PMC:7796058 / 36266-37001 JSONTXT

{"project":"LitCovid-PubTator","denotations":[{"id":"251","span":{"begin":202,"end":208},"obj":"Species"},{"id":"252","span":{"begin":407,"end":413},"obj":"Species"},{"id":"253","span":{"begin":219,"end":224},"obj":"Disease"}],"attributes":[{"id":"A251","pred":"tao:has_database_id","subj":"251","obj":"Tax:9606"},{"id":"A252","pred":"tao:has_database_id","subj":"252","obj":"Tax:9606"},{"id":"A253","pred":"tao:has_database_id","subj":"253","obj":"MESH:D003371"}],"namespaces":[{"prefix":"Tax","uri":"https://www.ncbi.nlm.nih.gov/taxonomy/"},{"prefix":"MESH","uri":"https://id.nlm.nih.gov/mesh/"},{"prefix":"Gene","uri":"https://www.ncbi.nlm.nih.gov/gene/"},{"prefix":"CVCL","uri":"https://web.expasy.org/cellosaurus/CVCL_"}],"text":"Moreover, the above Equation (1) does not take into account the duration of time spent occupying the space, the actions taken, and the decay of the virus particles over time. The algorithm restarts the people count and cough numbers after the space is cleaned. A slightly more complicated set of equations (Equation (2)) expands on the simple risk calculation by taking into account the amount of time that people remain in a particular space, and the decay of the virus particles over time (assuming the worst-case scenario of 72 h for all of the particles deposited to become inactive). (2) {C3= number_of_people × person_dwell_time(hours)×72− time_since_departure(hour)72 C4=AverageLastRiskyBehavior×72−TimeLastRiskyBehavior(hour)72"}

{"project":"LitCovid-PD-HP","denotations":[{"id":"T3","span":{"begin":219,"end":224},"obj":"Phenotype"}],"attributes":[{"id":"A3","pred":"hp_id","subj":"T3","obj":"http://purl.obolibrary.org/obo/HP_0012735"}],"text":"Moreover, the above Equation (1) does not take into account the duration of time spent occupying the space, the actions taken, and the decay of the virus particles over time. The algorithm restarts the people count and cough numbers after the space is cleaned. A slightly more complicated set of equations (Equation (2)) expands on the simple risk calculation by taking into account the amount of time that people remain in a particular space, and the decay of the virus particles over time (assuming the worst-case scenario of 72 h for all of the particles deposited to become inactive). (2) {C3= number_of_people × person_dwell_time(hours)×72− time_since_departure(hour)72 C4=AverageLastRiskyBehavior×72−TimeLastRiskyBehavior(hour)72"}

{"project":"LitCovid-sentences","denotations":[{"id":"T243","span":{"begin":0,"end":174},"obj":"Sentence"},{"id":"T244","span":{"begin":175,"end":260},"obj":"Sentence"},{"id":"T245","span":{"begin":261,"end":735},"obj":"Sentence"}],"namespaces":[{"prefix":"_base","uri":"http://pubannotation.org/ontology/tao.owl#"}],"text":"Moreover, the above Equation (1) does not take into account the duration of time spent occupying the space, the actions taken, and the decay of the virus particles over time. The algorithm restarts the people count and cough numbers after the space is cleaned. A slightly more complicated set of equations (Equation (2)) expands on the simple risk calculation by taking into account the amount of time that people remain in a particular space, and the decay of the virus particles over time (assuming the worst-case scenario of 72 h for all of the particles deposited to become inactive). (2) {C3= number_of_people × person_dwell_time(hours)×72− time_since_departure(hour)72 C4=AverageLastRiskyBehavior×72−TimeLastRiskyBehavior(hour)72"}