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LitCovid-PubTator

Id Subject Object Predicate Lexical cue tao:has_database_id
185 5-13 Disease denotes COVID-19 MESH:C000657245
190 1020-1030 Species denotes SARS-CoV-2 Tax:2697049
191 697-705 Disease denotes COVID-19 MESH:C000657245
192 975-984 Disease denotes infection MESH:D007239
193 1454-1463 Disease denotes infection MESH:D007239
200 1604-1610 Species denotes people Tax:9606
201 1689-1695 Species denotes people Tax:9606
202 1733-1739 Species denotes people Tax:9606
203 1783-1789 Species denotes people Tax:9606
204 1614-1622 Disease denotes infected MESH:D007239
205 1840-1848 Disease denotes coughing MESH:D003371
234 4451-4462 Species denotes coronavirus Tax:11118
235 4338-4343 Disease denotes COVID MESH:C000657245
236 4400-4408 Disease denotes COVID-19 MESH:C000657245
237 4815-4823 Disease denotes COVID-19 MESH:C000657245
239 5610-5618 Disease denotes COVID-19 MESH:C000657245
242 5785-5791 Species denotes People Tax:9606
243 5809-5815 Species denotes people Tax:9606
246 5970-5976 Species denotes people Tax:9606
247 6075-6081 Species denotes people Tax:9606
251 6381-6387 Species denotes people Tax:9606
252 6586-6592 Species denotes people Tax:9606
253 6398-6403 Disease denotes cough MESH:D003371
256 7092-7098 Species denotes people Tax:9606
257 7072-7080 Disease denotes COVID-19 MESH:C000657245

LitCovid-PD-HP

Id Subject Object Predicate Lexical cue hp_id
T1 1840-1848 Phenotype denotes coughing http://purl.obolibrary.org/obo/HP_0012735
T2 3961-3969 Phenotype denotes coughing http://purl.obolibrary.org/obo/HP_0012735
T3 6398-6403 Phenotype denotes cough http://purl.obolibrary.org/obo/HP_0012735

