Predicting Dengue Patterns: A Community-Based Prospective Study in Bangladesh

Background:
Dengue continues to pose a major public health threat in Bangladesh,
particularly during seasonal outbreaks exacerbated by climatic and
environmental conditions.
Objective:
This study aimed to predict dengue transmission patterns using a
community-based prospective design that integrates demographic,
environmental, entomological, and laboratory variables.
Methods:
A total of 300 participants were enrolled for this study which was
conducted in President Abdul Hamid Medical College, Kishoregonj,
Bangladesh, from March 2023 to October 2023. Data were collected on
sociodemographics, vector breeding sites, preventive practices, climate
parameters, and laboratory confirmation of dengue cases. Multiple
linear regressions were used to identify significant predictors of dengue
incidence.
Results:
Among confirmed cases, 70% were mild, 25% hospitalized, and 5%
resulted in death. Most cases (60%) occurred during the monsoon
season. Larvae were identified in 70% of breeding sites, with
abandoned tires and water tanks as major sources. While 40% of
households showed high awareness, preventive practices were
inconsistently followed. NS1 and IgM positivity were recorded in 65%
and 70% of cases, respectively. Rainfall (p=0.001) and breeding sites
(p=0.028) were statistically significant predictors in the regression
model, which achieved 75% prediction accuracy.
Conclusion:
Rainfall and breeding site density are key predictors of dengue
outbreaks. Community-based models integrating environmental,
behavioral, and diagnostic data are crucial for effective dengue
forecasting and control in Bangladesh.