Sensitivity of the Dengue Surveillance System in Brazil for Detecting Hospitalized Cases

Profile and temporal dynamics of the feline sporotrichosis epidemic in southern Brazil: A forecasting analysis

A detailed clinical-epidemiological analysis of feline sporotrichosis was conducted, and 288 cases reported between the years 2007 and 2018 were analyzed. The studied cases primarily involved mongrel cats (240/260), males (212/282), and adults (121/200). The main objectives were to identify the risk factors, calculate the monthly incidence rates, and establish a predictive model using the seasonal autoregressive integrated moving average (SARIMA) approach. The statistical analysis revealed significant associations (p < 0.05) between prolonged lesion evolution times and factors such as respiratory signs, prior treatments, and lesion contact. Empirical treatment was identified as a significant risk factor for disease progression. Moreover, the number of cases demonstrated an increasing trend over the study period, with annual peaks noted in disease incidence. The SARIMA model proved to be an effective tool for forecasting the incidence of sporotrichosis, offering robust support for epidemiological surveillance and facilitating targeted public health interventions in endemic regions. The predictive accuracy of the developed model underscored its utility in enhancing disease monitoring and supporting proactive health measures for the effective management of sporotrichosis.

The macroeconomic impact of a dengue outbreak: Case studies from Thailand and Brazil

BACKGROUND

Dengue is spreading in (sub)tropical areas, and half of the global population is at risk. The macroeconomic impact of dengue extends beyond healthcare costs. This study evaluated the impact of dengue on gross domestic product (GDP) based on approaches tailored to two dengue-endemic countries, Thailand and Brazil, from the tourism and workforce perspectives, respectively.

FINDINGS

Because the tourism industry is a critical economic sector for Thailand, lost tourism revenues were estimated to analyze the impact of a dengue outbreak. An input-output model estimated that the direct effects (on international tourism) and indirect effects (on suppliers) of dengue on tourism reduced overall GDP by 1.43 billion US dollars (USD) (0.26%) in the outbreak year 2019. The induced effect (reduced employee income/spending) reduced Thailand's GDP by 375 million USD (0.07%). Overall, lost tourism revenues reduced Thailand's GDP by an estimated 1.81 billion USD (0.33%) in 2019 (3% of annual tourism revenue). An inoperability input-output model was used to analyze the effect of workforce absenteeism on GDP due to a dengue outbreak in Brazil. This model calculates the number of lost workdays associated with ambulatory and hospitalized dengue. Input was collected from state-level epidemiological and economic data for 2019. An estimated 22.4 million workdays were lost in the employed population; 39% associated with the informal sector. Lost workdays due to dengue reduced Brazil's GDP by 876 million USD (0.05%).

CONCLUSIONS

The economic costs of dengue outbreaks far surpass the direct medical costs. Dengue reduces overall GDP and inflicts national economic losses. With a high proportion of the population lacking formal employment in both countries and low income being a barrier to seeking care, dengue also poses an equity challenge. A combination of public health measures, like vector control and vaccination, against dengue is recommended to mitigate the broader economic impact of dengue.

Temporal trends of dengue cases and deaths from 2007 to 2020 in Belo Horizonte, Brazil

Dengue, a disease with multifactorial determinants, is linked to population susceptibility to circulating viruses and the extent of vector infestation. This study aimed to analyze the temporal trends of dengue cases and deaths in Belo Horizonte, Minas Gerais, Brazil, from 2007 to 2020. Data from the Notifiable Diseases Information System (Sinan) were utilized for the investigation. To assess the disease's progression over the study period and predict its future incidence, time series analyses were conducted using a generalized additive model (GAM) and a seasonal autoregressive integrated moving average (SARIMA) model. Over the study period, a total of 463,566 dengue cases and 125 deaths were reported. Notably, there was an increase in severe cases and deaths, marking hyperendemics characterized by simultaneous virus circulation (79.17% in 2016-50% in 2019). The generalized additive model revealed a non-linear pattern with epidemic peaks in 2010, 2013, 2016, and 2019, indicating an explosive pattern of dengue incidence. The SARIMA (3,1,1) (0,0,0)12 model was validated for each year (2015 to 2019). Comparing the actual and predicted numbers of dengue cases, the model demonstrated its effectiveness for predicting cases in the municipality. The rising number of dengue cases emphasizes the importance of vector surveillance and control. Enhanced models and predictions by local health services will aid in anticipating necessary control measures to combat future epidemics.

Record Linkage for Malaria Deaths Data Recovery and Surveillance in Brazil

OBJECTIVE

The objective is to describe the results and the methodological processes of record linkage for matching deaths and malaria cases.

