👉 dx:infection is a computational model used to simulate the spread of infectious diseases within a population. It extends traditional compartmental models, such as SIR (Susceptible-Infected-Recovered), by incorporating more detailed dynamics, including age structure, spatial distribution, and the impact of interventions. The model divides the population into discrete compartments based on age or other relevant factors, allowing for a more nuanced understanding of how different groups are affected and how diseases spread through various contact networks. dx:infection specifically focuses on the transmission processes, using parameters like the infection rate (β) and recovery rate (γ) to model how individuals move between susceptible, infected, and recovered states. It can also account for factors such as asymptomatic transmission, waning immunity, and the effects of public health measures like quarantine and vaccination, providing a comprehensive tool for epidemiologists and policymakers to predict disease outbreaks and evaluate intervention strategies.
