Development and user testing study of MozzHub: a bipartite network-based dengue hotspot detector.

Area of Science:

• Public Health
• Epidemiology
• Environmental Science

Background:

• Dengue control traditionally relies on residential fogging, which may not target primary infection sources effectively.
• The COVID-19 movement control order in Malaysia led to a significant decrease in dengue cases, suggesting residential areas are not the main mosquito breeding grounds.
• Existing dengue early warning systems lack micro-level spatial analysis and do not incorporate human mobility patterns.

Purpose of the Study:

• To design and implement MozzHub, a web-based application for identifying dengue infection sources at a small spatial level (400m).
• To integrate human mobility and environmental predictors into a bipartite network-based dengue model within the MozzHub system.
• To evaluate the preliminary pilot test results and user acceptance of the MozzHub system among health officials in Malaysia.

Main Methods:

• Development of MozzHub, a web-based application utilizing a bipartite network-based dengue model.
• Integration of human mobility data and environmental predictors for micro-level spatial analysis (400m).
• Pilot testing and user acceptance evaluation in six district health offices in Malaysia.

Main Results:

• The MozzHub system was successfully designed and implemented.
• Preliminary pilot testing demonstrated the system's utility and ease of operation.
• End-users reported high satisfaction with the MozzHub system, with 77.4% finding it useful and 80.6% finding it easy to operate.

Conclusions:

• MozzHub offers a novel approach to dengue control by pinpointing infection sources at a micro-spatial level.
• The system effectively integrates human mobility and environmental factors, addressing limitations of traditional methods.
• MozzHub shows significant potential for improving dengue surveillance and control strategies, evidenced by positive user feedback and acceptance.