COVID-19 Forecasts for Cuba Using Logistic Regression and Gompertz Curves

Abstract

INTRODUCTION On March 11, 2020, WHO declared COVID-19 a pandemic and called on governments to impose drastic measures to fi ght it. It is vitally important for government health authorities and leaders to have reliable estimates of infected cases and deaths in order to apply the necessary measures with the resources at their disposal.OBJECTIVE Test the validity of the logistic regression and Gompertz curve to forecast peaks of confi rmed cases and deaths in Cuba, as well as total number of cases.METHODS An inferential, predictive study was conducted using logistic and Gompertz growth curves, adjusted with the least squares method and informatics tools for analysis and prediction of growth in COVID-19 cases and deaths. Italy and Spain-countries that have passed the initial peak of infection rates-were studied, and it was inferred from the results of these countries that their models were applicable to Cuba. This hypothesis was tested by applying goodnessof-fi t and signifi cance tests on its parameters. RESULTSBoth models showed good fi t, low mean square errors, and all parameters were highly signifi cant. CONCLUSIONSThe validity of models was confi rmed based on logistic regression and the Gompertz curve to forecast the dates of peak infections and deaths, as well as total number of cases in Cuba.

Comparison of Two Statistical Models for Predicting Mortality in COVID-19 Patients in Iran

“…In addition, ML algorithms may minimize uncertainties and ambiguities by offering evidence-based medicine for risk analysis, screening, prediction, and care plans; they also support reliable clinical decision-making through health care quality improvement (14,15). In this regard, supervised classification of learning algorithms (both statistical and computational) can be utilized to address these uncertainties by providing patient risk stratification for tailored clinical decision-making support (ie, measuring the probability of a disease, assessing disease likelihood, forecasting disease spread, and predicting fatality) (8,9,(16)(17)(18).…”

Using logistic regression to develop a diagnostic model for COVID-19

“…Both models showed good fit, low mean square errors, and all parameters were highly significant. [ 20 ] Hu and Li showed that the AUC, sensitivity, and specificity of a death prediction model based on a logistic regression model for predicting the mortality of COVID-19 cases during hospitalization were 0.804, 83.8%, and 82.3%. [ 30 ]…”

“…[ 15 16 17 ] To address the uncertainties in COVID-19 diagnosis, logistic regression as a supervised learning classification algorithm can be utilized to provide patient risk stratification to support tailored clinical decision-making including measuring the probability of a disease, assessing the disease likelihood, forecasting the spread, and predicting fatality. [ 13 18 19 20 21 ] To study the risk factors associated with COVID-19, logistic regressions have become a fundamental section of any data analysis related to the explanation of association among an outcome variable and one or more predictor variables. Therefore, this study was undertaken to develop a diagnostic model to predict the risk of the development of COVID-19.…”

Fractional Growth Model Applied to COVID-19 Data

“…Understanding population growth phenomena has been a task that over the time has provided various challenges to mathematicians, physicists, biologists, medics, economists and many others. From economic areas, where applying growth models to poultry allows making imperative predictions for the profitability of operations [1], to biological and medical areas, where growth models have been applied to the growth of animals, plants, yeast cells, tumors and recently to adjust and model COVID-19 pandemic data [2][3][4][5].…”

“…Finally, COVID-19 is a recent disease caused by the SARS-CoV-2 virus and declared as a public health emergency of international importance by the World Health Organization (WHO) on 30 January 2020 [21]; since then, many countries have implemented complex models in order to understand the behavior of this phenomenon and thus be able to make predictions [22][23][24]; even for these arduous modeling tasks, models as simple as Logistics and Gompertz continue to be used, showing a good fit to describe cumulative data on confirmed cases and deaths [3,4,25].…”