Estimates of Social Contact in a Middle School Based on Self-Report and Wireless Sensor Data.
Estimates of contact among children, used for infectious disease transmission models and understanding social patterns, historically rely on self-report logs. Recently, wireless sensor technology has enabled objective measurement of proximal contact and comparison of data from the two methods. These are mostly small-scale studies, and knowledge gaps remain in understanding contact and mixing patterns and also in the advantages and disadvantages of data collection methods. We collected contact data from a middle school, with 7th and 8th grades, for one day using self-report contact logs and wireless sensors. The data were linked for students with unique initials, gender, and grade within the school. This paper presents the results of a comparison of two approaches to characterize school contact networks, wireless proximity sensors and self-report logs. Accounting for incomplete capture and lack of participation, we estimate that "sensor-detectable", proximal contacts longer than 20 seconds during lunch and class-time occurred at 2 fold higher frequency than "self-reportable" talk/touch contacts. Overall, 55% of estimated talk-touch contacts were also sensor-detectable whereas only 15% of estimated sensor-detectable contacts were also talk-touch. Contacts detected by sensors and also in self-report logs had longer mean duration than contacts detected only by sensors (6.3 vs 2.4 minutes). During both lunch and class-time, sensor-detectable contacts demonstrated substantially less gender and grade assortativity than talk-touch contacts. Hallway contacts, which were ascertainable only by proximity sensors, were characterized by extremely high degree and short duration. We conclude that the use of wireless sensors and self-report logs provide complementary insight on in-school mixing patterns and contact frequency.
Department of Internal Medicine, University of Utah, Salt Lake City, Utah, United States of America.|Veterans Affairs Salt Lake City Health Care System, Salt Lake City, Utah, United States of America.
Molly
M
Leecaster
Department of Internal Medicine, University of Utah, Salt Lake City, Utah, United States of America.|Veterans Affairs Salt Lake City Health Care System, Salt Lake City, Utah, United States of America.|Department of Mathematics, University of Utah, Salt Lake City, Utah, United States of America.
Damon J A
DJ
Toth
Department of Internal Medicine, University of Utah, Salt Lake City, Utah, United States of America.|Veterans Affairs Salt Lake City Health Care System, Salt Lake City, Utah, United States of America.
Warren B P
WB
Pettey
Department of Global Migration and Quarantine, Centers for Disease Control and Prevention, Atlanta, Georgia, United States of America.
Jeanette J
JJ
Rainey
Department of Global Migration and Quarantine, Centers for Disease Control and Prevention, Atlanta, Georgia, United States of America.
Hongjiang
H
Gao
Department of Global Migration and Quarantine, Centers for Disease Control and Prevention, Atlanta, Georgia, United States of America.
Amra
A
Uzicanin
Department of Internal Medicine, University of Utah, Salt Lake City, Utah, United States of America.|Veterans Affairs Salt Lake City Health Care System, Salt Lake City, Utah, United States of America.|Department of Biomedical Informatics, University of Utah, Salt Lake City, United States of America.
Matthew
M
Samore
D002648:Child; D016358:Contact Tracing / Q000295:instrumentation* / Q000379:methods; D003625:Data Collection / Q000295:instrumentation* / Q000379:methods; D005260:Female; D006801:Humans; D008297:Male; D008962:Models, Theoretical; D012141:Respiratory Tract Infections / Q000635:transmission*; D012574:Schools / Q000706:statistics & numerical data; D057566:Self Report; D012919:Social Behavior; D013334:Students / Q000706:statistics & numerical data; D011795:Surveys and Questionnaires; D013997:Time Factors; D059015:Wireless Technology