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Background: Ioannidis et al. (2020) reported a standardized estimate of scientific productivity obtained from a worldwide database of 6,880,389 scientists who published at least 5 papers picked up by the Scopus database, and elaborated a ranking of ca. 120,000 scientists by both whole trajectory (career-long) impact and their current impact at year 2019. The goal of our paper is to contextualize Latin American ecologists' contribution at the world level based on the four most scientifically productive countries in the region. Methods and findings: Ioannidis et al. (2020) proposed a composite index that is the sum of six scientometric indicators: (1) The number of allocites, (2) the h index, (3) a per capita corrected version of h, (4) the allocites received as single author, (5) those received as single + first author, and (6) those as single + first + last author. We selected data for ecologists from Argentina, Brazil, Chile, and Mexico and comparatively analyzed their productivity according to the proposed index. We also compared these data with those obtained from a global sample of the top ecologists worldwide. Conclusions: Based on Ioannidis et al.'s proposition to evaluate scientific productivity we extract three lessons: (1) It does not pay to publish many papers; what counts is the number of allocites (i.e., self-citations do not add up). (2) Either be single, first, or last author; it does not pay to be in the middle of an authorship line. (3) Even worse it is to be among many co-authors because the proposed index allocates credits on a per capita basis. [ABSTRACT FROM AUTHOR]

In this study, our aim is to explore the dynamics of COVID-19 or 2019-nCOV in Argentina considering the parameter values based on the real data of this virus from March 03, 2020 to March 29, 2021 which is a data range of more than one complete year. We propose a Atangana–Baleanu type fractional-order model and simulate it by using predictor–corrector (P-C) method. First we introduce the biological nature of this virus in theoretical way and then formulate a mathematical model to define its dynamics. We use a well-known effective optimization scheme based on the renowned trust-region-reflective (TRR) method to perform the model calibration. We have plotted the real cases of COVID-19 and compared our integer-order model with the simulated data along with the calculation of basic reproductive number. Concerning fractional-order simulations, first we prove the existence and uniqueness of solution and then write the solution along with the stability of the given P-C method. A number of graphs at various fractional-order values are simulated to predict the future dynamics of the virus in Argentina which is the main contribution of this paper. [ABSTRACT FROM AUTHOR]