From 0db7624b2348b3072f0b69f4216de80f2467fd56 Mon Sep 17 00:00:00 2001 From: Luis Aleixo Date: Thu, 4 Aug 2022 16:19:29 +0200 Subject: [PATCH 01/21] added co2 concentration time --- caimira/models.py | 34 ++++++++++++++++++++++++++++++++++ 1 file changed, 34 insertions(+) diff --git a/caimira/models.py b/caimira/models.py index 36da66f5..756f62d1 100644 --- a/caimira/models.py +++ b/caimira/models.py @@ -982,6 +982,9 @@ def infectious_virus_removal_rate(self, time: float) -> _VectorisedFloat: + self.ventilation.air_exchange(self.room, time) ) + def air_exch_virus_removal_rate(self, time: float) -> _VectorisedFloat: + return self.ventilation.air_exchange(self.room, time) + @method_cache def _normed_concentration_limit(self, time: float) -> _VectorisedFloat: """ @@ -1000,6 +1003,15 @@ def _normed_concentration_limit(self, time: float) -> _VectorisedFloat: return 1. / (IVRR * V) + @method_cache + def _CO2_concentration_limit(self, time: float) -> _VectorisedFloat: + if not self.infected.person_present(time): + return 0. + V = self.room.volume + IVRR = self.air_exch_virus_removal_rate(time) + + return 1. / (IVRR * V) + @method_cache def state_change_times(self) -> typing.List[float]: """ @@ -1400,6 +1412,28 @@ def concentration(self, time: float) -> _VectorisedFloat: concentration += interaction.short_range_concentration(self.concentration_model, time) return concentration + def _CO2_concentration_cached(self, time: float) -> _VectorisedFloat: + return self._CO2_concentration(time) + + def _CO2_concentration(self, time: float) -> _VectorisedFloat: + if time <= self.concentration_model._first_presence_time(): + return 0.0 + next_state_change_time = self.concentration_model._next_state_change(time) + IVRR = self.concentration_model.air_exch_virus_removal_rate(next_state_change_time) + CO2_conc_limit = (self.concentration_model._CO2_concentration_limit(next_state_change_time) * + (self.concentration_model.infected.activity.exhalation_rate * (self.exposed.number + self.concentration_model.infected.number))) + + t_last_state_change = self.concentration_model.last_state_change(time) + co2_conc_at_last_state_change = self._CO2_concentration_cached(t_last_state_change) + + delta_time = time - t_last_state_change + fac = np.exp(-IVRR * delta_time) + + return (CO2_conc_limit * (1 - fac) + (co2_conc_at_last_state_change - 0.0004) * fac) + 0.0004 + + def CO2_concentration(self, time: float) -> _VectorisedFloat: + return max(440, self._CO2_concentration(time) * 10**6) + def long_range_deposited_exposure_between_bounds(self, time1: float, time2: float) -> _VectorisedFloat: deposited_exposure = 0. From 2ad971d7040f40ec237a6a80f250a3d01d3c172a Mon Sep 17 00:00:00 2001 From: Luis Aleixo Date: Thu, 4 Aug 2022 16:39:52 +0200 Subject: [PATCH 02/21] correction of initial value --- caimira/models.py | 2 +- caimira/monte_carlo/data.py | 8 ++++++++ 2 files changed, 9 insertions(+), 1 deletion(-) diff --git a/caimira/models.py b/caimira/models.py index 756f62d1..4559809b 100644 --- a/caimira/models.py +++ b/caimira/models.py @@ -1432,7 +1432,7 @@ def _CO2_concentration(self, time: float) -> _VectorisedFloat: return (CO2_conc_limit * (1 - fac) + (co2_conc_at_last_state_change - 0.0004) * fac) + 0.0004 def CO2_concentration(self, time: float) -> _VectorisedFloat: - return max(440, self._CO2_concentration(time) * 10**6) + return self._CO2_concentration(time) * 10**6 def long_range_deposited_exposure_between_bounds(self, time1: float, time2: float) -> _VectorisedFloat: deposited_exposure = 0. diff --git a/caimira/monte_carlo/data.py b/caimira/monte_carlo/data.py index e0eaa09a..35dc3eb4 100644 --- a/caimira/monte_carlo/data.py +++ b/caimira/monte_carlo/data.py @@ -86,6 +86,14 @@ def integrate(self, dmin, dmax): } +co2_activity = { + 'Sleep': mc.Activity(0.013, 0.013), + 'Resting': mc.Activity(0.02, 0.02), + 'Normal work': mc.Activity(LogNormal(0.08, 0.13), LogNormal(0.08, 0.13)), + 'Hard work': mc.Activity(LogNormal(0.33, 0.38), LogNormal(0.33, 0.38)), +} + + # From https://doi.org/10.1101/2021.10.14.21264988 and references therein symptomatic_vl_frequencies = LogCustomKernel( np.array((2.46032, 2.67431, 2.85434, 3.06155, 3.25856, 3.47256, 3.66957, 3.85979, 4.09927, 4.27081, From b26b8d5f6c90f2b7b508bd99458486afa8e8c4e5 Mon Sep 17 00:00:00 2001 From: Luis Aleixo Date: Thu, 4 Aug 2022 17:00:21 +0200 Subject: [PATCH 03/21] added initial state change --- caimira/models.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/caimira/models.py b/caimira/models.py index 4559809b..2a766e6b 100644 --- a/caimira/models.py +++ b/caimira/models.py @@ -1424,7 +1424,7 @@ def _CO2_concentration(self, time: float) -> _VectorisedFloat: (self.concentration_model.infected.activity.exhalation_rate * (self.exposed.number + self.concentration_model.infected.number))) t_last_state_change = self.concentration_model.last_state_change(time) - co2_conc_at_last_state_change = self._CO2_concentration_cached(t_last_state_change) + co2_conc_at_last_state_change = 0.00044 delta_time = time - t_last_state_change fac = np.exp(-IVRR * delta_time) @@ -1432,7 +1432,8 @@ def _CO2_concentration(self, time: float) -> _VectorisedFloat: return (CO2_conc_limit * (1 - fac) + (co2_conc_at_last_state_change - 0.0004) * fac) + 0.0004 def CO2_concentration(self, time: float) -> _VectorisedFloat: - return self._CO2_concentration(time) * 10**6 + # Correction due to the number of generated points. + return max(440, self._CO2_concentration(time) * 10**6) def long_range_deposited_exposure_between_bounds(self, time1: float, time2: float) -> _VectorisedFloat: deposited_exposure = 0. From a9929b38abb41ff02c1d5180541ee1ee1afd68d4 Mon Sep 17 00:00:00 2001 From: Luis Aleixo Date: Mon, 26 Sep 2022 14:12:46 +0200 Subject: [PATCH 04/21] adapted co2 formula to have an initial state --- caimira/models.py | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/caimira/models.py b/caimira/models.py index 2a766e6b..f43153fc 100644 --- a/caimira/models.py +++ b/caimira/models.py @@ -1417,23 +1417,23 @@ def _CO2_concentration_cached(self, time: float) -> _VectorisedFloat: def _CO2_concentration(self, time: float) -> _VectorisedFloat: if time <= self.concentration_model._first_presence_time(): - return 0.0 + return 440.44e-6 # carbon dioxide concentration in the make up air (m3/m3 person) - 440ppm next_state_change_time = self.concentration_model._next_state_change(time) IVRR = self.concentration_model.air_exch_virus_removal_rate(next_state_change_time) - CO2_conc_limit = (self.concentration_model._CO2_concentration_limit(next_state_change_time) * + co2_conc_limit = (self.concentration_model._CO2_concentration_limit(next_state_change_time) * (self.concentration_model.infected.activity.exhalation_rate * (self.exposed.number + self.concentration_model.infected.number))) t_last_state_change = self.concentration_model.last_state_change(time) - co2_conc_at_last_state_change = 0.00044 + co2_conc_at_last_state_change = self._CO2_concentration_cached(t_last_state_change) # CO2 contribution in the room at start delta_time = time - t_last_state_change fac = np.exp(-IVRR * delta_time) - return (CO2_conc_limit * (1 - fac) + (co2_conc_at_last_state_change - 0.0004) * fac) + 0.0004 + return co2_conc_limit * (1 - fac) + (co2_conc_at_last_state_change * fac) def CO2_concentration(self, time: float) -> _VectorisedFloat: # Correction due to the number of generated points. - return max(440, self._CO2_concentration(time) * 10**6) + return self._CO2_concentration(time) * 1e6 def long_range_deposited_exposure_between_bounds(self, time1: float, time2: float) -> _VectorisedFloat: deposited_exposure = 0. From e01342796d665f0ebe1cdd62f12f342490b02e59 Mon Sep 17 00:00:00 2001 From: Luis Aleixo Date: Tue, 27 Sep 2022 09:49:51 +0200 Subject: [PATCH 05/21] added CO2 initial room concentration on the formula --- caimira/models.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/caimira/models.py b/caimira/models.py index f43153fc..99969d5d 100644 --- a/caimira/models.py +++ b/caimira/models.py @@ -1429,7 +1429,7 @@ def _CO2_concentration(self, time: float) -> _VectorisedFloat: delta_time = time - t_last_state_change fac = np.exp(-IVRR * delta_time) - return co2_conc_limit * (1 - fac) + (co2_conc_at_last_state_change * fac) + return co2_conc_limit * (1 - fac) + ((co2_conc_at_last_state_change - 440.44e-6) * fac) + 440.44e-6 def CO2_concentration(self, time: float) -> _VectorisedFloat: # Correction due to the number of generated points. From d495f111763b3c383660cd21e191718c84e08d4b Mon Sep 17 00:00:00 2001 From: Luis Aleixo Date: Wed, 28 Sep 2022 14:28:47 +0200 Subject: [PATCH 06/21] Removed co2 activities and adapted formula --- caimira/models.py | 14 ++++++++------ caimira/monte_carlo/data.py | 8 -------- 2 files changed, 8 insertions(+), 14 deletions(-) diff --git a/caimira/models.py b/caimira/models.py index 99969d5d..ce2e949a 100644 --- a/caimira/models.py +++ b/caimira/models.py @@ -1004,7 +1004,7 @@ def _normed_concentration_limit(self, time: float) -> _VectorisedFloat: return 1. / (IVRR * V) @method_cache - def _CO2_concentration_limit(self, time: float) -> _VectorisedFloat: + def _CO2_normed_concentration_limit(self, time: float) -> _VectorisedFloat: if not self.infected.person_present(time): return 0. V = self.room.volume @@ -1416,20 +1416,22 @@ def _CO2_concentration_cached(self, time: float) -> _VectorisedFloat: return self._CO2_concentration(time) def _CO2_concentration(self, time: float) -> _VectorisedFloat: + make_up_air_concentration = 440.44e-6 # carbon dioxide concentration in the make up air (m3/m3 person) - 440ppm if time <= self.concentration_model._first_presence_time(): - return 440.44e-6 # carbon dioxide concentration in the make up air (m3/m3 person) - 440ppm + return make_up_air_concentration next_state_change_time = self.concentration_model._next_state_change(time) IVRR = self.concentration_model.air_exch_virus_removal_rate(next_state_change_time) - co2_conc_limit = (self.concentration_model._CO2_concentration_limit(next_state_change_time) * - (self.concentration_model.infected.activity.exhalation_rate * (self.exposed.number + self.concentration_model.infected.number))) - + co2_conc_limit = (self.concentration_model._CO2_normed_concentration_limit(next_state_change_time) * + ((self.exposed.number*self.exposed.activity.exhalation_rate*0.1 + + self.concentration_model.infected.number*self.concentration_model.infected.activity.exhalation_rate*0.1))) + t_last_state_change = self.concentration_model.last_state_change(time) co2_conc_at_last_state_change = self._CO2_concentration_cached(t_last_state_change) # CO2 contribution in the room at start delta_time = time - t_last_state_change fac = np.exp(-IVRR * delta_time) - return co2_conc_limit * (1 - fac) + ((co2_conc_at_last_state_change - 440.44e-6) * fac) + 440.44e-6 + return co2_conc_limit * (1 - fac) + ((co2_conc_at_last_state_change - make_up_air_concentration) * fac) + make_up_air_concentration def CO2_concentration(self, time: float) -> _VectorisedFloat: # Correction