LitCovid-sentences  

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Id Subject Object Predicate Lexical cue
T11 0-2 Sentence denotes 1.
T12 3-15 Sentence denotes Introduction
T13 16-145 Sentence denotes Interactions between the life history of a pathogen and the environment in which it is embedded drive the evolution of virulence.
T14 146-298 Sentence denotes These interactions thus dictate both the experience of disease at the individual host level and the shape of disease dynamics in host populations [1,2].
T15 299-446 Sentence denotes The nature of the interaction between virulence and transmission has been the object of both theoretical and empirical examination [2,3,4,5,6,7,8].
T16 447-617 Sentence denotes Free-living survival, here defined as the ability of a pathogen to persist outside of its host, is one of many transmission life-history traits associated with virulence.
T17 618-724 Sentence denotes The relationship between the two varies between host–pathogen types and different environments [4,8,9,10].
T18 725-847 Sentence denotes Several hypotheses serve as the canon in the evolution of virulence, theorizing its relationship with transmission traits.
T19 848-1200 Sentence denotes The Curse of the Pharaoh hypothesis—Named after a tale about a mythical curse that torments individuals who dig up tombs of Egyptian pharaohs [11]—Suggests that, if a parasite has high free-living survival, then it is far less dependent on its host for transmission and, consequently, will have no evolutionary incentive to decrease virulence [2,4,12].
T20 1201-1390 Sentence denotes The potential negative fitness consequences of killing hosts rapidly (being highly virulent) can be counteracted by persisting in the environment until the arrival of new susceptible hosts.
T21 1391-1590 Sentence denotes Any presumptive selection on beneficence may be relaxed: parasites can detrimentally affect the health of hosts at no cost to transmission because most of their life cycle is spent outside of a host.
T22 1591-1732 Sentence denotes Previous studies support a positive correlation between free-living survival and mortality per infection (a common proxy for virulence) [13].
T23 1733-1888 Sentence denotes Alternatively, the “tradeoff” hypothesis suggests that there is some intermediate level of parasite virulence [3,6,14] that is optimal for a given setting.
T24 1889-2009 Sentence denotes In this scenario, too high a virulence kills the host and parasite and too low a virulence leads to failure to transmit.
T25 2010-2232 Sentence denotes Applying this hypothesis specifically to free-living survival would suggest that selection for increased free-living survival should come at the expense of virulence (producing a pathogen that is less harmful to the host).
T26 2233-2375 Sentence denotes Mechanistically, as a consequence of increased adaptation to a nonhost environment, a virus may be less fit to replicate inside a host [9,15].
T27 2376-2522 Sentence denotes For example, a more robust viral capsid may help to survive harsh environmental conditions but may make it more difficult to package RNA/DNA [15].
T28 2523-2710 Sentence denotes More generally, the tradeoff hypothesis can be framed in the context of a life-history tradeoff: investment in certain parts of the life cycle often comes at the expense of others [2,16].
T29 2711-2858 Sentence denotes Theoretical studies have explored varying evolutionary relationships between heightened virulence and extreme pathogen longevity [4,5,12,17,18,19].
T30 2859-2979 Sentence denotes One critical component of these studies revolves around whether virulence evolves independently of free-living survival.
T31 2980-3330 Sentence denotes For example, some models have argued [4] that pathogen virulence is independent of survival under a set of conditions: when the host–pathogen system is at an equilibrium (evolutionary and ecological), if host density fluctuates around an equilibrium, or if turnover of the infected host population is fast relative to the pathogen in the environment.
T32 3331-3580 Sentence denotes However, if the host–pathogen system is at disequilibrium and if the dynamics of propagules in the environment are fast compared to the dynamics of infected hosts, then virulence is, as hypothesized, an increasing function of propagule survival [4].
T33 3581-3847 Sentence denotes Kamo and Boots [17] examined this hypothesis by incorporating a spatial structure in the environment using a cellular, automata model and found that, if virulence evolution is independent of transmission, then long-lived infective stages select for higher virulence.
