Symptoms and Movement Patterns During the COVID-19 Pandemic

dhawkin — Mon, 22 Feb 2021 21:55:42 +0000

Symptoms and Movement Patterns During the COVID-19 Pandemic dhawkin Mon, 02/22/2021 - 16:55

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How can we better understand how people move during the pandemic and how they spread COVID-19? Dr. Janusz Wojtusiak, associate professor of health informatics and director of the Machine Learning Inference Lab is leading one of the first individual-level studies on social distancing.

Participants use the Mason COVID Health Check^TM to record symptoms of possible COVID-19 infection and GPS and WiFi data to provide information on how they move during the pandemic. This allows the researchers to model and predict movements during the pandemic and in conjunction with any reported possible COVID-19 symptoms. This could help inform effective public health interventions to reduce infection.

Initial findings from the first wave of the study, including data collected through September 2020, were published in the Journal of Healthcare Informatics Research. Wojtusiak and colleagues found that headache was the most frequently reported symptom and headache was always listed as a symptom when any other symptoms were reported. The next most commonly reported symptoms were cough and sore throat.

Movement patterns varied among participants, with some only going out for essential trips while others moved about more. As a group, movement was consistent over the study period, which included a period when Virginia was under a stay-at-home order and when it was not. Participants traveled a total average of 139 miles per week, visiting an average of less than six locations per week. This low average mileage and number of sites visited does suggest that COVID-19-related restrictions affected their movement. However, they also found that even when participants reported symptoms of COVID-19 or contact with others with COVID-19, they did not change their movements as recommended by public health guidance.

This research is possible thanks to the dedication of study participants who share their data to allow for movement modeling. Recruitment of Mason faculty, staff, and students for the second wave of the study has begun. Learn more and sign up: https://www.mli.gmu.edu/sd/.

��AV has a very low COVID-19 infection rate, and during the period none of the study participants reported COVID-19 infection, so researchers weren’t able to link COVID-19 positive tests and movement. Future analysis will include data from the 2020 winter so may provide more information on movement after COVID-19 infection. The researchers will also conduct surveys and interviews to provide richer data including reasons for complying or not complying with social distancing.

In a related study supported by the National Cancer Institute, Wojtusiak’s team analyzed individual movements of people on campus. Such micro-scale movements within buildings can be modeled using WiFi data collected each time a mobile phone or laptop is connected to the internet. In over 150 simulated scenarios they were able to reconstruct movements of volunteers within Mason’s Peterson Hall. This technology is intended to support contact elicitation as part of contact tracing for COVID-19 or other infectious diseases.

Promising preliminary results show that the technology can change how public health officials think about contact tracing. Tests are now being conducted across other locations on Fairfax campus. You can learn more about the project at https://www.mli.gmu.edu/wifi.

Topics

CHHS Research

Machine Learning

Machine Learning in Health Care

coronavirus; covid-19

College of Health and Human Services

College of Health and Human Services Department of Health Administration and Policy

CHHS News