due to the number of generated points. diff --git a/caimira/monte_carlo/data.py b/caimira/monte_carlo/data.py index 35dc3eb4..e0eaa09a 100644 --- a/caimira/monte_carlo/data.py +++ b/caimira/monte_carlo/data.py @@ -86,14 +86,6 @@ def integrate(self, dmin, dmax): } -co2_activity = { - 'Sleep': mc.Activity(0.013, 0.013), - 'Resting': mc.Activity(0.02, 0.02), - 'Normal work': mc.Activity(LogNormal(0.08, 0.13), LogNormal(0.08, 0.13)), - 'Hard work': mc.Activity(LogNormal(0.33, 0.38), LogNormal(0.33, 0.38)), -} - - # From https://doi.org/10.1101/2021.10.14.21264988 and references therein symptomatic_vl_frequencies = LogCustomKernel( np.array((2.46032, 2.67431, 2.85434, 3.06155, 3.25856, 3.47256, 3.66957, 3.85979, 4.09927, 4.27081, From 9a1949c8512a53a76677c219f2b214682b2bf17f Mon Sep 17 00:00:00 2001 From: Luis Aleixo Date: Thu, 29 Sep 2022 11:53:49 +0200 Subject: [PATCH 07/21] added percentage of BR for CO2 as input parameter --- caimira/models.py | 9 ++++----- 1 file changed, 4 insertions(+), 5 deletions(-) diff --git a/caimira/models.py b/caimira/models.py index ce2e949a..b13fdf35 100644 --- a/caimira/models.py +++ b/caimira/models.py @@ -1415,15 +1415,14 @@ def concentration(self, time: float) -> _VectorisedFloat: def _CO2_concentration_cached(self, time: float) -> _VectorisedFloat: return self._CO2_concentration(time) - def _CO2_concentration(self, time: float) -> _VectorisedFloat: - make_up_air_concentration = 440.44e-6 # carbon dioxide concentration in the make up air (m3/m3 person) - 440ppm + def _CO2_concentration(self, time: float, make_up_air_concentration: float = 440.44e-6, CO2_fraction: float = 0.042) -> _VectorisedFloat: if time <= self.concentration_model._first_presence_time(): - return make_up_air_concentration + return make_up_air_concentration # carbon dioxide concentration in the make up air (m3/m3 person) - 440ppm next_state_change_time = self.concentration_model._next_state_change(time) IVRR = self.concentration_model.air_exch_virus_removal_rate(next_state_change_time) co2_conc_limit = (self.concentration_model._CO2_normed_concentration_limit(next_state_change_time) * - ((self.exposed.number*self.exposed.activity.exhalation_rate*0.1 + - self.concentration_model.infected.number*self.concentration_model.infected.activity.exhalation_rate*0.1))) + ((self.exposed.number*self.exposed.activity.exhalation_rate*CO2_fraction + + self.concentration_model.infected.number*self.concentration_model.infected.activity.exhalation_rate*CO2_fraction))) t_last_state_change = self.concentration_model.last_state_change(time) co2_conc_at_last_state_change = self._CO2_concentration_cached(t_last_state_change) # CO2 contribution in the room at start From cd60e58528c6e8f9d407bae7288fbe016f41e310 Mon Sep 17 00:00:00 2001 From: Nicolas Mounet Date: Tue, 13 Dec 2022 20:59:49 +0100 Subject: [PATCH 08/21] Changing structure of CO2 concentration model. Renaming one input of ConcentrationModel (infected->population) since it is now inherited from _ConcentrationModelBase, which requires subsequent changes in the apps. --- caimira/apps/expert.py | 2 +- caimira/models.py | 214 ++++++++++++-------- caimira/tests/models/test_exposure_model.py | 2 +- 3 files changed, 133 insertions(+), 85 deletions(-) diff --git a/caimira/apps/expert.py b/caimira/apps/expert.py index 6d489697..f48d7aaa 100644 --- a/caimira/apps/expert.py +++ b/caimira/apps/expert.py @@ -1091,4 +1091,4 @@ def models_start_end(models: typing.Sequence[models.ExposureModel]) -> typing.Tu """ infected_start = min(model.concentration_model.infected.presence.boundaries()[0][0] for model in models) infected_finish = min(model.concentration_model.infected.presence.boundaries()[-1][1] for model in models) - return infected_start, infected_finish \ No newline at end of file + return infected_start, infected_finish diff --git a/caimira/models.py b/caimira/models.py index b13fdf35..e2561354 100644 --- a/caimira/models.py +++ b/caimira/models.py @@ -956,34 +956,38 @@ def probability_meet_infected_person(self, virus: Virus, n_infected: int, event_ @dataclass(frozen=True) -class ConcentrationModel: +class _ConcentrationModelBase: room: Room ventilation: _VentilationBase - infected: InfectedPopulation - - #: evaporation factor: the particles' diameter is multiplied by this - # factor as soon as they are in the air (but AFTER going out of the, - # mask, if any). - evaporation_factor: float = 0.3 @property - def virus(self): - return self.infected.virus + def population(self) -> Population: + """ + Population in the room (the emitters of what we compute the + concentration of) + """ + raise NotImplementedError("Subclass must implement") - def infectious_virus_removal_rate(self, time: float) -> _VectorisedFloat: - # Equilibrium velocity of particle motion toward the floor - vg = self.infected.particle.settling_velocity(self.evaporation_factor) - # Height of the emission source to the floor - i.e. mouth/nose (m) - h = 1.5 - # Deposition rate (h^-1) - k = (vg * 3600) / h - return ( - k + self.virus.decay_constant(self.room.humidity, self.room.inside_temp.value(time)) - + self.ventilation.air_exchange(self.room, time) - ) + def removal_rate(self, time: float) -> _VectorisedFloat: + """ + Remove rate of the species considered, in h^-1 + """ + raise NotImplementedError("Subclass must implement") - def air_exch_virus_removal_rate(self, time: float) -> _VectorisedFloat: - return self.ventilation.air_exchange(self.room, time) + def atmosphere_concentration(self) -> _VectorisedFloat: + """ + Background concentration in the atmosphere + (in the same unit as the concentration) + """ + return 0. + + def normalization_factor(self) -> _VectorisedFloat: + """ + Normalization factor (in the same unit as the concentration). + This factor is applied to the normalized concentration only + at the very end. + """ + raise NotImplementedError("Subclass must implement") @method_cache def _normed_concentration_limit(self, time: float) -> _VectorisedFloat: @@ -991,36 +995,27 @@ def _normed_concentration_limit(self, time: float) -> _VectorisedFloat: Provides a constant that represents the theoretical asymptotic value reached by the concentration when time goes to infinity, if all parameters were to stay time-independent. - This is normalized by the emission rate, the latter acting as a + This is normalized by the normalization factor, the latter acting as a multiplicative constant factor for the concentration model that can be put back in front of the concentration after the time dependence has been solved for. """ - if not self.infected.person_present(time): - return 0. - V = self.room.volume - IVRR = self.infectious_virus_removal_rate(time) - - return 1. / (IVRR * V) - - @method_cache - def _CO2_normed_concentration_limit(self, time: float) -> _VectorisedFloat: - if not self.infected.person_present(time): + if not self.population.person_present(time): return 0. V = self.room.volume - IVRR = self.air_exch_virus_removal_rate(time) + RR = self.removal_rate(time) - return 1. / (IVRR * V) + return (1. / (RR * V) + self.atmosphere_concentration()/ + self.normalization_factor()) @method_cache def state_change_times(self) -> typing.List[float]: """ All time dependent entities on this model must provide information about the times at which their state changes. - """ state_change_times = {0.} - state_change_times.update(self.infected.presence.transition_times()) + state_change_times.update(self.population.presence.transition_times()) state_change_times.update(self.ventilation.transition_times(self.room)) return sorted(state_change_times) @@ -1028,9 +1023,8 @@ def state_change_times(self) -> typing.List[float]: def _first_presence_time(self) -> float: """ First presence time. Before that, the concentration is zero. - """ - return self.infected.presence.boundaries()[0][0] + return self.population.presence.boundaries()[0][0] def last_state_change(self, time: float) -> float: """ @@ -1039,7 +1033,6 @@ def last_state_change(self, time: float) -> float: Find the nearest time less than the given one. If there is a state change exactly at ``time`` the previous state change is returned (except at ``time == 0``). - """ times = self.state_change_times() t_index: int = np.searchsorted(times, time) # type: ignore @@ -1052,7 +1045,6 @@ def last_state_change(self, time: float) -> float: def _next_state_change(self, time: float) -> float: """ Find the nearest future state change. - """ for change_time in self.state_change_times(): if change_time >= time: @@ -1064,15 +1056,17 @@ def _next_state_change(self, time: float) -> float: @method_cache def _normed_concentration_cached(self, time: float) -> _VectorisedFloat: - # A cached version of the _normed_concentration method. Use this - # method if you expect that there may be multiple concentration - # calculations for the same time (e.g. at state change times). + """ + A cached version of the _normed_concentration method. Use this + method if you expect that there may be multiple concentration + calculations for the same time (e.g. at state change times). + """ return self._normed_concentration(time) def _normed_concentration(self, time: float) -> _VectorisedFloat: """ - Virus long-range exposure concentration, as a function of time, and - normalized by the emission rate. + Concentration as a function of time, and normalized by + normalization_factor. The formulas used here assume that all parameters (ventilation, emission rate) are constant between two state changes - only the value of these parameters at the next state change, are used. @@ -1083,33 +1077,34 @@ def _normed_concentration(self, time: float) -> _VectorisedFloat: # The model always starts at t=0, but we avoid running concentration calculations # before the first presence as an optimisation. if time <= self._first_presence_time(): - return 0.0 + return self.atmosphere_concentration() next_state_change_time = self._next_state_change(time) - IVRR = self.infectious_virus_removal_rate(next_state_change_time) + RR = self.removal_rate(next_state_change_time) conc_limit = self._normed_concentration_limit(next_state_change_time) t_last_state_change = self.last_state_change(time) conc_at_last_state_change = self._normed_concentration_cached(t_last_state_change) delta_time = time - t_last_state_change - fac = np.exp(-IVRR * delta_time) + fac = np.exp(-RR * delta_time) return conc_limit * (1 - fac) + conc_at_last_state_change * fac def concentration(self, time: float) -> _VectorisedFloat: """ - Virus long-range exposure concentration, as a function of time. + Total concentration as a function of time. The normalization + factor has been put back. Note that time is not vectorised. You can only pass a single float to this method. """ return (self._normed_concentration_cached(time) * - self.infected.emission_rate_when_present()) + self.normalization_factor()) @method_cache def normed_integrated_concentration(self, start: float, stop: float) -> _VectorisedFloat: """ - Get the integrated long-range concentration of viruses in the air between