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Simulations in health & medicine

People working in health and medicine want to save lives and improve well-being. They search for solutions to medical issues, but they can't risk experiments that will have the opposite effect. At the same time, they also don't want to spend too much time coming up with a solution when people's lives are at stake.
When real-world experiments would be too deadly or too slow, computer simulations can be used instead.

Epidemiology simulations

Governmental health organizations use simulations to predict the effect of an infectious disease on a community, and those predictions help them prepare for the impact and make recommendations.
The simulation below shows how an epidemic would affect a community. Try different infection and population parameters to see what changes.
During the COVID-19 pandemic of 2020, researchers all over the world created simulations to better understand the effects of that pandemic.
One of the most pivotal research papers came out on March 16, 2020, and made predictions based on simulating various interventions:
A graph with six lines overlaid. The red line is straight and the other lines are curves. The black curve has the highest peak, then the green curve, then orange curve, then yellow, then blue.
Mitigation strategy scenarios for Great Britain showing critical care (ICU) bed requirements. The black line shows the unmitigated epidemic. The green line shows a mitigation strategy incorporating closure of schools and universities; orange line shows case isolation; yellow line shows case isolation and household quarantine; and the blue line shows case isolation, home quarantine and social distancing of those aged over 70. The blue shading shows the 3-month period in which these interventions are assumed to remain in place. Source: Imperial College Report
The results from those simulations spread through the government and press, and likely influenced the issuing of stay-at-home orders soon after. 1

Pharmaceutical simulations

Pharmaceutical companies spend a large part of their yearly budgets on the research and development of new drugs. A 2013 analysis found that the big drug companies spent an average of 5 billion dollars for each drug they eventually brought to market. 2
Fortunately, pharmaceutical companies now use computational simulations to speed up the drug development process. According to the FDA commissioner in 2017, “Almost 100 percent of all new drug applications for new molecular entities have components of modeling and simulation." 3
In computer-aided drug design, computational chemists use computer models to identify potential drug targets from large databases of molecular structures. The results can be further refined through modeling that predicts the properties of the candidate drugs or simulates the interactions of the drugs with the target molecules. Once the chemist identifies a promising drug target, another chemist can synthesize and test the potential drug in the lab.
Diagram of a drug discovery process. Starts on the left with databases and a molecular target, then arrow points to an computer generated image of molecular docking, then to a synthesis of the molecule, then experimental evaluation, and then an arrow points back to the molecular docking.
A drug discovery process. Image source: Ferreira and Andricopulo
Oftentimes, the drug discovery process will take multiple iterations to find a drug promising enough for clinical trials, but the computer-aided drug design process still significantly increases the rate of drug discovery.
Once a drug is selected for the next stage of clinical trials, pharmaceutical companies then use computer simulations to optimize clinical trial design. The simulations help them determine aspects of the trial such as the optimal dosage and treatment schedule, the effect of patient inclusion/exclusion criteria on outcomes, and the minimal length and number of trials.
Since saving time can save pharmaceutical companies billions of dollars, there are a growing number of companies that develop software for computer-aided drug design and clinical trial design.

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