Dataset Information

Epidemiology of Dengue Among Children Aged < 18 Months-Puerto Rico, 1999-2011.

ABSTRACT: Dengue, a mosquito-borne viral illness caused by dengue virus types (DENV)-1 to DENV-4, is endemic in Puerto Rico. Severe dengue usually occurs in individuals previously infected with DENV or among infants born to previously infected mothers. To describe clinical features of dengue in infants, we retrospectively characterized dengue patients aged < 18 months reported to the Passive Dengue Surveillance System (PDSS) during 1999-2011. To determine frequency of signs, symptoms, and disease severity, case report forms and medical records were evaluated for patients who tested positive for dengue by reverse transcriptase polymerase chain reaction or anti-DENV immunoglobulin Menzyme-linked immunosorbent assay. Of 4,178 reported patients aged < 18 months, 813 (19%) were laboratory positive. Of these, most had fever (92%), rash (53%), bleeding manifestations (52%), and thrombocytopenia (52%). Medical records were available for 145 (31%) of 472 hospitalized patients, of which 40% had dengue, 23% had dengue with warning signs, and 33% had severe dengue. Mean age of patients with severe dengue was 8 months. Anti-DENV immunoglobulin G (IgG) titers were not statistically different in patients with (50%) and without (59%) severe dengue. In this study, one-third of DENV-infected infants met the severe dengue case definition. The role of maternal anti-DENV IgG in development of severe disease warrants further study in prospective cohorts of mother-infant pairs.

SUBMITTER: Hause AM

PROVIDER: S-EPMC4751950 | biostudies-literature | 2016 Feb

REPOSITORIES: biostudies-literature

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Epidemiology of Dengue Among Children Aged < 18 Months-Puerto Rico, 1999-2011.

Hause Anne M AM Perez-Padilla Janice J Horiuchi Kalanthe K Han George S GS Hunsperger Elizabeth E Aiwazian Jonathan J Margolis Harold S HS Tomashek Kay M KM

The American journal of tropical medicine and hygiene 20151228 2

Dengue, a mosquito-borne viral illness caused by dengue virus types (DENV)-1 to DENV-4, is endemic in Puerto Rico. Severe dengue usually occurs in individuals previously infected with DENV or among infants born to previously infected mothers. To describe clinical features of dengue in infants, we retrospectively characterized dengue patients aged < 18 months reported to the Passive Dengue Surveillance System (PDSS) during 1999-2011. To determine frequency of signs, symptoms, and disease severity ...[more]

PMID: 26711519

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Project description:BackgroundDistinguishing between non-severe and severe dengue is crucial for timely intervention and reducing morbidity and mortality. World Health Organization (WHO)-recommended warning signs offer a practical approach for clinicians but have limited sensitivity and specificity. This study aims to evaluate machine learning (ML) model performance compared to WHO-recommended warning signs in predicting severe dengue among laboratory-confirmed cases in Puerto Rico.MethodsWe analyzed data from Puerto Rico's Sentinel Enhanced Dengue Surveillance System (May 2012-August 2024), using 40 clinical, demographic, and laboratory variables. Nine ML models, including Decision Trees, K-Nearest Neighbors, Naïve Bayes, Support Vector Machines, Artificial Neural Networks, AdaBoost, CatBoost, LightGBM, and XGBoost, were trained using fivefold cross-validation and evaluated with area under the receiver operating characteristic curve (AUC-ROC), sensitivity, and specificity. A subanalysis excluded hemoconcentration and leukopenia to assess performance in resource-limited settings. An AUC-ROC value of 0.5 indicates no discriminative power, while values closer to 1.0 reflect better performance.ResultsAmong the 1708 laboratory-confirmed dengue cases, 24.3% were classified as severe. Gradient boosting algorithms achieved the highest predictive performance, with an AUC-ROC of 97.1% (95% CI: 96.0-98.3%) for CatBoost using the full 40-variable feature set. Feature importance analysis identified hemoconcentration (≥ 20% increase during illness or ≥ 20% above baseline for age and sex), leukopenia (white blood cell count < 4000/mm3), and timing of presentation at 4-6 days post-symptom onset as key predictors. When excluding hemoconcentration and leukopenia, the CatBoost AUC-ROC was 96.7% (95% CI: 95.5-98.0%), demonstrating minimal reduction in performance. Individual warning signs like abdominal pain and restlessness had sensitivities of 79.0% and 64.6%, but lower specificities of 48.4% and 59.1%, respectively. Combining ≥ 3 warning signs improved specificity (80.9%) while maintaining moderate sensitivity (78.6%), resulting in an AUC-ROC of 74.0%.ConclusionsML models, especially gradient boosting algorithms, outperformed traditional warning signs in predicting severe dengue. Integrating these models into clinical decision-support tools could help clinicians better identify high-risk patients, guiding timely interventions like hospitalization, closer monitoring, or the administration of intravenous fluids. The subanalysis excluding hemoconcentration confirmed the models' applicability in resource-limited settings, where access to laboratory data may be limited.