the times start and stop, - normalized by the emission rate. + Get the integrated concentration between the times start and stop, + normalized by normalization_factor. """ if stop <= self._first_presence_time(): return 0.0 @@ -1127,11 +1122,11 @@ def normed_integrated_concentration(self, start: float, stop: float) -> _Vectori next_conc_state = self._next_state_change(stop) conc_limit = self._normed_concentration_limit(next_conc_state) - IVRR = self.infectious_virus_removal_rate(next_conc_state) + RR = self.removal_rate(next_conc_state) delta_time = stop - start total_normed_concentration += ( conc_limit * delta_time + - (conc_limit - conc_start) * (np.exp(-IVRR*delta_time)-1) / IVRR + (conc_limit - conc_start) * (np.exp(-RR*delta_time)-1) / RR ) return total_normed_concentration @@ -1140,7 +1135,85 @@ def integrated_concentration(self, start: float, stop: float) -> _VectorisedFloa Get the integrated concentration of viruses in the air between the times start and stop. """ return (self.normed_integrated_concentration(start, stop) * - self.infected.emission_rate_when_present()) + self.normalization_factor()) + + +@dataclass(frozen=True) +class ConcentrationModel(_ConcentrationModelBase): + """ + Class used for the computation of the long-range virus concentration. + """ + + #: Infected population in the room, emitting virions + infected: InfectedPopulation + + #: evaporation factor: the particles' diameter is multiplied by this + # factor as soon as they are in the air (but AFTER going out of the, + # mask, if any). + evaporation_factor: float = 0.3 + + @property + def population(self) -> InfectedPopulation: + return self.infected + + @property + def virus(self) -> Virus: + return self.infected.virus + + def normalization_factor(self) -> _VectorisedFloat: + # we normalize by the emission rate + return self.infected.emission_rate_when_present() + + def removal_rate(self, time: float) -> _VectorisedFloat: + # Equilibrium velocity of particle motion toward the floor + vg = self.infected.particle.settling_velocity(self.evaporation_factor) + # Height of the emission source to the floor - i.e. mouth/nose (m) + h = 1.5 + # Deposition rate (h^-1) + k = (vg * 3600) / h + return ( + k + self.virus.decay_constant(self.room.humidity, self.room.inside_temp.value(time)) + + self.ventilation.air_exchange(self.room, time) + ) + + def infectious_virus_removal_rate(self, time: float) -> _VectorisedFloat: + # defined for back-compatibility purposes + return self.removal_rate(time) + + +@dataclass(frozen=True) +class CO2ConcentrationModel(_ConcentrationModelBase): + """ + Class used for the computation of the CO2 concentration. + """ + #: Population in the room emitting CO2 + CO2_emitters: Population + + #: CO2 concentration in the atmosphere (in ppm) + CO2_atmosphere_concentration: float = 440.44 + + #: CO2 fraction in the exhaled air + CO2_fraction_exhaled: float = 0.042 + + @property + def population(self) -> Population: + return self.CO2_emitters + + def removal_rate(self, time: float) -> _VectorisedFloat: + return self.ventilation.air_exchange(self.room, time) + + def atmosphere_concentration(self) -> _VectorisedFloat: + """ + Background CO2 concentration in the atmosphere (in ppm) + """ + return self.CO2_atmosphere_concentration + + def normalization_factor(self) -> _VectorisedFloat: + # normalization by the CO2 exhaled. + # CO2 concentration given in ppm, hence the 1e6 factor. + return (1e6*self.population.number + *self.population.activity.exhalation_rate + *self.CO2_fraction_exhaled) @dataclass(frozen=True) @@ -1177,7 +1250,7 @@ def dilution_factor(self) -> _VectorisedFloat: φ = 2 # Exhalation airflow, as per Jia et al. (2022) - Q_exh = φ * BR + Q_exh: _VectorisedFloat = φ * BR # Area of the mouth assuming a perfect circle (m2) Am = np.pi*(mouth_diameter**2)/4 @@ -1361,14 +1434,13 @@ def __post_init__(self): """ c_model = self.concentration_model # Check if the diameter is vectorised. - if (isinstance(c_model.infected, InfectedPopulation) and not np.isscalar(c_model.infected.expiration.diameter) + if (isinstance(c_model.infected, InfectedPopulation) and not np.isscalar(c_model.infected.expiration.diameter) # Check if the diameter-independent elements of the infectious_virus_removal_rate method are vectorised. and not ( all(np.isscalar(c_model.virus.decay_constant(c_model.room.humidity, c_model.room.inside_temp.value(time)) + c_model.ventilation.air_exchange(c_model.room, time)) for time in c_model.state_change_times()))): raise ValueError("If the diameter is an array, none of the ventilation parameters " "or virus decay constant can be arrays at the same time.") - def long_range_fraction_deposited(self) -> _VectorisedFloat: """ @@ -1412,30 +1484,6 @@ def concentration(self, time: float) -> _VectorisedFloat: concentration += interaction.short_range_concentration(self.concentration_model, time) return concentration - def _CO2_concentration_cached(self, time: float) -> _VectorisedFloat: - return self._CO2_concentration(time) - - def _CO2_concentration(self, time: float, make_up_air_concentration: float = 440.44e-6, CO2_fraction: float = 0.042) -> _VectorisedFloat: - if time <= self.concentration_model._first_presence_time(): - return make_up_air_concentration # carbon dioxide concentration in the make up air (m3/m3 person) - 440ppm - next_state_change_time = self.concentration_model._next_state_change(time) - IVRR = self.concentration_model.air_exch_virus_removal_rate(next_state_change_time) - co2_conc_limit = (self.concentration_model._CO2_normed_concentration_limit(next_state_change_time) * - ((self.exposed.number*self.exposed.activity.exhalation_rate*CO2_fraction + - self.concentration_model.infected.number*self.concentration_model.infected.activity.exhalation_rate*CO2_fraction))) - - t_last_state_change = self.concentration_model.last_state_change(time) - co2_conc_at_last_state_change = self._CO2_concentration_cached(t_last_state_change) # CO2 contribution in the room at start - - delta_time = time - t_last_state_change - fac = np.exp(-IVRR * delta_time) - - return co2_conc_limit * (1 - fac) + ((co2_conc_at_last_state_change - make_up_air_concentration) * fac) + make_up_air_concentration - - def CO2_concentration(self, time: float) -> _VectorisedFloat: - # Correction due to the number of generated points. - return self._CO2_concentration(time) * 1e6 - def long_range_deposited_exposure_between_bounds(self, time1: float, time2: float) -> _VectorisedFloat: deposited_exposure = 0. diff --git a/caimira/tests/models/test_exposure_model.py b/caimira/tests/models/test_exposure_model.py index 1a986815..ef9d0c4e 100644 --- a/caimira/tests/models/test_exposure_model.py +++ b/caimira/tests/models/test_exposure_model.py @@ -19,7 +19,7 @@ class KnownNormedconcentration(models.ConcentrationModel): """ normed_concentration_function: typing.Callable = lambda x: 0 - def infectious_virus_removal_rate(self, time: float) -> models._VectorisedFloat: + def removal_rate(self, time: float) -> models._VectorisedFloat: # Very large decay constant -> same as constant concentration return 1.e50 From 39cb0b3de9b3e5862dc54a709ca24d71ed438c7b Mon Sep 17 00:00:00 2001 From: Nicolas Mounet Date: Wed, 14 Dec 2022 10:22:33 +0100 Subject: [PATCH 09/21] Solving a failing mypy test in test_full_algorithm --- caimira/tests/test_full_algorithm.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/caimira/tests/test_full_algorithm.py b/caimira/tests/test_full_algorithm.py index 51a3da72..9f2ee70d 100644 --- a/caimira/tests/test_full_algorithm.py +++ b/caimira/tests/test_full_algorithm.py @@ -229,7 +229,7 @@ def dilution_factor(self) -> _VectorisedFloat: x = np.array(self.distance) dilution = np.empty(x.shape, dtype=np.float64) # Exhalation airflow, as per Jia et al. (2022), m^3/s - Q_exh = self.φ * np.array(self.breathing_rate/3600) + Q_exh: _VectorisedFloat = self.φ * np.array(self.breathing_rate/3600) # The expired flow velocity at the noozle (mouth opening), m/s u0 = np.array(Q_exh/(np.pi/4. * self.mouth_diameter**2)) # Parameters in the jet-like stage From f0298183d31f4c9c531dfa10c46ed6d1a57c2253 Mon Sep 17 00:00:00 2001 From: Luis Aleixo Date: Thu, 15 Dec 2022 08:39:38 +0000 Subject: [PATCH 10/21] Initial atmosphere_concentration normalized --- caimira/models.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/caimira/models.py b/caimira/models.py index e2561354..83711bfd 100644 --- a/caimira/models.py +++ b/caimira/models.py @@ -1077,7 +1077,7 @@ def _normed_concentration(self, time: float) -> _VectorisedFloat: # The model always starts at t=0, but we avoid running concentration calculations # before the first presence as an optimisation. if time <= self._first_presence_time(): - return self.atmosphere_concentration() + return self.atmosphere_concentration()/self.normalization_factor() next_state_change_time = self._next_state_change(time) RR = self.removal_rate(next_state_change_time) conc_limit = self._normed_concentration_limit(next_state_change_time) From 9f219bd64d5bcaf8c12cb5af8ef9902c06200de1 Mon Sep 17 00:00:00 2001 From: Nicolas Mounet Date: Thu, 15 Dec 2022 17:03:12 +0100 Subject: [PATCH 11/21] Correcting bug in models._ConcentrationModelBase (wrong value of concentration_limit when no one is present, for non-zero atmospheric concentration) --- caimira/models.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/caimira/models.py b/caimira/models.py index 83711bfd..43cab84f 100644 --- a/caimira/models.py +++ b/caimira/models.py @@ -1001,7 +1001,7 @@ def _normed_concentration_limit(self, time: float) -> _VectorisedFloat: dependence has been solved for. """ if not self.population.person_present(time): - return 0. + return self.atmosphere_concentration()/self.normalization_factor() V = self.room.volume RR = self.removal_rate(time) From 97e3540f51e937b7bde08b8494f171cd4b863e20 Mon Sep 17 00:00:00 2001 From: Luis Aleixo Date: Thu, 15 Dec 2022 16:22:27 +0000 Subject: [PATCH 12/21] increased version number --- caimira/apps/calculator/__init__.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/caimira/apps/calculator/__init__.py b/caimira/apps/calculator/__init__.py index 50790196..2f42bd0d 100644 --- a/caimira/apps/calculator/__init__.py +++ b/caimira/apps/calculator/__init__.py @@ -35,7 +35,7 @@ # calculator version. If the calculator needs to make breaking changes (e.g. change # form attributes) then it can also increase its MAJOR version without needing to # increase the overall CAiMIRA version (found at ``caimira.__version__``). -__version__ = "4.5" +__version__ = "4.6" class BaseRequestHandler(RequestHandler): From 24b9ffe68f0cdbdc3eeead492d6dae9275ba51e1 Mon Sep 17 00:00:00 2001 From: Luis Aleixo Date: Mon, 9 Jan 2023 17:06:22 +0100 Subject: [PATCH 13/21] updated documentation and added tests for the integrated concentration --- caimira/docs/UML-CAiMIRA.png | Bin 706307 -> 819772 bytes caimira/docs/full_diameter_dependence.rst | 35 ++++++---- caimira/models.py | 2 +- .../models/test_co2_concentration_model.py | 44 +++++++++++++ .../tests/models/test_concentration_model.py | 61 +++++++++++++++++- 5 files changed, 126 insertions(+), 16 deletions(-) create mode 100644 caimira/tests/models/test_co2_concentration_model.py diff --git a/caimira/docs/UML-CAiMIRA.png b/caimira/docs/UML-CAiMIRA.png 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zukx~d0B!