Dataset Information

A data-driven approach to identify risk profiles and protective drugs in COVID-19.

ABSTRACT: As the COVID-19 pandemic is spreading around the world, increasing evidence highlights the role of cardiometabolic risk factors in determining the susceptibility to the disease. The fragmented data collected during the initial emergency limited the possibility of investigating the effect of highly correlated covariates and of modeling the interplay between risk factors and medication. The present study is based on comprehensive monitoring of 576 COVID-19 patients. Different statistical approaches were applied to gain a comprehensive insight in terms of both the identification of risk factors and the analysis of dependency structure among clinical and demographic characteristics. The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus enters host cells by binding to the angiotensin-converting enzyme 2 (ACE2), but whether or not renin-angiotensin-aldosterone system inhibitors (RAASi) would be beneficial to COVID-19 cases remains controversial. The survival tree approach was applied to define a multilayer risk stratification and better profile patient survival with respect to drug regimens, showing a significant protective effect of RAASi with a reduced risk of in-hospital death. Bayesian networks were estimated, to uncover complex interrelationships and confounding effects. The results confirmed the role of RAASi in reducing the risk of death in COVID-19 patients. De novo treatment with RAASi in patients hospitalized with COVID-19 should be prospectively investigated in a randomized controlled trial to ascertain the extent of risk reduction for in-hospital death in COVID-19.

SUBMITTER: Cippa PE

PROVIDER: S-EPMC7817222 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature

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A data-driven approach to identify risk profiles and protective drugs in COVID-19.

Cippà Pietro E PE Cugnata Federica F Ferrari Paolo P Brombin Chiara C Ruinelli Lorenzo L Bianchi Giorgia G Beria Nicola N Schulz Lukas L Bernasconi Enos E Merlani Paolo P Ceschi Alessandro A Di Serio Clelia C

Proceedings of the National Academy of Sciences of the United States of America 20201210 1

As the COVID-19 pandemic is spreading around the world, increasing evidence highlights the role of cardiometabolic risk factors in determining the susceptibility to the disease. The fragmented data collected during the initial emergency limited the possibility of investigating the effect of highly correlated covariates and of modeling the interplay between risk factors and medication. The present study is based on comprehensive monitoring of 576 COVID-19 patients. Different statistical approache ...[more]

PMID: 33303654

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Project description:This study presents a new risk-averse multi-stage stochastic epidemics-ventilator-logistics compartmental model to address the resource allocation challenges of mitigating COVID-19. This epidemiological logistics model involves the uncertainty of untested asymptomatic infections and incorporates short-term human migration. Disease transmission is also forecasted through a new formulation of transmission rates that evolve over space and time with respect to various non-pharmaceutical interventions, such as wearing masks, social distancing, and lockdown. The proposed multi-stage stochastic model overviews different scenarios on the number of asymptomatic individuals while optimizing the distribution of resources, such as ventilators, to minimize the total expected number of newly infected and deceased people. The Conditional Value at Risk (CVaR) is also incorporated into the multi-stage mean-risk model to allow for a trade-off between the weighted expected loss due to the outbreak and the expected risks associated with experiencing disastrous pandemic scenarios. We apply our multi-stage mean-risk epidemics-ventilator-logistics model to the case of controlling COVID-19 in highly-impacted counties of New York and New Jersey. We calibrate, validate, and test our model using actual infection, population, and migration data. We also define a new region-based sub-problem and bounds on the problem and then show their computational benefits in terms of the optimality and relaxation gaps. The computational results indicate that short-term migration influences the transmission of the disease significantly. The optimal number of ventilators allocated to each region depends on various factors, including the number of initial infections, disease transmission rates, initial ICU capacity, the population of a geographical location, and the availability of ventilator supply. Our data-driven modeling framework can be adapted to study the disease transmission dynamics and logistics of other similar epidemics and pandemics.

Project description:The growth of COVID-19 pandemic throughout more than 213 countries around the world have put a lot of pressures on governments and health services to try to stop the rapid expansion of the pandemic. During 2009, H1N1 Influenza pandemic, statistical and mathematical methods were used to track how the virus spreads around countries. Most of these models that were developed at the beginning of the XXI century are based on the classical susceptible-infected-recovered (SIR) model developed almost a hundred years ago. The evolution of this model allows us to forecast and compute basic and effective reproduction numbers (R t and R 0 ), measures that quantify the epidemic potential of a pathogen and estimates different scenarios. In this study, we present a traditional estimation technique for R0 with statistical distributions by best fitting and a Bayesian approach based on continuous feed of prior distributions to obtain posterior distributions and computing real time Rt . We use data from COVID-19 officially reported cases in Ecuador since the first confirmed case on February 29th. Because of the lack of data, in the case of R0 we compare two methods for the estimation of these parameters below exponential growth and maximum likelihood estimation. We do not make any assumption about the evolution of cases due to limited information and we use previous methods to compare scenarios about R0 and in the case of Rt we used Bayesian inference to model uncertainty in contagious proposing a new modification to the well-known model of Bettencourt and Ribeiro based on a time window of m days to improve estimations. Ecuadorian R0 with exponential growth criteria was 3.45 and with the maximum likelihood estimation method was 2.93. The results show that Guayas, Pichincha and Manabí were the provinces with the highest number of cases due to COVID-19. Some reasons explain the increased transmissibility in these localities: massive events, population density, cities dispersion patterns, and the delayed time of public health actions to contain pandemic. In conclusion, this is a novel approach that allow us to measure infection dynamics and outbreak distribution when not enough detailed data is available. The use of this model can be used to predict pandemic distribution and to implement data-based effective measures.

Dataset Information

A data-driven approach to identify risk profiles and protective drugs in COVID-19.

Publications

A data-driven approach to identify risk profiles and protective drugs in COVID-19.

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