Agent-based modelling offers opportunities to explore the complex social interactions at the heart of the addiction crisis.
With the tip of her syringe, Brandi pokes at a grey lump of heroin in a spoon. It’s a new variety of the drug that has shown up on the market in the past few days, and Brandi likes it. “I feel this more, I feel more of the pain resistance,” she says.
Once it has dissolved into a liquid, she injects it into her arm, then uses a fresh needle to inject the skinny arm of another woman. “She does it better than the hospital,” the woman comments.
“I’ll help anybody who needs it,” Brandi explains to public-health researcher Daniel Ciccarone of the University of California, San Francisco, who has been filming the entire process.
Ciccarone’s team has embedded with Brandi — whose name has been changed for this story — in Charleston, West Virginia, documenting her interactions without judgement or interference. Later, the group will analyse this video, in addition to half a dozen other videos of drug users from across the city, logging details big and small. Brandi does not heat the solution on the spoon, for instance, and that may increase the likelihood of spreading viruses such as HIV. And tests reveal that what she’s taking has been laced with fentanyl, a synthetic drug up to 50 times more powerful than heroin.
The researchers will plug these data into powerful computer simulations of Charleston, populated by thousands of virtual Brandis — heroin users and dealers going about their daily routines. They will watch these digital agents buy more heroin as their tolerance increases, form networks with sellers and users and, in some cases, accidentally overdose.
Ciccarone’s is one of several groups using agent-based models to understand what is driving the US opioid epidemic — the dramatic rise over the past two decades in the use of opioids, including prescription pain medications and illegal drugs such as heroin. By studying the motivations and practices of real drug dealers and users, the researchers hope to build agents whose behaviour in the virtual world mimics that in real life.
Agent-based models promise to provide a more granular view of the opioid crisis than standard modelling, which is based on average populations, and to capture some of the complexity of the driving forces. This could prove important for demonstrating the effects of opening or closing methadone clinics or needle exchanges. The models allow scientists to compare interventions at almost no cost and could help policymakers to decide how to proceed in the real world. “It’s a very classic and useful way to try and see where is the best place to deploy an intervention to have the biggest effect,” says John Brooks, a medical adviser for the division of HIV/AIDS prevention at the US Centers for Disease Control and Prevention (CDC) in Atlanta, Georgia.
Although such simulations have long been used to model disease outbreaks and have, in some instances, guided public policy, their track record with more complex social behaviour such as drug use is limited, largely owing to sparse data and the breadth of parameters to consider. (...)
To create an agent-based model, researchers first ‘build’ a virtual town or region, sometimes based on a real place, including buildings such as schools and food shops. They then populate it with agents, using census data to give each one its own characteristics, such as age, race and income, and to distribute the agents throughout the virtual town.
The agents are autonomous but operate within pre-programmed routines — going to work five times a week, for instance. Some behaviours may be more random, such as a 5% chance per day of skipping work, or a 50% chance of meeting a certain person in the agent’s network. Once the system is as realistic as possible, the researchers introduce a variable such as a flu virus, with a rate and pattern of spread based on its real-life characteristics. They then run the simulation to test how the agents’ behaviour shifts when a school is closed or a vaccination campaign is started, repeating it thousands of times to determine the likelihood of different outcomes. (...)
In response to the opioid epidemic, Bobashev’s group has constructed Pain Town — a generic city complete with 10,000 people suffering from chronic pain, 70 drug dealers, 30 doctors, 10 emergency rooms and 10 pharmacies. The researchers run the model over five simulated years, recording how the situation changes each virtual day.
During this time, the patients’ drug tolerance increases, leading them to find different ways of acquiring drugs. Their behaviour is driven by variables such as the chance that a doctor will increase their prescription, or the likelihood that a dealer will have enough heroin. At a certain threshold, patients become addicted or more likely to overdose. Bobashev’s early data suggest, for example, that requiring doctors to track patients’ medication history can be effective over the long term, but not immediately.