_rojGg*9M{sWnLzHLw`GTeY4*`-c|0owTMskS=BpksB=j8Bm3@d z-Q+dau8qK`i8FjMBK1JoS>Jhj0i8!)qDr| zL-et?ezr1Ts+9_6TVL%wqzI(ufZG-a_Y`QR(Bo8OBiE|QlP&w0C(aSI0J+nrS;?v1($g!0F%=~HQcu9HWAj*Ot@s5j)R%kCxuFp$}eBv z-RyY~a)ab$&ldT#!iD-JzX~Xfg3`Ej2-xFTIkfM(zC?P(FmyMjZn_`T=o-^8bjpOs zifyi~vvvwHC8KyjlUg$~hj`TX)bkBCJ`oNwat`x}djIly3F$Z8;osGHn|Hy9^ex}t zg2(w`F!#4-3YwL?@kZmQ0tU=~XLRw|j*=5&G|}R7K)rCk!EL|=?mOcFYcx`q!-%Bs zgTeb+!~ne>dv?!L=-?kgW9Ttl*>@^xXUV^7$N%Q55N8GGJU%NV37XKDI1v;14(no| zaXd1(y4nf+EVCs8UtL`GfuX13I}NYn4Ml zuHyf_n5_$jNiWL)80A@iVY zbs!260o-RE^EDW8FM6MMt|h@UgeM@1Z>h8QKl~&;RC>6Xirv7dSinu+f5fWK;yR+K zJQ~USyO;Yve=ki8KI(SQsD)74Z4CC-89svI7zW(S5@$%mdjDv7a`QY+FXM2pb&Bax T$?VMKf Date: Fri, 27 Jan 2023 16:46:52 +0100 Subject: [PATCH 19/21] added a test for the zero ventilation rate case --- .../tests/models/test_concentration_model.py | 28 +++++++++++++++++++ 1 file changed, 28 insertions(+) diff --git a/caimira/tests/models/test_concentration_model.py b/caimira/tests/models/test_concentration_model.py index fc9e304e..17228588 100644 --- a/caimira/tests/models/test_concentration_model.py +++ b/caimira/tests/models/test_concentration_model.py @@ -240,3 +240,31 @@ def test_normed_integrated_concentration_vectorisation( assert isinstance(integrated_concentration, np.ndarray) assert integrated_concentration.shape == (2, ) npt.assert_almost_equal(integrated_concentration, expected_normed_integrated_concentration) + + +@pytest.mark.parametrize([ + "known_removal_rate", + "known_min_background_concentration", + "expected_concentration"], + [ + [0., 240., 240.], + [0., np.array([240., 240.]), np.array([240., 240.])] + ] +) +def test_zero_ventilation_rate( + simple_conc_model: models.ConcentrationModel, + dummy_population: models.Population, + known_removal_rate: float, + known_min_background_concentration: float, + expected_concentration: float): + + known_conc_model = KnownConcentrationModelBase( + room = simple_conc_model.room, + ventilation = simple_conc_model.ventilation, + known_population = dummy_population, + known_removal_rate = known_removal_rate, + known_normalization_factor=10., + known_min_background_concentration = known_min_background_concentration) + + normed_concentration = known_conc_model.concentration(1) + npt.assert_almost_equal(normed_concentration, expected_concentration) From 80713d3197eeae7c1585fa0f8428b4543e4f6fae Mon Sep 17 00:00:00 2001 From: Luis Aleixo Date: Fri, 27 Jan 2023 16:47:39 +0100 Subject: [PATCH 20/21] handled the case when the removal rate is 0 and changed documentation --- caimira/docs/full_diameter_dependence.rst | 2 ++ caimira/models.py | 7 ++++++- 2 files changed, 8 insertions(+), 1 deletion(-) diff --git a/caimira/docs/full_diameter_dependence.rst b/caimira/docs/full_diameter_dependence.rst index 09561bee..626f21c2 100644 --- a/caimira/docs/full_diameter_dependence.rst +++ b/caimira/docs/full_diameter_dependence.rst @@ -317,6 +317,8 @@ The estimate of the concentration of CO\ :sub:`2` in a given room to indicate th Note that in order to calculate the CO\ :sub:`2` concentration one should use the concentration method defined in the superclass - :meth:`caimira.models._ConcentrationModelBase.concentration` - for a dedicated :class:`caimira.models.CO2ConcentrationModel` scenario. A fraction of 4.2% of the exhalation rate of the defined activity was considered as the supplied to the room (:meth:`caimira.models.CO2ConcentrationModel.CO2_fraction_exhaled`). +Note still that nothing depends on the aerosol diameter :math:`D` in this case (no particles are involved) - hence in this class all parameters are constant w.r.t :math:`D`. + Since the CO\ :sub:`2` concentration differs from the virus concentration, the specific removal rate, CO\ :sub:`2` atmospheric concentration and normalization factors are respectively defined in :meth:`caimira.models.CO2ConcentrationModel.removal_rate`, :meth:`caimira.models.CO2ConcentrationModel.min_background_concentration` and :meth:`caimira.models.CO2ConcentrationModel.normalization_factor`. diff --git a/caimira/models.py b/caimira/models.py index 03602ca9..a9e5ae01 100644 --- a/caimira/models.py +++ b/caimira/models.py @@ -1085,7 +1085,12 @@ def _normed_concentration(self, time: float) -> _VectorisedFloat: return self.min_background_concentration()/self.normalization_factor() next_state_change_time = self._next_state_change(time) RR = self.removal_rate(next_state_change_time) - conc_limit = self._normed_concentration_limit(next_state_change_time) + # If RR is 0, conc_limit does not play a role but its computation + # would raise an error -> we set it to zero. + try: + conc_limit = self._normed_concentration_limit(next_state_change_time) + except ZeroDivisionError: + conc_limit = 0. t_last_state_change = self.last_state_change(time) conc_at_last_state_change = self._normed_concentration_cached(t_last_state_change) From 18b3e3bc1d660565bf5cbd013b0090a984ec48a3 Mon Sep 17 00:00:00 2001 From: Luis Aleixo Date: Mon, 30 Jan 2023 16:30:31 +0100 Subject: [PATCH 21/21] Updated documentation and added a docstring to the newly created tests --- caimira/docs/full_diameter_dependence.rst | 4 ++-- caimira/tests/models/test_concentration_model.py | 4 ++++ 2 files changed, 6 insertions(+), 2 deletions(-) diff --git a/caimira/docs/full_diameter_dependence.rst b/caimira/docs/full_diameter_dependence.rst index 626f21c2..1e684ebb 100644 --- a/caimira/docs/full_diameter_dependence.rst +++ b/caimira/docs/full_diameter_dependence.rst @@ -314,8 +314,8 @@ CO\ :sub:`2` Concentration The estimate of the concentration of CO\ :sub:`2` in a given room to indicate the air quality is given by the same approach as for the long-range virus concentration, :math:`C_{\mathrm{LR}}(t, D)`, where :math:`C_0(D)` is considered to be the background (outdoor) CO\ :sub:`2` concentration (:meth:`caimira.models.CO2ConcentrationModel.CO2_atmosphere_concentration`). -Note that in order to calculate the CO\ :sub:`2` concentration one should use the concentration method defined in the superclass - :meth:`caimira.models._ConcentrationModelBase.concentration` - for a dedicated :class:`caimira.models.CO2ConcentrationModel` scenario. -A fraction of 4.2% of the exhalation rate of the defined activity was considered as the supplied to the room (:meth:`caimira.models.CO2ConcentrationModel.CO2_fraction_exhaled`). +In order to compute the CO\ :sub:`2` concentration one should then simply use the :meth:`caimira.models.CO2ConcentrationModel.concentration` method. +A fraction of 4.2% of the exhalation rate of the defined activity was considered as supplied to the room (:meth:`caimira.models.CO2ConcentrationModel.CO2_fraction_exhaled`). Note still that nothing depends on the aerosol diameter :math:`D` in this case (no particles are involved) - hence in this class all parameters are constant w.r.t :math:`D`. diff --git a/caimira/tests/models/test_concentration_model.py b/caimira/tests/models/test_concentration_model.py index 17228588..568e6757 100644 --- a/caimira/tests/models/test_concentration_model.py +++ b/caimira/tests/models/test_concentration_model.py @@ -179,6 +179,8 @@ def test_integrated_concentration(simple_conc_model): npt.assert_almost_equal(c1, c2 + c3, decimal=15) +# The expected numbers were obtained via the quad integration of the +# normed_integrated_concentration method with 0 (start) and 2 (stop) as limits. @pytest.mark.parametrize([ "known_min_background_concentration", "expected_normed_integrated_concentration"], @@ -206,6 +208,8 @@ def test_normed_integrated_concentration_with_background_concentration( npt.assert_almost_equal(known_conc_model.normed_integrated_concentration(0, 2), expected_normed_integrated_concentration) +# The expected numbers were obtained via the quad integration of the +# normed_integrated_concentration method with 0 (start) and 2 (stop) as limits. @pytest.mark.parametrize([ "known_removal_rate", "known_min_background_concentration",

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ztYNyVn^WtMZf-Plm)c`1fFynqgMRz71bIMZLIaq0Z7w43bTG9`ywC=aeX+m45`X)m zU;f7)JYa3t{CA~aLA~2c^FI~j=2G||1(*#9E#F*!`HJ6uPpmuqe%%dLw^7E9@J<)q z!VSt%z*N(fZr=E_>-?|Yk|+un-Koa*2QGgc%V$kmPfPeW`q44`9 zm`q&|YjK8mG7cWB7!$*enccV9A@wg$f+pm64^jDA9UY<@90e!VwMnX&4*UP*p;ApB w23%l5d=4B}KH<)7`LEiI|BW*-+jYkHiP#54adE}#1>ny%Tl-A~c+c4X0i}Fo(*OVf diff --git a/caimira/docs/full_diameter_dependence.rst b/caimira/docs/full_diameter_dependence.rst index 3fbea5ba..bbe5cbef 100644 --- a/caimira/docs/full_diameter_dependence.rst +++ b/caimira/docs/full_diameter_dependence.rst @@ -69,14 +69,16 @@ In the code, for a given Expiration, we use different methods to perform the cal Note that the diameter-dependence is kept at this stage. Since other parameters downstream in code are also diameter-dependent, the Monte-Carlo integration over the aerosol sizes is computed at the level of the dose :math:`\mathrm{vD^{total}}`. In case one would like to have intermediate results for emission rate, perform the Monte-Carlo integration of :math:`E_{c, j}^{\mathrm{total}}` and compute :math:`\mathrm{vR^{total}} =\mathrm{vl_{in}} \cdot E_{c, j}^{\mathrm{total}} \cdot \mathrm{BR_k}`. -Concentration - C(t, D) -======================= +Virus Concentration - C(t, D) +============================= The estimate of the concentration of virus-laden particles in a given room is based on a two-box exposure model: * **Box 1** - long-range exposure: also known as the *background* concentration, corresponds to the exposure of airborne virions where the susceptible (exposed) host is more than 2 m away from the infected host(s), considering the result of a mass balance equation between the emission rate of the infected host(s) and the removal rates from the environmental/virological characteristics. * **Box 2** - short-range exposure: also known as the *exhaled jet* concentration in close-proximity, corresponds to the exposure of airborne virions where the susceptible (exposed) host is distanced between 0.5 and 2 m from an infected host, considering the result of a two-stage exhaled jet model. +Note that most of the methods used to calculate the concentration are defined in the superclass :meth:`caimira.models._ConcentrationModelBase`, while the specific methods for the long-range virus concentration are part of the subclass :meth:`caimira.models.ConcentrationModel`. + Long-range approach ******************* @@ -84,8 +86,8 @@ The long-range concentration of virus-laden aerosols of a given size :math:`D`, :math:`C_{\mathrm{LR}}(t, D)=\frac{\mathrm{vR}(D) \cdot N_{\mathrm{inf}}}{\lambda_{\mathrm{vRR}}(D) \cdot V_r}-\left (\frac{\mathrm{vR}(D) \cdot N_{\mathrm{inf}}}{\lambda_{\mathrm{vRR}}(D) \cdot V_r}-C_0(D) \right )e^{-\lambda_{\mathrm{vRR}}(D)t}` , -and computed, as a function