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

{Background} Colombia, like many developing nations, does not have a strong health system able to respond to a pandemic of the magnitude of Covid-19. There is an increasing need to create a model that allows particular clinics and hospitals to the number of patients that require Intensive Care Units-ICU care (critical), and the number of patients that require hospital care (severe), but not ICU care. {Methods} This paper presents a prediction of the total number of ICU and regular beds that will be needed during the pandemic COVID-19 for Bogot\'a-Colombia. We use a SEIR model that includes three different compartments of infection: those who can stay at home, those in regular hospital beds and those in need of ICU treatment. The model allows for a time varying transmission rate which we use to incorporate the measures introduced by the government over the period of one year. {Results} The model predicts that by mid July 2020, the city will reach the peak of the epidemic with a total of 22 526 prevalent ICUs needed and 84 816 regular hospital beds needed. By the end of May 2020, the number of patients that need ICUs will overpass the current capacity set at 2000 beds for ICU hospital beds in the city. The model predicts that the death toll by the same date will reach 1752 people and the number of cases will be 54652 inhabitants by then. We provide a Shiny app available in \texttt{https://claudia-rivera-rodriguez.shinyapps.io/shinyappcovidclinic/}. {Conclusions} COVID-19 has posed too many challenges to health systems around the globe, this model is an useful tool for cities, hospitals and clinics in Colombia that need to prepare for the excess demand of services that a pandemic like this one generates. Unfortunately, the model predicts that by July capacity of the system in Bogot\'a will not be enough. We expect the lock-down rules strength in the future days, so the death toll is not as bad as predicted by this model.