LitCovid-sentences  

PMC:7449695 / 34768-36199 JSONTXT 6 Projects

Annnotations TAB TSV DIC JSON TextAE

Id Subject Object Predicate Lexical cue
T243 0-235 Sentence denotes The forecasting method combines an SEEIIR (susceptible-exposed-infectious-recovered) population model of infection with daily COVID-19 case notification counts, through the use of a bootstrap particle filter (Arulampalam et al., 2002).
T244 236-401 Sentence denotes This approach is similar to that implemented and described in Moss et al., 2019b, in the context of seasonal influenza forecasts for several major Australian cities.
T245 402-541 Sentence denotes Briefly, the particle filter method uses post-regularisation (Doucet et al., 2001), with a deterministic resampling stage (Kitagawa, 1996).
T246 542-622 Sentence denotes Code and documentation are available at https://epifx.readthedocs.io/en/latest/.
T247 623-941 Sentence denotes The daily case counts by date of diagnosis were modelled using a negative binomial distribution with a fixed dispersion parameter k, and the expected number of cases was proportional to the daily incidence of symptomatic infections in the SEEIIR model; this proportion was characterised by the observation probability.
T248 942-1276 Sentence denotes Natural disease history parameters were sampled from narrow uniform priors, based on values reported in the literature for COVID-19 (Table 3), and each particle was associated with an Rš‘’š‘“š‘“ trajectory that was drawn from the state/territory Rš‘’š‘“š‘“ trajectories in Figure 2 up to 5 April, from which point they are assumed to be constant.
T249 1277-1431 Sentence denotes The model was subsequently projected forward from April 14 to April 28, to forecast the number of reported cases, assuming a detection probability of 80%.