The model contains many assumptions and simplifications, Bobashev says. For example, it doesn’t capture the fact that the rate at which people develop tolerance and addiction can depend on factors such as genetics, and that whether a person switches from prescription drugs to heroin can depend on the relative availability of the two drugs.
But researchers can adjust models such as Pain Town to test various interventions, such as increasing access to emergency rooms, arresting a dealer or equipping police with naloxone (a drug that reverses opioid overdoses), to see how the system reacts and whether it affects the number of deaths over time. And as models become more sophisticated, the researchers may be able to incorporate more factors, such as people who are not taking pain medications but are susceptible to trying opioids for the first time. (...)
Data drought
The models face numerous challenges before they will be ready for widespread adoption, primarily data gaps. Marshall says that researchers struggle to get access to data on opioid prescriptions that are held by manufacturers, pharmacies and law-enforcement agencies. It is also difficult to obtain government information on drug cartels and the type and rate of drugs flowing into the country. Other data simply do not exist in usable form: agencies may record deaths due to drug overdose, for instance, but fail to specify which drug was responsible.
Observing drug users such as Brandi can provide certain types of information more quickly and accurately. “Drug users know their chemicals intimately,” Ciccarone says.
Lee Hoffer is a cultural anthropologist at Case Western Reserve University in Cleveland, Ohio, who studies heroin markets and collaborates with Bobashev. He says the ethnographic data that his group and others are collecting could help to fill some of the information gaps: “We’re trying to enter their world as interlopers to see how they see their life.” After an initial awkward period, he says, drug users tend to become more honest with the researchers, telling them crucial information such as how they form networks with dealers and the cost of drugs.
Understanding the psychology of drug users is also crucial, says Epstein. Most decision-making models assume rational behaviours. In reality, emotions, misinformation and irrational calculations play a major part. “When you put them together you get collections of dynamics that are very dysfunctional.”
by Sara Reardon, Nature | Read more:
Image: Jerome Sessini/Magnum
With the tip of her syringe, Brandi pokes at a grey lump of heroin in a spoon. It’s a new variety of the drug that has shown up on the market in the past few days, and Brandi likes it. “I feel this more, I feel more of the pain resistance,” she says.
Once it has dissolved into a liquid, she injects it into her arm, then uses a fresh needle to inject the skinny arm of another woman. “She does it better than the hospital,” the woman comments.
“I’ll help anybody who needs it,” Brandi explains to public-health researcher Daniel Ciccarone of the University of California, San Francisco, who has been filming the entire process.Ciccarone’s team has embedded with Brandi — whose name has been changed for this story — in Charleston, West Virginia, documenting her interactions without judgement or interference. Later, the group will analyse this video, in addition to half a dozen other videos of drug users from across the city, logging details big and small. Brandi does not heat the solution on the spoon, for instance, and that may increase the likelihood of spreading viruses such as HIV. And tests reveal that what she’s taking has been laced with fentanyl, a synthetic drug up to 50 times more powerful than heroin.
The researchers will plug these data into powerful computer simulations of Charleston, populated by thousands of virtual Brandis — heroin users and dealers going about their daily routines. They will watch these digital agents buy more heroin as their tolerance increases, form networks with sellers and users and, in some cases, accidentally overdose.
Ciccarone’s is one of several groups using agent-based models to understand what is driving the US opioid epidemic — the dramatic rise over the past two decades in the use of opioids, including prescription pain medications and illegal drugs such as heroin. By studying the motivations and practices of real drug dealers and users, the researchers hope to build agents whose behaviour in the virtual world mimics that in real life.
Agent-based models promise to provide a more granular view of the opioid crisis than standard modelling, which is based on average populations, and to capture some of the complexity of the driving forces. This could prove important for demonstrating the effects of opening or closing methadone clinics or needle exchanges. The models allow scientists to compare interventions at almost no cost and could help policymakers to decide how to proceed in the real world. “It’s a very classic and useful way to try and see where is the best place to deploy an intervention to have the biggest effect,” says John Brooks, a medical adviser for the division of HIV/AIDS prevention at the US Centers for Disease Control and Prevention (CDC) in Atlanta, Georgia.