Project description:BackgroundThe incidence and severity of dengue in Latin America has increased substantially in recent decades and data from Puerto Rico suggests an increase in severe cases. Successful clinical management of severe dengue requires early recognition and supportive care.MethodsFatal cases were identified among suspected dengue cases reported to two disease surveillance systems and from death certificates. To be included, fatal cases had to have specimen submitted for dengue diagnostic testing including nucleic acid amplification for dengue virus (DENV) in serum or tissue, immunohistochemical testing of tissue, and immunoassay detection of anti-DENV IgM from serum. Medical records from laboratory-positive dengue fatal case-patients were reviewed to identify possible determinants for death.ResultsAmong 10,576 reported dengue cases, 40 suspect fatal cases were identified, of which 11 were laboratory-positive, 14 were laboratory-negative, and 15 laboratory-indeterminate. The median age of laboratory-positive case-patients was 26 years (range 5 months to 78 years), including five children aged < 15 years; 7 sought medical care at least once prior to hospital admission, 9 were admitted to hospital and 2 died upon arrival. The nine hospitalized case-patients stayed a mean of 15 hours (range: 3-48 hours) in the emergency department (ED) before inpatient admission. Five of the nine case-patients received intravenous methylprednisolone and four received non-isotonic saline while in shock. Eight case-patients died in the hospital; five had their terminal event on the inpatient ward and six died during a weekend. Dengue was listed on the death certificate in only 5 instances.ConclusionsDuring a dengue epidemic in an endemic area, none of the 11 laboratory-positive case-patients who died were managed according to current WHO Guidelines. Management issues identified in this case-series included failure to recognize warning signs for severe dengue and shock, prolonged ED stays, and infrequent patient monitoring.

Project description:BackgroundPredictive models can serve as early warning systems and can be used to forecast future risk of various infectious diseases. Conventionally, regression and time series models are used to forecast dengue incidence, using dengue surveillance (e.g., case counts) and weather data. However, these models may be limited in terms of model assumptions and the number of predictors that can be included. Machine learning (ML) methods are designed to work with a large number of predictors and thus offer an appealing alternative. Here, we compared the performance of ML algorithms with that of regression models in predicting dengue cases and outbreaks from 4 to up to 12 weeks in advance. Many countries lack sufficient health surveillance infrastructure, as such we evaluated the contribution of dengue surveillance and weather data on the predictive power of these models.MethodsWe developed ML, regression, and time series models to forecast weekly dengue case counts and outbreaks in Iquitos, Peru; San Juan, Puerto Rico; and Singapore from 1990-2016. Forecasts were generated using available weekly dengue surveillance, and weather data. We evaluated the agreement between model forecasts and actual dengue observations using Mean Absolute Error and Matthew's Correlation Coefficient (MCC).ResultsFor near term predictions of weekly case counts and when using surveillance data, ML models had 21% and 33% less error than regression and time series models respectively. However, using weather data only, ML models did not demonstrate a practical advantage. When forecasting weekly dengue outbreaks 12 weeks in advance, ML models achieved a maximum MCC of 0.61.ConclusionsOur results identified 2 scenarios when ML models are advantageous over regression model: 1) predicting dengue weekly case counts 4 weeks ahead when dengue surveillance data are available and 2) predicting weekly dengue outbreaks 12 weeks ahead when dengue surveillance data are unavailable. Given the advantages of ML models, dengue early warning systems may be improved by the inclusion of these models.

Dataset Information

Epidemiology of Dengue Among Children Aged < 18 Months-Puerto Rico, 1999-2011.

Publications

Epidemiology of Dengue Among Children Aged < 18 Months-Puerto Rico, 1999-2011.

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