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= {version = "^1.2.4", optional = true} oyaml = "^1.0" +mkdocstrings = {version = "^0.25.1", extras = ["python"], optional = true} +mkdocs-material = {version = "^9.5.26", optional = true} [tool.poetry.dev-dependencies] pytest = "^6.2" diff --git a/tests/params/basic.yml b/tests/params/basic.yml index 586b8141..9044e5d7 100644 --- a/tests/params/basic.yml +++ b/tests/params/basic.yml @@ -355,7 +355,7 @@ features: timeline_scaling: true model: - num_pop: 100 + num_pop: 1000 network: enable: true num_reps: 1 diff --git a/titan/exposures/hiv.py b/titan/exposures/hiv.py index 84679790..46c2b248 100644 --- a/titan/exposures/hiv.py +++ b/titan/exposures/hiv.py @@ -9,7 +9,7 @@ class HIV(base_exposure.BaseExposure): name: str = "hiv" - stats: List[str] = ["hiv", "hiv_dx", "hiv_aids", "hiv_new", "hiv_dx_new"] + stats: List[str] = ["hiv", "hiv_dx", "hiv_aids", "hiv_new", "hiv_dx_new", "hiv_tested_negative_new"] """ HIV collects the following stats: @@ -34,7 +34,9 @@ def __init__(self, agent: "agent.Agent"): self.dx = False self.dx_time: Optional[int] = None self.aids = False - + self.tested_negative = False #has this agent tested negative for hiv? + self.tested_negative_time = None #time agent tested negative + @classmethod def init_class(cls, params): """ @@ -67,7 +69,7 @@ def init_agent(self, pop: "population.Population", time: int): # HIV if ( - pop.pop_random.random() < agent_params.hiv.init + pop.pop_random.random() < agent_params.hiv.init #0.13 for white, 0.175 for latino and 0.434 for blacks and time >= pop.params.hiv.start_time ): self.active = True @@ -82,7 +84,7 @@ def init_agent(self, pop: "population.Population", time: int): if pop.pop_random.random() < agent_params.hiv.aids.init: self.aids = True - if pop.pop_random.random() < agent_params.hiv.dx.init: + if pop.pop_random.random() < agent_params.hiv.dx.init: #0.6 - 0.8 depending on race self.dx = True # agent was diagnosed at a random time between conversion and now self.dx_time = utils.safe_random_int(self.time, time, pop.pop_random) @@ -99,23 +101,34 @@ def update_agent(self, model: "model.TITAN"): args: model: the instance of TITAN currently being run """ + if self.active and model.time >= model.params.hiv.start_time: - if not self.dx: - test_prob = ( - self.agent.location.params.demographics[self.agent.race] - .sex_type[self.agent.sex_type] - .drug_type[self.agent.drug_type] - .hiv.dx.prob - ) - - # Rescale based on calibration param - test_prob *= model.calibration.test_frequency - - if model.run_random.random() < test_prob: - self.diagnose(model) - self.progress_to_aids(model) + + test_prob = ( + self.agent.location.params.demographics[self.agent.race] + .sex_type[self.agent.sex_type] + .drug_type[self.agent.drug_type] + .hiv.dx.prob + ) + + # Rescale based on calibration param + test_prob *= model.calibration.test_frequency #freq is 1 so test_prob remains at 0.025 + if ( + model.run_random.random() < test_prob + and not self.dx # has not yet been diagnosed + ): + if self.active and model.time >= model.params.hiv.start_time: + self.diagnose(model) + else: + self.tested_negative = True + self.tested_negative_time = model.time + + if self.tested_negative and self.tested_negative_time < model.time: + self.tested_negative = False + self.tested_negative_time = None + @classmethod def add_agent(cls, agent: "agent.Agent"): """ @@ -157,7 +170,9 @@ def set_stats(self, stats: Dict[str, int], time: int): stats["hiv_dx"] += 1 if self.dx_time == time: stats["hiv_dx_new"] += 1 - + if self.tested_negative and self.tested_negative_time == time: + stats["hiv_tested_negative_new"] += 1 + @staticmethod def expose( model: "model.TITAN", diff --git a/titan/features/haart.py b/titan/features/haart.py index 48951d65..cc05e3db 100644 --- a/titan/features/haart.py +++ b/titan/features/haart.py @@ -13,7 +13,7 @@ class HAART(base_feature.BaseFeature): """ name = "haart" - stats = ["haart"] + stats = ["haart", "ps_haart_count"] """ HAART collects the following stats: @@ -28,7 +28,7 @@ def __init__(self, agent: "agent.Agent"): self.active = False self.ever = False self.adherent = False - + @classmethod def init_class(cls, params: "ObjMap"): """ @@ -41,6 +41,8 @@ def init_class(cls, params: "ObjMap"): race: {sex_type: 0 for sex_type in params.classes.sex_types} for race in params.classes.races } + + cls.ps_haart_count = 0 def init_agent(self, pop: "population.Population", time: int): """ @@ -132,10 +134,25 @@ def remove_agent(cls, agent: "agent.Agent"): """ cls.counts[agent.race][agent.sex_type] -= 1 + @classmethod + def ps_add_stat(cls): + cls.ps_haart_count += 1 + + @classmethod + def ps_remove_stat(cls): # do we subtract agents who stop using haart? + pass + #cls.ps_haart_count -= 1 + + @classmethod + def ps_get_stat(cls): + return cls.ps_haart_count + def set_stats(self, stats: Dict[str, int], time: int): if self.active: stats["haart"] += 1 - + + stats["ps_haart_count"] = self.ps_get_stat() + def get_transmission_risk_multiplier(self, time: int, interaction_type: str): """ Get a multiplier for how haart reduces hiv transmission risk based on interaction type and params. @@ -182,6 +199,7 @@ def enroll(self, model: "model.TITAN", haart_params: ObjMap): model: the instance of TITAN currently being run haart_params: the HAART demographic params for this agent """ + if self.agent.location.params.haart.use_cap: self.enroll_cap(model, haart_params) else: @@ -201,11 +219,22 @@ def enroll_cap(self, model: "model.TITAN", haart_params: ObjMap): # HAART agents based on % of diagnosed agents num_dx_agents = self.agent.hiv.dx_counts[race][sex_type] # type: ignore[attr-defined] num_haart_agents = self.counts[race][sex_type] - - # take value from dictionary for cap - if num_haart_agents < (haart_params.cap * num_dx_agents): - self.initiate(model.run_random, haart_params, "prob") - + + # if agent is diagnosed through PS, use PS parameter + if self.agent.partner_tracing.ps_dx: + cap = self.agent.partner_tracing.get_positive_count() * self.agent.location.params.partner_tracing.treatment_prob + # initiate agent only if required capacity is not yet met + if self.ps_get_stat() < cap: + self.initiate(model.run_random, haart_params, "prob") + self.ps_add_stat() + + # else, use base parameter + else: + cap = haart_params.cap + # initiate agent only if required capacity is not yet met + if num_haart_agents < (cap * num_dx_agents): + self.initiate(model.run_random, haart_params, "prob") + def enroll_prob(self, model: "model.TITAN", haart_params: ObjMap): """ Determine whether to enroll an agent in HAART using probability method. @@ -214,9 +243,19 @@ def enroll_prob(self, model: "model.TITAN", haart_params: ObjMap): model: the instance of TITAN currently being run haart_params: the HAART demographic params for this agent """ + # if agent has ever been on haart and use_reinit is true, use reinit probability if self.ever and self.agent.location.params.haart.use_reinit: if model.run_random.random() < haart_params.reinit.prob: self.initiate(model.run_random, haart_params, "prob") + + # if agent has been diagnosed through PS, use PS treatment (haart) probability + elif self.agent.partner_tracing.ps_dx: + enroll_prob = self.agent.location.params.partner_tracing.treatment_prob + if model.run_random.random() < enroll_prob: + self.initiate(model.run_random, haart_params, "prob") + self.ps_add_stat() + + # else, use normal base probability scaled by time since diagnoses else: # Find enroll probability based on time since diagnosis enroll_prob = 0.0 diff --git a/titan/features/partner_tracing.py b/titan/features/partner_tracing.py index 2f1ac74e..2c44534e 100644 --- a/titan/features/partner_tracing.py +++ b/titan/features/partner_tracing.py @@ -1,17 +1,58 @@ +from typing import List, Dict from . import base_feature from .. import agent from .. import model +from ..parse_params import ObjMap class PartnerTracing(base_feature.BaseFeature): - name = "partner_tracing" - + name: str = "partner_tracing" + stats: List[str] = ["step_1", "step_3", "step_4", "ps_dx", "ps_negative", "ps_prev_dx", "ps_participants", "ps_negative_count", "ps_positive_count", "re_haart", "prev_dx_not_on_haart"] + def __init__(self, agent: "agent.Agent"): super().