METHODS

A descriptive cross-sectional study was conducted with probabilistic record linkage of death and malaria cases data in Brazil from 2011 to 2020 using death records from the Mortality Information System (SIM) and epidemiological data from the Notifiable Diseases Information System (Sinan) and Epidemiological Surveillance Information Systems for malaria (Sivep-Malaria). Three matching keys were used: patient's name, date of birth, and mother's name, with an analysis of cosine and Levenshtein dissimilarity measures.

RESULTS

A total of 490 malaria deaths were recorded in Brazil between 2011 and 2020. The record linkage resulted in the pairing of 216 deaths (44.0%). Pairings where all three matching keys were identical accounted for 30.1% of the total matched deaths, 39.4% of the matched deaths had two identical variables, and 30.5% had only one of the three key variables identical. The distribution of the variables of the matched deaths (216) was similar to the distribution of all recorded deaths (490). Out of the 216 matched deaths, 80 (37.0%) had poorly specified causes of death in the SIM.

CONCLUSIONS

The record linkage allowed for the detailing of the data with additional information from other epidemiological systems. Record linkage enables data linkage between information systems that lack interoperability and is an extremely useful tool for refining health situation analyses and improving malaria death surveillance in Brazil.

A machine learning model to assess potential misdiagnosed dengue hospitalization

Dengue, like other arboviruses with broad clinical spectra, can easily be misdiagnosed as other infectious diseases due to the overlap of signs and symptoms. During large outbreaks, severe dengue cases have the potential to overwhelm the health care system and understanding the burden of dengue hospitalizations is therefore important to better allocate medical care and public health resources. A machine learning model that used data from the Brazilian public healthcare system database and the National Institute of Meteorology (INMET) was developed to estimate potential misdiagnosed dengue hospitalizations in Brazil. The data was modeled into a hospitalization level linked dataset. Then, Random Forest, Logistic Regression and Support Vector Machine algorithms were assessed. The algorithms were trained by dividing the dataset in training/test set and performing a cross validation to select the best hyperparameters in each algorithm tested. The evaluation was done based on accuracy, precision, recall, F1 score, sensitivity, and specificity. The best model developed was Random Forest with an accuracy of 85% on the final reviewed test. This model shows that 3.4% (13,608) of all hospitalizations in the public healthcare system from 2014 to 2020 could have been dengue misdiagnosed as other diseases. The model was helpful in finding potentially misdiagnosed dengue and might be a useful tool to help public health decision makers in planning resource allocation.

Real world data study of prevalence and direct costs related to dengue management in Brazil's private healthcare from 2015 to 2020

BACKGROUND

The burden of dengue in Brazil is poorly documented and is based on data from the public health care setting. This study estimated the prevalence and costs of dengue management in the private health care system in Brazil from 2015 to 2020 using a large claims database from Orizon.

METHODS

We selected claims with dengue ICD codes (ICD-10 A90 or A91) from January 2015 to December 2020. Prevalence was estimated based on the population enrolled in health insurance plans in the given year. Costs were adjusted for the inflation up to December 2021 and evaluated by measures of central tendency and dispersion.

RESULTS

A total of 63,882 unique beneficiaries were included, with a total of 64,186 dengue cases. The year with the highest prevalence was 2015 (1.6% of patients who used health plans), and there was also an increase in cases in 2016 and 2019. The median cost per hospitalization in 2015 was US$486.17, and in 2020, it reached US$696.72. The median cost of a case seen at an emergency room ranged from US$ 97.78 in 2015 to US$ 118.16 in 2017.

CONCLUSIONS

The estimated prevalence of dengue in this population of private health-insured patients followed the epidemiological trends of the general population in Brazil, with the highest rates in 2015, 2016, and 2019. The cost of dengue management has increased in the private health care setting over the years.

A Privacy-Preserved Internet-of-Medical-Things Scheme for Eradication and Control of Dengue Using UAV

Dengue is a mosquito-borne viral infection, found in tropical and sub-tropical climates worldwide, mostly in urban and semi-urban areas. Countries like Pakistan receive heavy rains annually resulting in floods in urban cities due to poor drainage systems. Currently, different cities of Pakistan are at high risk of dengue outbreaks, as multiple dengue cases have been reported due to poor flood control and drainage systems. After heavy rain in urban areas, mosquitoes are provided with a favorable environment for their breeding and transmission through stagnant water due to poor maintenance of the drainage system. The history of the dengue virus in Pakistan shows that there is a closed relationship between dengue outbreaks and a rainfall. There is no specific treatment for dengue; however, the outbreak can be controlled through internet of medical things (IoMT). In this paper, we propose a novel privacy-preserved IoMT model to control dengue virus outbreaks by tracking dengue virus-infected patients based on bedding location extracted using call data record analysis (CDRA). Once the bedding location of the patient is identified, then the actual infected spot can be easily located by using geographic information system mapping. Once the targeted spots are identified, then it is very easy to eliminate the dengue by spraying the affected areas with the help of unmanned aerial vehicles (UAVs). The proposed model identifies the targeted spots up to 100%, based on the bedding location of the patient using CDRA.