T34 3848-4080 Sentence denotes However, if there is a tradeoff between virulence and transmission, there is no evidence for the Curse of the Pharaoh hypothesis, and in fact, higher virulence may be selected for by shorter rather than long-lived infectious stages.
T35 4081-4194 Sentence denotes Further, the evolution of high virulence does not have to occur solely through a transmission–virulence tradeoff.
T36 4195-4380 Sentence denotes Day [18] demonstrated how pathogens can evolve high virulence and even select for traits to kill the host (e.g., toxins) if pathogen transmission and reproductive success are decoupled.
T37 4381-4465 Sentence denotes These studies emphasized the context-dependence of virulence–survival relationships.
T38 4466-4660 Sentence denotes Understanding where in the relationship between virulence and survival a given pathogen population exists may allow one to understand how virus evolution will manifest at the level of epidemics.
T39 4661-4857 Sentence denotes In this study, we examine the epidemic consequences of different virulence–survival relationships—Positive and negative correlation—In a viral disease with an environmental transmission component.
T40 4858-5189 Sentence denotes In order to measure how pathogen survival influences disease dynamics, we included an environmental compartment in our model, which represents contaminated environments that act as a reservoir for persisting pathogens, causing disease spread when they come in contact with susceptible individuals (infection via “fomites”) [20,21].
T41 5190-5355 Sentence denotes We find that the identity of the virulence–free-living survival relationship (e.g., positive vs. negative) has distinct implications for how an epidemic will unfold.
T42 5356-5491 Sentence denotes Some, but not all, features of an outbreak are dramatically influenced by the nature of the underlying virulence–survival relationship.
T43 5492-5650 Sentence denotes This indicates that signatures for evolution (adaptive or other) in a pathogen population will manifest more conspicuously in certain features of an outbreak.
T44 5651-5837 Sentence denotes We reflect on these findings in light of their theoretical implications on the evolution and ecology of infectious disease and for their potential utility in public health interventions.
T45 5839-5841 Sentence denotes 2.
T46 5842-5863 Sentence denotes Materials and Methods
T47 5865-5869 Sentence denotes 2.1.
T48 5870-5902 Sentence denotes Model Motivation and Application
T49 5903-6198 Sentence denotes The mathematical model explored in this study is adapted from a recent one developed to investigate environmental transmission of SARS-CoV-2 during the early-stage outbreak dynamics of coronavirus disease 2019 (COVID-19), with parameter values based on fits to actual country outbreak data [22].
T50 6199-6301 Sentence denotes In this study, we utilize this model to examine questions about the evolution of free-living survival.
T51 6302-6546 Sentence denotes While the phenomenon we examine is a very relevant one that manifests in the real world, we want to emphasize that none of the methods or results in this study are intended to be applied to the current COVID-19 pandemic (as of September, 2020).
T52 6547-6687 Sentence denotes This study is an attempt at responsible theoretical biology, with data-informed models and inferences that are germane to the natural world.
T53 6688-6840 Sentence denotes However, neither do we support the extrapolation of these findings to any particular aspect of COVID-19 nor should they inform a policy or intervention.
T54 6841-6934 Sentence denotes The model applies to a number of scenarios that include outbreaks in a naïve host population.
T55 6935-7162 Sentence denotes This describes situations such as the evolution of novel viral lineages, viral spillover events, or host shifts, where a virus with a preexisting relationship between virulence and survival emerges in a population of new hosts.
T56 7163-7422 Sentence denotes Another such scenario where this model applies is one where a virus has already emerged but evolves in a subpopulation in the novel hosts before a migration event of some kind introduces the evolved virus population to a fully susceptible population of hosts.
T57 7424-7428 Sentence denotes 2.2.
T58 7429-7446 Sentence denotes Model Description
T59 7447-7547 Sentence denotes The model is implemented via a set of ordinary differential equations, defined by Equations (1)–(6).
