Modeling Intraday Aedes-human exposure dynamics enhances dengue risk prediction.

Area of Science:

• Epidemiology
• Public Health
• Urban Ecology

Background:

• Cities are primary hubs for global dengue transmission.
• Human mobility data offers new avenues for disease control.
• Understanding human-vector interactions is crucial for mosquito-borne diseases.

Purpose of the Study:

• To evaluate the impact of hourly human movement patterns on dengue occurrence at a sub-neighborhood level.
• To integrate human mobility, Aedes biting behavior, and spatial vulnerability indicators for improved dengue modeling.
• To enhance urban vector control strategies using mobile phone data.

Main Methods:

• Utilized hourly mobile phone records from 3 million residents and daily dengue case data (2015-2022) in Rio de Janeiro.
• Developed a spatial model incorporating human movement, Aedes biting patterns, spatial eigenvectors, and vulnerability indicators.
• Analyzed sub-neighborhood dengue occurrence (DENV infections).

Main Results:

• Integrating human movement with Aedes biting behavior significantly improved dengue occurrence predictions.
• The spatial model, including vulnerability indicators, explained 77% of the deviance in sub-neighborhood DENV infections.
• Identified potential dengue transmission zones based on connectivity and Aedes peak biting hours.

Conclusions:

• Hourly human movement data is vital for understanding urban dengue transmission dynamics.
• Optimized vector control can be achieved by combining human mobility data with knowledge of mosquito behavior.
• This approach offers significant epidemiological implications for reducing Aedes-borne disease rates in urban areas.