InfoDengue: a nowcasting system for the surveillance of dengue fever transmission

Codeço, Cláudia Torres; Cruz, Oswaldo Gonçalves; Riback, Thaís Irene Souza; Degener, Carolin Marlen; Gomes, Maria Auxiliadora de Souza Mendes; Villela, Daniel A. M.; Bastos, Leonardo Soares; Camargo, Sabrina; Saraceni, Valéria; Lemos, Maria Cristina Ferreira; Coelho, Flávio Codeço

doi:10.1101/046193

Cited by 17 publications

(18 citation statements)

References 19 publications

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“…The dataset of Zika cases used in this study was obtained from SINAN (the Brazilian national registry for diseases with mandatory reporting), through the Infodengue project 8 . We analyzed all reported Zika and dengue cases which took place in the city of Rio de Janeiro between January 2015 and July…”

Section: Methodsmentioning

confidence: 99%

Zika: an ongoing threat to women and infants

et al. 2018

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Recent data from the municipality of Rio de Janeiro, Brazil, shows a sharp drop in the number of reported occurrences of Zika during the summer of 2016/2017, compared to the previous summer. There is still a much higher incidence among women than men, almost certainly due to sexual transmission. An unexpected feature of the new data is that there are proportionally far more cases affecting children under 15 months than older age classes. By comparing incidence rates in 2016/2017 and 2015/2016, we were able to deduce the proportion of reported cases affecting men and women, and verify that gender disparity is still present. Women and children are still risk groups for Zika infection, even during non-epidemic seasons.

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Section: Methodsmentioning

confidence: 99%

Zika: an ongoing threat to women and infants

et al. 2018

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“…The field of infectious disease modeling has a rich literature of methodologies but has not previously focused on the challenge of estimating spatiotemporal reporting delays in real-time public health control applications. An integrated dengue monitoring system was developed in the city of Rio de Janeiro, Brazil, which corrects for reporting delays using a lognormal survival model (https ://info.dengu e.mat.br/infor macoe s/) [23]. The model has been further developed and is being used as a decision making tool by Brazilian authorities as warning systems, infoDengue (https ://info.dengu e.mat.…”

Section: Introductionmentioning

confidence: 99%

Bayesian spatiotemporal modeling with sliding windows to correct reporting delays for real-time dengue surveillance in Thailand

et al. 2020

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Background: The ability to produce timely and accurate estimation of dengue cases can significantly impact disease control programs. A key challenge for dengue control in Thailand is the systematic delay in reporting at different levels in the surveillance system. Efficient and reliable surveillance and notification systems are vital to monitor health outcome trends and early detection of disease outbreaks which vary in space and time.Methods: Predicting the trend in dengue cases in real-time is a challenging task in Thailand due to a combination of factors including reporting delays. We present decision support using a spatiotemporal nowcasting model which accounts for reporting delays in a Bayesian framework with sliding windows. A case study is presented to demonstrate the proposed nowcasting method using weekly dengue surveillance data in Bangkok at district level in 2010. Results:The overall real-time estimation accuracy was 70.69% with 59.05% and 79.59% accuracy during low and high seasons averaged across all weeks and districts. The results suggest the model was able to give a reasonable estimate of the true numbers of cases in the presence of delayed reports in the surveillance system. With sliding windows, models could also produce similar accuracy to estimation with the whole data. Conclusions:A persistent challenge for the statistical and epidemiological communities is to transform data into evidence-based knowledge that facilitates policy making about health improvements and disease control at the individual and population levels. Improving real-time estimation of infectious disease incidence is an important technical development. The effort in this work provides a template for nowcasting in practice to inform decision making for dengue control.

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“…Accurate, real-time forecasting can inform public health decision makers to allow for improved response, targeted surveillance, and improved mitigation 18 . Broadly speaking, dengue prediction studies have focused on the Americas 19 and Southeast Asia 20 . Time scales range from predicting categorical risk for an entire transmission season 21 to weekly case counts 19 .…”

Section: Introductionmentioning

confidence: 99%

“…Broadly speaking, dengue prediction studies have focused on the Americas 19 and Southeast Asia 20 . Time scales range from predicting categorical risk for an entire transmission season 21 to weekly case counts 19 . Spatial scales are most commonly limited to cities 22 , small countries such as Singapore 20 , or specific regions (e.g., county, state) within a country with particularly good human case counts and/or mosquito time series data 23 .…”

Section: Introductionmentioning

confidence: 99%

“…Spatial scales are most commonly limited to cities 22 , small countries such as Singapore 20 , or specific regions (e.g., county, state) within a country with particularly good human case counts and/or mosquito time series data 23 . Windows of prediction include nowcasting (necessary since reported case counts often have a lag of 1-4 weeks) 19 , forecasting weekly cases 4-12 weeks into the future 20 , and several-months ahead categorical risk forecasting 21 . Many of these studies have exploited multiple data streams including socioeconomic drivers 9 , weather data 6 , satellite imagery 11 , topography (e.g., altitude), entomological factors 24 , Internet data (e.g., social media) 13,25 , and clinical surveillance data 19 .…”

Section: Introductionmentioning

confidence: 99%

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Unlocking the Predictive Power of Heterogeneous Data to Build an Operational Dengue Forecasting System

Manore

Fairchild

Ziemann

et al. 2020

Preprint

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Predicting an infectious disease can help reduce its impact by advising public health interventions and personal preventive measures. While availability of heterogeneous data streams and sensors such as satellite imagery and the Internet have increased the opportunity to indirectly measure, understand, and predict global dynamics, the data may be prohibitively large and/or require intensive data management while also requiring subject matter experts to properly exploit the data sources (e.g., deriving features from fundamentally different data sets). Few efforts have quantitatively assessed the predictive benefit of novel data streams in comparison to more traditional data sources, especially at fine spatio-temporal resolutions. We have combined multiple traditional and non-traditional data streams (satellite imagery, Internet, weather, census, and clinical surveillance data) and assessed their combined ability to predict dengue in Brazil's 27 states on a weekly and yearly basis over seven years. For each state, we nowcast dengue based on several time series models, which vary in complexity and inclusion of exogenous data. We also predict yearly cumulative risk by municipality and state. The top-performing model and utility of predictive data varies by state, implying that forecasting and nowcasting efforts in the future may be made more robust by and benefit from the use of multiple data streams and models.

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InfoDengue: a nowcasting system for the surveillance of dengue fever transmission

Cited by 17 publications

References 19 publications

Zika: an ongoing threat to women and infants

Zika: an ongoing threat to women and infants

Bayesian spatiotemporal modeling with sliding windows to correct reporting delays for real-time dengue surveillance in Thailand

Unlocking the Predictive Power of Heterogeneous Data to Build an Operational Dengue Forecasting System

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