Ding (equation 15) link-function to connect virus load with transmission, assuming logarithmic

However it seemed real and interesting enough to ask the questio: ``How would such a potential trade-off lead to interactions on the within-host and between-host levels and affect Puerarin structure overall virus fitness.Ding (equation 15) link-function to connect virus load with transmission, assuming logarithmic relation (equation 16)doi:10.1371/journal.pcbi.1002989.tthe intercept on the decay price curve, a, (quantifying virus persistence at low temperature, especially at 00 C) against the value for the temperature-dependence in the decay rate, c, (quantifying virus persistence at higher temperature). To perform the fit, we assume that the infection was started by a 1 EID50 =mL (EID50 is the viral dose that results in a 50 chance of infecting an embryonated egg, assumed to correspond to 1 infectious virion) and that the initial number of uninfected target cells is 2:5|107 [71] (while this estimate is for chickens rather than ducks, the exact value is not qualitatively important: changes in the target cell numbers only rescale the model parameter p and otherwise produce the same dynamics). In figure 5, we show the best fit to the data, with parameter values presented in Table 1. We want to point out that while these parameter estimates are useful and accurate enough for the purpose of our study, they come with caveats.Ding (equation 15) link-function to connect virus load with transmission, assuming logarithmic relation (equation 16)doi:ten.1371/journal.pcbi.1002989.tthe intercept of the decay rate curve, a, (quantifying virus persistence at low temperature, specifically at 00 C) against the value for the temperature-dependence with the decay price, c, (quantifying virus persistence at high temperature). In figure 4C, we offer exactly the same facts, but for the rank of those parameters. These plots demonstrate a negative correlation in between persistence at low and higher temperatures. Since the center panel indicates a linear relation for the logarithm of a and c, we fitted a regression line log(c) gzk log(a) towards the information. We locate for the regression match g {3:28, k {0:26 (R2 0:70, p 0:00068). Similarly, computing a correlation coefficient for the rank-transformed data, we find a negative correlation of {0:72 (p 0:011). The analysis of this dataset can be taken as suggestion for the presence of a trade-off between stability at low and high temperatures at least for the panel of strains we investigated here. Since this is a small sample of strains, we do not want to over-emphasize the finding. However it seemed real and interesting enough to ask the questio: ``How would such a potential trade-off lead to interactions on the within-host and between-host levels and affect overall virus fitness. We address this question in the remainder of the paper. As a potentially interesting side question not further considered in the remainder of this paper we wondered whether there are systematic differences between strains belonging to different groups. Based on amino acid differences, strains with different HA types can be clustered into two groups, as indicated in Table 4 (see e.g. [668]). We were curious to see if systematic differences in the decay behavior between the two groups could be observed.