The variation of the energy influx with the RF power was found to follow a linear profile and a simple relation is proposed to model this variation for the considered plasma.
We have statistically modelled this variation in the soil faunal biomass distribution and biomass flux; showed that occurrence of livestock is a reliable allometric predictor; and assessed that cattle manure enhances lower body-size clumps in the faunal biomass distribution.
To model this variation we fitted a generalised additive model to the reporting rate as a function of week number (1 7 Jan = 1 to 25 31 Dec = 52) in the form of a thin-plate regression spline using gamm with a binomial error structure and logit link function [50].
The lack of data on diagnostic performance in different settings limits our ability to adequately model this variation, although it could theoretically be incorporated into these types of simulation models.
To model this variation, we simulated under the assumption that the loss-to-gain rate ratio is distributed uniformly between 0 and 2, so that the loss-to-gain rate ratio has equal probabilities to be higher or lower than 1 (simulation scenario code name ER_gVrl_1, Table 1).
While we here simply model this fitness variation as arising from circulating deleterious mutations, any of these non-antigenic phenotypes can similarly contribute to this fitness variation.
Analysis methods need to be able to cope with and appropriately model this variation.
In the adjusted models, this variation reduced to 0.44 and 0.12, respectively.
Assuming that countries with larger variation in mortality rates between their internal regions indicate greater inequalities than countries with smaller variation, we model this variation by relating it to the distribution of region populations within each country.
When proportion of health facility delivery is less than 50%, the effect size becomes 0.74 (95% CI: 0.59, 0.94), when the proportion of health facility delivery is 50% or above the effect size becomes 0.61 (95% CI: 0.43, 0.87) in the random effects model, however, in the fixed effects model this variation is much more significant (Table 2).
