LitCovid-sentences  

PMC:7551987 / 28535-32089 JSONTXT 3 Projects

Annnotations TAB TSV DIC JSON TextAE

Id Subject Object Predicate Lexical cue
T188 0-84 Sentence denotes Practical Implications for the Understanding of Outbreaks Caused by Emerging Viruses
T189 85-353 Sentence denotes That different features of an outbreak are differentially influenced by the endpoints of viral life-history evolution highlights how epidemiology should continue to consider principles in the evolution and ecology of infectious disease in its analyses and predictions.
T190 354-686 Sentence denotes As not all features of an epidemic are going to be equally reliable signatures of virus evolution, we should carefully consider the data on how the dynamics of an epidemic change when making inferences about whether a pathogen population is essentially different from prior iterations (e.g., prior outbreaks of the same virus type).
T191 687-964 Sentence denotes The results of this study suggest that carefully constructed, mechanistically sound models of epidemics are important, both for capturing the dynamics of an outbreak and for abetting our efforts to understand how evolution of survival and virulence influences disease dynamics.
T192 965-1122 Sentence denotes For example, the potential for adaptive evolution of SARS-CoV-2 has emerged as a possible explanation for different COVID-19 dynamics in different countries.
T193 1123-1264 Sentence denotes We suggest that such interpretations should be considered with caution and that they require very specific types of evidence to support them.
T194 1265-1437 Sentence denotes As of 1 July 2020, any conclusion that widespread SARS-CoV-2 evolution is an explanation for variation in disease patterns across settings (space and/or time) is premature.
T195 1438-1576 Sentence denotes The practical process of interpreting the evolutionary consequences of signals of virus evolution should encompass several discrete steps.
T196 1577-1664 Sentence denotes Firstly, we should determine whether molecular signatures exist for adaptive evolution.
T197 1665-1799 Sentence denotes Adaptive evolution would manifest in observable differences in genotype and phenotype and, perhaps, in the natural history of disease.
T198 1800-1918 Sentence denotes Secondly, we should aim to attain knowledge of the underlying mechanistic relationship between survival and virulence.
T199 1919-2202 Sentence denotes This knowledge is not necessarily easy to attain (it requires extensive laboratory studies) but would allow added biological insight: we may be able to extrapolate how changes in some traits (e.g., those that compose survival) influence others (e.g., those that influence virulence).
T200 2203-2452 Sentence denotes More generally, our findings suggest that the ability to detect the consequences of virus evolution would depend on which feature of an outbreak an epidemiologist measures: from our analysis, R0 is most impacted by changes in virulence and survival.
T201 2453-2639 Sentence denotes In addition, the total number of infected individuals in the early window and the size of the infected “peak” would each be impacted most readily by changes in virulence–survival traits.
T202 2640-2803 Sentence denotes The rate at which the epidemic peak was reached, on the other hand, showed relatively little change as survival increased or between the two correlation scenarios.
T203 2804-2875 Sentence denotes Consequently, it would not serve as a useful proxy for virus evolution.
T204 2876-3218 Sentence denotes While the stochastic, sometimes entropic nature of epidemics renders them very challenging to predict [36], we suggest that canons such as life-history theory and the evolution of virulence provide useful lenses that can aid in our ability to interpret how life-history changes in virus populations will manifest at the epidemiological scale.
T205 3219-3554 Sentence denotes We propose that, in an age of accumulating genomic and phenotypic data in many pathogen–host systems, we continue to responsibly apply or modify existing theory in order to collate said data into an organized picture for how different components of the host–parasite interaction influence the shape of viral outbreaks of various kinds.