Knowledge gaps in the epidemiology of severe dengue impede vaccine evaluation

The most severe consequences of dengue virus infection include shock, haemorrhage, and major organ failure; however, the frequency of these manifestations varies, and the relative contribution of pre-existing anti-dengue virus antibodies, virus characteristics, and host factors (including age and comorbidities) are not well understood. Reliable characterisation of the epidemiology of severe dengue first depends on the use of consistent definitions of disease severity. As vaccine trials have shown, severe dengue is a crucial interventional endpoint, yet the infrequency of its occurrence necessitates the inclusion of thousands of study participants to appropriately compare its frequency among participants who have and have not been vaccinated. Hospital admission is frequently used as a proxy for severe dengue; however, lack of specificity and variability in clinical practices limit the reliability of this approach. Although previous infection with a dengue virus is the best characterised risk factor for developing severe dengue, the influence of the timing between dengue virus infections and the sequence of dengue virus infections on disease severity is only beginning to be elucidated. To improve our understanding of the diverse factors that shape the clinical spectrum of disease resulting from dengue virus infection, prospective, community-based and clinic-based immunological, virological, genetic, and clinical studies across a range of ages and geographical regions are needed.

Improving Dengue Forecasts by Using Geospatial Big Data Analysis in Google Earth Engine and the Historical Dengue Information-Aided Long Short Term Memory Modeling

Simple Summary

Forecasting dengue cases often face challenges from (1) time-effectiveness due to time-consuming satellite data downloading and processing, (2) weak spatial representation due to data dependence on administrative unit-based statistics or weather station-based observations, and (3) stagnant accuracy without historical dengue cases. With the advance of the geospatial big data cloud computing in Google Earth Engine and deep learning, this study proposed an efficient framework of dengue prediction at an epidemiological week basis using geospatial big data analysis in Google Earth Engine and Long Short Term Memory modeling. We focused on the dengue epidemics in the Federal District of Brazil during 2007–2019. Based on Google Earth Engine and epidemiological calendar, we computed the weekly composite for each dengue driving factor, and spatially aggregated the pixel values into dengue transmission areas to generate the time series of driving factors. A multi-step-ahead Long Short Term Memory modeling was used, and the time-differenced natural log-transformed dengue cases and the time series of driving factors were considered as outcomes and explantary factors, respectively, with two modeling scenarios (with and without historical cases). The performance is better when historical cases were used, and the 5-weeks-ahead forecast has the best performance.

Abstract

Timely and accurate forecasts of dengue cases are of great importance for guiding disease prevention strategies, but still face challenges from (1) time-effectiveness due to time-consuming satellite data downloading and processing, (2) weak spatial representation capability due to data dependence on administrative unit-based statistics or weather station-based observations, and (3) stagnant accuracy without the application of historical case information. Geospatial big data, cloud computing platforms (e.g., Google Earth Engine, GEE), and emerging deep learning algorithms (e.g., long short term memory, LSTM) provide new opportunities for advancing these efforts. Here, we focused on the dengue epidemics in the urban agglomeration of the Federal District of Brazil (FDB) during 2007–2019. A new framework was proposed using geospatial big data analysis in the Google Earth Engine (GEE) platform and long short term memory (LSTM) modeling for dengue case forecasts over an epidemiological week basis. We first defined a buffer zone around an impervious area as the main area of dengue transmission by considering the impervious area as a human-dominated area and used the maximum distance of the flight range of Aedes aegypti and Aedes albopictus as a buffer distance. Those zones were used as units for further attribution analyses of dengue epidemics by aggregating the pixel values into the zones. The near weekly composite of potential driving factors was generated in GEE using the epidemiological weeks during 2007–2019, from the relevant geospatial data with daily or sub-daily temporal resolution. A multi-step-ahead LSTM model was used, and the time-differenced natural log-transformed dengue cases were used as outcomes. Two modeling scenarios (with and without historical dengue cases) were set to examine the potential of historical information on dengue forecasts. The results indicate that the performance was better when historical dengue cases were used and the 5-weeks-ahead forecast had the best performance, and the peak of a large outbreak in 2019 was accurately forecasted. The proposed framework in this study suggests the potential of the GEE platform, the LSTM algorithm, as well as historical information for dengue risk forecasting, which can easily be extensively applied to other regions or globally for timely and practical dengue forecasts.