T60 7548-7746 Sentence denotes It implements viral free-living survival via the “Waterborne Abiotic or other Indirect Transmission (WAIT)” modelling framework, coupling individuals and the pathogen within the environment [23,24].
T61 7747-7990 Sentence denotes Within the model, the βw term allows for individuals to become infected via viral pathogen deposited in the environment and terms 𝜎A and 𝜎I allow asymptomatic and symptomatic individuals to deposit pathogens into the environment, respectively.
T62 7991-8353 Sentence denotes Adapted from the more traditional SEIR (susceptible-exposed-infected-recovered) model, the SEAIR-W (susceptible-exposed-asymptomatic-infected-recovered-WAIT) model interrogates the consequences of the two hypotheses outlined above while representing the dynamics of a very relevant disease system (SARS-CoV-2) that includes an asymptomatic infectious population.
T63 8354-8511 Sentence denotes While the importance of asymptomatic transmission was debated early in the pandemic, many studies have affirmed its role in the spread of disease [25,26,27].
T64 8512-8768 Sentence denotes Though environmental transmission of SARS-CoV-2 remains a controversial topic, it is plausible that asymptomatic individuals may spread disease through frequent contact with the environment, thus increasing the proportion of virus that is free-living [28].
T65 8769-8934 Sentence denotes We acknowledge that mathematical models of epidemics can be limited by “identifiability,” which can obfuscate the relative importance of some routes of transmission.
T66 8935-9219 Sentence denotes In models that have both indirect and direct routes of transmission, it can be very difficult to conclude that one route is predominant [29,30,31]. (1) dSdt=μN−S−βAA+βIIN+βWWS (2) dEdt=βAA+βIIN+βWWS−ε+μE (3) dAdt=εE−ω+μA (4) dIdt=1−pωA−v+μII (5) dRdt=pωA+vI−μR (6) dWdt=σAA+σIIN1−W−kW
T67 9220-9277 Sentence denotes Figure 1 depicts the compartmental diagram for the model.
T68 9278-9385 Sentence denotes The direction of the arrows corresponds to the flow of the individuals and the pathogen through the system.
T69 9386-9574 Sentence denotes Note that individuals can move directly from the asymptomatically infected compartment to the recovered compartment (bypassing the symptomatic compartment) via what we call a “mild track”.
T70 9575-9636 Sentence denotes The dashed arrows represent WAIT coupling to the environment.
T71 9637-9737 Sentence denotes The model is inspired by one developed to interrogate environmental transmission of SARS-CoV-2 [22].
T72 9739-9743 Sentence denotes 2.3.
T73 9744-9768 Sentence denotes Simulations of Outbreaks
T74 9769-9891 Sentence denotes The system was numerically integrated using the “odeint” solver in the Scipy 1.4—Python scientific computation suite [32].
T75 9892-9978 Sentence denotes The simulations track the populations for each of the compartments listed in Figure 1.
T76 9979-10096 Sentence denotes Each model run occurred over 250 days, which amounts to over 8 months of the epidemic or 5× the peak of the outbreak.
T77 10097-10229 Sentence denotes This length of time is consistent with the antecedent SARS-CoV-2 model [22], long enough for the dynamics of the system to manifest.
T78 10230-10348 Sentence denotes Note however that, for this study, we are especially interested in the early window of an outbreak: the first 30 days.
T79 10349-10547 Sentence denotes We focus on this window because this is the time frame that best captures the underlying physics of an epidemic, as 30 days is often before populations are able to adjust their individual behaviors.
T80 10548-10674 Sentence denotes The code constructed for the analysis in this study is publicly available on github: https://github.com/OgPlexus/Pharaohlocks.
T81 10676-10680 Sentence denotes 2.4.
T82 10681-10724 Sentence denotes Population Definitions and Parameter Values
T83 10725-10869 Sentence denotes Table 1 outlines the definitions of each population and provides the initial population values used for all simulations conducted in this study.
