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This website houses outputs from the Accelerating the adoption of wingbeat-based sensing to quantify mosquito species abundance in the tropics project. Insights generated from this project are summarized below with full details available in the Full project report.


What we learnt – In a nutshell

The use of acoustic sensors, such as microphones, offers an affordable and widely available solution for mosquito species surveillance. While the quality of microphones can vary, research has demonstrated that specialized equipment is not necessary for capturing the wingbeat frequencies of mosquitoes. Multiple studies have successfully classified mosquito species and even their sex using low-cost, commercially available microphones.

Challenges in Transitioning from Research to Practice

To move from research environments to real-world applications, several key challenges must be addressed. The deployment of acoustic sensor technology for mosquito surveillance requires a phased approach to validation.

Phase 1: Proof of Concept – “Does It Work?”

Initial efforts must focus on demonstrating the technology’s effectiveness in real-world settings. Critical questions include:

  • Signal presence: Will mosquitoes fly close enough to the sensors to be detected?
  • Noise interference: Can the technology distinguish mosquito wingbeats from environmental noise?
  • Accuracy: Are the detection and classification systems reliable and precise enough?

The performance of the sensors hinges on the availability and quality of real-world mosquito data, which remains limited. This project aims to address this gap by generating initial datasets, as well as developing prototypes and associated resources that will contribute to expanding these datasets for further research and refinement.

Phase 2: Trial Feasibility – “Is It Worth Trying?”

Following proof of concept, the next step is to assess the feasibility of broader implementation. In this project, we developed and trialed a mobile phone app prototype in real homes. Mobile apps offer a cost-effective option, as they leverage existing infrastructure and require no additional investment. Based on user feedback, a more robust version is in development and will soon be available for further testing.

Community acceptance was high but depended on trust, which we fostered through partnerships with community health volunteers. Transparency about what was being recorded reassured residents and ensured privacy. Furthermore, we found that human noise filtering was unnecessary, as disjoint audio segments made words incomprehensible, further easing privacy concerns. Ensuring trust and community buy-in will be crucial for the success of future trials and widespread adoption.

Phase 3: Scalability – “Can It Be Sustained?”

Once the technology has proven its value, scaling the technology requires addressing several key factors:

  • Cost: The target per-unit cost is still unclear and will require further economic assessment, likely varying across different settings.
  • Maintenance: With staff shortages being a pressing issue, the need for any maintenance could significantly hinder sustainability. Minimizing maintenance is crucial.
  • Usability: While this project collaborated with a local entomological unit, the level of entomological expertise varies widely across staff. Relying on specialized knowledge will impact both feasibility and long-term adoption. The technology should be usable with minimal expertise to ensure success.


Datasets


Prototypes and development needs