A Binary Prototype for Time-Series Surveillance and Intervention

medRxiv (Cold Spring Harbor Laboratory), Год журнала: 2025, Номер unknown

Опубликована: Фев. 5, 2025

Abstract Despite much research on early detection of anomalies from surveillance data, a systematic framework for appropriately acting these signals is lacking. We addressed this gap by formulating hidden Markov-style model time-series surveillance, where the system state, observed and decision rule are all binary. incur delayed cost, c , whenever abnormal no action taken, or an immediate k with action, < . If costs too high, then detrimental, intervention should never occur. sufficiently low, always Only when intermediate low beneficial. Our equations provide assessing which approach may apply under range scenarios and, if warranted, facilitate methodical classification strategies. thus offers conceptual basis designing real-world public health systems.

Язык: Английский

Optimizing the detection of emerging infections using mobility-based spatial sampling

International Journal of Applied Earth Observation and Geoinformation, Год журнала: 2024, Номер 131, С. 103949 - 103949

Опубликована: Июнь 1, 2024

Timely and precise detection of emerging infections is imperative for effective outbreak management disease control. Human mobility significantly influences the spatial transmission dynamics infectious diseases. Spatial sampling, integrating structure target, holds promise as an approach testing allocation in detecting infections, leveraging information on individuals' movement contact behavior can enhance targeting precision. This study introduces a sampling framework informed by spatiotemporal analysis human data, aiming to optimize resources infections. Mobility patterns, derived from clustering point-of-interest travel are integrated into four approaches at community level. We evaluate proposed mobility-based analyzing both actual simulated outbreaks, considering scenarios transmissibility, intervention timing, population density cities. Results indicate that inter-community data initial case locations, Case Flow Intensity (CFI) Transmission (CTI)-informed enhances community-level efficiency reducing number individuals screened while maintaining high accuracy rate infection identification. Furthermore, prompt application CFI CTI within cities crucial detection, especially highly contagious densely populated areas. With widespread use responses, theoretical extends patterns providing cost-effective solution resource deployment containing

Язык: Английский