Using heterogeneous data to identify signatures of dengue outbreaks at fine spatio-temporal scales across Brazil

Dengue virus remains a significant public health challenge in Brazil, and seasonal preparation efforts are hindered by variable intra- and interseasonal dynamics. Here, we present a framework for characterizing weekly dengue activity at the Brazilian mesoregion level from 2010-2016 as time series properties that are relevant to forecasting efforts, focusing on outbreak shape, seasonal timing, and pairwise correlations in magnitude and onset. In addition, we use a combination of 18 satellite remote sensing imagery, weather, clinical, mobility, and census data streams and regression methods to identify a parsimonious set of covariates that explain each time series property. The models explained 54% of the variation in outbreak shape, 38% of seasonal onset, 34% of pairwise correlation in outbreak timing, and 11% of pairwise correlation in outbreak magnitude. Regions that have experienced longer periods of drought sensitivity, as captured by the "normalized burn ratio," experienced less intense outbreaks, while regions with regular fluctuations in relative humidity had less regular seasonal outbreaks. Both the pairwise correlations in outbreak timing and outbreak trend between mesoresgions were best predicted by distance. Our analysis also revealed the presence of distinct geographic clusters where dengue properties tend to be spatially correlated. Forecasting models aimed at predicting the dynamics of dengue activity need to identify the most salient variables capable of contributing to accurate predictions. Our findings show that successful models may need to leverage distinct variables in different locations and be catered to a specific task, such as predicting outbreak magnitude or timing characteristics, to be useful. This advocates in favor of "adaptive models" rather than "one-size-fits-all" models. The results of this study can be applied to improving spatial hierarchical or target-focused forecasting models of dengue activity across Brazil.

A prospective, multicentre, cohort study to assess the incidence of dengue illness in households from selected communities in Brazil (2014-2018)

OBJECTIVES

To estimate the incidence of dengue infection across geographically distinct areas of Brazil.

METHODS

This prospective, household-based, cohort study enrolled participants in five areas and followed them up for up to 4 years (2014-2018). Dengue seroprevalence was assessed at each scheduled visit. Suspected dengue cases were identified through enhanced passive and active surveillance. Acute symptomatic dengue infection was confirmed through reverse-transcriptase quantitative polymerase chain reaction in combination with an antigenic assay (non-structural protein 1) and serology.

RESULTS

Among 3300 participants enrolled, baseline seroprevalence was 76.2%, although only 23.3% of participants reported a history of dengue. Of 1284 suspected symptomatic dengue cases detected, 50 (3.9%) were laboratory-confirmed. Based on 8166.5 person-years (PY) of follow-up, the incidence of laboratory-confirmed symptomatic infection (primary endpoint) was 6.1 per 1000 PY (95% confidence interval [CI]: 4.5, 8.1). Incidence varied substantially in different years (1.8-7.4 per 1000 PY). The incidence of inapparent primary dengue infection was substantially higher: 41.7 per 1000 PY (95% CI: 31.1, 54.6).

CONCLUSIONS

Our findings, highlighting that the incidence of dengue infection is underestimated in Brazil, will inform the design and implementation of future dengue vaccine trials.

CLINICAL TRIAL REGISTRATION

NCT01751139.

Visual analytics of COVID-19 dissemination in São Paulo state, Brazil

Visual analytics techniques are useful tools to support decision-making and cope with increasing data, particularly to monitor natural or artificial phenomena. When monitoring disease progression, visual analytics approaches help decision-makers to understand or even prevent dissemination paths. In this paper, we propose a new visual analytics tool for monitoring COVID-19 dissemination. We use k-nearest neighbors of cities to mimic neighboring cities and analyze COVID-19 dissemination based on comparing a city under consideration and its neighborhood. Moreover, such analysis is performed within periods, which facilitates the assessment of isolation policies. We validate our tool by analyzing the progression of COVID-19 in neighboring cities of São Paulo state, Brazil.

Development and Evaluation of a Fully Automated Surveillance System for Influenza-Associated Hospitalization at a Multihospital Health System in Northeast Ohio

BACKGROUND

Performing high-quality surveillance for influenza-associated hospitalization (IAH) is challenging, time-consuming, and essential.

