The assumptions in epidemiological models can have significant implications for their outcomes:
- Overestimation or Underestimation of Cases: Homogeneous mixing can lead to an overestimation of disease spread in populations with structured interactions, while ignoring demographic changes can underestimate the number of susceptible individuals over time.
- Policy Implications: Incorrect assumptions can lead to misguided public health policies. For example, assuming no latency period might lead to the neglect of quarantine measures that target the incubation period.
- Planning and Resource Allocation: Models that do not account for demographic changes may misguide resource allocation, affecting healthcare planning and intervention strategies.
