Risk Factors of Dengue Fever in Urban Areas of Rawalpindi District in Pakistan During 2017: A Case Control Study

Space-time scanning statistics in the prediction and evaluation of dengue epidemic clusters

Objectives

To detect clusters of dengue hemorrhagic fever in an urbanized district of Hai Phong City, Vietnam using Poisson space-time retrospective and prospective analysis.

Methods

A cross-sectional and retrospective study analyzed dengue surveillance data in the period from January 01, 2018, to December 31, 2022. Spatial-temporal scanning statistics were performed using the free software SatScan v10.1.2.

Results

A total of 519 cases were recorded. The cumulative incidence per 100,000 inhabitants was 3.37, 127.36, 10.96, 0, and 296.04 in 2018, 2019, 2020, 2021, and 2022, respectively. By retrospective Poisson model-based analysis, seven clusters were detected. Six of these seven detected outbreaks occurred in November and December 2022. The largest cluster had a relative risk (RR) of 1539.5 (P <0.00001). The smallest cluster has a RR of 316.1 (P = 0.006). Prospective analysis using the Poisson model significantly detected four active case clusters at the time of the study. The largest cluster of cases with RR was 47.7 (P <0.00001) and the smallest cluster with RR was 18.2 (P <0.00001).

Conclusions

This study provides a basis for improving the effectiveness of interventions and conducting further investigations into risk factors in the study area, as well as in other urban and suburban areas nationwide.

Support Vector Machine Outperforms Other Machine Learning Models in Early Diagnosis of Dengue Using Routine Clinical Data

Background: There is a dire need for the establishment of active dengue surveillance to continuously detect cases, circulating serotypes, and determine the disease burden of dengue fever (DF) in the country and region. Predicting dengue PCR results using machine learning (ML) models represents a significant advancement in pre-emptive healthcare measures. This study outlines the comprehensive process of data preprocessing, model selection, and the underlying mechanisms of each algorithm employed to accurately predict dengue PCR outcomes. Methods: We analyzed data from 300 suspected dengue patients in Islamabad and Rawalpindi, Pakistan, from August to October 2023. NS1 antigen ELISA, IgM and IgG antibody tests, and serotype-specific real-time polymerase chain reaction (RT-PCR) were used to detect the dengue virus (DENV). Representative PCR-positive samples were sequenced by Sanger sequencing to confirm the circulation of various dengue serotypes. Demographic information, serological test results, and hematological parameters were used as inputs to the ML models, with the dengue PCR result serving as the output to be predicted. The models used were logistic regression, XGBoost, LightGBM, random forest, support vector machine (SVM), and CatBoost. Results: Of the 300 patients, 184 (61.33%) were PCR positive. Among the total positive cases detected by PCR, 9 (4.89%), 171 (92.93%), and 4 (2.17%) were infected with serotypes 1, 2, and 3, respectively. A total of 147 (79.89%) males and 37 (20.11%) females were infected, with a mean age of 33 ± 16 years. In addition, the mean platelet and leukocyte counts and the hematocrit percentages were 75,447%, 4189.02%, and 46.05%, respectively. The SVM was the best-performing ML model for predicting RT-PCR results, with 71.4% accuracy, 97.4% recall, and 71.6% precision. Hyperparameter tuning improved the recall to 100%. Conclusion: Our study documents three circulating serotypes in the capital territory of Pakistan and highlights that the SVM outperformed other models, potentially serving as a valuable tool in clinical settings to aid in the rapid diagnosis of DF.

Severity of Dengue Viral Infection Based on Clinical and Hematological Parameters among Pakistani Patients

The global burden of dengue infections has increased dramatically. Early diagnosis of dengue infection is critical to proper medical management to avoid further complications in patients. This study was geared to assess the severity of dengue infections based on clinical and hematological examinations. A cross-sectional study was conducted among febrile patients with dengue infection in a teaching hospital in Pakistan. Blood samples were investigated for dengue-specific antibodies (IgM and IgG) and the nonstructural 1 antigen. The clinical findings of each subject were noted to assess the severity of the infection. Tests for hematological parameters were performed. Of 130 patients with confirmed dengue infection, 23 had severe and 107 had nonsevere dengue. Patients with severe dengue experienced mucosal bleeding (71.4%), fluid accumulation (57.1%), shock (35.7%), and gastrointestinal bleeding (28.6%). The most significant hematological findings among severe and nonsevere patients with dengue infection were thrombocytopenia, leukopenia, and a raised hematocrit level (P < 0.001). Patients with severe dengue infection showed marked thrombocytopenia, with a mean platelet count of 49.96 × 109 platelets/L. The clinical presentation of patients with dengue infection along with hematological markers are the most important clues for the diagnosis of, prognosis of, and therapy for dengue infection. Thrombocytopenia, leukopenia, and raised hematocrit levels were the most significant hematological parameters when assessing the severity of dengue infection.

Demographic and clinical variables in the dengue epidemic in Punjab, Pakistan

Objectives

To identify the latest trends in the clinical picture and severity of the disease, which will help better understand and manage dengue.

Methodology

It was a cross-sectional, hospital-based study performed in the tertiary care hospitals of Punjab from August 21 to December 2022, in which serologically and polymerase chain reaction (PCR) confirmed patients with dengue infection, were enrolled. Demographic and clinical variables were recorded on a pre-tested Performa, processed and presented in frequency and percentages, and graphs were generated. Mean and standard deviation was used to present continuous variables.

Results

Out of a total of 580 patients, 472 were diagnosed with Dengue Fever (DF) and 108 with Dengue Hemorrhagic Fever (DHF). About 79.31% of the patients were male and 20.69% were females. The mean age of patients was 32.5±9 years. Among the clinical features the percentage of high-grade fever, body aches, and vomiting were the highest. The liver function profile showed that serum bilirubin, Serum aspartate transaminase (AST), serum alanine transaminase (ALT,) and alkaline phosphatase (ALP) levels were markedly raised.

Conclusion

This study showed that with time the trends in the presentation of dengue are slowly shifting, which will help us better manage the disease burden in the future.