OBJECTIVES

Our objectives were to develop a fully automated surveillance system for laboratory-confirmed IAH at our multihospital health system, to evaluate the performance of the automated system during the 2018 to 2019 influenza season at eight hospitals by comparing its sensitivity and positive predictive value to that of manual surveillance, and to estimate the time and cost savings associated with reliance on the automated surveillance system.

METHODS

Infection preventionists (IPs) perform manual surveillance for IAH by reviewing laboratory records and making a determination about each result. For automated surveillance, we programmed a query against our Enterprise Data Vault (EDV) for cases of IAH. The EDV query was established as a dynamic data source to feed our data visualization software, automatically updating every 24 hours.To establish a gold standard of cases of IAH against which to evaluate the performance of manual and automated surveillance systems, we generated a master list of possible IAH by querying four independent information systems. We reviewed medical records and adjudicated whether each possible case represented a true case of IAH.

RESULTS

We found 844 true cases of IAH, 577 (68.4%) of which were detected by the manual system and 774 (91.7%) of which were detected by the automated system. The positive predictive values of the manual and automated systems were 89.3 and 88.3%, respectively.Relying on the automated surveillance system for IAH resulted in an average recoup of 82 minutes per day for each IP and an estimated system-wide payroll redirection of $32,880 over the four heaviest weeks of influenza activity.

CONCLUSION

Surveillance for IAH can be entirely automated at multihospital health systems, saving time, and money while improving case detection.

Evaluating the validity of dengue clinical-epidemiological criteria for diagnosis in patients residing in a Brazilian endemic area

BACKGROUND

We evaluated the validity of clinical diagnosis compared with laboratory diagnosis of dengue in a retrospective sample of patients in São José do Rio Preto, Brazil.

METHODS

Our sample included 148 299 clinically (56.3%) or laboratory-diagnosed (43.7%) dengue cases. We compared the sensitivity, specificity, positive and negative predictive value (PPV and NPV) of dengue patients' demographic and clinical characteristics with laboratory-based diagnosis. We used logistic regressions to estimate the correlation between clinical and laboratory diagnosis of dengue and a full set of dengue signs and symptoms.

RESULTS

We found substantial variability in sensitivity and specificity of signs and symptoms ranging from 0.8-81.1 and 21.5-99.6, respectively. Thrombocytopenia exhibited the highest PPV (92.0) and lowest NPV (42.2) and was the only symptom showing agreement with laboratory-confirmed dengue (φ = 0.38). The presence of exanthema and thrombocytopenia led to a greater likelihood of concordant clinical and laboratory diagnoses (exanthema: OR: 4.23; 95% CI: 2.09 to 8.57; thrombocytopenia: OR: 4.02; 95% CI: 1.32 to 12.27).

CONCLUSIONS

We found substantial variation in sensitivity, specificity, PPV and NPV of dengue signs and symptoms. For accuracy, clinical and laboratory diagnosis of dengue should be performed concurrently. When laboratory tests are not available, we suggest focusing on the clinical manifestations most associated with dengue.

Colares JK, C. B. Coelho Z, M. C. Araújo F, Dias Schwarcz W, Duarte AC, Borges B, Minguet C, Guignard A. Dengue Infection in Children in Fortaleza, Brazil: A 3-Year School-Based Prospective Cohort Study

Dengue is endemic in Brazil. The dengue surveillance system's reliance on passive reporting may underestimate disease incidence and cannot detect asymptomatic/pauci-symptomatic cases. In this 3-year prospective cohort study (NCT01391819) in 5- to 13-year-old children from nine schools in Fortaleza (N = 2,117), we assessed dengue virus (DENV) infection seroprevalence by IgG indirect ELISA at yearly visits and disease incidence through active and enhanced passive surveillance. Real-time quantitative polymerase chain reaction (RT-qPCR) and DENV IgM/IgG capture ELISA were used for diagnosis. We further characterized confirmed and probable cases with a plaque reduction neutralization test. At enrollment, 54.1% (95% CI: 46.6, 61.4) of children were DENV IgG positive. The annual incidence of laboratory-confirmed symptomatic dengue cases was 11.0 (95% CI: 7.3, 14.7), 18.1 (10.4, 25.7), and 10.2 (0.7, 19.7), and of laboratory-confirmed or probable dengue cases with neutralizing antibody profile evocative of dengue exposure was 13.2 (6.6, 19.9), 18.7 (5.3, 32.2), and 8.4 (2.4, 19.2) per 1,000 child-years in 2012, 2013, and 2014, respectively. By RT-qPCR, we identified 14 DENV-4 cases in 2012-2013 and seven DENV-1 cases in 2014. During the course of the study, 32.8% of dengue-naive children experienced a primary infection. Primary inapparent dengue infection was detected in 20.3% (95% CI: 13.6, 29.1) of dengue-naive children in 2012, 8.7% (6.9, 10.9) in 2013, and 5.1% (4.4, 6.0) in 2014. Our results confirmed the high dengue endemicity in Fortaleza, with active and enhanced passive surveillance detecting three to five times more cases than the National System of Disease Notification.

