Title : Effectiveness of surveillance tools in determining drivers of dengue case Incidence and vector density in Gujranwala Pakistan
Abstract:
Background: Dengue fever remains a significant public health concern in Pakistan, with twelve large outbreaks over the past three decades, culminating in an astonishing total of 286,262 morbidities and 1,108 mortalities. Haphazard urbanization, climate change, and insufficient vector control have contributed to the spread of the disease. This study focuses on the temporal and spatial dynamics of Dengue incidence in Gujranwala, Punjab, from 2020 to 2023, to understand year-over-year trends in disease spread and vector density.
Methods: This secondary data analysis utilizes Dengue case records and vector surveillance data collected between 2020 and 2023 in Gujranwala, Pakistan. Time-trend analysis was conducted to compare case incidence and vector density across years, exploring changes in patterns due to environmental and societal factors. A logistic regression model was also developed to assess the contribution of variables such as geographic location, fever, associated symptoms, and blood markers (WBC count and platelet count) to Dengue diagnosis, aiming to identify potential areas for surveillance improvements.
Results: The sequential yearly comparison showed a marked decline in confirmed Dengue cases from 2020 to 2023, despite an increase in suspected cases and improved vector surveillance. The regression analysis revealed significant predictors of Dengue cases, including geographic region, fever, and thrombocytopenia, whilst the ROC curve suggested a strong diagnostic accuracy with an AUC of 0.80. These findings indicate that although the number of confirmed cases decreased, enhanced surveillance efforts have uncovered more potential hotspots, improving overall Dengue management.
Conclusion: The analysis highlights the importance of long-term monitoring of vector density and case incidence to identify patterns in Dengue transmission. Improvements in vector surveillance, combined with the logistic regression findings, suggest areas for enhancing early detection and targeted interventions, particularly in high-risk towns of Gujranwala.