__init__(agent) - + self.active = False self.time = None - + self.participate = False #willing to participate in PS (i.e name your partners and have them contacted)? + self.participate_time = None + self.ps_dx = False #diagnosed with HIV through PS? + self.ps_dx_time = None #time this agent was diagnosed through PS + self.tested_negative = False #tested negative through PS? + self.tested_negative_time = None #time this agent tested negative through PS + self.info = False # will the agent provide contact info about a partner? + self.contact = False # will the partner be contacted by PS? + self.info_stat = 0 #for collecting stats only + self.contact_stat = 0 #for collecting stats only + self.ps_prev_dx = False #was this traced agent already previously diagnosed with hiv? (for collecting stats only) + self.ps_prev_dx_time = None #when did we become aware of the fact that the agent was previously diagnosed? (for collecting stats only) + self.ps_participant = False #did this partner come back for testing after being contacted? (for collecting stats only) + self.ps_participant_time = None #when did this agent come back for testing? (for collecting stats only) + self.re_initiated = False #renitiated in haart after stopping treatment? + self.prev_dx_not_on_haart = False #is this agent previously diagnosed and is not on haart? + + # these stats are used for collecting the number of people who test positive and negative through PS. Note these stats are strictly increasing and should never go down. + @classmethod + def init_class(cls, params: "ObjMap"): + cls.tested_negative_count = 0 + cls.tested_positive_count = 0 + + @classmethod + def add_negative_count(cls): + cls.tested_negative_count += 1 + + @classmethod + def get_negative_count(cls): + return cls.tested_negative_count + + @classmethod + def add_positive_count(cls): + cls.tested_positive_count += 1 + + @classmethod + def get_positive_count(cls): + return cls.tested_positive_count + def update_agent(self, model: "model.TITAN"): """ Update the agent for this feature for a time step. Called once per time step in `TITAN.update_all_agents`. Agent level updates are done after population level updates. Called on only features that are enabled per the params. @@ -21,35 +62,156 @@ def update_agent(self, model: "model.TITAN"): args: model: the instance of TITAN currently being run """ + params = self.agent.location.params.partner_tracing - + if model.time < params.start_time or model.time > params.stop_time: return agent_exposure = getattr(self.agent, params.exposure) + + # if the agent was diagnosed with hiv in the previous time step and is willing to participate, trace their partners + if ( + agent_exposure.dx + and agent_exposure.dx_time == model.time - 1 + ): + # step 1 + self.participate = True if model.run_random.random() < params.participate_prob[self.agent.race] else False - if agent_exposure.active: - # was this agent diagnosed with the target exposure last time step - if agent_exposure.dx and agent_exposure.dx_time == model.time - 1: - for ptnr in self.agent.get_partners(params.bond_type): - partner_exposure = getattr(ptnr, params.exposure) - if ( - not partner_exposure.dx - and model.run_random.random() < params.prob - ): - ptnr.partner_tracing.active = True # type: ignore[attr-defined] - ptnr.partner_tracing.time = model.time # type: ignore[attr-defined] - - # second chance at diagnosis if traced + if self.participate: + self.participate_time = model.time + + self.trace_partners(params, model) + + + # if this agent is traced (i.e. has been contacted by PS) and comes back for testing + if ( + self.active + and self.time < model.time + and model.run_random.random() < params.partner_participate_prob[self.agent.race] + ): + # mark agents who participate + self.ps_participant = True + self.ps_participant_time = model.time + + # if this traced agent was already previously diagnosed, mark as previously diagnosed (every agent is allowed to enter this if statemnet no more than once) if ( - self.active - and self.time < model.time - and not agent_exposure.dx - and model.run_random.random() < params.dx_prob + agent_exposure.dx + and agent_exposure.dx_time < model.time - 1 + and not self.ps_prev_dx # and not already marked ): - agent_exposure.diagnose(model) - + self.ps_prev_dx = True + self.ps_prev_dx_time = model.time + # is this agent on haart? if not, mark + if not self.agent.haart.active: + self.prev_dx_not_on_haart = True + + # if this traced agent has not been diagnosed, test agent + if not agent_exposure.dx: + self.test_agent(agent_exposure, params, model) + + + # if agent was traced via PS and was previously diagnosed with hiv and is not on haart, stochastically enroll in haart + if ( + self.active + and self.time < model.time + and agent_exposure.dx + and agent_exposure.dx_time < model.time + and not self.agent.haart.active + and model.run_random.random() < params.re_haart_prob + ): + haart_params = ( + self.agent.location.params.demographics[self.agent.race] + .sex_type[self.agent.sex_type] + .drug_type[self.agent.drug_type] + .haart + ) + self.agent.haart.initiate(model.run_random, haart_params, "prob") + self.re_initiated = True + # stop tracing of this agent if time if self.active and model.time >= self.time + params.trace_duration: self.active = False self.time = None + + # this is to ensure agents are able to test negative multiple times throughout the simulation if contacted. On the otherhand, agents cannot test positive multiple times + if self.tested_negative and model.time > self.tested_negative_time: + self.tested_negative = False + self.tested_negative_time = None + + # this is to ensure agents are able to participate multiple times throughout the simulation + if self.ps_participant and model.time > self.ps_participant_time: + self.ps_participant = False + self.ps_participant_time = None + + def trace_partners(self, params, model: "model.TITAN"): + for ptnr in self.agent.get_partners(params.bond_type): + + # step 3 + self.info = True if model.run_random.random() < params.tracing_prob else False # can only trace if participant provides information + # step 4 + self.contact = True if model.run_random.random() < params.contact_prob[ptnr.race] else False # contact stochastically + + if self.info: + self.info_stat += 1 + + if ( + self.info + and self.contact + ): + self.contact_stat += 1 + ptnr.partner_tracing.active = True # type: ignore[attr-defined] + ptnr.partner_tracing.time = model.time # type: ignore[attr-defined] + + def test_agent(self, agent_exposure, params, model: "model.TITAN"): + + # if agent has hiv, mark as diagnosed + if ( + agent_exposure.active + and model.run_random.random() < params.dx_prob + ): + agent_exposure.diagnose(model) # markes agent with positive hiv diagnoses (not stochastic) + self.ps_dx = True + self.ps_dx_time = model.time + self.add_positive_count() + + # if agent does not have hiv, mark as tested negative + else: + self.tested_negative = True + self.tested_negative_time = model.time + self.add_negative_count() + + def set_stats(self, stats: Dict[str, int], time: int): + + if self.participate: + if self.participate_time == time: + stats["step_1"] += 1 + + stats["step_3"] += self.info_stat + stats["step_4"] += self.contact_stat + + if self.ps_participant: + if self.ps_participant_time == time: + stats["ps_participants"] += 1 + + if self.ps_dx: + if self.ps_dx_time == time: + stats["ps_dx"] += 1 + + if self.tested_negative: + if self.tested_negative_time == time: + stats["ps_negative"] += 1 + + if self.ps_prev_dx: + if self.ps_prev_dx_time == time: + stats["ps_prev_dx"] += 1 + if self.prev_dx_not_on_haart: + stats["prev_dx_not_on_haart"] += 1 + + if self.re_initiated: + stats["re_haart"] += 1 + + stats["ps_negative_count"] = self.get_negative_count() + + stats["ps_positive_count"] = self.get_positive_count() + \ No newline