👉 Cdt:infection is a computational model used to simulate the spread of infectious diseases within a population, particularly focusing on the early stages of an outbreak. It extends traditional compartmental models like SIR (Susceptible-Infected-Recovered) by incorporating detailed contact patterns and transmission dynamics. The model divides the population into compartments based on infection status but also considers the heterogeneity in contact rates among individuals, such as age, social behavior, and geographic distribution. Cdt:infection typically includes parameters for infection probability, recovery rate, and the time it takes for an infected individual to become infectious again, allowing for a more nuanced understanding of how infections spread through complex social networks. This approach helps researchers predict the impact of interventions like quarantine, vaccination, and social distancing by simulating various scenarios and assessing their effectiveness in controlling the spread of the disease.