Effectiveness of an intervention for Aedes aegypti control scaled-up under an inter-sectoral approach in a Colombian city hyper-endemic for dengue virus

Aedes aegypti transmitted arboviral diseases are of significant importance in Colombia, particularly since the 2014/2015 introduction of chikungunya and Zika in the Americas and the increasing spread of dengue. In response, the Colombian government initiated the scaling-up of a community-based intervention under inter and multi-sector partnerships in two out of four sectors in Girardot, one of the most hyper-endemic dengue cities in the country. Using a quasi-experimental research design a scaled-up community-led Aedes control intervention was assessed for its capacity to reduce dengue from January 2010 to August 2017 in Girardot, Colombia. Reported dengue cases, and associated factors were analysed from available data sets from the Colombian disease surveillance systems. We estimated the reduction in dengue cases before and after the intervention using, Propensity Score Matching and an Autoregressive Moving Average model for robustness. In addition, the differences in dengue incidence among scaling-up phases (pre-implementation vs sustainability) and between treatment groups (intervention and control areas) were modelled. Evidence was found in favour of the intervention, although to maximise impact the scaling-up of the intervention should continue until it covers the remaining sectors. It is expected that a greater impact of the intervention can be documented in the next outbreak of dengue in Girardot.

Google Health Trends performance reflecting dengue incidence for the Brazilian states

Background

Dengue fever is a mosquito-borne infection transmitted by Aedes aegypti and mainly found in tropical and subtropical regions worldwide. Since its re-introduction in 1986, Brazil has become a hotspot for dengue and has experienced yearly epidemics. As a notifiable infectious disease, Brazil uses a passive epidemiological surveillance system to collect and report cases; however, dengue burden is underestimated. Thus, Internet data streams may complement surveillance activities by providing real-time information in the face of reporting lags.

Methods

We analyzed 19 terms related to dengue using Google Health Trends (GHT), a free-Internet data-source, and compared it with weekly dengue incidence between 2011 to 2016. We correlated GHT data with dengue incidence at the national and state-level for Brazil while using the adjusted R squared statistic as primary outcome measure (0/1). We used survey data on Internet access and variables from the official census of 2010 to identify where GHT could be useful in tracking dengue dynamics. Finally, we used a standardized volatility index on dengue incidence and developed models with different variables with the same objective.

Results

From the 19 terms explored with GHT, only seven were able to consistently track dengue. From the 27 states, only 12 reported an adjusted R squared higher than 0.8; these states were distributed mainly in the Northeast, Southeast, and South of Brazil. The usefulness of GHT was explained by the logarithm of the number of Internet users in the last 3 months, the total population per state, and the standardized volatility index.

Conclusions

The potential contribution of GHT in complementing traditional established surveillance strategies should be analyzed in the context of geographical resolutions smaller than countries. For Brazil, GHT implementation should be analyzed in a case-by-case basis. State variables including total population, Internet usage in the last 3 months, and the standardized volatility index could serve as indicators determining when GHT could complement dengue state level surveillance in other countries.

Dengue Surveillance System in Brazil: A Qualitative Study in the Federal District

Dengue's increasing trends raise concerns over global health and pose a challenge to the Brazilian health system, highlighting the necessity of a strong surveillance system to reduce morbidity, mortality, and the economic burden of this disease. Although the Brazilian surveillance system reports more dengue cases than any other country, recent studies suggest that non-reported cases are the majority. The aim of the study is to explore the strengths and weaknesses of the Brazilian surveillance system, particularly looking at the functioning of data collection and reporting. This was done through qualitative semi-structured interviews with 17 experts in dengue surveillance, supported by quantitative data from the official notification system. To select the interviewees, purposive and theoretical sampling were used. Data were analyzed through thematic analysis. The research highlighted that a lack of human and technological resources in healthcare units and surveillance departments slows down the notification process and data analysis. Due to a lack of integration in the private sector, the surveillance system fails to detect the socioeconomic profile of the patients. Investments in public healthcare, human and technological resources for surveillance and better integration in the private healthcare system, and vector surveillance may improve dengue surveillance.

