COVID-19 most vulnerable Mexican cities lack the public health infrastructure to face the pandemic: a new temporally-explicit model
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Abstract
Recently, a wide array of epidemiological models have been developed to guide public health actors in containing the rapid dissemination of the new severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), cause of COVID-19. Despite their usefulness, many epidemiological models recently developed to understand the spread of SARS-CoV-2 and infection rates of COVID-19 fall short as they ignore human mobility, limiting our understanding of the spread of the disease, together with the vulnerability of population centers in a broad scale. We developed a new temporally-explicit model and simulated several social distancing scenarios to predict the vulnerability to COVID-19 of 50 Mexican cities that are interconnected by their air transportation network. Additionally, we assessed the sufficiency of the public health infrastructure in the focal cities to face the pandemic over time. Based on our model, we show that the most important cities within the Mexican air transportation network are the most vulnerable to COVID-19, with all assessed public health infrastructure being insufficient to face the modeled scenario for the pandemic after 100 days. Despite these alarming findings, our results show that social distancing could dramatically decrease the total number of infected people (77% drop-off for the 45% distancing scenario when contrasted with no distancing), flattening the growth of infection rate. Thus, we consider that this study provides useful information that may help decision-makers to timely implement health policies to anticipate and lessen the impact of the current pandemic in Mexico.
Significance Statement
We used a new temporally-explicit model focused on air transportation networks to predict the vulnerability of 50 focal Mexican cities to COVID-19. We found that most vulnerable cities lack of the required public health infrastructure (i.e., number of inpatient and intensive care unit beds) to face this new pandemic, overloading in all cases after 100 days. However, our results show that a 45% social distancing scenario can reduce the number of infected people by up to 78.7%, flattening the growth rate of people with COVID-19 before infection rates soar exponentially countrywide.
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SciScore for 10.1101/2020.04.10.20061192: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
No key resources detected.
Results from OddPub: Thank you for sharing your data.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Thus, we consider that it is important to highlight some of the 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 …
SciScore for 10.1101/2020.04.10.20061192: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
No key resources detected.
Results from OddPub: Thank you for sharing your data.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Thus, we consider that it is important to highlight some of the 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 ...
Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
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