T84 10870-10927 Sentence denotes The nominal parameter values used are defined in Table 2.
T85 10928-11075 Sentence denotes The initial values are drawn from the aforementioned COVID-19 outbreak study, derived from empirical findings and country-level outbreak data [22].
T86 11077-11081 Sentence denotes 2.5.
T87 11082-11102 Sentence denotes Virulence Definition
T88 11103-11173 Sentence denotes In this study, we define virulence as the capacity to cause a disease.
T89 11174-11360 Sentence denotes In order to measure it, we utilize a set of parameters that uniformly increase the rate or probability of causing symptomatic disease or the severity of those symptoms (including death).
T90 11361-11661 Sentence denotes Our definition is more comprehensive than many other models of parasite virulence (e.g., [4,13]), which tend to focus on a single aspect of the natural history of disease associated with harm to a host (e.g., the fitness consequences of an infection on the host population or the case fatality rate).
T91 11662-11913 Sentence denotes Instead of having to justify a definition built around a single term (e.g., the term associated with fatality), we took a collective approach to defining virulence through all terms that foment the viral-induced onset of symptomatic disease and death.
T92 11914-12081 Sentence denotes This definition allows for the reality of pleiotropic effects in viral pathogens, where adaptations can have multiple effects on the natural history of disease [2,33].
T93 12082-12187 Sentence denotes Our definition of virulence emphasizes terms that influence host wellness and/or are symptoms of disease.
T94 12188-12362 Sentence denotes The iteration of virulence used in this study also undermines the potential for overly weighting only one or a small number of parameters under a large umbrella of virulence.
T95 12363-12670 Sentence denotes Because so many varying definitions exist for virulence, we have also performed calculations according to a different definition of virulence, one that exclusively considers terms that have a detrimental direct effect on the host and neither of the terms that reflect symptoms of severe disease (𝜎a and 𝜎I).
T96 12671-12734 Sentence denotes These calculations can be found in the Supplementary Materials.
T97 12735-13733 Sentence denotes The collection of parameters that we use to define virulence are as follows: the infected population death rate (𝜇I), the incubation period of SARS-CoV-2 (𝜂), the rate of transfer from asymptomatic to symptomatic (1/⍵), the infected population recovery rate (ν), the percent of individuals that move from the asymptomatic to the recovered compartment without showing symptoms (the “mild” recovery track, p), the contact rate of people with people × the transmission probability of people to people by an asymptomatic individual (βA), the contact rate of people with people × the transmission probability of people to people by an asymptomatically infected person (βI), the contact rate of people with the environment × the probability of shedding by an asymptomatic individual to the environmental (𝜎A), the contact rate of people with the environment × the probability of symptomatically infected individuals shedding in the environment (𝜎I), and the average number of days before infection (1/ε).
T98 13734-13868 Sentence denotes Table 3 outlines the direction in which each of the virulence-associated parameters are modulated as virulence decreases or increases.
T99 13869-13984 Sentence denotes An up arrow (↑) indicates the parameter increases (by an equivalent percent) when the percent virulence is changed.
T100 13985-14111 Sentence denotes A down arrow (↓) indicates the parameter decreases (by an equivalent percent) when the percent change in virulence is applied.
T101 14112-14247 Sentence denotes Changes in virulence are then defined, in this study, as an equivalent uniform (percent) change in each of the parameters listed above.
T102 14248-14358 Sentence denotes For the purposes of our study, we modify virulence by changing all parameters associated with virulence by 5%.
T103 14359-14492 Sentence denotes One could also disambiguate virulence into changes in individual subcomponents; however, that is not the focus of this current study.
T104 14494-14498 Sentence denotes 2.6.
T105 14499-14518 Sentence denotes Survival Definition
T106 14519-14712 Sentence denotes Survival is defined as the set of parameters that, when uniformly modulated, increases the pathogen’s probability of surviving the outside environment and successfully infecting a new host [2].
