A Comprehensive Dengue Dataset of Bangladesh
Description
Description A major public health concern in Bangladesh, especially in the Dhaka area, is dengue fever, a virus spread by mosquitoes. This dataset offers methodically gathered real-world data to support targeted public health actions, predictive modeling, and epidemiological research. In order to ensure ethical data collection techniques, information was obtained from community members who freely provided their information and from patients who visited nearby hospitals. Environmental, serological, and demographic parameters linked to dengue incidence are included in the dataset. In order to determine the most important diagnostic markers for Dengue categorization, medical experts were also consulted. For researchers, decision-makers, and data scientists battling Dengue outbreaks, this dataset is an invaluable resource. Data Collection Methodology Data were gathered through direct surveys of individuals visiting local hospitals and their communities. Participants willingly contributed information after being informed about the study's significance. No coercion was used, ensuring ethical data collection. Additionally, consultations with medical professionals helped identify key diagnostic features essential for Dengue classification. Key Features Demographics: Gender and age of respondents. Serological Data: NS1, IgG, and IgM test results (binary values: 1 = positive, 0 = negative). Geographical Information: Area, area type (Developed/Undeveloped), district. Housing Conditions: House type (e.g., Building, Tin-Shed, Others). Dengue Outcome: Presence (1) or absence (0) of Dengue cases. Scientific Basis for Data Collection This dataset follows a cross-sectional observational study approach. The NS1, IgG, and IgM test results were included based on clinical consultations, ensuring relevance for Dengue identification. Environmental and demographic factors were incorporated due to their established role in disease transmission. Potential Applications Epidemiological Research: Identifying high-risk areas and analyzing disease spread patterns. Machine Learning Models: Developing predictive models for Dengue diagnosis. Public Health Interventions: Assisting government agencies and NGOs in outbreak response and resource allocation. Conclusion This dataset serves as a valuable resource for researchers, public health professionals, and policymakers to analyze Dengue trends and improve outbreak preparedness in Bangladesh.
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This dataset was collected through a cross-sectional observational study in the Dhaka region of Bangladesh. Data were gathered via structured surveys conducted among hospital visitors seeking medical attention for suspected Dengue symptoms and community members who voluntarily shared their information. No personal identifiers were collected to maintain privacy, and participants provided data willingly after understanding the study’s significance. Key clinical markers such as NS1, IgG, and IgM were selected in consultation with medical professionals to ensure relevance for Dengue identification. Additional demographic and environmental factors (age, gender, house type, area classification) were included based on their potential role in disease transmission. Field surveys involved direct interviews with respondents, while test result validation was conducted through available hospital records. The collected data underwent a quality control process, where inconsistencies and incomplete entries were reviewed and removed. Finally, the dataset was compiled in CSV format, ensuring its usability for epidemiological research, predictive modeling, and public health analysis. This methodology can be replicated in similar urban settings with high Dengue prevalence by following structured questionnaires, direct hospital surveys, and geographical data mapping techniques. Future research may enhance this dataset by incorporating climatic variables, vector density analysis, and spatial mapping tools for a more comprehensive understanding of Dengue outbreaks.