of the exposure time and particle diameter, in the :meth:`caimira.models.ConcentrationModel.concentration` method. -The long-range concentration, integrated over the exposure time (in piecewise constant steps), :math:`C(D)`, is given by :meth:`caimira.models.ConcentrationModel.integrated_concentration`. +and computed, as a function of the exposure time and particle diameter, in the :meth:`caimira.models._ConcentrationModelBase.concentration` method. +The long-range concentration, integrated over the exposure time (in piecewise constant steps), :math:`C(D)`, is given by :meth:`caimira.models._ConcentrationModelBase.integrated_concentration`. In the :math:`C_{\mathrm{LR}}(t, D)` equation above, the **emission rate** - :math:`\mathrm{vR}(D)` - and the **viral removal rate** - :math:`\lambda_{\mathrm{vRR}}(D)`, :meth:`caimira.models.ConcentrationModel.infectious_virus_removal_rate` - are both diameter-dependent. One can show that the resulting concentration is always proportional to the emission rate :math:`\mathrm{vR}(D)`. Hence, for computational speed-up purposes @@ -93,8 +95,8 @@ the code computes first a normalized version of the concentration, i.e. divided To summarize, we can split the concentration in two different formulations: -* Normalized concentration :meth:`caimira.models.ConcentrationModel._normed_concentration`: :math:`\mathrm{C_\mathrm{LR, normed}}(t, D)` that computes the concentration without including the emission rate. -* Concentration :meth:`caimira.models.ConcentrationModel.concentration` : :math:`C_{\mathrm{LR}}(t, D) = \mathrm{C_\mathrm{LR, normed}}(t, D) \cdot \mathrm{vR}(D)`, where :math:`\mathrm{vR}(D)` is the result of the :meth:`caimira.models._PopulationWithVirus.emission_rate_when_present` method. +* Normalized concentration :meth:`caimira.models._ConcentrationModelBase._normed_concentration`: :math:`\mathrm{C_\mathrm{LR, normed}}(t, D)` that computes the concentration without including the emission rate. +* Concentration :meth:`caimira.models._ConcentrationModelBase.concentration` : :math:`C_{\mathrm{LR}}(t, D) = \mathrm{C_\mathrm{LR, normed}}(t, D) \cdot \mathrm{vR}(D)`, where :math:`\mathrm{vR}(D)` is the result of the :meth:`caimira.models._PopulationWithVirus.emission_rate_when_present` method. Note that in order to get the total concentration value in this stage, the final result should be averaged over the particle diameters (i.e. Monte-Carlo integration over diameters, see above). For the calculator app report, the total concentration (MC integral over the diameter) is performed only when generating the plot. @@ -102,8 +104,8 @@ Otherwise, the diameter-dependence continues until we compute the inhaled dose i The following methods calculate the integrated concentration between two times. They are mostly used when calculating the **dose**: -* :meth:`caimira.models.ConcentrationModel.normed_integrated_concentration`, :math:`\mathrm{C_\mathrm{normed}}(D)` that returns the integrated long-range concentration of viruses in the air, between any two times, normalized by the emission rate. Note that this method performs the integral between any two times of the previously mentioned :meth:`caimira.models.ConcentrationModel._normed_concentration` method. -* :meth:`caimira.models.ConcentrationModel.integrated_concentration`, :math:`C(D)`, that returns the same result as the previous one, but multiplied by the emission rate. +* :meth:`caimira.models._ConcentrationModelBase.normed_integrated_concentration`, :math:`\mathrm{C_\mathrm{normed}}(D)` that returns the integrated long-range concentration of viruses in the air, between any two times, normalized by the emission rate. Note that this method performs the integral between any two times of the previously mentioned :meth:`caimira.models._ConcentrationModelBase._normed_concentration` method. +* :meth:`caimira.models._ConcentrationModelBase.integrated_concentration`, :math:`C(D)`, that returns the same result as the previous one, but multiplied by the emission rate. The integral over the exposure times is calculated directly in the class (integrated methods). @@ -115,7 +117,7 @@ The short-range concentration is the result of a two-stage exhaled jet model dev :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))` , where :math:`S(x)` is the dilution factor due to jet dynamics, as a function of the interpersonal distance :math:`x` and :math:`C_{0, \mathrm{SR}}(D)` corresponds to the initial concentration of virions at the mouth/nose outlet during exhalation. -:math:`C_{\mathrm{LR}, 100μm}(t, D)` is the long-range concentration, calculated in :meth:`caimira.models.ConcentrationModel.concentration` method but **interpolated** to the diameter range used for close-proximity (from 0 to 100μm). +:math:`C_{\mathrm{LR}, 100μm}(t, D)` is the long-range concentration, calculated in :meth:`caimira.models._ConcentrationModelBase.concentration` method but **interpolated** to the diameter range used for close-proximity (from 0 to 100μm). Note that :math:`C_{0, \mathrm{SR}}(D)` is constant over time, hence only dependent on the particle diameter distribution. For code simplification, we split the :math:`C_{\mathrm{SR}}(t, D)` equation into two components: @@ -182,9 +184,9 @@ To summarize, in the code, :math:`C_{\mathrm{SR}}(t, D)` is computed as follows: * calculate the `dilution_factor` - :math:`S({x})` - in the method :meth:`caimira.models.ShortRangeModel.dilution_factor`, with the distance :math:`x` as a random variable (log normal distribution in :meth:`caimira.monte_carlo.data.short_range_distances`) * compute :math:`\frac{1}{S({x})} \cdot (C_{0, \mathrm{SR}}(D) - C_{\mathrm{LR}, 100\mathrm{μm}}(t, D))` in method :meth:`caimira.models.ShortRangeModel.normed_concentration`, * multiply by the diameter-independent parameter, viral load, in method :meth:`caimira.models.ShortRangeModel.short_range_concentration` -* complete the equation of :math:`C_{\mathrm{SR}}(t, D)` by adding the long-range concentration from the :meth:`caimira.models.ConcentrationModel.concentration` (all integrated over :math:`D`), returning the final short-range concentration value for a given time and expiration activity. This is done at the level of the Exposure Model (:meth:`caimira.models.ExposureModel.concentration`). +* complete the equation of :math:`C_{\mathrm{SR}}(t, D)` by adding the long-range concentration from the :meth:`caimira.models._ConcentrationModelBase.concentration` (all integrated over :math:`D`), returning the final short-range concentration value for a given time and expiration activity. This is done at the level of the Exposure Model (:meth:`caimira.models.ExposureModel.concentration`). -Note that :meth:`caimira.models.ShortRangeModel._normed_concentration` method is different from :meth:`caimira.models.ConcentrationModel._normed_concentration` and :meth:`caimira.models.ConcentrationModel.concentration` differs from :meth:`caimira.models.ExposureModel.concentration`. +Note that :meth:`caimira.models.ShortRangeModel._normed_concentration` method is different from :meth:`caimira.models._ConcentrationModelBase._normed_concentration` and :meth:`caimira.models._ConcentrationModelBase.concentration` differs from :meth:`caimira.models.ExposureModel.concentration`. Unless one is computing the mean concentration values (e.g. for the plots in the report), the diameter-dependence is kept at this stage. Since other parameters downstream in the code are also diameter-dependent, the Monte-Carlo integration over the particle sizes is computed at the level of the dose :math:`\mathrm{vD^{total}}`. In case one would like to have intermediate results for the initial short-range concentration, this is done at the :class:`caimira.models.ExposureModel` class level. @@ -217,7 +219,7 @@ Long-range approach ******************* Regarding the concentration part of the long-range exposure (concentration integrated over time, :math:`\int_{t1}^{t2}C_{\mathrm{LR}}(t, D)\;\mathrm{d}t`), the respective method is :meth:`caimira.models.ExposureModel._long_range_normed_exposure_between_bounds`, -which uses the long-range exposure (concentration) between two bounds (time1 and time2), normalized by the emission rate of the infected population, calculated from :meth:`caimira.models.ConcentrationModel.normed_integrated_concentration`. +which uses the long-range exposure (concentration) between two bounds (time1 and time2), normalized by the emission rate of the infected population, calculated from :meth:`caimira.models._ConcentrationModelBase.normed_integrated_concentration`. The former method filters out the given bounds considering the breaks through the day (i.e. the time intervals during which there is no exposition to the virus) and retrieves the integrated long-range concentration of viruses in the air between any two times. After the calculations of the integrated concentration over the time, in order to calculate the final dose, we have to compute the remaining factors in the above equation. @@ -304,6 +306,15 @@ If short-range interactions exist: the long-range component is added to the alre If the are no short-range interactions: the short-range component (`deposited_exposure`) is zero, hence the result is equal solely to the long-range component :math:`C_{\mathrm{LR}}`. +:math:`\mathrm{CO}_{2}` Concentration +===================================== + +The estimate of the concentration of :math:`\mathrm{CO}_{2}` in a given room to indicate the air quality is given by the same equation of the virus concentration for the long-range approach, :math:`C_{\mathrm{LR}}(t, D)`, where :math:`C_0(D)` is the background :math:`\mathrm{CO}_{2}` concentration in the atmosphere (initially defined as `440.44 ppm`). +Note that in order to calculate the :math:`\mathrm{CO}_{2}` concentration one should use the concentration method defined in the superclass - :meth:`caimira.models._ConcentrationModelBase.concentration` - for a dedicated :class:`caimira.models.CO2ConcentrationModel` scenario. +A fraction of 4.2% of the exhalation rate of the defined activity was considered as the :math:`\mathrm{CO}_{2}` supplied to the room. + +Since the :math:`\mathrm{CO}_{2}` concentration differs from the virus concentration, the specific removal rate, atmospheric concentration and normalization factors are respectively defined in :meth:`caimira.models.CO2ConcentrationModel.removal_rate`, :meth:`caimira.models.CO2ConcentrationModel.atmosphere_concentration` and :meth:`caimira.models.CO2ConcentrationModel.normalization_factor`. + .. _caimira-uml-diagram: CAiMIRA UML Diagram diff --git a/caimira/models.py b/caimira/models.py index 43cab84f..b6875d53 100644 --- a/caimira/models.py +++ b/caimira/models.py @@ -1107,7 +1107,7 @@ def normed_integrated_concentration(self, start: float, stop: float) -> _Vectori normalized by normalization_factor. """ if stop <= self._first_presence_time(): - return 0.0 + return (stop - start)*self.atmosphere_concentration()/self.normalization_factor() state_change_times = self.state_change_times() req_start, req_stop = start, stop total_normed_concentration = 0. diff --git a/caimira/tests/models/test_co2_concentration_model.py b/caimira/tests/models/test_co2_concentration_model.py new file mode 100644 index 00000000..ad4a348c --- /dev/null +++ b/caimira/tests/models/test_co2_concentration_model.py @@ -0,0 +1,44 @@ +import numpy.testing as npt +import pytest + +from caimira import models + + +@pytest.fixture +def simple_co2_conc_model(): + return models.CO2ConcentrationModel( + room=models.Room(200, models.PiecewiseConstant((0., 24.), (293,))), + ventilation=models.AirChange(models.PeriodicInterval(period=120, duration=120), 0.25), + CO2_emitters=models.Population( + number=5, + presence=models.SpecificInterval((([0., 4.], ))), + mask=models.Mask.types['No mask'], + activity=models.Activity.types['Seated'], + host_immunity=0., + ), + ) + + +@pytest.mark.parametrize( + "time, expected_co2_concentration", [ + [0., 440.44], + [1., 914.2487227], + [2., 1283.251327], + [3., 1570.630844], + [4., 1794.442237], + ] +) +def test_co2_concentration( + simple_co2_conc_model: models.CO2ConcentrationModel, + time: float, + expected_co2_concentration: float, +): + npt.assert_almost_equal(simple_co2_conc_model.concentration(time), expected_co2_concentration) + + +def test_integrated_concentration(simple_co2_conc_model): + c1 = simple_co2_conc_model.integrated_concentration(0, 2) + c2 = simple_co2_conc_model.integrated_concentration(0, 1) + c3 = simple_co2_conc_model.integrated_concentration(1, 2) + assert c1 != 0 + npt.assert_almost_equal(c1, c2 + c3) diff --git a/caimira/tests/models/test_concentration_model.py b/caimira/tests/models/test_concentration_model.py index 0f6acb37..4141e6c9 100644 --- a/caimira/tests/models/test_concentration_model.py +++ b/caimira/tests/models/test_concentration_model.py @@ -3,9 +3,34 @@ import numpy as np import numpy.testing as npt import pytest +from dataclasses import dataclass from caimira import models +@dataclass(frozen=True) +class KnownConcentrationModelBase(models._ConcentrationModelBase): + """ + A ConcentrationModel where the atmosphere_concentration method is + redefined with a value taken from a new parameter. Useful for testing. + + """ + known_population: models.Population + + known_atmosphere_concentration: float = 0.0 + + @property + def population(self) -> models.Population: + return self.known_population + + def removal_rate(self, time: float): + return 10. + + def atmosphere_concentration(self): + return self.known_atmosphere_concentration + + def normalization_factor(self): + return 1e2 + @pytest.mark.parametrize( "override_params", [ @@ -59,9 +84,9 @@ def test_concentration_model_vectorisation(override_params): def simple_conc_model(): interesting_times = models.SpecificInterval(([0.5, 1.], [1.1, 2], [2., 3.]), ) return models.ConcentrationModel( - models.Room(75, models.PiecewiseConstant((0., 24.), (293,))), - models.AirChange(interesting_times, 100), - models.InfectedPopulation( + room = models.Room(75, models.PiecewiseConstant((0., 24.), (293,))), + ventilation = models.AirChange(interesting_times, 100), + infected = models.InfectedPopulation( number=1, presence=interesting_times, mask=models.Mask.types['Type I'], @@ -137,3 +162,33 @@ def test_integrated_concentration(simple_conc_model): c3 = simple_conc_model.integrated_concentration(1, 2) assert c1 != 0 npt.assert_almost_equal(c1, c2 + c3, decimal=15) + + +@pytest.mark.parametrize( + "known_atmosphere_concentration, expected_normed_integrated_concentration", [ + [0.0, 0.0017333437605308818], + [240.0, 4.801733343835203], + [440.0, 8.801733343835203], + [600., 12.001733343835202], + [1000., 20.00173334429238], + ] +) +def test_normed_integrated_concentration( + simple_conc_model: models.ConcentrationModel, + known_atmosphere_concentration: float, + expected_normed_integrated_concentration: float): + + dummy_population = models.Population( + number=10, + presence=simple_conc_model.infected.presence, + mask=models.Mask.types['Type I'], + activity=models.Activity.types['Seated'], + host_immunity=0., + ) + + known_conc_model = KnownConcentrationModelBase( + simple_conc_model.room, + simple_conc_model.ventilation, + dummy_population, + known_atmosphere_concentration) + npt.assert_almost_equal(known_conc_model.normed_integrated_concentration(0, 2), expected_normed_integrated_concentration) From cdf91ad01176905183d5ddcf2d5bc23a91b8884b Mon Sep 17 00:00:00 2001 From: Luis Aleixo Date: Tue, 10 Jan 2023 12:31:24 +0100 Subject: [PATCH 14/21] added tests on vectorisation for an integrated concentration --- .../tests/models/test_concentration_model.py | 106 +++++++++++++----- 1 file changed, 77 insertions(+), 29 deletions(-) diff --git a/caimira/tests/models/test_concentration_model.py b/caimira/tests/models/test_concentration_model.py index 4141e6c9..0a22bca2 100644 --- a/caimira/tests/models/test_concentration_model.py +++ b/caimira/tests/models/test_concentration_model.py @@ -10,26 +10,30 @@ @dataclass(frozen=True) class KnownConcentrationModelBase(models._ConcentrationModelBase): """ - A ConcentrationModel where the atmosphere_concentration method is - redefined with a value taken from a new parameter. Useful for testing. + A _ConcentrationModelBase where all the class methods are + redefined with a value taken from new parameters. Useful for testing. """ known_population: models.Population - known_atmosphere_concentration: float = 0.0 + known_removal_rate: float + + known_atmosphere_concentration: float + + known_normalization_factor: float @property def population(self) -> models.Population: return self.known_population - def removal_rate(self, time: float): - return 10. + def removal_rate(self, time: float) -> float: + return self.known_removal_rate - def atmosphere_concentration(self): + def atmosphere_concentration(self) -> float: return self.known_atmosphere_concentration - def normalization_factor(self): - return 1e2 + def normalization_factor(self) -> float: + return self.known_normalization_factor @pytest.mark.parametrize( @@ -99,6 +103,17 @@ def simple_conc_model(): ) +@pytest.fixture +def dummy_population(simple_conc_model) -> models.Population: + return models.Population( + number=10, + presence=simple_conc_model.infected.presence, + mask=models.Mask.types['Type I'], + activity=models.Activity.types['Seated'], + host_immunity=0., + ) + + @pytest.mark.parametrize( "time, expected_last_state_change", [ [-15., 0.], # Out of range goes to the first state. @@ -164,31 +179,64 @@ def test_integrated_concentration(simple_conc_model): npt.assert_almost_equal(c1, c2 + c3, decimal=15) -@pytest.mark.parametrize( - "known_atmosphere_concentration, expected_normed_integrated_concentration", [ - [0.0, 0.0017333437605308818], - [240.0, 4.801733343835203], - [440.0, 8.801733343835203], - [600., 12.001733343835202], - [1000., 20.00173334429238], +@pytest.mark.parametrize([ + "known_atmosphere_concentration", + "expected_normed_integrated_concentration"], + [ + [0.0, 0.00018533333708996207], + [240.0, 48.000185340695275], + [440.0, 88.00018534069527], + [600., 120.00018534069527], + [1000., 200.0001853407918], ] ) -def test_normed_integrated_concentration( - simple_conc_model: models.ConcentrationModel, +def test_normed_integrated_concentration_with_atmosphere_concentration( + simple_conc_model: models.ConcentrationModel, + dummy_population: models.Population, known_atmosphere_concentration: float, expected_normed_integrated_concentration: float): - dummy_population = models.Population( - number=10, - presence=simple_conc_model.infected.presence, - mask=models.Mask.types['Type I'], - activity=models.Activity.types['Seated'], - host_immunity=0., - ) - known_conc_model = KnownConcentrationModelBase( - simple_conc_model.room, - simple_conc_model.ventilation, - dummy_population, - known_atmosphere_concentration) + room = simple_conc_model.room, + ventilation = simple_conc_model.ventilation, + known_population = dummy_population, + known_removal_rate = 100., + known_atmosphere_concentration = known_atmosphere_concentration, + known_normalization_factor = 10.) npt.assert_almost_equal(known_conc_model.normed_integrated_concentration(0, 2), expected_normed_integrated_concentration) + + +@pytest.mark.parametrize([ + "known_removal_rate", + "known_atmosphere_concentration", + "known_normalization_factor", + "expected_normed_integrated_concentration"], + [ + [np.array([0.25, 10]), 0.0, 10., np.array([0.012161005755130391, 0.0017333437605308818])], + [100, np.array([0, 240.0]), 10., np.array([0.00018533333708996207, 48.000185340695275])], + [100, 440.0, np.array([10., 20.]), np.array([88.00018534069527, 44.000185340695275])], + [np.array([10, 100]), np.array([600., 800.]), 10., np.array([120.00173334473946, 160.0001853406953])], + [np.array([50, 100,]), np.array([1000.,1100.]), np.array([10., 20.]), np.array([200.00036800764332, 110.00018534069527])], + ] +) +def test_normed_integrated_concentration_vectorisation( + simple_conc_model: models.ConcentrationModel, + dummy_population: models.Population, + known_removal_rate: float, + known_atmosphere_concentration: float, + known_normalization_factor: float, + expected_normed_integrated_concentration: float): + + known_conc_model = KnownConcentrationModelBase( + room = simple_conc_model.room, + ventilation = simple_conc_model.ventilation, + known_population = dummy_population, + known_removal_rate = known_removal_rate, + known_atmosphere_concentration = known_atmosphere_concentration, + known_normalization_factor = known_normalization_factor) + + integrated_concentration = known_conc_model.normed_integrated_concentration(0, 2) + + assert isinstance(integrated_concentration, np.ndarray) + assert integrated_concentration.shape == (2, ) + npt.assert_almost_equal(integrated_concentration, expected_normed_integrated_concentration) From 71c7d4137d88daaa4ecfa6f1eefc0826debbb9ac Mon Sep 17 00:00:00 2001 From: "CERN\\Andrejh" Date: Tue, 10 Jan 2023 11:52:44 +0100 Subject: [PATCH 15/21] update documentation --- caimira/docs/full_diameter_dependence.rst | 15 ++++++++++----- 1 file changed, 10 insertions(+), 5 deletions(-) diff --git a/caimira/docs/full_diameter_dependence.rst b/caimira/docs/full_diameter_dependence.rst index bbe5cbef..90b10d96 100644 --- a/caimira/docs/full_diameter_dependence.rst +++ b/caimira/docs/full_diameter_dependence.rst @@ -78,6 +78,8 @@ The estimate of the concentration of virus-laden particles in a given room is ba * **Box 2** - short-range exposure: also known as the *exhaled jet* concentration in close-proximity, corresponds to the exposure of airborne virions where the susceptible (exposed) host is distanced between 0.5 and 2 m from an infected host, considering the result of a two-stage exhaled jet model. Note that most of the methods used to calculate the concentration are defined in the superclass :meth:`caimira.models._ConcentrationModelBase`, while the specific methods for the long-range virus concentration are part of the subclass :meth:`caimira.models.ConcentrationModel`. +The