Although such simulations have long been used to model disease outbreaks and have, in some instances, guided public policy, their track record with more complex social behaviour such as drug use is limited, largely owing to sparse data and the breadth of parameters to consider. (...)
To create an agent-based model, researchers first ‘build’ a virtual town or region, sometimes based on a real place, including buildings such as schools and food shops. They then populate it with agents, using census data to give each one its own characteristics, such as age, race and income, and to distribute the agents throughout the virtual town.
The agents are autonomous but operate within pre-programmed routines — going to work five times a week, for instance. Some behaviours may be more random, such as a 5% chance per day of skipping work, or a 50% chance of meeting a certain person in the agent’s network. Once the system is as realistic as possible, the researchers introduce a variable such as a flu virus, with a rate and pattern of spread based on its real-life characteristics. They then run the simulation to test how the agents’ behaviour shifts when a school is closed or a vaccination campaign is started, repeating it thousands of times to determine the likelihood of different outcomes. (...)
In response to the opioid epidemic, Bobashev’s group has constructed Pain Town — a generic city complete with 10,000 people suffering from chronic pain, 70 drug dealers, 30 doctors, 10 emergency rooms and 10 pharmacies. The researchers run the model over five simulated years, recording how the situation changes each virtual day.
During this time, the patients’ drug tolerance increases, leading them to find different ways of acquiring drugs. Their behaviour is driven by variables such as the chance that a doctor will increase their prescription, or the likelihood that a dealer will have enough heroin. At a certain threshold, patients become addicted or more likely to overdose. Bobashev’s early data suggest, for example, that requiring doctors to track patients’ medication history can be effective over the long term, but not immediately.
The model contains many assumptions and simplifications, Bobashev says. For example, it doesn’t capture the fact that the rate at which people develop tolerance and addiction can depend on factors such as genetics, and that whether a person switches from prescription drugs to heroin can depend on the relative availability of the two drugs.
But researchers can adjust models such as Pain Town to test various interventions, such as increasing access to emergency rooms, arresting a dealer or equipping police with naloxone (a drug that reverses opioid overdoses), to see how the system reacts and whether it affects the number of deaths over time. And as models become more sophisticated, the researchers may be able to incorporate more factors, such as people who are not taking pain medications but are susceptible to trying opioids for the first time. (...)
Data drought
The models face numerous challenges before they will be ready for widespread adoption, primarily data gaps. Marshall says that researchers struggle to get access to data on opioid prescriptions that are held by manufacturers, pharmacies and law-enforcement agencies. It is also difficult to obtain government information on drug cartels and the type and rate of drugs flowing into the country. Other data simply do not exist in usable form: agencies may record deaths due to drug overdose, for instance, but fail to specify which drug was responsible.
Observing drug users such as Brandi can provide certain types of information more quickly and accurately. “Drug users know their chemicals intimately,” Ciccarone says.
Lee Hoffer is a cultural anthropologist at Case Western Reserve University in Cleveland, Ohio, who studies heroin markets and collaborates with Bobashev. He says the ethnographic data that his group and others are collecting could help to fill some of the information gaps: “We’re trying to enter their world as interlopers to see how they see their life.” After an initial awkward period, he says, drug users tend to become more honest with the researchers, telling them crucial information such as how they form networks with dealers and the cost of drugs.
Understanding the psychology of drug users is also crucial, says Epstein. Most decision-making models assume rational behaviours. In reality, emotions, misinformation and irrational calculations play a major part. “When you put them together you get collections of dynamics that are very dysfunctional.”
by Sara Reardon, Nature | Read more:
Image: Jerome Sessini/Magnum
