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. • Demographic data: Epidemiological indicators require population size. Demographic data of Brazilian municipalities are updated each year in Infodengue, using estimates IBGE.
• 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.
• 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.
Evigilancia aimed to consolidate the alert system for arboviruses based on hybrid data generated through integrated data analysis, mined from web climatic and epidemiological data, the InfoDengue.
Financiamento: INOVA Fiocruz
2020-2021
Predicting epidemics of mosquito-borne diseases
This project aims to provide medium-term projections for the incidence of new cases of mosquito-borne diseases in Brazil. The project adapts to the Brazilian reality, the experience implemented in Vietnam, where we propose the use of Bayesian space-time hierarchical models to predict the incidence of dengue cases, and the support of auxiliary variables, such as climatic variables (Colon-Gonzalez, Bastos, et al. al. 2021). In this project, we will extend the predictive modeling of dengue in Vietnam to mosquito-borne diseases, in which we will separately explore arboviruses in Brazil and malaria in the Amazon region, which are diseases of great impact and relevance in the country's public health. The models to be proposed will be built and validated using open data, where medium-term projections will be made, that is, six to twelve months ahead for the health microregions. The project will prioritize the use of open data, in addition to the use and development of codes in free environments, so that epidemiological surveillance teams can replicate and adapt according to their needs. In addition to the projections, a specific dashboard for each disease will be developed in parallel, so that the forecasts are accessible to decision makers and the general population.
Financiamento: INOVA Fiocruz
2022-2023
ARBOFRONTEIRA: Predicting epidemics of mosquito-borne diseases in the southern arc of the country
The objective of this proposal is to generate indicators for the surveillance of arboviruses that are specific to the southern border of the country and guidelines for its use in strengthening the surveillance of these municipalities. For this, it is necessary to understand the process of expansion of arboviruses in the three countries and to identify future expansion scenarios. Per Through statistical and mathematical models, we will investigate possible scenarios for the spread of arboviruses and the importance of binational and trinational protocols to the highest precision of control actions. From a practical point of view, models will be applied of nowcasting and prediction at two points: the triple border Foz do Iguaçu, Iguazu and city of the east; and on the border formed by Barracão (PR), D Cerqueira (SC) and Bernardo de Irigoyen (Arg). The models will be incorporated into Infodengue and will be made available specific reports for each region.
Financiamento: INOVA Fiocruz
How to quote 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. @article{CODECO2018S386, 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
2016
Coelho et al. Epidemiological data accessibility in Brazil, The Lancet ID,
link
2017
Lana R.M., Gomes M.F.C., Lima T.F.M., Honório N.A., and Codeço C.T.
The introduction of dengue follows transportation infrastructure changes in the state
of acre, brazil: A network-based analysis. PLOS Neglected Tropical Diseases.
link
2018
Santos, B.C., Coelho F.C., Armstrong M, Sarraceni V, Lemos C. Zika: an
ongoing threat to women and infant. Cadernos de Saude Publica.
link
Codeço, C.T., Villela A.M.D. , Coelho F.C. Estimating the effective reproduction
number of dengue considering temperature-dependent generation intervals. Epidemics.
link
Codeço, C.T., Coelho F.C., Cruz, O.G., Oliveira, S. Castro, T. Bastos, L.S.
Infodengue: A nowcasting system for the surveillance of arboviruses in Brazil. Revue
d'Épidémiologie et de Santé Publique.
link
Lana R.M., Morais M.M., Lima T.F.M., Carneiro T.G.S., Stolerman L.M.,
Santos J.P.C., Cortês J.J.C., Eiras A.E., and Codeço C.T. Assessment of a trap based
aedes aegypti surveillance program using mathematical modeling. PLOS ONE,
link
2019
Bastos LS, Economou T, Gomes MG, Villela DAM, Coelho OG, Stoner O,
Bailey T, Codeço CT. A modelling approach for correcting reporting delays in
disease surveillance data. Statistics in Medicine,
link
Coelho FC, Codeço CT. Precision epidemiology of arboviral diseases.
Journal of Public Health and Emergency,
link
2020
Elisa Mussumeci, Flávio Codeço Coelho. Large-scale multivariate forecasting
models for Dengue - LSTM versus random forest regression, Spatial and Spatio-temporal
Epidemiology.
link
Lowe R., Lee S., Lana R.M., Codeço C.T., Castro M.C., and Pascual M.
Emerging arboviruses in the urbanized amazon rainforest. BMJ,
link
2021
Alves L.D., Lana R.M., and Coelho F.C. A framework for weather-driven
dengue virus transmission dynamics in different Brazilian regions.
International Journal of Environmental Research and Public Health,
link
Colon-Gonzalez, Bastos, et al. (2021) Probabilistic seasonal dengue
forecasting in Vietnam: A modelling study using superensembles PLOS Medicine.
link
2022
Codeco C.T., Oliveira S.S., Ferreira D.A.C., Riback T.I.S., Bastos L.S.,
Lana R.M., Almeida I.F., Godinho V.B., Cruz O.G., and Coelho F.C. Fast expansion of
dengue in brazil. The Lancet Regional Health - Americas,
link
Miller S, Preis T, Mizzi G, Bastos LS, Gomes, MFC, Coelho FC, Codeço CT, Moat SH.
Faster indicators of chikungunya incidence using Google searches Plos Computational Biology,
link
• 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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