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Viral transmission and associated healthcare outcomes are an important area of study in the ongoing COVID-19 pandemic. Regeneron Pharmaceuticals is working to understand how treatments may impact viral spread and the resulting healthcare outcomes. With a finite drug supply, they wanted to determine who to prioritize for receiving treatment during the pandemic and when patients should receive treatment to maximize its effectiveness.

Certara CS Agent Based Model Predicts Impact 1
Certara CS Agent Based Model Predicts Impact 2

Certara partnered with the Regeneron Pharmaceuticals team to develop an agent-based model to help determine the potential impact of combining monoclonal antibody (mAb) treatment, prophylaxis, and vaccines to reduce the spread of SARS-CoV-2.

Agent-based models can simulate interactions between individual agents (e.g., patients) and the spread of the virus within a population, considering factors such as the efficacy of treatments and vaccines, individual compliance, frequency of new viral mutations, geographic location, population density, and the availability of healthcare resources. Additionally, this model was designed to help evaluate the effectiveness of different treatment strategies and identify potential bottlenecks or challenges in the distribution process.2

Certara partnered with the Regeneron Pharmaceuticals team to develop an agent-based model to help determine the potential impact of combining monoclonal antibody (mAb) treatment, prophylaxis, and vaccines to reduce the spread of SARS-CoV-2.

What are the Components of a COVID-19 Agent-Based Model?
To simulate the spread of SARS-CoV-2, the model included the following components:3

● Population structure: The population is organized in a specific way within the model. For example, the population may be divided by age groups, gender, or other demographic characteristics.
● Disease model: Describes how the disease progresses and spreads within the population. It will include information on viral transmission, the incubation period, and the disease symptoms.
● Contact network: This describes the interactions and connections between individuals within the population. For example, it includes information on who is most likely to contact one another, such as family members, coworkers, and friends.
● SARS-CoV-2 infection: Refers to the specific infection by the virus that causes COVID-19. The model includes information on viral load and time to symptom improvement and resolution depending on whether treatment was initiated.

Certara CS Agent Based Model Predicts Impact 3
Certara CS Agent Based Model Predicts Impact 4

By simulating SARS-CoV-2 viral spread and the effectiveness of using mAb treatment and PEP as well as vaccines, the model helped estimate the impact of different treatment strategies on the number of infections and hospitalizations. It can also be used to evaluate the cost-effectiveness of these interventions and the long-term impact on public health. These results can guide resource allocation and patient management decisions for COVID-19 and can also be used to inform public health policy for current and future pandemic preparedness.

References

  1. Kerr CC, Stuart RM, Mistry D, et al. Covasim: An agent-based model of COVID-19 dynamics and interventions. PLoS Comput Biol. 2021;17(7):e1009149. Published 2021 Jul 26. doi:10.1371/journal.pcbi.1009149
  2. Kamal MA, Kuznik A, Qi L, et al. Assessing the Combined Public Health Impact of Pharmaceutical Interventions on Pandemic Transmission and Mortality: An Example in SARS CoV-2. Clin Pharmacol Ther. 2022;112(6):1224-1235. doi:10.1002/cpt.2728
  3. A Case Study of Agent-Based Model: Monoclonal Antibody Treatment, Prophylaxis, and Vaccines Combined to Reduce SARS CoV-2 Spread, (PDF)
Certara CS Agent Based Model Predicts Impact 5

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