T107 14713-14915 Sentence denotes In our model, this includes both the waning virus rate in the environment (k) and the contact rate of an individual with the environment × the transmission probability of the environment to people (βw).
T108 14916-15054 Sentence denotes Table 4 outlines the direction (increasing or decreasing) in which these parameters are modulated when survival is decreased or increased.
T109 15055-15186 Sentence denotes Within both models, a (percent) change in survival is defined as an equivalent uniform (percent) change in the survival parameters.
T110 15187-15643 Sentence denotes Throughout this study, the impact of changes in virulence and survival (and the relationship between these traits) are assessed with respect to the following four epidemic metrics: the number of infected individuals (asymptomatic and symptomatic) at the maximum (when the outbreak is at its most severe), the rate at which the peak infected population is reached (tmax−1), the total infected population after 30 days, and the basic reproductive ratio (R0).
T111 15644-15829 Sentence denotes Importantly, among these signatures, the basic reproductive ratio is the most frequently used in epidemiology and benefits from familiarity and mathematical formalism (see Section 2.7).
T112 15830-15937 Sentence denotes The other signatures are determined through simulations of an epidemic for a given set of parameter values.
T113 15938-16139 Sentence denotes Nonetheless, this study’s inclusion of multiple features of the epidemic allows us to examine how variation in virus life-history traits may influence different aspects of an epidemic in peculiar ways.
T114 16141-16145 Sentence denotes 2.7.
T115 16146-16170 Sentence denotes Basic Reproductive Ratio
T116 16171-16288 Sentence denotes Equations (7)–(9) give the analytic expression of the basic reproductive ratio (R0) for the model used in this study.
T117 16289-16353 Sentence denotes This expression for R0 can be deconstructed into two components.
T118 16354-16545 Sentence denotes Equation (8) only contains parameters associated with person to person transmission (Rp), while Equation (9) solely contains parameters associated with transmission from the environment (Re).
T119 16546-16649 Sentence denotes In the Supplementary Materials, we provide additional information on these terms and their derivations.
T120 16650-16854 Sentence denotes Applying the parameters values in Table 2, the numerical value of the basic reproductive ratio is given as R0 ~ 2.82. (7) R0=RpRp2+4Re22 (8) Rp=εβAμI+v+βI1−pωμ+εμ+ωμI+v (9) Re2=εβWσAμI+v+σI1−pωkμ+εμ+ωμI+v
T121 16856-16858 Sentence denotes 3.
T122 16859-16866 Sentence denotes Results
T123 16868-16872 Sentence denotes 3.1.
T124 16873-16899 Sentence denotes Model Sensitivity Analysis
T125 16900-17009 Sentence denotes Figure 2 depicts a tornado plot that communicates the sensitivity of the model to permutations in parameters.
T126 17010-17197 Sentence denotes Across features, the model is most sensitive to parameters that are considered virulence-associated (Table 3) and is relatively less sensitive to survival-associated parameters (Table 4).
T127 17198-17302 Sentence denotes Similar to other features, R0 (Figure 2D) of the model is most sensitive to the parameters ⍵, βA, and ν.
T128 17303-17434 Sentence denotes The sensitivity of R0 to changes in ⍵ reflects the importance of the rate of conversion to the symptomatic state on model dynamics.
T129 17435-17634 Sentence denotes In addition, βA has a very important influence on the model, consistent with other findings for COVID-19 that have emphasized the importance of asymptomatic transmission in disease spread [25,26,27].
T130 17636-17640 Sentence denotes 3.2.
T131 17641-17678 Sentence denotes Illustrative Dynamics of Model System
T132 17679-17897 Sentence denotes Based on the parameter values in Table 2, Figure 3A demonstrates the base dynamics of the model playing out over the first 100 days while Figure 3B shows the dynamics within the environment over the course of 250 days.
T133 17898-17973 Sentence denotes In these dynamics, the population begins to be fixed for susceptible hosts.
