LitCovid-sentences  

PMC:7551987 / 25809-32089 JSONTXT 3 Projects

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Id Subject Object Predicate Lexical cue
T175 0-2 Sentence denotes 4.
T176 3-13 Sentence denotes Discussion
T177 14-126 Sentence denotes The virulence–survival relationship drives the consequences of virus evolution on the trajectory of an outbreak.
T178 127-337 Sentence denotes In this study, we examined how different virulence–survival relationships may dictate different features of outbreaks at the endpoints of evolution (according to the positive or negative correlation scenarios).
T179 338-650 Sentence denotes When the parameter space for virulence and survival is mapped, we find that certain outbreak metrics are more sensitive to change in free-living survival and virulence than others and that the nature of this sensitivity differs depending on whether survival and virulence are positively or negatively correlated.
T180 651-977 Sentence denotes For the positive correlation scenario, when free-living survival varies between 5% below and above the nominal value, we observed a dramatic change in the total number of infected individuals in the first 30 days (98% increase from minimum survival to maximum survival; Table 5), and R0 nearly doubles (94% increase; Table 5).
T181 978-1226 Sentence denotes These two traits are, of course, connected: the theoretical construction of the R0 metric specifically applies to settings where a pathogen spreads in a population of susceptible hosts [34,35], an early window that is captured in the first 30 days.
T182 1227-1502 Sentence denotes When survival and virulence are negatively correlated, different outbreak dynamics emerge: while the R0 difference between minimum and maximum survival is significant (approximately 84% decrease), the total number of infected individuals only changes by roughly 3% (Table 6).
T183 1503-1721 Sentence denotes This large difference between R0 at higher and lower survival values also does not translate to a difference in the total number of infected individuals in the first 30 days of an infection (the early outbreak window).
T184 1722-1959 Sentence denotes In a scenario where survival and virulence are negatively correlated, a highly virulent and less virulent virus population can have similar signatures on a population with respect to the number of infected individuals in the first month.
T185 1960-2224 Sentence denotes Thus, simply measuring the number of infected individuals in the first month of an outbreak is unlikely to reveal whether a pathogen population has undergone adaptive evolution or has evolved in a manner that meaningfully influences the natural history of disease.
T186 2225-2576 Sentence denotes Notably, for scenarios where survival and virulence are both positively and negatively correlated, the time that it takes for an epidemic to reach its maximum number of infected individuals changes little across extreme values of survival (12% in the positive correlation scenario; 0.15% in the negative correlation scenario; see Table 5 and Table 6).
T187 2577-2724 Sentence denotes That is, the time that it takes for an epidemic to reach its peak (however high) is not especially sensitive to evolution in virulence or survival.
T188 2726-2810 Sentence denotes Practical Implications for the Understanding of Outbreaks Caused by Emerging Viruses
T189 2811-3079 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 3080-3412 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 3413-3690 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 3691-3848 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 3849-3990 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 3991-4163 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 4164-4302 Sentence denotes The practical process of interpreting the evolutionary consequences of signals of virus evolution should encompass several discrete steps.
T196 4303-4390 Sentence denotes Firstly, we should determine whether molecular signatures exist for adaptive evolution.
T197 4391-4525 Sentence denotes Adaptive evolution would manifest in observable differences in genotype and phenotype and, perhaps, in the natural history of disease.
T198 4526-4644 Sentence denotes Secondly, we should aim to attain knowledge of the underlying mechanistic relationship between survival and virulence.
T199 4645-4928 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 4929-5178 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 5179-5365 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 5366-5529 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 5530-5601 Sentence denotes Consequently, it would not serve as a useful proxy for virus evolution.
T204 5602-5944 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 5945-6280 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.