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Last Updated: 08/09/2022
PREMONITION – Enhanced Entomological Monitoring in Tanzania
Objectives
The overall aim of this study is to demonstrate the capabilities of Premonition Technologies as an integrated system for sampling malaria vectors in both semi-fields and field settings in Tanzania.
The project is designed to address current surveillance gaps, by providing high-quality, passive, yet continuous monitoring of mosquito species distributions, abundance, and transmission activity.
Specific aims:
- Develop and test machine learning classifiers for different Anopheles species
- Validate the sampling system in the field and semi-field systems
- Identify important covariates that may influence the performance of field-deployed Premotion systems
This project will provide evidence for the development of novel entomological monitoring systems capable of automated luring, detection, quantification, and identification of adult mosquito species in Tanzania and other malaria-endemic countries. Furthermore, the Premonition Biological Weather Stations’ (BWSs) will enhance mosquito surveillance by providing real-time mosquito data from networks deployed in rural and remote settings. This project is in line with the WHO’s Global Malaria Technical Strategy 2016-2030, which stresses the need for robust and sustainable surveillance as one of the core malaria interventions.
This project is designed to address current surveillance gaps, by providing high-quality, passive, yet continuous monitoring of mosquito species distributions, abundance, and transmission activity. Microsoft Premonition is building entomological sensing and identification platforms that can autonomously sample mosquitoes and relay the data for integration with other data streams to guide decisions. We propose to demonstrate the capabilities of the Premonition Technologies in both semi-field and field settings in the Kilombero Valley in Tanzania, an area of moderate to high malaria transmission. We intend to demonstrate the efficacy of the Premonition ’Biological Weather Stations’ (BWSs), use multi-species observations to train and calibrate the system and validate the full system in a small-scale study in the Kilombero valley, in villages with heterogeneous malaria transmission representative of many high-burned settings in Africa.
Microsoft Premonition is building entomological sensing and identification platforms that can autonomously sample mosquitoes and relay the data for integration with other data streams to guide decisions. The study intends to demonstrate the efficacy of the Premonition BWSs, use multi-species observations to train and calibrate the system and validate the full system.
The proposed study locations are Ulanga and Kilombero districts in south-eastern Tanzania and will include both semi-field and field settings in the Kilombero Valley – an area of moderate to high malaria transmission. The study duration is 3 years and is set to begin in August 2022 to August 2025. The project timeline is divided into three phases.
Aug 2022 — Aug 2025
$1.52M
Related Resources
