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PMC:7738161 / 33163-34387 JSONTXT

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LitCovid-PubTator

Id Subject Object Predicate Lexical cue tao:has_database_id
103 520-526 Disease denotes deaths MESH:D003643
104 564-570 Disease denotes deaths MESH:D003643
105 621-627 Disease denotes deaths MESH:D003643

LitCovid-sentences

Id Subject Object Predicate Lexical cue
T227 0-39 Sentence denotes There are also limitations in our data.
T228 40-194 Sentence denotes We have used an observation model to link reported cases to the modelled prevalence, and we included variation in the portion of cases detected over time.
T229 195-409 Sentence denotes Modelling and forecasting based on reported cases faces challenges when testing is driven by clinical needs, testing capacities, and other constraints (and in particular is not designed to test population samples).
T230 410-534 Sentence denotes Cases in long-term care facilities (LTCF) represent a substantial fraction of the cases, and particularly the deaths, in BC.
T231 535-639 Sentence denotes Along with the low number of deaths in total, this is one rationale for not modelling deaths explicitly.
T232 640-745 Sentence denotes We included LTCF cases but also modelled a wide range of under-reporting to account for potential biases.
T233 746-898 Sentence denotes If many cases in an LTCF cluster were all reported on the same day (or within a short time frame) this could increase the noise in reported case counts.
T234 899-1224 Sentence denotes We have modelled case counts as over-dispersed compared to a Poisson distribution to account for such variation; we have developed the R package to model a range of data types individually or in combination (e.g., reported cases, hospitalizations, ICU admissions), which could help to overcome limitations of particular data.