T134 17974-18100 Sentence denotes The disease dynamics manifest in the shapes of the curves corresponding to exposed, asymptomatic, and symptomatic individuals.
T135 18101-18183 Sentence denotes Note the long tail of the curve corresponding to contamination by the environment.
T136 18184-18279 Sentence denotes The environment remains infectious even after the infected populations have declined in number.
T137 18280-18370 Sentence denotes The length and shape of this tail are influenced by the free-living survival of the virus.
T138 18372-18376 Sentence denotes 3.3.
T139 18377-18436 Sentence denotes The Epidemic Consequences of Varying Virulence and Survival
T140 18437-18553 Sentence denotes In the next analysis, we examine the epidemic consequences of varying traits associated with survival and virulence.
T141 18554-18806 Sentence denotes One can consider this as a scenario where we compare the endpoints of evolution of different virus populations (corresponding to combinations of values of survival and virulence) and calculating how these evolved populations manifest in epidemic terms.
T142 18807-19097 Sentence denotes In Figure 4, we observe how dynamics of the outbreak are influenced across a space of combinations of traits altering virulence (see Table 3 for a list of virulence-associated parameters) and survival (see Table 4 for a list of survival-associated parameters), changed by ±5% (10% overall).
T143 19098-19273 Sentence denotes In Figure 4D, we demonstrate how changes in virulence and free-living survival traits influence R0, with variation in virulence-related traits having the largest effect on R0.
T144 19274-19395 Sentence denotes Of note is how the range in R0 values varies widely across virulence–survival values, from nearly 2.0 to 3.7 (Figure 4D).
T145 19397-19401 Sentence denotes 3.4.
T146 19402-19477 Sentence denotes Implications of Virulence–Survival Relationships at Their Relative Extremes
T147 19478-19717 Sentence denotes Having observed how outbreak dynamics are influenced by variation in traits that alter virulence–survival phenotypes, we then examined how each outbreak metric is influenced by the extreme (±5%) values of the trait combinations considered.
T148 19718-19877 Sentence denotes Specifically, we assess how a change in pathogen survival affects outbreak dynamics, based on two expected relationships between survival and virulence traits.
T149 19879-19883 Sentence denotes 3.5.
T150 19884-19935 Sentence denotes Positive Correlation Between Survival and Virulence
T151 19936-20055 Sentence denotes In a positive correlation scenario, high values for survival would be associated with high values for virulence [4,13].
T152 20056-20217 Sentence denotes Because the correlations we observe are often not exactly linear, we utilize quadrants to express a trend, allowing for some variance around the expected “line”.
T153 20218-20358 Sentence denotes In Figure 5, the positive correlation scenario can be represented by combinations of virulence and survival residing in quadrants I and III.
T154 20359-20537 Sentence denotes If host–pathogen evolution proceeds according to a positive correlation scenario, all outbreak metrics would show an increase in severity as both survival and virulence increase.
T155 20538-20942 Sentence denotes Across the range of variation in virulence and survival traits considered (5% above and below the nominal value), the peak number of infected individuals increases by approximately 35%, the rate at which the peak is reached increases by approximately 16%, the total number of infected individuals after 30 days increases by approximately 98%, and R0 increases by approximately 94% (Figure 6 and Table 5).
T156 20944-20948 Sentence denotes 3.6.
T157 20949-21000 Sentence denotes Negative Correlation Between Survival and Virulence
T158 21001-21165 Sentence denotes In a negative correlation scenario, high values for survival would be associated with low values for virulence [2,9,15] and a low peak in total infected population.
T159 21166-21306 Sentence denotes Pathogens with a life history that exhibits negative virulence–survival associations would likely appear in quadrants II and IV in Figure 5.
T160 21307-21385 Sentence denotes Under negative correlation, outbreak severity decreases as survival increases.
