Designing Aedes (Diptera: Culicidae) Mosquito Traps: The Evolution of the Male Aedes Sound Trap by Iterative Evaluation

Facilitating the use of the target product profile in academic research: a systematic review

BACKGROUND

The Target Product Profile (TPP) is a tool used in industry to guide development strategies by addressing user needs and fostering effective communication among stakeholders. However, they are not frequently used in academic research, where they may be equally useful. This systematic review aims to extract the features of accessible TPPs, to identify commonalities and facilitate their integration in academic research methodology.

METHODS

We searched peer-reviewed papers published in English developing TPPs for different products and health conditions in four biomedical databases. Interrater agreement, computed on random abstract and paper sets (Cohen's Kappa; percentage agreement with zero tolerance) was > 0.91. We interviewed experts from industry contexts to gain insight on the process of TPP development, and extracted general and specific features on TPP use and structure.

RESULTS

138 papers were eligible for data extraction. Of them, 92% (n = 128) developed a new TPP, with 41.3% (n = 57) focusing on therapeutics. The addressed disease categories were diverse; the largest (47.1%, n = 65) was infectious diseases. Only one TPP was identified for several fields, including global priorities like dementia. Our analyses found that 56.5% of papers (n = 78) was authored by academics, and 57.8% of TPPs (n = 80) featured one threshold level of product performance. The number of TPP features varied widely across and within product types (n = 3-44). Common features included purpose/context of use, shelf life for drug stability and validation aspects. Most papers did not describe the methods used to develop the TPP. We identified aspects to be taken into account to build and report TPPs, as a starting point for more focused initiatives guiding use by academics.

DISCUSSION

TPPs are used in academic research mostly for infectious diseases and have heterogeneous features. Our extraction of key features and common structures helps to understand the tool and widen its use in academia. This is of particular relevance for areas of notable unmet needs, like dementia. Collaboration between stakeholders is key for innovation. Tools to streamline communication such as TPPs would support the development of products and services in academia as well as industry.

An IoT-based smart mosquito trap system embedded with real-time mosquito image processing by neural networks for mosquito surveillance

An essential aspect of controlling and preventing mosquito-borne diseases is to reduce mosquitoes that carry viruses. We designed a smart mosquito trap system to reduce the density of mosquito vectors and the spread of mosquito-borne diseases. This smart trap uses computer vision technology and deep learning networks to identify features of live Aedes aegypti and Culex quinquefasciatus in real-time. A unique mechanical design based on the rotation concept is also proposed and implemented to capture specific living mosquitoes into the corresponding chambers successfully. Moreover, this system is equipped with sensors to detect environmental data, such as CO₂ concentration, temperature, and humidity. We successfully demonstrated the implementation of such a tool and paired it with a reliable capture mechanism for live mosquitos without destroying important morphological features. The neural network achieved 91.57% accuracy with test set images. When the trap prototype was applied in a tent, the accuracy rate in distinguishing live Ae. aegypti was 92%, with a capture rate reaching 44%. When the prototype was placed into a BG trap to produce a smart mosquito trap, it achieved a 97% recognition rate and a 67% catch rate when placed in the tent. In a simulated living room, the recognition and capture rates were 90% and 49%, respectively. This smart trap correctly differentiated between Cx. quinquefasciatus and Ae. aegypti mosquitoes, and may also help control mosquito-borne diseases and predict their possible outbreak.

Comparison of Fan-Traps and Gravitraps for Aedes Mosquito Surveillance in Taiwan

A key component of integrated vector management strategies is the efficient implementation of mosquito traps for surveillance and control. Numerous trap types have been created with distinct designs and capture mechanisms, but identification of the most effective trap type is critical for effective implementation. For dengue vector surveillance, previous studies have demonstrated that active traps utilizing CO₂ attractant are more effective than passive traps for capturing Aedes mosquitoes. However, maintaining CO₂ supply in traps is so labor intensive as to be likely unfeasible in crowded residential areas, and it is unclear how much more effective active traps lacking attractants are than purely passive traps. In this study, we analyzed Aedes capture data collected in 2019 from six urban areas in Kaohsiung City to compare Aedes mosquito catch rates between (passive) gravitraps and (active) fan-traps. The average gravitrap index (GI) and fan-trap index (FI) values were 0.68 and 3.39 respectively at peak catch times from June to August 2019, with consistently higher FI values calculated in all areas studied. We compared trap indices to reported cases of dengue fever and correlated them with weekly fluctuations in temperature and rainfall. We found that FI trends aligned more closely with case numbers and rainfall than GI values, supporting the use of fan-traps for Aedes mosquito surveillance and control as part of broader vector management strategies. Furthermore, combining fan-trap catch data with rapid testing for dengue infections may improve the early identification and prevention of future disease outbreaks.