COVID-19 most vulnerable Mexican cities lack the public health infrastructure to face the pandemic: a new temporally-explicit model

Wesley Dattilo; Alcides Castro e Silva; Roger Guevara; Ian MacGregor-Fors; Servio Pontes Ribeiro

is the (which was not peer-reviewed) The copyright holder for this preprint . https://doi.org/10.1101/2020.04. 10.20061192 doi: medRxiv preprint main limitations of our model. First, we only consider air transport to estimate the mobility of people between cities; however, the we are well aware that the Mexican road network is one important means of communication, representing an additional potential driver for the spatial spread of the disease. Second, our model only considers the health infrastructure in terms of inpatient and intensive care unit beds from the public healthcare network. Most recently, the Mexican Government increased hospital capacity in some cities, which could mold the outcome of our results if considered in the model. Third, there are additional factors that have been identified as important in facing the spread of COVID-19 (e.g., protective equipment, medical personnel, and mechanical ventilators; 23) that are not considered in our model. Finally, we assessed social distancing as a general grouping factor behind the decrease of contagion rates, which could not only include well known specific drivers of such phenomenon (e.g., thorough hand washing, staying at home, keeping distance when leaving home). Taking into account all of the limitations of our model and the available data used to construct it, we show how the Mexican air transport network could increase the vulnerability of cities to COVID-19. Also, our simulations suggest that the available public healthcare infrastructure in the most vulnerable studied focal cities is very likely to be insufficient to face this pandemic. Despite most scenarios derived from our model are not optimistic for the coming months, our model clearly shows that social distancing is a remarkable measure to control the growth rate of total COVID-19 cases before infection rates soar exponentially in the country. We observed important differences between 0-30% social distancing scenarios and that of 45%, where the curve is clearly flattened.