T161 21386-21713 Sentence denotes Across the measured range of variation in virulence–survival traits, the peak number of infected individuals decreases by approximately 23%, the rate at which the epidemic peak is reached decreases by 0.15%, the total number of infected individuals decreases by 3%, and R0 decreases by approximately 84% (Figure 6 and Table 6).
T162 21714-21904 Sentence denotes Across all metrics considered, the effects of increased viral survival on outbreak dynamics is more extreme under the positive correlation than the negative correlation scenarios (Figure 6).
T163 21906-21910 Sentence denotes 3.7.
T164 21911-21981 Sentence denotes Dynamics of Epidemics at Extreme Values for Virus Free-Living Survival
T165 21982-22167 Sentence denotes In Figure 7, we observe the disease dynamics at extreme values for survival and the dynamics corresponding to the fraction of the environment that is contaminated with infectious virus.
T166 22168-22403 Sentence denotes Consistent with the data represented in Figure 6, we observe that minimum and maximum simulations differ more substantially for extreme survival scenarios in the positive correlation scenario than for the negative correlation scenario.
T167 22404-22569 Sentence denotes The feature of different outbreaks that varies most ostensibly between the correlation scenarios is the time needed to reach the peak number of infected individuals.
T168 22570-22745 Sentence denotes In positive correlation simulations, one can observe that the low virulence, low survival scenario (Figure 7A,B) takes longer to reach the peak number of infected individuals.
T169 22746-22950 Sentence denotes Most notably, however, the low virulence, low survival setting has a far smaller peak of environmental contamination and shorter tail relative to its high virulence, high survival counterpart (Figure 7D).
T170 22951-23110 Sentence denotes Similarly, intriguing findings exist in the comparison between the simulation sets corresponding to extremes in the negative correlation setting (Figure 7E–H).
T171 23111-23338 Sentence denotes Especially notable is the difference in the length of the tail of the environmental contamination for the high virulence, low survival combination (Figure 7F) vs. the low virulence, low survival combination variant (Figure 7H).
T172 23339-23549 Sentence denotes The explanation is that, in this model, higher virulence influences (among many other things) the rate at which the virus is shed into the environment from either the asymptomatic (𝜎A) or symptomatic (𝜎I) host.
T173 23550-23745 Sentence denotes We observe how the high virulence, low survival simulation (Figure 7E) features a symptomatic peak that is larger in size and is prolonged relative to the lower virulence counterpart (Figure 7G).
T174 23746-23945 Sentence denotes This relatively large symptomatic population sheds infectious virus into the environment for a longer period of time, contributing to the long tail of contaminated environments observed in Figure 5F.
T175 23947-23949 Sentence denotes 4.
T176 23950-23960 Sentence denotes Discussion
T177 23961-24073 Sentence denotes The virulence–survival relationship drives the consequences of virus evolution on the trajectory of an outbreak.
T178 24074-24284 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 24285-24597 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 24598-24924 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 24925-25173 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 25174-25449 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 25450-25668 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 25669-25906 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 25907-26171 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 26172-26523 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 26524-26671 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 26673-26757 Sentence denotes Practical Implications for the Understanding of Outbreaks Caused by Emerging Viruses
T189 26758-27026 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 27027-27359 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 27360-27637 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 27638-27795 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 27796-27937 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 27938-28110 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 28111-28249 Sentence denotes The practical process of interpreting the evolutionary consequences of signals of virus evolution should encompass several discrete steps.
T196 28250-28337 Sentence denotes Firstly, we should determine whether molecular signatures exist for adaptive evolution.
T197 28338-28472 Sentence denotes Adaptive evolution would manifest in observable differences in genotype and phenotype and, perhaps, in the natural history of disease.
T198 28473-28591 Sentence denotes Secondly, we should aim to attain knowledge of the underlying mechanistic relationship between survival and virulence.
T199 28592-28875 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 28876-29125 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 29126-29312 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 29313-29476 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 29477-29548 Sentence denotes Consequently, it would not serve as a useful proxy for virus evolution.
T204 29549-29891 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 29892-30227 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.