LitCovid-sentences

Id Subject Object Predicate Lexical cue
T190 0-4 Sentence denotes 4.1.
T191 5-45 Sentence denotes COVID-19 Risk Assessment Using IndoorGML
T192 46-173 Sentence denotes The SMCDA simultaneously represents decision spaces as well as criteria values based on attribute and geographic topology [50].
T193 174-312 Sentence denotes For this research, topological relationships from the OGC IndoorGML dual graph were used for risk aggregation for the multi-sensor system.
T194 313-401 Sentence denotes A scientific SMCDA process can be put in place using the different steps shown Figure 6.
T195 402-536 Sentence denotes In order to initialize the decision-making process for this paper, equal weights for various risk criteria map layers were considered.
T196 537-603 Sentence denotes This helped ensure fast implementation and quick proof of concept.
T197 604-849 Sentence denotes The main step for risk criteria assessment was determining the factors affecting the risk of COVID-19 spread based on information from existing studies from both the World Health Organization (WHO) [27] and the Government of Canada website [28].
T198 850-990 Sentence denotes The number of active virus particles present in a place was considered the most important factor for determining the risk of infection [27].
T199 991-1201 Sentence denotes Various transmission ways of SARS-CoV-2 transmission include airborne transmission caused by small droplets, and larger droplet transmission (droplets can survive up to several days on different surfaces) [54].
T200 1202-1310 Sentence denotes The term “viral load” will also be used to refer to the number of active virus particles present in a space.
T201 1311-1445 Sentence denotes Virus particles live for different lengths of time, depending on a number of factors, the most significant one being surface material.
T202 1446-1552 Sentence denotes Risk of infection for any particular IndoorGML cell space was modelled as the viral load within the space.
T203 1553-1807 Sentence denotes Assuming that a proportion of any average group of people is infected, the viral load within a space increases along with the number of people occupying it, the amount of time the people spend in the space, and the actions of the people within the space.
T204 1808-1963 Sentence denotes Talking loudly, exercising, and coughing expel more droplets into the environment than other activities, and thus increase the viral load within the space.
T205 1964-2127 Sentence denotes The virus also passes from surface to surface through touch, so touching surfaces without cleaning hands in between also increases the viral load within the space.
T206 2128-2269 Sentence denotes The viral load was broken down into a hierarchy of smaller cause similar to a root cause analysis in order to evaluate the different factors.
T207 2270-2331 Sentence denotes The following layers represent the respective criterion maps.
T208 2332-2459 Sentence denotes Effective parameters were identified based on available sensors and data according to the implemented IoCT multi-sensor system.
T209 2460-2510 Sentence denotes The viral load risk criteria are listed as follow:
T210 2511-2514 Sentence denotes C1:
T211 2515-2534 Sentence denotes Risk from Cleaning:
T212 2535-2644 Sentence denotes Cleaning schedule reported on a smartphone app based on the time that had elapsed from the previous cleaning.
T213 2645-2817 Sentence denotes For this paper, the cleaning frequency for each room was every 6 h, meaning that after six hours the risk is maximized at one whereas immediately after cleaning it is at 0.
T214 2818-2912 Sentence denotes C1 is a spatiotemporal map layer comprised of OGC IndoorGML cells with values between 0 and 1.
T215 2913-2916 Sentence denotes C2:
T216 2917-2943 Sentence denotes Risk from Contact Tracing:
T217 2944-3064 Sentence denotes Proximity tracing map extracted from beacons which includes a trajectory map of traced people on an OGC IndoorGML graph.
T218 3065-3153 Sentence denotes These trajectories show the location of the cleaner and the number of people in a place.
T219 3154-3313 Sentence denotes If a person identifies himself or herself as a COVID-19 infected person, the historical trajectories can be used for the contact tracing map layer calculation.
T220 3314-3317 Sentence denotes C3:
T221 3318-3343 Sentence denotes Risk from People Density:
T222 3344-3450 Sentence denotes Gathering restriction map from smart cameras which includes the number of people over each IndoorGML node.
T223 3451-3642 Sentence denotes This value changes over a range of 0–1 based on the number of people divided by the capacity of the room (which can be assigned or generated from the area property of an IndoorGML cell node).
T224 3643-3719 Sentence denotes This information is reported online and aggregated once the room is cleaned.
T225 3720-3749 Sentence denotes C4: COVID-19 Risky Behaviors:
T226 3750-3985 Sentence denotes Risky behavior violation map which includes the number of incidents or violations (number of people violating social distancing, hugging, people touching common surfaces and objects, talking loudly, exercising, coughing, and sneezing).
T227 3986-4138 Sentence denotes This value is a weighted average of the risky behavior factors (number of violations) over the frequency of cleaning (normally six hours for each room).
T228 4139-4317 Sentence denotes This layer was generated using smart cameras and audio sensors based on the number of detected risky events using deep learning algorithms as described in the following sections.
T229 4318-4429 Sentence denotes As we progress with COVID, various criteria have been introduced and evaluated in COVID-19 spread risk [54,55].
T230 4430-4594 Sentence denotes Transmission ways of coronavirus and prioritizing their importance are still under debate and more studies are underway to understand transmission ways better [56].
T231 4595-4810 Sentence denotes Although the risk calculation could be very complicated based on time, room volume, air circulation, etc. [57,58], our research’s scope includes a general risk assessment function which is a simple weighted average.
T232 4811-4974 Sentence denotes The COVID-19 viral load risk function was modelled using a weighted average of risky criteria (as mentioned above) over each IndoorGML node (e.g., a meeting room).
T233 4975-5119 Sentence denotes For the initial prototype calculation of risk, Equation (1) was used:(1) { RiskCOVID19 = W1C1+W2C2+W3C3+W4C4W1=W2=W3=W4=1, to simplify the model
T234 5120-5288 Sentence denotes For simplicity sake, we assigned each of the above map layers with a similar weight and computed the risk factor over each IndoorGML node using an aggregation function.
T235 5289-5390 Sentence denotes However, this risk function can be easily manipulated and configured by the users on the client side.
T236 5391-5469 Sentence denotes So, we evaluated a set of different weights and evaluated them in Section 5.7.
T237 5470-5520 Sentence denotes In this new risk model, the wights are as follows:
T238 5521-5692 Sentence denotes Risk from Cleaning : W1=0.1: since cleaning is a measure of potentially unknown risks of COVID-19 transmission, such as airborne particles passing through the ventilation.
T239 5693-5774 Sentence denotes Risk from Contact Tracing: W2=0.4: it is one of the strongest risks for the place
T240 5775-5918 Sentence denotes Risk from People Density: W3=0.3: people using the space or engaging in risky behaviors in the space are stronger indicators and higher weight.
T241 5919-5944 Sentence denotes Risky behaviours: W4=0.2:
T242 5945-6178 Sentence denotes We assumed the number of people being the strongest due to the prevalence of airborne virus being multiplicative on the number of people in the room, whereas risky behaviors are less frequent, and therefore harder to weight (W3=0.2).
T243 6179-6353 Sentence denotes 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.
T244 6354-6439 Sentence denotes The algorithm restarts the people count and cough numbers after the space is cleaned.
T245 6440-6914 Sentence denotes 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
T246 6915-7100 Sentence denotes For future research, we will consider different risk profiles (i.e., optimistic and pessimistic) for various user groups (e.g., pessimistic risk profile for COVID-19 vulnerable people).