AI Article Synopsis

  • - The study emphasizes the importance of quickly detecting new infections for effective outbreak management and highlights how human mobility affects infection risks and spread, using spatial sampling to guide testing efforts in specific areas.
  • - Researchers combined mobility data with different spatial sampling methods to optimize testing strategies for emerging infections, testing their effectiveness through analysis of real and simulated outbreak scenarios.
  • - Results show that using case flow and transmission intensity data can significantly reduce the number of tests needed while maintaining accuracy, making this approach a cost-effective way to enhance community-level infection detection.

Article Abstract

Background: Timely and precise detection of emerging infections is crucial for effective outbreak management and disease control. Human mobility significantly influences infection risks and transmission dynamics, and spatial sampling is a valuable tool for pinpointing potential infections in specific areas. This study explored spatial sampling methods, informed by various mobility patterns, to optimize the allocation of testing resources for detecting emerging infections.

Methods: Mobility patterns, derived from clustering point-of-interest data and travel data, were integrated into four spatial sampling approaches to detect emerging infections at the community level. To evaluate the effectiveness of the proposed mobility-based spatial sampling, we conducted analyses using actual and simulated outbreaks under different scenarios of transmissibility, intervention timing, and population density in cities.

Results: By leveraging inter-community movement data and initial case locations, the proposed case flow intensity (CFI) and case transmission intensity (CTI)-informed sampling approaches could considerably reduce the number of tests required for both actual and simulated outbreaks. Nonetheless, the prompt use of CFI and CTI within communities is imperative for effective detection, particularly for highly contagious infections in densely populated areas.

Conclusions: The mobility-based spatial sampling approach can substantially improve the efficiency of community-level testing for detecting emerging infections. It achieves this by reducing the number of individuals screened while maintaining a high accuracy rate of infection identification. It represents a cost-effective solution to optimize the deployment of testing resources, when necessary, to contain emerging infectious diseases in diverse settings.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10680910PMC
http://dx.doi.org/10.21203/rs.3.rs-3597070/v1DOI Listing

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