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

Claudia Rivera-Rodriguez; Beatriz Piedad Urdinola

We implemented a SEIR model (Susceptible -Exposed -Infectious -Recovered) to forecast the number of cases in Bogotá, the largest city in Colombia 40 and the one with the most numbers of cases to date. The model includes three different compartments of infection: Infected that require ICU care, Infected that require hospital care, but not ICU care and Infected that only require Home care. The model accounts for the effect of control strategies introduced by the government by changing the transmission rate over time. We developed 45 a Shiny app that displays the results from the model (https://claudia-riverarodriguez.shinyapps.io/shinyappcovidclinic/). The initial parameters are the ones used in this work. However, users can change the parameters according to their needs. The Shiny app can be used as a forecast tool for specific clinics by specifying the market share (percentage) of the population corresponding to 50 the clinic. During the outbreak some clinics should be ready to see an increase in their market share because they may have more resources, such as ICU beds and the model allows for each clinic to adjust it. The model can be used for specific cities or towns, the user only needs to change the population size, and 3 . CC-BY-NC 4.0 International license It is made available under a author/funder, who has granted medRxiv a license to display the preprint in perpetuity.