Administrative Data Linkage in Brazil: Potentials for Health Technology Assessment

Health technology assessment (HTA) is the systematic evaluation of the properties and impacts of health technologies and interventions. In this article, we presented a discussion of HTA and its evolution in Brazil, as well as a description of secondary data sources available in Brazil with potential applications to generate evidence for HTA and policy decisions. Furthermore, we highlighted record linkage, ongoing record linkage initiatives in Brazil, and the main linkage tools developed and/or used in Brazilian data. Finally, we discussed the challenges and opportunities of using secondary data for research in the Brazilian context. In conclusion, we emphasized the availability of high quality data and an open, modern attitude toward the use of data for research and policy. This is supported by a rigorous but enabling legal framework that will allow the conduct of large-scale observational studies to evaluate clinical, economical, and social impacts of health technologies and social policies.

Spatio-temporal dynamics of dengue in Brazil: Seasonal travelling waves and determinants of regional synchrony

Dengue continues to be the most important vector-borne viral disease globally and in Brazil, where more than 1.4 million cases and over 500 deaths were reported in 2016. Mosquito control programmes and other interventions have not stopped the alarming trend of increasingly large epidemics in the past few years. Here, we analyzed monthly dengue cases reported in Brazil between 2001 and 2016 to better characterise the key drivers of dengue epidemics. Spatio-temporal analysis revealed recurring travelling waves of disease occurrence. Using wavelet methods, we characterised the average seasonal pattern of dengue in Brazil, which starts in the western states of Acre and Rondônia, then travels eastward to the coast before reaching the northeast of the country. Only two states in the north of Brazil (Roraima and Amapá) did not follow the countrywide pattern and had inconsistent timing of dengue epidemics throughout the study period. We also explored epidemic synchrony and timing of annual dengue cycles in Brazilian regions. Using gravity style models combined with climate factors, we showed that both human mobility and vector ecology contribute to spatial patterns of dengue occurrence. This study offers a characterization of the spatial dynamics of dengue in Brazil and its drivers, which could inform intervention strategies against dengue and other arboviruses.

Systematic review of health economic evaluation studies of dengue vaccines

OBJECTIVES

To review the literature on economic evaluation of dengue vaccination to produce evidence to support a local cost-effectiveness study and to subsidize the decision to introduce a dengue vaccine in the Brazilian National Immunization Program.

METHODS

We systematically searched multiple databases (MEDLINE (via PubMed), EMBASE, SCOPUS, NHS Economic Evaluation Database (NHS EED), HTA Database (via Centre for Reviews and Dissemination - CRD) and LILACS), selecting full HEEs of dengue vaccine. Two independent reviewers screened articles for relevance and extracted the data. The methodology for the quality reporting was assessed using CHEERS checklist. We performed a qualitative narrative synthesis.

RESULTS

Thirteen studies conducted in Asian and Latin America countries were reviewed. All studies were favorable to the incorporation of the vaccine. However, the assumptions and values assumed for vaccine efficacy, safety and duration of protection, as well as the choice of the study population and the type of model used in the analyses, associated to an insufficient reporting of the methodological steps, affect the validity of the studies' results. The quality reporting appraisal showed that the majority (8/13) of the studies reported less than 55% of the CHEERS checklists' items.

CONCLUSIONS

This systematic review shows that the economic evaluation of dengue vaccination did not adhere to key recommended general methods for economic evaluation. The presented cost-effectiveness results should not be transferred to other countries. It is recommended to conduct studies with local epidemiological and cost data, as well as assumptions about vaccination that reflect the results observed in clinical trials.

Time series analysis of dengue surveillance data in two Brazilian cities

The aim of the study was to evaluate the temporal patterns of dengue incidence from 2001 to 2014 and forecast for 2015 in two Brazilian cities. We analysed dengue surveillance data (SINAN) from Recife, 1.6 million population, and Goiania, 1.4 million population. We used Auto-Regressive Integrated Moving Average (ARIMA) modelling of monthly notified dengue incidence (2001-2014). Forecasting models (95% prediction interval) were developed to predict numbers of dengue cases for 2015. During the study period, 73,479 dengue cases were reported in Recife varying from 11 cases/100,000 inhab (2004) to 2418 cases/100,000 inhab (2002). In Goiania, 253,008 dengue cases were reported and the yearly incidence varied from 293 cases/100,000 inhab (2004) to 3927 cases/100,000 inhab (2013). Trend was the most important component for Recife, while seasonality was the most important one in Goiania. For Recife, the best fitted model was ARIMA (1,1,3)¹² and for Goiania Seasonal ARIMA (1,0,2) (1,1,2)¹². The model predicted 4254 dengue cases for Recife in 2015; SINAN registered 35,724 cases. For Goiania the model predicted 33,757 cases for 2015; the reported number of cases by SINAN was 74,095, within the 95% prediction interval. The difference between notified and forecasted dengue cases in Recife can be explained by the co-circulation of dengue and Zika virus in 2015. In this year, all cases with rash were notified as "dengue-like" illness. The ARIMA models may be considered a baseline for the time series analysis of dengue incidence before the Zika epidemic.

