What features of epidemics in human populations are NOT possible to model with a simulator on a computer?
Simulating epidemics on a computer enables us to simulate various aspects of epidemics, but there are certain features that are challenging to accurately model. Here are some examples of features that are difficult to capture with a computer simulator:
1. Human behavior: Modeling human behavior accurately is a complex task. People's actions, such as following preventive measures, social distancing, or their movement patterns, can significantly affect the spread of an epidemic. It is challenging to accurately capture the diversity of human behavior in a computer simulation.
2. Local context: Epidemics can be influenced by local factors, such as population density, cultural practices, healthcare infrastructure, and government policies. Capturing these specific local contexts accurately in a simulation is challenging since they can vary widely across different regions.
3. Unknown variables: In some cases, there may be unknown or poorly understood variables affecting an epidemic. For example, a novel virus may have unique features or there might be variations in individual immune responses that are not yet fully understood. These unknown variables pose challenges for accurate modeling.
4. Interactions with the environment: Epidemics can interact with the environment in various ways. For example, seasonality, climate, and environmental factors may influence the spread of certain diseases. Modeling the intricate interactions between epidemics and the environment is a complex task that often involves multiple disciplines.
5. Complex transmission dynamics: Some epidemics exhibit complex transmission dynamics that are challenging to model accurately. These dynamics may include factors such as multiple transmission routes, super-spreaders, and non-linear relationships between the number of infectious individuals and the rate of transmission.
While computer simulations can provide valuable insights into epidemics, they have limitations due to the complexity and uncertainties involved. It is crucial to interpret the results of simulations with caution and consider other sources of information, such as real-world data and epidemiological studies, to validate and refine the models.