Mathematical modeling of COVID-19 in 14.8 million individuals in Bahia, Brazil
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Abstract
COVID-19 is affecting healthcare resources worldwide, with lower and middle-income countries being particularly disadvantaged to mitigate the challenges imposed by the disease, including the availability of a sufficient number of infirmary/ICU hospital beds, ventilators, and medical supplies. Here, we use mathematical modelling to study the dynamics of COVID-19 in Bahia, a state in northeastern Brazil, considering the influences of asymptomatic/non-detected cases, hospitalizations, and mortality. The impacts of policies on the transmission rate were also examined. Our results underscore the difficulties in maintaining a fully operational health infrastructure amidst the pandemic. Lowering the transmission rate is paramount to this objective, but current local efforts, leading to a 36% decrease, remain insufficient to prevent systemic collapse at peak demand, which could be accomplished using periodic interventions. Non-detected cases contribute to a ∽55% increase in R 0 . Finally, we discuss our results in light of epidemiological data that became available after the initial analyses.
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SciScore for 10.1101/2020.05.25.20105213: (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
Software and Algorithms Sentences Resources PSO was implemented using pyswarms library version 1.1.0 for Python 3 (http://python.org)28, and was executed with 300 particles through 1,000 iterations with cognitive parameter 0.1, social parameter 0.3, inertia parameter 0.9, evaluating five closest neighbors through Euclidean (or L2) distance metric. Pythonsuggested: (IPython, RRID:SCR_001658)Results from OddPub: Thank you for sharing your code and data.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Our findings have some limitations. First, this study was carried out with …
SciScore for 10.1101/2020.05.25.20105213: (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
Software and Algorithms Sentences Resources PSO was implemented using pyswarms library version 1.1.0 for Python 3 (http://python.org)28, and was executed with 300 particles through 1,000 iterations with cognitive parameter 0.1, social parameter 0.3, inertia parameter 0.9, evaluating five closest neighbors through Euclidean (or L2) distance metric. Pythonsuggested: (IPython, RRID:SCR_001658)Results from OddPub: Thank you for sharing your code and data.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Our findings have some limitations. First, this study was carried out with routine local data, which may result in an underestimation of the real incidence of COVID-19, an avoidable problem also detected in other diseases34. Mass testing is still not performed in the country, and current policies recommend that individuals suspected of COVID-19 infection should only seek health care assistance when presenting with mild-to-moderate symptoms. However, the current available national surveillance data can be considered adequate for the identification of trends of the disease, as this system is standardized and implemented in all municipalities in the country. Nevertheless, we were able to parametrize our model to a more realistic setting by using hospitalization data from a local reference infectious disease hospital currently dedicated to the care of COVID-19 patients, and the results were compared to model parameters obtained from the state of Bahia as well as the literature. Thus, our modelling strategy has as an advantage being locally-informed, yielding more realistic results. The implemented model does not consider transmission of infected individuals undergoing hospitalization, although it is known that health care workers are more at-risk of many airborne infections, and transmission is particularly high during procedures that generate aerosols35. In spite of these limitations, given the general character of the mathematical model described herein, it may be readily appli...
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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