Clinical, laboratory, and demographic determinants of hospitalization due to dengue in 7613 patients: A retrospective study based on hierarchical models

In Brazil, the incidence of hospitalization due to dengue, as an indicator of severity, has drastically increased since 1998. The objective of our study was to identify risk factors associated with subsequent hospitalization related to dengue. We analyzed 7613 dengue confirmed via serology (ELISA), non-structural protein 1, or polymerase chain reaction amplification. We used a hierarchical framework to generate a multivariate logistic regression based on a variety of risk variables. This was followed by multiple statistical analyses to assess hierarchical model accuracy, variance, goodness of fit, and whether or not this model reliably represented the population. The final model, which included age, sex, ethnicity, previous dengue infection, hemorrhagic manifestations, plasma leakage, and organ failure, showed that all measured parameters, with the exception of previous dengue, were statistically significant. The presence of organ failure was associated with the highest risk of subsequent dengue hospitalization (OR=5·75; CI=3·53-9·37). Therefore, plasma leakage and organ failure were the main indicators of hospitalization due to dengue, although other variables of minor importance should also be considered to refer dengue patients to hospital treatment, which may lead to a reduction in avoidable deaths as well as costs related to dengue.

Dengue hospitalisations in Brazil: annual wave from West to East and recent increase among children

The number of dengue epidemics in Brazil has increased dramatically in the last 15 years. In this study, we analysed the seasonal patterns in the incidence of hospitalisations due to dengue across the different states of Brazil and compared these with the corresponding climatic patterns. We discovered that the seasonality of dengue hospitalisations in Brazil has a clear zonal gradient, characterised by the progression of primary peaks from West to East during the first half of the year, which may be associated with the increased vapour pressure and rainfall during this period, leading to increased mosquito abundance and activity. We also found that the proportion of children among hospitalised individuals was especially high during the peak outbreaks in 2007/2008 and 2010. This may be due to the emergence and spread of the new DENV-2 Southeast Asian genotype lineage II from 2007, which has probably arrived from the Caribbean and may have caused an increase in incidence and severity of the disease, particularly among children. Our findings may allow health systems to improve control interventions and contribute to reducing dengue morbidity and mortality by using integrated vector control in conjunction with early diagnosis and prompt supportive care.

Dengue prediction by the web: Tweets are a useful tool for estimating and forecasting Dengue at country and city level

BACKGROUND

Infectious diseases are a leading threat to public health. Accurate and timely monitoring of disease risk and progress can reduce their impact. Mentioning a disease in social networks is correlated with physician visits by patients, and can be used to estimate disease activity. Dengue is the fastest growing mosquito-borne viral disease, with an estimated annual incidence of 390 million infections, of which 96 million manifest clinically. Dengue burden is likely to increase in the future owing to trends toward increased urbanization, scarce water supplies and, possibly, environmental change. The epidemiological dynamic of Dengue is complex and difficult to predict, partly due to costly and slow surveillance systems.

METHODOLOGY / PRINCIPAL FINDINGS

In this study, we aimed to quantitatively assess the usefulness of data acquired by Twitter for the early detection and monitoring of Dengue epidemics, both at country and city level at a weekly basis. Here, we evaluated and demonstrated the potential of tweets modeling for Dengue estimation and forecast, in comparison with other available web-based data, Google Trends and Wikipedia access logs. Also, we studied the factors that might influence the goodness-of-fit of the model. We built a simple model based on tweets that was able to 'nowcast', i.e. estimate disease numbers in the same week, but also 'forecast' disease in future weeks. At the country level, tweets are strongly associated with Dengue cases, and can estimate present and future Dengue cases until 8 weeks in advance. At city level, tweets are also useful for estimating Dengue activity. Our model can be applied successfully to small and less developed cities, suggesting a robust construction, even though it may be influenced by the incidence of the disease, the activity of Twitter locally, and social factors, including human development index and internet access.

CONCLUSIONS

Tweets association with Dengue cases is valuable to assist traditional Dengue surveillance at real-time and low-cost. Tweets are able to successfully nowcast, i.e. estimate Dengue in the present week, but also forecast, i.e. predict Dengue at until 8 weeks in the future, both at country and city level with high estimation capacity.