New World Screwworm Networked Detection, Identification, and Surveillance System

New World Screwworm (NWS or Cochliomyia hominivorax) larvae infest the living tissue of warm-blooded animals. NWS is prevalent in South America and the Caribbean, and it is estimated that re-entry of NWS into the United States could cost US cattle producers $732 million per year [USDA, 2025]. Screwworm flies lay eggs in open wounds. The eggs hatch into maggots (larvae) that eat live tissue, causing a worsening, often painful and foul-smelling wounds [CDC, 2025]. Wildlife and free-ranging animals are an important pathway for New World Screwworm to spread, making surveillance beyond livestock essential [Arizona, 2026]. However, the current state-of-the-art for NWS detection and tracking being used at the US Southern Border involves three types of traps (sticky fly traps, liver-baited net traps, and gator traps), all of which require manual servicing, fly collection, and fly delivery to a laboratory [USDA, 2026a] [USDA, 2026b]. The state-of-the-art lure to attract NWS flies to a trap is Swormlure-5 [Hickner, 2023], a synthetic bait based on a mix of volatile organic and volatile sulfur compounds; however, such baits are not specific to NWS and can lure other species [Hickner, 2023]. The US Department of Agriculture (USDA) has deployed over 100 NWS-specific traps and lures across high-risk areas of U.S. Border States and is leveraging thousands of fruit fly/insect traps all along the Southern Border. Suspected NWS samples are forwarded for official confirmation to the USDA National Veterinary Services Laboratories (NVSL) in Ames, Iowa, the national reference lab responsible for screwworm diagnostics [USDA, 2026c]. Thus, a real-time, autonomous, persistent, networked NWS detection, identification, and surveillance system is necessary for accurate and timely tracking and forecasting of the movement of the New World Screwworm. Such a system must operate reliably in rugged, remote regions lacking power and communications infrastructure by leveraging on-board data processing and machine learning analysis. This can enable on-board insect identification (e.g., using low-power, localized optical, acoustic, electrical, and/or chemical sensing) and eliminate the need for manual sample collection and laboratory diagnostics. Furthermore, these devices must form resilient, low-bandwidth ad-hoc networks to transmit real-time, highly compressed detection telemetry, enabling NWS movement forecasting and biosecurity intervention even in remote and disconnected environments.