Modelling strategies to predict hospital demand during the COVID-19 outbreak in Bogotá, Colombia

Claudia Rivera-Rodriguez; Beatriz Piedad Urdinola

SEIR models are a variation that relaxes some of those assumptions, includ-65 ing closed populations and account for communicable diseases that transmit in transitions, starting from the entire population (Susceptible) that incubate the disease for a period of time (Exposed) making the person infected but not infectious (I) and finally become Recovered (R) [4] . Each transition holds a rate based on what is observed from a population, that is a susceptible person gets 70 infected at a transmission rate once in contact with an infected individual, and become exposed. Once exposed transition to infected happens at a rate that captures the inverse of the mean latent period of the disease. The final transition is recovery with permanent immunity. We chose this model to estimate bed demands per institution in Colombia separating regular and ICU (Intensive 75 Care Units) beds, which allows a distinction for each type that reflects differential transition rates, preparedness and logistics for health providers. Similar works have been used to forecast similar needs in Europe and United States of America, but none has been done for a developing country, such as Colombia [5, 6, 7] .