at end of file diff --git a/titan/features/prep.py b/titan/features/prep.py index b4ba8574..b1c4241e 100644 --- a/titan/features/prep.py +++ b/titan/features/prep.py @@ -12,7 +12,7 @@ class Prep(base_feature.BaseFeature): name = "prep" - stats = ["prep", "prep_new", "prep_injectable", "prep_oral"] + stats = ["prep", "prep_new", "prep_injectable", "prep_oral", "ps_prep_count"] """ PrEP collects the following stats: @@ -33,7 +33,8 @@ def __init__(self, agent: "agent.Agent"): self.type = "" self.time = None self.last_dose_time: Optional[int] = None - + self.ps_prep = False # was this agent referred to prep through ps? + @classmethod def init_class(cls, params: "ObjMap"): """ @@ -43,6 +44,7 @@ def init_class(cls, params: "ObjMap"): params: the population params """ cls.counts = {race: 0 for race in params.classes.races} + cls.ps_prep_count = 0 def init_agent(self, pop: "population.Population", time: int): """ @@ -109,7 +111,20 @@ def remove_agent(cls, agent): agent: the agent to remove from the class attributes """ cls.counts[agent.race] -= 1 - + + @classmethod + def ps_add_stat(cls): + cls.ps_prep_count += 1 + + @classmethod + def ps_remove_stat(cls): # do we subtract agents who stop using prep? + pass + #cls.ps_prep_count -= 1 + + @classmethod + def ps_get_stat(cls): + return cls.ps_prep_count + def set_stats(self, stats: Dict[str, int], time: int): if self.active: stats["prep"] += 1 @@ -121,7 +136,9 @@ def set_stats(self, stats: Dict[str, int], time: int): stats["prep_injectable"] += 1 elif self.type == "Oral": stats["prep_oral"] += 1 - + + stats["ps_prep_count"] = self.ps_get_stat() + def get_acquisition_risk_multiplier(self, time: int, interaction_type: str): """ Get a multiplier for how prep reduces risk of HIV acquisition. @@ -150,7 +167,7 @@ def get_acquisition_risk_multiplier(self, time: int, interaction_type: str): return 1.0 # =============== HELPER METHODS =================== - + def initiate(self, model: "model.TITAN", force: bool = False): """ Place agents onto PrEP treatment. PrEP treatment assumes that the agent knows their HIV status is negative. @@ -168,19 +185,38 @@ def initiate(self, model: "model.TITAN", force: bool = False): if force: self.enroll(model.run_random, model.time) elif params.prep.cap_as_prob: - if "Racial" in params.prep.target_model: - if ( - model.run_random.random() - <= params.demographics[self.agent.race] - .sex_type[self.agent.sex_type] - .prep.cap - ): + + # if target model is racial, use appropriate proabability + if "Racial" in params.prep.target_model and not self.agent.partner_tracing.tested_negative: + prep_prob = params.demographics[self.agent.race].sex_type[self.agent.sex_type].prep.cap + if model.run_random.random() <= prep_prob: self.enroll(model.run_random, model.time) - else: - if model.run_random.random() <= params.prep.cap: + + # else if agent tested negative through PS, use appropriate probability + elif "Racial" in params.prep.target_model and self.agent.partner_tracing.tested_negative: + prep_prob = self.agent.location.params.partner_tracing.prep_prob[self.agent.race] + if model.run_random.random() <= prep_prob: self.enroll(model.run_random, model.time) + self.ps_prep = True + self.ps_add_stat() + + else: + if self.agent.partner_tracing.tested_negative: + prep_prob = self.agent.location.params.partner_tracing.prep_prob[self.agent.race] + if model.run_random.random() <= prep_prob: + self.enroll(model.run_random, model.time) + self.ps_prep = True + self.ps_add_stat() + + # else, use base probability + else: + prep_prob = params.prep.cap + if model.run_random.random() <= prep_prob: + self.enroll(model.run_random, model.time) + else: - if "Racial" in params.prep.target_model: + # racial model, but not tested through PS + if "Racial" in params.prep.target_model and not self.agent.partner_tracing.tested_negative: num_prep_agents = self.counts[self.agent.race] all_hiv_agents = exposures.HIV.agents all_race = { @@ -191,15 +227,27 @@ def initiate(self, model: "model.TITAN", force: bool = False): target_prep = (len(all_race) - num_hiv_agents) * params.demographics[ self.agent.race ].sex_type[self.agent.sex_type].prep.cap + if num_prep_agents < target_prep: + self.enroll(model.run_random, model.time) + + # tested through PS + elif self.agent.partner_tracing.tested_negative: + target_prep = self.agent.partner_tracing.get_negative_count() * self.agent.location.params.partner_tracing.prep_prob[self.agent.race] + + if self.ps_get_stat() < target_prep: + self.enroll(model.run_random, model.time) + self.ps_prep = True + self.ps_add_stat() + + # base case else: num_prep_agents = sum(self.counts.values()) target_prep = int( (model.pop.all_agents.num_members() - len(exposures.HIV.agents)) * params.prep.cap ) - - if num_prep_agents < target_prep: - self.enroll(model.run_random, model.time) + if num_prep_agents < target_prep: + self.enroll(model.run_random, model.time) def enroll(self, rand_gen, time): """ @@ -263,7 +311,7 @@ def progress(self, model: "model.TITAN", force: bool = False): == model.time ): self.discontinue() - + def discontinue(self): """ Discontinue PrEP usage @@ -272,7 +320,10 @@ def discontinue(self): self.type = "" self.time = None self.last_dose_time = None - + + if self.ps_prep: + self.ps_prep = False # expected to inflate stats because it allowes re enrollment in prep + self.ps_remove_stat() # subtracts from stats self.remove_agent(self.agent) def eligible(self, time) -> bool: diff --git a/titan/params/partner_tracing.yml b/titan/params/partner_tracing.yml index 4ac6341c..3c6185a6 100644 --- a/titan/params/partner_tracing.yml +++ b/titan/params/partner_tracing.yml @@ -37,3 +37,57 @@ partner_tracing: description: "For how many time steps after partner diagnosis a partner is traced" type: int default: 1 + participate_prob: + description: Probability that a given agent is willing to participate in the partner services program + type: float + default: + white: 0.59 + black: 0.525 + latino: 0.543 + min: 0 + max: 1 + tracing_prob: + description: Probability of tracing a partner based on the amount of information provided by agent + type: float + default: 0.66 # to be calibrated + min: 0 + max: 1 + contact_prob: + description: Probability that a named partner will be contacted + type: float + default: + white: 0.7 + black: 0.69 + latino: 0.75 + min: 0 + max: 1 + partner_participate_prob: + description: Probability that a contacted partner will participate in the PS and get tested + type: float + default: + white: 0.56 + black: 0.49 + latino: 0.54 + min: 0 + max: 1 + treatment_prob: + description: Probability that a partner who has been diagnosed through PS will receive treatment + type: float + default: 0.82 + min: 0 + max: 1 + prep_prob: + description: Probability that a partner who tested negative for HIV through PS will be referred to prep + type: float + default: + white: 0.49 + black: 0.5 + latino: 0.4 + min: 0 + max: 1 + re_haart_prob: + description: Probability that a traced agent who has been previously diagnosed and not on haart reengages haart + type: float + default: 0.70 + min: 0 + max: 1 diff --git a/titan/settings/atlanta-latino/demographics.yml b/titan/settings/atlanta-latino/demographics.yml index 48674d00..815bb3c8 100644 --- a/titan/settings/atlanta-latino/demographics.yml +++ b/titan/settings/atlanta-latino/demographics.yml @@ -42,7 +42,7 @@ demographics: hiv: 17.2 aids: 34.4 haart: - init: 0.625 + init: 0.680 cap: 0.35 adherence: init: 0.885 @@ -91,7 +91,7 @@ demographics: hiv: 17.2 aids: 34.4 haart: - init: 0.585 + init: 0.751 cap: 0.65 adherence: init: 0.851 diff --git a/titan/settings/atlanta-latino/model.yml b/titan/settings/atlanta-latino/model.yml index 903c91b5..5c410a2a 100644 --- a/titan/settings/atlanta-latino/model.yml +++ b/titan/settings/atlanta-latino/model.yml @@ -4,9 +4,9 @@ model: run: 0 num_pop: 26937 time: - num_steps: 70 + num_steps: 120 steps_per_year: 12 - burn_steps: 0 + burn_steps: 12 network: type: scale_free @@ -27,6 +27,9 @@ prep: target_model: - Racial +haart: + use_cap: true + hiv: aids: prob: 0.05