Identifying pre-outbreak signals of hand, foot and mouth disease based on landscape dynamic network marker.

ABSTRACT:

Background

The high incidence, seasonal pattern and frequent outbreaks of hand, foot and mouth disease (HFMD) represent a threat for billions of children around the world. Detecting pre-outbreak signals of HFMD facilitates the timely implementation of appropriate control measures. However, real-time prediction of HFMD outbreaks is usually challenging because of its complexity intertwining both biological systems and social systems.

Results

By mining the dynamical information from city networks and horizontal high-dimensional data, we developed the landscape dynamic network marker (L-DNM) method to detect pre-outbreak signals prior to the catastrophic transition into HFMD outbreaks. In addition, we set up multi-level early warnings to achieve the purpose of distinguishing the

SUBMITTER: Zhang X 

PROVIDER: S-EPMC7809731 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature