now includes international research groups seeking solutions for arboviruses in other countries and civil society organizations. By sharing experiences and methodologies, we expand our capacity to innovate on arbovirus surveillance.

The several technical and scientific challenges faced during the development of InfoDengue resulted in theses and dissertations. As the challenges do not end, we continue and will always continue to investigate new solutions to generate useful information for the management and advancement of knowledge of arboviruses and their control.

About the system

The InfoDengue system is a semi-automated data collection, harmonization and analysis pipeline that generates indicators of the epidemiological situation of dengue and other arboviruses at the municipal level.


Where does the data come from?

• Reported cases of dengue, zika or chikungunya. These are notifiable diseases, that is, the health worker diagnosing a suspicious case needs to fill out a notification form that feeds a municipal database which is then consolidated at the state level and finally, federally by the Ministry of Health. Only a fraction of these cases are laboratory confirmed, most receive final classification based on clinical and epidemiological criteria. From the notified cases, the incidence indicators that feed the InfoDengue are calculated.

• Weather data: The transmission of arboviruses is greatly influenced by the climate. The transmitting mosquito, Aedes aegypti, requires high temperature and humidity to reproduce and live. The virus infecting the mosquito will also reproduce better at higher temperatures. Temperature and humidity data are obtained from airport weather stations as well as satellite images.

• Mention analysis of dengue in social networks: People generally seek information or tell about their health status on social networks before seeking medical help. Thus, knowing the number of mentions of dengue in social networks helps to identify when cases are increasing in the population. Infodengue has partnered with the Dengue Observatory hat captures and analyzes tweets from geolocalized people for the mention of dengue symptoms.

• Demographic data: Epidemiological indicators require population size. Demographic data of Brazilian municipalities are updated each year in Infodengue, using estimates IBGE.

What to do?
Nowcasting: Disease notification data are always delayed for a variety of reasons including the time between getting sick and seeking medical attention, the time it takes for the healthcare professional to report, and the time it takes to aggregate information into databases. This delay makes decision making difficult because one always has to look at outdated data. To circumvent this problem, a Bayesian inference-based statistical method was developed that combines the delay estimate with the historical temporal pattern of incidence to calculate the expected number of cases each week. This process of nowcasting is described Bastos, Economou et al (2019) ”A modelling approach for correcting reporting delays in disease surveillance data”. A modelling approach for correcting reporting delays in disease surveillance data”. The figure below (taken from Bastos et al 2019) shows in black the true incidence, in red, that observed late and in gray the nowcasting. For further technical details, see the publication.


Sustained Transmission Detection: Estimation of reproductive number is important in many ways. Sequence of weeks with critical transmission (Rt> 1) indicates sustained transmission. That is, it indicates the phase of spread of the disease, and that appropriate control actions need to be implemented. On the other hand, sub-critical periods (Rt <1) indicate that there is no sustained transmission. To estimate Rt from the incidence data, we developed our own methodology, described in Codeço et al. 2018 "Estimating the effective reproduction number of dengue considering temperature-dependent generation intervals".

Receptivity Analysis: The transmission of arboviruses requires appropriate weather conditions, which may vary from place to place. In Rio de Janeiro, for example, a temperature higher than 22 degrees is a necessary condition for sustained transmission. In Fortaleza, maximum humidity above 85% is a necessary condition. We have adjusted decision tree models to evaluate which combination of climate and environmental variables are indicative of transmission conditions. This information serves as an early warning of the arrival of the transmission season.

Mention analysis of dengue in social networks: Social network indicators calculated by the Dengue Observatory are used as auxiliary variables in InfoDengue. The capture and analysis methodology is described Marques-Toledo et al (2017).

Atypical Incidence Detection: To estimate epidemic thresholds for dengue, we adapted the Moving Epidemics Method (MEM) Vega et al. 2012, which allows estimating of various quantities of interest based on local historical data such as pre-threshold. - epidemic, typical activity levels, expected values ​​for each week, etc. Because this threshold is derived from the data history, locations with different case volumes have different thresholds. The methodology also provides estimates for the expected number of cases per week, together with confidence intervals that allow the definition of weekly activity zones. These regions are defined similarly to activity levels, but are calculated based on the typical case distribution for each week. This construction allows us to visually identify seasonal patterns, since they present the typical temporal evolution based on the median of expected cases for each week and lower and upper confidence intervals (we use 10% and 90%, respectively), defining activity corridors as a control diagram.

Disease code


All code generated for InfoDengue, mostly R and Python, is available at:

How to Participate in InfoDengue

C. Codeco, F. Coelho, O. Cruz, S. Oliveira, T. Castro, L. Bastos, Infodengue:
A nowcasting system for the surveillance of arboviruses in Brazil, Revue d'Épidémiologie et de Santé Publique, Vol 66, Suppl 5, 2018, Page S386,​https://doi.org/10.1016/j.respe.2018.05.408.

title = "Infodengue: A nowcasting system for the surveillance of arboviruses in Brazil",
journal = "Revue d'Épidémiologie et de Santé Publique",
volume = "66",
pages = "S386",
year = "2018",
note = "European Congress of Epidemiology “Crises, epidemiological transitions and the role of epidemiologists”",
issn = "0398-7620",
doi = "https://doi.org/10.1016/j.respe.2018.05.408",
url = "http://www.sciencedirect.com/science/article/pii/S0398762018311088",
author = "C. Codeco and F. Coelho and O. Cruz and S. Oliveira and T. Castro and L. Bastos"}

Scientific productions associated with Infodengue

• Infodengue: ​ A nowcasting system for the surveillance of arboviruses in Brazil


• E-vigilância 2021: ​ http://e-vigilancia.dengue.mat.br/

How to Collaborate

• If your city in Brazil is not Infodengue, contact the Health Department
• If you are a student or researcher and would like to contribute analysis and models, write us
• If you would like to contribute sponsorships, write us
• If you want to learn how to use infodengue, have suggestions, criticism or if you noticed any errors on the site, write us

Overcoming arboviruses is everyone's job, all collaboration is welcome!

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