Impact of Indoor Mobility Behavior on the Respiratory Infectious
Diseases Transmission Trends
- URL: http://arxiv.org/abs/2311.17318v1
- Date: Wed, 29 Nov 2023 02:16:06 GMT
- Title: Impact of Indoor Mobility Behavior on the Respiratory Infectious
Diseases Transmission Trends
- Authors: Ziwei Cui, Ming Cai, Zheng Zhu, Gongbo Chen, and Yao Xiao
- Abstract summary: The importance of indoor human mobility in the transmission dynamics of respiratory infectious diseases has been acknowledged.
This study considers people's mobility behaviors in a general scenario, abstracting them into two categories: crowding behavior, related to the spatial aspect, and stopping, related to the temporal aspect.
This study investigates their impacts on disease spreading and the impact of individual-temporal distribution resulting from these mobility behaviors on epidemic transmission.
- Score: 26.806334364100074
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The importance of indoor human mobility in the transmission dynamics of
respiratory infectious diseases has been acknowledged. Previous studies have
predominantly addressed a single type of mobility behavior such as queueing and
a series of behaviors under specific scenarios. However, these studies ignore
the abstraction of mobility behavior in various scenes and the critical
examination of how these abstracted behaviors impact disease propagation. To
address these problems, this study considers people's mobility behaviors in a
general scenario, abstracting them into two main categories: crowding behavior,
related to the spatial aspect, and stopping behavior, related to the temporal
aspect. Accordingly, this study investigates their impacts on disease spreading
and the impact of individual spatio-temporal distribution resulting from these
mobility behaviors on epidemic transmission. First, a point of interest (POI)
method is introduced to quantify the crowding-related spatial POI factors
(i.e., the number of crowdings and the distance between crowdings) and
stopping-related temporal POI factors (i.e., the number of stoppings and the
duration of each stopping). Besides, a personal space determined with Voronoi
diagrams is used to construct the individual spatio-temporal distribution
factor. Second, two indicators (i.e., the daily number of new cases and the
average exposure risk of people) are applied to quantify epidemic transmission.
These indicators are derived from a fundamental model which accurately predicts
disease transmission between moving individuals. Third, a set of 200 indoor
scenarios is constructed and simulated to help determine variable values.
Concurrently, the influences and underlying mechanisms of these behavioral
factors on disease transmission are examined using structural equation modeling
and causal inference modeling......
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