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Luis Aleixo 2022-08-02 10:16:09 +02:00
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16
cara/docs/full_diameter_dependence.rst
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@ -26,7 +26,7 @@ provided the sample size is large enough. Example of the MC integration over the
It is important to distinguish between 1) Monte-Carlo random variables (which are vectorised independently on its diameter-dependence) and 2) numerical Monte-Carlo integration for the diameter-dependence.
Since the integral of the diameter-dependent variables are solved when computing the dose -- :math:`\mathrm{vD^{total}}` -- while performing some of the intermediate calculations,
we normalize the results by *dividing* by the Monte-Carlo variables that are diameter-independent, so that they are not considered in the Monte-Carlo integration (e.g. :meth:`cara.models.InfectedPopulation.aerosols`).
we normalize the results by *dividing* by the Monte-Carlo variables that are diameter-independent, so that they are not considered in the Monte-Carlo integration (e.g. the **viral load** parameter, or the result of the :meth:`cara.models.InfectedPopulation.emission_rate_per_aerosol_when_present` method).
Expiration
==========
@ -50,9 +50,7 @@ To summarize, the Expiration object contains, as a vectorised float, a sample of
Emission Rate - vR(D)
=====================
The mathematical equations to calculate :math:`\mathrm{vR}(D)` are defined in the paper
(Henriques A et al, Modelling airborne transmission of SARS-CoV-2 using CARA: risk assessment for enclosed spaces.
Interface Focus 20210076, https://doi.org/10.1098/rsfs.2021.0076), as follows:
The mathematical equations to calculate :math:`\mathrm{vR}(D)` are defined in the paper - Henriques, A. et al. [2]_ - as follows:
:math:`\mathrm{vR}(D)_j= \mathrm{vl_{in}} \cdot E_{c,j}(D,f_{\mathrm{amp}},\eta_{\mathrm{out}}(D)) \cdot {\mathrm{BR}}_{\mathrm{k}}` ,
@ -112,7 +110,7 @@ The integral over the exposure times is calculated directly in the class (integr
Short-range approach
********************
The short-range concentration is the result of a two-stage exhaled jet model developed by *JIA W. et al.* and is expressed as:
The short-range concentration is the result of a two-stage exhaled jet model developed by Jia, W. et al. [1]_ and is expressed as:
:math:`C_{\mathrm{SR}}(t, D) = C_{\mathrm{LR}} (t, D) + \frac{1}{S({x})} \cdot (C_{0, \mathrm{SR}}(D) - C_{\mathrm{LR}, 100μm}(t, D))` ,
@ -131,7 +129,7 @@ When generating a full model, the short-range class is defined with a new **Expi
given that the **min** and **max** diameters for the short-range interactions are different from those used in the long-range concentration (the idea is that very large particles should not be considered in the long-range case as they fall rapidly on the floor,
while they must be in for the short-range case).
As mentioned in *JIA W. et al.*, the jet concentration depends on the **long-range concentration** of viruses.
As mentioned in Jia, W. et al. [1]_, the jet concentration depends on the **long-range concentration** of viruses.
Here, once again, we shall normalize the short-range concentration to the diameter-independent quantities.
IMPORTANT NOTE: since the susceptible host is physically closer to the infector, the emitted particles are larger in size,
hence a new distribution of diameters should be taken into consideration.
@ -298,3 +296,9 @@ The following diagram describes all the data classes and their relations under t
:align: center
CARA `models.py` file UML diagram.
REFERENCES
==========
.. [1] Jia, Wei, et al. "Exposure and respiratory infection risk via the short-range airborne route." Building and environment 219 (2022): 109166.
.. [2] Henriques, Andre, et al. "Modelling airborne transmission of SARS-CoV-2 using CARA: risk assessment for enclosed spaces." Interface Focus 12.2 (2022): 20210076.

6
cara/models.py
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@ -1086,6 +1086,11 @@ class ConcentrationModel:
@dataclass(frozen=True)
class ShortRangeModel:
'''
Based on the two-stage (jet/puff) expiratory jet model by
Jia et al (2022) - https://doi.org/10.1016/j.buildenv.2022.109166
'''
#: Expiration type
expiration: _ExpirationBase
@ -1101,7 +1106,6 @@ class ShortRangeModel:
def dilution_factor(self) -> _VectorisedFloat:
'''
The dilution factor for the respective expiratory activity type.
Based on the two-stage (jet/puff) expiratory jet model by Jia et al (2022) - https://doi.org/10.1016/j.buildenv.2022.109166
'''
# Average mouth diameter
D = 0.02