Emphasizing visual tools: using R to understand dynamic models of biology and epidemiology

Epidemiological models have enhanced relevance because of the COVID-19 pandemic. In this note, we emphasize visual tools that can be part of a learning module geared to teaching the SIR epidemiological model, suitable for advanced undergraduates or beginning graduate students in disciplines where the level of prior mathematical knowledge of students may not be very strong. Visual tools – phase portrait, flow field and trajectory and line plots – available in the R software are presented in a step by step manner, moving from the exponential growth model to the logistic growth model and then to the SIR model. Code for numerical simulation of differential equations and estimation of parameters is presented for the SIR model. Suggestions for students to connect the learning from these examples with research papers on COVID-19 are provided.

Machine learning and automatic ARIMA/Prophet models-based forecasting of COVID-19: methodology, evaluation, and case study in SAARC countries.

Machine learning (ML) has proved to be a prominent study field while solving complex real-world problems. The whole globe has suffered and continues suffering from Coronavirus disease 2019 (COVID-19), and its projections need to be forecasted. In this article, we propose and derive an autoregressive modeling framework based on ML and statistical methods to predict confirmed cases of COVID-19 in the South Asian Association for Regional Cooperation (SAARC) countries. Automatic forecasting models based on autoregressive integrated moving average (ARIMA) and Prophet time series structures, as well as extreme gradient boosting, generalized linear model elastic net (GLMNet), and random forest ML techniques, are introduced and applied to COVID-19 data from the SAARC countries. Different forecasting models are compared by means of selection criteria. By using evaluation metrics, the best and suitable models are selected. Results prove that the ARIMA model is found to be suitable and ideal for forecasting confirmed infected cases of COVID-19 in these countries. For the confirmed cases in Afghanistan, Bangladesh, India, Maldives, and Sri Lanka, the ARIMA model is superior to the other models. In Bhutan, the Prophet time series model is appropriate for predicting such cases. The GLMNet model is more accurate than other time-series models for Nepal and Pakistan. The random forest model is excluded from forecasting because of its poor fit.

An overview on parametric quantile regression models and their computational implementation with applications to biomedical problems including COVID-19 data.

Quantile regression allows us to estimate the relationship between covariates and any quantile of the response variable rather than the mean. Recently, several statistical distributions have been considered for quantile modeling. The objective of this study is to provide a new computational package, two biomedical applications, one of them with COVID-19 data, and an up-to-date overview of parametric quantile regression. A fully parametric quantile regression is formulated by first parameterizing the baseline distribution in terms of a quantile. Then, we introduce a regression-based functional form through a link function. The density, distribution, and quantile functions, as well as the main properties of each distribution, are presented. We consider 18 distributions related to normal and non-normal settings for quantile modeling of continuous responses on the unit interval, four distributions for continuous response, and one distribution for discrete response. We implement an R package that includes estimation and model checking, density, distribution, and quantile functions, as well as random number generators, for distributions using quantile regression in both location and shape parameters. In summary, a number of studies have recently appeared applying parametric quantile regression as an alternative to the distribution-free quantile regression proposed in the literature. We have reviewed a wide body of parametric quantile regression models, developed an R package which allows us, in a simple way, to fit a variety of distributions, and applied these models to two examples with biomedical real-world data from Brazil and COVID-19 data from US for illustrative purposes. Parametric and non-parametric quantile regressions are compared with these two data sets.

A statistical analysis for the epidemiological surveillance of COVID-19 in Chile

The emergence of COVID-19 so far and in the immediate future has brought significant uncertainties that negatively impact institutions and individuals in developing and planning their activities worldwide. The uncertainty of the effectiveness of vaccines has forced the authorities to adopt different protocols, the most relevant of which is the isolation of people through quarantine to avoid contagion, drastically affecting our way of life. For this reason, it is crucial to evaluate the effectiveness of quarantines. In this paper, we analyze the spread of the disease in Chile according to the quarantines decreed by the sanitary authority. An inferential study is used to estimate the trend changes in COVID-19 cases and their basic and instantaneous reproduction numbers, which allows us to evaluate the decreed measures and establish vaccination policies. According to the data obtained until 03 March 2021 of confirmed COVID-19 cases disaggregated at a regional level in Chile, we observe a heterogeneous spread in most Chilean regions. When incorporating the dynamic quarantines decreed, effectiveness is detected in most regions, except in a few of them. Our results indicate that we are unable to identify the measures in the step-by-step protocols partly responsible for non-compliance with quarantines. However, our specific findings that can be extrapolated to daily practice and enlighten the ways of other countries are as follows. On the one hand, a measure that has been effective in curbing the spread of the disease is the strict early quarantine as detected in some Chilean regions. Therefore, indexes are needed to measure the mobility of citizens. On the other hand, as time passes without stopping infections, quarantines lose effectiveness even if the estimated instantaneous reproduction number is negligible and stable. In addition, other factors can cause this number to not be within the expected ranges, which must be further studied. Also, we have estimated the basic reproduction number whose value confirms the suitability of the pandemic declaration. [ FROM AUTHOR] Copyright of Signa Vitae is the property of Pharmamed Mado Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

AIC Algorithm for Entrepreneurial Intention in Covid19 Pandemic

This paper applied the AIC algorithm pointed out that the factors impacted the entrepreneurial intention in the Covid19 Pandemic. The COVID-19 epidemic had caused great harm to the start-up community when up to 50% of startups confirmed that they were operating in moderation and generating negligible income;while 23% of start-ups think that they are losing capital raising opportunities and expanding their market, 20% of start-ups choose to freeze their activities. We selected 178 students living in Ho Chi Minh City, Vietnam, to survey. The results suggest that the research results show that the factors affecting the Entrepreneurship intention of students from strong impact to weak impact are as follows: personality characteristics, subjective norm, feasibility perception, attitude towards entrepreneurship, financial approach impact entrepreneurship intention. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022

Disjoint and Functional Principal Component Analysis for Infected Cases and Deaths Due to COVID-19 in South American Countries with Sensor-Related Data.

In this paper, we group South American countries based on the number of infected cases and deaths due to COVID-19. The countries considered are: Argentina, Bolivia, Brazil, Chile, Colombia, Ecuador, Peru, Paraguay, Uruguay, and Venezuela. The data used are collected from a database of Johns Hopkins University, an institution that is dedicated to sensing and monitoring the evolution of the COVID-19 pandemic. A statistical analysis, based on principal components with modern and recent techniques, is conducted. Initially, utilizing the correlation matrix, standard components and varimax rotations are calculated. Then, by using disjoint components and functional components, the countries are grouped. An algorithm that allows us to keep the principal component analysis updated with a sensor in the data warehouse is designed. As reported in the conclusions, this grouping changes depending on the number of components considered, the type of principal component (standard, disjoint or functional) and the variable to be considered (infected cases or deaths). The results obtained are compared to the k-means technique. The COVID-19 cases and their deaths vary in the different countries due to diverse reasons, as reported in the conclusions.