Impacts of observation frequency on proximity contact data and modeled transmission dynamics.
PLoS Comput Biol
; 19(2): e1010917, 2023 02.
Article
in English
| MEDLINE | ID: covidwho-2318361
ABSTRACT
Transmission of many communicable diseases depends on proximity contacts among humans. Modeling the dynamics of proximity contacts can help determine whether an outbreak is likely to trigger an epidemic. While the advent of commodity mobile devices has eased the collection of proximity contact data, battery capacity and associated costs impose tradeoffs between the observation frequency and scanning duration used for contact detection. The choice of observation frequency should depend on the characteristics of a particular pathogen and accompanying disease. We downsampled data from five contact network studies, each measuring participant-participant contact every 5 minutes for durations of four or more weeks. These studies included a total of 284 participants and exhibited different community structures. We found that for epidemiological models employing high-resolution proximity data, both the observation method and observation frequency configured to collect proximity data impact the simulation results. This impact is subject to the population's characteristics as well as pathogen infectiousness. By comparing the performance of two observation methods, we found that in most cases, half-hourly Bluetooth discovery for one minute can collect proximity data that allows agent-based transmission models to produce a reasonable estimation of the attack rate, but more frequent Bluetooth discovery is preferred to model individual infection risks or for highly transmissible pathogens. Our findings inform the empirical basis for guidelines to inform data collection that is both efficient and effective.
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Communicable Diseases
/
Epidemics
Type of study:
Experimental Studies
/
Observational study
/
Prognostic study
Limits:
Humans
Language:
English
Journal:
PLoS Comput Biol
Journal subject:
Biology
/
Medical Informatics
Year:
2023
Document Type:
Article
Affiliation country:
Journal.pcbi.1010917
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