LitCovid-sentences  

PMC:7551987 / 7701-18716 JSONTXT 3 Projects

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Id Subject Object Predicate Lexical cue
T45 0-2 Sentence denotes 2.
T46 3-24 Sentence denotes Materials and Methods
T47 26-30 Sentence denotes 2.1.
T48 31-63 Sentence denotes Model Motivation and Application
T49 64-359 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 360-462 Sentence denotes In this study, we utilize this model to examine questions about the evolution of free-living survival.
T51 463-707 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 708-848 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 849-1001 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 1002-1095 Sentence denotes The model applies to a number of scenarios that include outbreaks in a naïve host population.
T55 1096-1323 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 1324-1583 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 1585-1589 Sentence denotes 2.2.
T58 1590-1607 Sentence denotes Model Description
T59 1608-1708 Sentence denotes The model is implemented via a set of ordinary differential equations, defined by Equations (1)–(6).
T60 1709-1907 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 1908-2151 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 2152-2514 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 2515-2672 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 2673-2929 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 2930-3095 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 3096-3380 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 3381-3438 Sentence denotes Figure 1 depicts the compartmental diagram for the model.
T68 3439-3546 Sentence denotes The direction of the arrows corresponds to the flow of the individuals and the pathogen through the system.
T69 3547-3735 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 3736-3797 Sentence denotes The dashed arrows represent WAIT coupling to the environment.
T71 3798-3898 Sentence denotes The model is inspired by one developed to interrogate environmental transmission of SARS-CoV-2 [22].
T72 3900-3904 Sentence denotes 2.3.
T73 3905-3929 Sentence denotes Simulations of Outbreaks
T74 3930-4052 Sentence denotes The system was numerically integrated using the “odeint” solver in the Scipy 1.4—Python scientific computation suite [32].
T75 4053-4139 Sentence denotes The simulations track the populations for each of the compartments listed in Figure 1.
T76 4140-4257 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 4258-4390 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 4391-4509 Sentence denotes Note however that, for this study, we are especially interested in the early window of an outbreak: the first 30 days.
T79 4510-4708 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 4709-4835 Sentence denotes The code constructed for the analysis in this study is publicly available on github: https://github.com/OgPlexus/Pharaohlocks.
T81 4837-4841 Sentence denotes 2.4.
T82 4842-4885 Sentence denotes Population Definitions and Parameter Values
T83 4886-5030 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 5031-5088 Sentence denotes The nominal parameter values used are defined in Table 2.
T85 5089-5236 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 5238-5242 Sentence denotes 2.5.
T87 5243-5263 Sentence denotes Virulence Definition
T88 5264-5334 Sentence denotes In this study, we define virulence as the capacity to cause a disease.
T89 5335-5521 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 5522-5822 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 5823-6074 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 6075-6242 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 6243-6348 Sentence denotes Our definition of virulence emphasizes terms that influence host wellness and/or are symptoms of disease.
T94 6349-6523 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 6524-6831 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 6832-6895 Sentence denotes These calculations can be found in the Supplementary Materials.
T97 6896-7894 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 7895-8029 Sentence denotes Table 3 outlines the direction in which each of the virulence-associated parameters are modulated as virulence decreases or increases.
T99 8030-8145 Sentence denotes An up arrow (↑) indicates the parameter increases (by an equivalent percent) when the percent virulence is changed.
T100 8146-8272 Sentence denotes A down arrow (↓) indicates the parameter decreases (by an equivalent percent) when the percent change in virulence is applied.
T101 8273-8408 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 8409-8519 Sentence denotes For the purposes of our study, we modify virulence by changing all parameters associated with virulence by 5%.
T103 8520-8653 Sentence denotes One could also disambiguate virulence into changes in individual subcomponents; however, that is not the focus of this current study.
T104 8655-8659 Sentence denotes 2.6.
T105 8660-8679 Sentence denotes Survival Definition
T106 8680-8873 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 8874-9076 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 9077-9215 Sentence denotes Table 4 outlines the direction (increasing or decreasing) in which these parameters are modulated when survival is decreased or increased.
T109 9216-9347 Sentence denotes Within both models, a (percent) change in survival is defined as an equivalent uniform (percent) change in the survival parameters.
T110 9348-9804 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 9805-9990 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 9991-10098 Sentence denotes The other signatures are determined through simulations of an epidemic for a given set of parameter values.
T113 10099-10300 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 10302-10306 Sentence denotes 2.7.
T115 10307-10331 Sentence denotes Basic Reproductive Ratio
T116 10332-10449 Sentence denotes Equations (7)–(9) give the analytic expression of the basic reproductive ratio (R0) for the model used in this study.
T117 10450-10514 Sentence denotes This expression for R0 can be deconstructed into two components.
T118 10515-10706 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 10707-10810 Sentence denotes In the Supplementary Materials, we provide additional information on these terms and their derivations.
T120 10811-11015 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