specific removal rate, atmospheric concentration and normalization factors will depend on what concentration is being calculated (e.g. viral concentration or CO2 concentration) and are respectively defined in :meth:`caimira.models._ConcentrationModelBase.removal_rate`, +:meth:`caimira.models._ConcentrationModelBase.atmosphere_concentration` and :meth:`caimira.models._ConcentrationModelBase.normalization_factor`. Long-range approach ******************* @@ -306,14 +308,17 @@ If short-range interactions exist: the long-range component is added to the alre If the are no short-range interactions: the short-range component (`deposited_exposure`) is zero, hence the result is equal solely to the long-range component :math:`C_{\mathrm{LR}}`. -:math:`\mathrm{CO}_{2}` Concentration +CO\ :sub:`2` Concentration ===================================== -The estimate of the concentration of :math:`\mathrm{CO}_{2}` in a given room to indicate the air quality is given by the same equation of the virus concentration for the long-range approach, :math:`C_{\mathrm{LR}}(t, D)`, where :math:`C_0(D)` is the background :math:`\mathrm{CO}_{2}` concentration in the atmosphere (initially defined as `440.44 ppm`). -Note that in order to calculate the :math:`\mathrm{CO}_{2}` concentration one should use the concentration method defined in the superclass - :meth:`caimira.models._ConcentrationModelBase.concentration` - for a dedicated :class:`caimira.models.CO2ConcentrationModel` scenario. -A fraction of 4.2% of the exhalation rate of the defined activity was considered as the :math:`\mathrm{CO}_{2}` supplied to the room. +The estimate of the concentration of CO\ :sub:`2` in a given room to indicate the air quality is given by the same approach as for the long-range virus concentration, +:math:`C_{\mathrm{LR}}(t, D)`, where :math:`C_0(D)` is considered to be the background (outdoor) CO\ :sub:`2` concentration (:meth:`caimira.models.CO2ConcentrationModel.CO2_atmosphere_concentration`). -Since the :math:`\mathrm{CO}_{2}` concentration differs from the virus concentration, the specific removal rate, atmospheric concentration and normalization factors are respectively defined in :meth:`caimira.models.CO2ConcentrationModel.removal_rate`, :meth:`caimira.models.CO2ConcentrationModel.atmosphere_concentration` and :meth:`caimira.models.CO2ConcentrationModel.normalization_factor`. +Note that in order to calculate the CO\ :sub:`2` concentration one should use the concentration method defined in the superclass - :meth:`caimira.models._ConcentrationModelBase.concentration` - for a dedicated :class:`caimira.models.CO2ConcentrationModel` scenario. +A fraction of 4.2% of the exhalation rate of the defined activity was considered as the supplied to the room (:meth:`caimira.models.CO2ConcentrationModel.CO2_fraction_exhaled`). + +Since the CO\ :sub:`2` concentration differs from the virus concentration, the specific removal rate, atmospheric concentration and normalization factors are respectively defined in :meth:`caimira.models.CO2ConcentrationModel.removal_rate`, +:meth:`caimira.models.CO2ConcentrationModel.atmosphere_concentration` and :meth:`caimira.models.CO2ConcentrationModel.normalization_factor`. .. _caimira-uml-diagram: From fcf5590a5ebef23e69a2fb5995217f5e58904f48 Mon Sep 17 00:00:00 2001 From: Luis Aleixo Date: Tue, 10 Jan 2023 13:01:34 +0100 Subject: [PATCH 16/21] updated atmospheric concentration to background concentration (more generic) --- caimira/docs/full_diameter_dependence.rst | 8 +++---- caimira/models.py | 18 +++++++++------ .../tests/models/test_concentration_model.py | 22 +++++++++---------- 3 files changed, 26 insertions(+), 22 deletions(-) diff --git a/caimira/docs/full_diameter_dependence.rst b/caimira/docs/full_diameter_dependence.rst index 90b10d96..9abb73c6 100644 --- a/caimira/docs/full_diameter_dependence.rst +++ b/caimira/docs/full_diameter_dependence.rst @@ -78,8 +78,8 @@ The estimate of the concentration of virus-laden particles in a given room is ba * **Box 2** - short-range exposure: also known as the *exhaled jet* concentration in close-proximity, corresponds to the exposure of airborne virions where the susceptible (exposed) host is distanced between 0.5 and 2 m from an infected host, considering the result of a two-stage exhaled jet model. Note that most of the methods used to calculate the concentration are defined in the superclass :meth:`caimira.models._ConcentrationModelBase`, while the specific methods for the long-range virus concentration are part of the subclass :meth:`caimira.models.ConcentrationModel`. -The specific removal rate, atmospheric concentration and normalization factors will depend on what concentration is being calculated (e.g. viral concentration or CO2 concentration) and are respectively defined in :meth:`caimira.models._ConcentrationModelBase.removal_rate`, -:meth:`caimira.models._ConcentrationModelBase.atmosphere_concentration` and :meth:`caimira.models._ConcentrationModelBase.normalization_factor`. +The specific removal rate, background concentration and normalization factors will depend on what concentration is being calculated (e.g. viral concentration or CO\ :sub:`2` concentration) and are respectively defined in :meth:`caimira.models._ConcentrationModelBase.removal_rate`, +:meth:`caimira.models._ConcentrationModelBase.background_concentration` and :meth:`caimira.models._ConcentrationModelBase.normalization_factor`. Long-range approach ******************* @@ -317,8 +317,8 @@ The estimate of the concentration of CO\ :sub:`2` in a given room to indicate th Note that in order to calculate the CO\ :sub:`2` concentration one should use the concentration method defined in the superclass - :meth:`caimira.models._ConcentrationModelBase.concentration` - for a dedicated :class:`caimira.models.CO2ConcentrationModel` scenario. A fraction of 4.2% of the exhalation rate of the defined activity was considered as the supplied to the room (:meth:`caimira.models.CO2ConcentrationModel.CO2_fraction_exhaled`). -Since the CO\ :sub:`2` concentration differs from the virus concentration, the specific removal rate, atmospheric concentration and normalization factors are respectively defined in :meth:`caimira.models.CO2ConcentrationModel.removal_rate`, -:meth:`caimira.models.CO2ConcentrationModel.atmosphere_concentration` and :meth:`caimira.models.CO2ConcentrationModel.normalization_factor`. +Since the CO\ :sub:`2` concentration differs from the virus concentration, the specific removal rate, CO\ :sub:`2` atmospheric concentration and normalization factors are respectively defined in :meth:`caimira.models.CO2ConcentrationModel.removal_rate`, +:meth:`caimira.models.CO2ConcentrationModel.background_concentration` and :meth:`caimira.models.CO2ConcentrationModel.normalization_factor`. .. _caimira-uml-diagram: diff --git a/caimira/models.py b/caimira/models.py index b6875d53..e869b7a3 100644 --- a/caimira/models.py +++ b/caimira/models.py @@ -957,6 +957,10 @@ def probability_meet_infected_person(self, virus: Virus, n_infected: int, event_ @dataclass(frozen=True) class _ConcentrationModelBase: + """ + A generic superclass that contains the methods to calculate the + concentration (e.g. viral concentration or CO2 concentration). + """ room: Room ventilation: _VentilationBase @@ -974,9 +978,9 @@ def removal_rate(self, time: float) -> _VectorisedFloat: """ raise NotImplementedError("Subclass must implement") - def atmosphere_concentration(self) -> _VectorisedFloat: + def background_concentration(self) -> _VectorisedFloat: """ - Background concentration in the atmosphere + Background concentration in the scenario (in the same unit as the concentration) """ return 0. @@ -1001,11 +1005,11 @@ def _normed_concentration_limit(self, time: float) -> _VectorisedFloat: dependence has been solved for. """ if not self.population.person_present(time): - return self.atmosphere_concentration()/self.normalization_factor() + return self.background_concentration()/self.normalization_factor() V = self.room.volume RR = self.removal_rate(time) - return (1. / (RR * V) + self.atmosphere_concentration()/ + return (1. / (RR * V) + self.background_concentration()/ self.normalization_factor()) @method_cache @@ -1077,7 +1081,7 @@ def _normed_concentration(self, time: float) -> _VectorisedFloat: # The model always starts at t=0, but we avoid running concentration calculations # before the first presence as an optimisation. if time <= self._first_presence_time(): - return self.atmosphere_concentration()/self.normalization_factor() + return self.background_concentration()/self.normalization_factor() next_state_change_time = self._next_state_change(time) RR = self.removal_rate(next_state_change_time) conc_limit = self._normed_concentration_limit(next_state_change_time) @@ -1107,7 +1111,7 @@ def normed_integrated_concentration(self, start: float, stop: float) -> _Vectori normalized by normalization_factor. """ if stop <= self._first_presence_time(): - return (stop - start)*self.atmosphere_concentration()/self.normalization_factor() + return (stop - start)*self.background_concentration()/self.normalization_factor() state_change_times = self.state_change_times() req_start, req_stop = start, stop total_normed_concentration = 0. @@ -1202,7 +1206,7 @@ def population(self) -> Population: def removal_rate(self, time: float) -> _VectorisedFloat: return self.ventilation.air_exchange(self.room, time) - def atmosphere_concentration(self) -> _VectorisedFloat: + def background_concentration(self) -> _VectorisedFloat: """ Background CO2 concentration in the atmosphere (in ppm) """ diff --git a/caimira/tests/models/test_concentration_model.py b/caimira/tests/models/test_concentration_model.py index 0a22bca2..7f3d5052 100644 --- a/caimira/tests/models/test_concentration_model.py +++ b/caimira/tests/models/test_concentration_model.py @@ -10,7 +10,7 @@ @dataclass(frozen=True) class KnownConcentrationModelBase(models._ConcentrationModelBase): """ - A _ConcentrationModelBase where all the class methods are + A _ConcentrationModelBase class where all the class methods are redefined with a value taken from new parameters. Useful for testing. """ @@ -18,7 +18,7 @@ class KnownConcentrationModelBase(models._ConcentrationModelBase): known_removal_rate: float - known_atmosphere_concentration: float + known_background_concentration: float known_normalization_factor: float @@ -29,8 +29,8 @@ def population(self) -> models.Population: def removal_rate(self, time: float) -> float: return self.known_removal_rate - def atmosphere_concentration(self) -> float: - return self.known_atmosphere_concentration + def background_concentration(self) -> float: + return self.known_background_concentration def normalization_factor(self) -> float: return self.known_normalization_factor @@ -180,7 +180,7 @@ def test_integrated_concentration(simple_conc_model): @pytest.mark.parametrize([ - "known_atmosphere_concentration", + "known_background_concentration", "expected_normed_integrated_concentration"], [ [0.0, 0.00018533333708996207], @@ -190,10 +190,10 @@ def test_integrated_concentration(simple_conc_model): [1000., 200.0001853407918], ] ) -def test_normed_integrated_concentration_with_atmosphere_concentration( +def test_normed_integrated_concentration_with_background_concentration( simple_conc_model: models.ConcentrationModel, dummy_population: models.Population, - known_atmosphere_concentration: float, + known_background_concentration: float, expected_normed_integrated_concentration: float): known_conc_model = KnownConcentrationModelBase( @@ -201,14 +201,14 @@ def test_normed_integrated_concentration_with_atmosphere_concentration( ventilation = simple_conc_model.ventilation, known_population = dummy_population, known_removal_rate = 100., - known_atmosphere_concentration = known_atmosphere_concentration, + known_background_concentration = known_background_concentration, known_normalization_factor = 10.) npt.assert_almost_equal(known_conc_model.normed_integrated_concentration(0, 2), expected_normed_integrated_concentration) @pytest.mark.parametrize([ "known_removal_rate", - "known_atmosphere_concentration", + "known_background_concentration", "known_normalization_factor", "expected_normed_integrated_concentration"], [ @@ -223,7 +223,7 @@ def test_normed_integrated_concentration_vectorisation( simple_conc_model: models.ConcentrationModel, dummy_population: models.Population, known_removal_rate: float, - known_atmosphere_concentration: float, + known_background_concentration: float, known_normalization_factor: float, expected_normed_integrated_concentration: float): @@ -232,7 +232,7 @@ def test_normed_integrated_concentration_vectorisation( ventilation = simple_conc_model.ventilation, known_population = dummy_population, known_removal_rate = known_removal_rate, - known_atmosphere_concentration = known_atmosphere_concentration, + known_background_concentration = known_background_concentration, known_normalization_factor = known_normalization_factor) integrated_concentration = known_conc_model.normed_integrated_concentration(0, 2) From 4e6b66d9a911bd5ef1884ddaf043f22c54e23cbb Mon Sep 17 00:00:00 2001 From: Luis Aleixo Date: Wed, 11 Jan 2023 13:54:05 +0100 Subject: [PATCH 17/21] updated background_concentration name to min_background_concentration --- caimira/docs/full_diameter_dependence.rst | 6 +++--- caimira/models.py | 17 +++++++++-------- .../tests/models/test_concentration_model.py | 18 +++++++++--------- 3 files changed, 21 insertions(+), 20 deletions(-) diff --git a/caimira/docs/full_diameter_dependence.rst b/caimira/docs/full_diameter_dependence.rst index 9abb73c6..09561bee 100644 --- a/caimira/docs/full_diameter_dependence.rst +++ b/caimira/docs/full_diameter_dependence.rst @@ -78,8 +78,8 @@ The estimate of the concentration of virus-laden particles in a given room is ba * **Box 2** - short-range exposure: also known as the *exhaled jet* concentration in close-proximity, corresponds to the exposure of airborne virions where the susceptible (exposed) host is distanced between 0.5 and 2 m from an infected host, considering the result of a two-stage exhaled jet model. Note that most of the methods used to calculate the concentration are defined in the superclass :meth:`caimira.models._ConcentrationModelBase`, while the specific methods for the long-range virus concentration are part of the subclass :meth:`caimira.models.ConcentrationModel`. -The specific removal rate, background concentration and normalization factors will depend on what concentration is being calculated (e.g. viral concentration or CO\ :sub:`2` concentration) and are respectively defined in :meth:`caimira.models._ConcentrationModelBase.removal_rate`, -:meth:`caimira.models._ConcentrationModelBase.background_concentration` and :meth:`caimira.models._ConcentrationModelBase.normalization_factor`. +The specific removal rate, minimum background concentration and normalization factors will depend on what concentration is being calculated (e.g. viral concentration or CO\ :sub:`2` concentration) and are respectively defined in :meth:`caimira.models._ConcentrationModelBase.removal_rate`, +:meth:`caimira.models._ConcentrationModelBase.min_background_concentration` and :meth:`caimira.models._ConcentrationModelBase.normalization_factor`. Long-range approach ******************* @@ -318,7 +318,7 @@ Note that in order to calculate the CO\ :sub:`2` concentration one should use th A fraction of 4.2% of the exhalation rate of the defined activity was considered as the supplied to the room (:meth:`caimira.models.CO2ConcentrationModel.CO2_fraction_exhaled`). Since the CO\ :sub:`2` concentration differs from the virus concentration, the specific removal rate, CO\ :sub:`2` atmospheric concentration and normalization factors are respectively defined in :meth:`caimira.models.CO2ConcentrationModel.removal_rate`, -:meth:`caimira.models.CO2ConcentrationModel.background_concentration` and :meth:`caimira.models.CO2ConcentrationModel.normalization_factor`. +:meth:`caimira.models.CO2ConcentrationModel.min_background_concentration` and :meth:`caimira.models.CO2ConcentrationModel.normalization_factor`. .. _caimira-uml-diagram: diff --git a/caimira/models.py b/caimira/models.py index e869b7a3..03602ca9 100644 --- a/caimira/models.py +++ b/caimira/models.py @@ -978,10 +978,11 @@ def removal_rate(self, time: float) -> _VectorisedFloat: """ raise NotImplementedError("Subclass must implement") - def background_concentration(self) -> _VectorisedFloat: + def min_background_concentration(self) -> _VectorisedFloat: """ - Background concentration in the scenario - (in the same unit as the concentration) + Minimum background concentration in the room for a given scenario + (in the same unit as the concentration). Its the value towards which + the concentration will decay to. """ return 0. @@ -1005,11 +1006,11 @@ def _normed_concentration_limit(self, time: float) -> _VectorisedFloat: dependence has been solved for. """ if not self.population.person_present(time): - return self.background_concentration()/self.normalization_factor() + return self.min_background_concentration()/self.normalization_factor() V = self.room.volume RR = self.removal_rate(time) - return (1. / (RR * V) + self.background_concentration()/ + return (1. / (RR * V) + self.min_background_concentration()/ self.normalization_factor()) @method_cache @@ -1081,7 +1082,7 @@ def _normed_concentration(self, time: float) -> _VectorisedFloat: # The model always starts at t=0, but we avoid running concentration calculations # before the first presence as an optimisation. if time <= self._first_presence_time(): - return self.background_concentration()/self.normalization_factor() + return self.min_background_concentration()/self.normalization_factor() next_state_change_time = self._next_state_change(time) RR = self.removal_rate(next_state_change_time) conc_limit = self._normed_concentration_limit(next_state_change_time) @@ -1111,7 +1112,7 @@ def normed_integrated_concentration(self, start: float, stop: float) -> _Vectori normalized by normalization_factor. """ if stop <= self._first_presence_time(): - return (stop - start)*self.background_concentration()/self.normalization_factor() + return (stop - start)*self.min_background_concentration()/self.normalization_factor() state_change_times = self.state_change_times() req_start, req_stop = start, stop total_normed_concentration = 0. @@ -1206,7 +1207,7 @@ def population(self) -> Population: def removal_rate(self, time: float) -> _VectorisedFloat: return self.ventilation.air_exchange(self.room, time) - def background_concentration(self) -> _VectorisedFloat: + def min_background_concentration(self) -> _VectorisedFloat: """ Background CO2 concentration in the atmosphere (in ppm) """ diff --git a/caimira/tests/models/test_concentration_model.py b/caimira/tests/models/test_concentration_model.py index 7f3d5052..fc9e304e 100644 --- a/caimira/tests/models/test_concentration_model.py +++ b/caimira/tests/models/test_concentration_model.py @@ -18,7 +18,7 @@ class KnownConcentrationModelBase(models._ConcentrationModelBase): known_removal_rate: float - known_background_concentration: float + known_min_background_concentration: float known_normalization_factor: float @@ -29,8 +29,8 @@ def population(self) -> models.Population: def removal_rate(self, time: float) -> float: return self.known_removal_rate - def background_concentration(self) -> float: - return self.known_background_concentration + def min_background_concentration(self) -> float: + return self.known_min_background_concentration def normalization_factor(self) -> float: return self.known_normalization_factor @@ -180,7 +180,7 @@ def test_integrated_concentration(simple_conc_model): @pytest.mark.parametrize([ - "known_background_concentration", + "known_min_background_concentration", "expected_normed_integrated_concentration"], [ [0.0, 0.00018533333708996207], @@ -193,7 +193,7 @@ def test_integrated_concentration(simple_conc_model): def test_normed_integrated_concentration_with_background_concentration( simple_conc_model: models.ConcentrationModel, dummy_population: models.Population, - known_background_concentration: float, + known_min_background_concentration: float, expected_normed_integrated_concentration: float): known_conc_model = KnownConcentrationModelBase( @@ -201,14 +201,14 @@ def test_normed_integrated_concentration_with_background_concentration( ventilation = simple_conc_model.ventilation, known_population = dummy_population, known_removal_rate = 100., - known_background_concentration = known_background_concentration, + known_min_background_concentration = known_min_background_concentration, known_normalization_factor = 10.) npt.assert_almost_equal(known_conc_model.normed_integrated_concentration(0, 2), expected_normed_integrated_concentration) @pytest.mark.parametrize([ "known_removal_rate", - "known_background_concentration", + "known_min_background_concentration", "known_normalization_factor", "expected_normed_integrated_concentration"], [ @@ -223,7 +223,7 @@ def test_normed_integrated_concentration_vectorisation( simple_conc_model: models.ConcentrationModel, dummy_population: models.Population, known_removal_rate: float, - known_background_concentration: float, + known_min_background_concentration: float, known_normalization_factor: float, expected_normed_integrated_concentration: float): @@ -232,7 +232,7 @@ def test_normed_integrated_concentration_vectorisation( ventilation = simple_conc_model.ventilation, known_population = dummy_population, known_removal_rate = known_removal_rate, - known_background_concentration = known_background_concentration, + known_min_background_concentration = known_min_background_concentration, known_normalization_factor = known_normalization_factor) integrated_concentration = known_conc_model.normed_integrated_concentration(0, 2) From 46d1382ec5496d84ba48f09773b5521efcb853a3 Mon Sep 17 00:00:00 2001 From: Luis Aleixo Date: Wed, 11 Jan 2023 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