Search

During the COVID-19 pandemic, regions were affected by a combination of economic crises: weak demand and constrained supply. Several studies have sought to analyse the heterogeneous effects of supply and demand shocks on the labour market, economic growth, and the environment. This study has a different focus, estimating both direct and indirect effects of demand and supply shocks adopted during the pandemic in Brazil and Chile. Afterwards, the paper compares the degree of regional absorption (leakage) of income resulting from each of these shocks, applying an interregional input–output model for each country. The results of this study show that income absorption by the poorest regions is relatively greater in the case of a supply shock. It can be said, therefore, that this type of shock improves the retention of income generated in the poorest regions, favouring the development of these localities and the reduction in regional inequalities. The main reason for this result is that supply policies have restricted essential sectors to a lesser extent, and these sectors are generally less concentrated in large urban centres in both Brazil and Chile. In other words, much of the interregional leakage is driven by the demand for non-essential products, mainly in the richest urban economy centres. Finally, the geographical dimension of regional inequalities leads to the economic benefit of prosperous areas in the country when shocks occur in vulnerable regions, highlighting the centre–periphery pattern in both countries. [ABSTRACT FROM AUTHOR]

This paper designs and implements a methodology to model the evolution of the COVID-19 pandemic, produced by the SARS-CoV-2 virus, in what was called the first wave in Chile, which lasted from March 2 to 31 October 2020. The models are based on sigmoidal growth curves and can be used to predict the number of daily infections and deaths in future days, making them a useful tool for sanitary authorities to manage an epidemic. The methodology is applied to the entire country and to each of its most affected regions. In addition, the dynamics of these models allow it to be nurtured with the new information that is being produced and forecast a tentative date on which there would be some control over the pandemic. Moreover, these models allow for predicting the total number of infected and deceased people at the time the pandemic is under control. However, the simplicity of these models, which consider only the accumulated data of those infected and deceased, does not contemplate an intervention analysis such as vaccinations, which, as is known, are being effective in controlling the pandemic. [ABSTRACT FROM AUTHOR]

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]

The number of research papers interested in studying the social dimension of supply chain sustainability and resilience is increasing in the literature. However, the social dimension is complex, with several uncertainty variables that cannot be expressed with a traditional Boolean logic of totally true or false. To cope with uncertainty, Fuzzy Logic allows the development of models to obtain crisp values from the concept of fuzzy linguistic variables. Using the Structural Equation Model by Partial Least Squares (SEM-PLS) and Evolutionary Fuzzy Knowledge, this research aims to analyze the predictive power of social sustainability characteristics on supply chain resilience performance in the context of the COVID-19 pandemic with representative cases from Mexico and Chile. We validate our approach using the Chile database for training our model and the Mexico database for testing. The fuzzy knowledge database has a predictive power of more than 80%, using social sustainability features as inputs regarding supply chain resilience in the context of the COVID-19 pandemic disruption. To our knowledge, no works in the literature use fuzzy evolutionary knowledge to study social sustainability in correlation with resilience. Moreover, our proposed approach is the only one that does not require a priori expert knowledge or a systematic mathematical setup. [ABSTRACT FROM AUTHOR]

In this paper, the Extended Half-Power Exponential (EHPE) distribution is built on the basis of the Power Exponential model. The properties of the EHPE model are discussed: the cumulative distribution function, the hazard function, moments, and the skewness and kurtosis coefficients. Estimation is carried out by applying maximum likelihood (ML) methods. A Monte Carlo simulation study is carried out to assess the performance of ML estimates. To illustrate the usefulness and applicability of EHPE distribution, two real applications to COVID-19 data in Chile are discussed. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This paper investigates the causal effect of COVID-19 quarantine measures on the electricity consumption of different types of regulated customers. In particular, the average energy consumption of a group of customers in Chile is examined, exploiting the weekly variations in the application of the stages of the Step by Step Plan as a natural experiment. Step by Step Plan was the Chile COVID containment strategy, which involved implementing partial lockdowns based on the COVID incidence in the municipalities, and adjusted on a weekly basis. An econometric model is specified, and estimated using the technique Difference-in-Difference-in-Difference (DDD) to quantify the causal impact of these measures. After applying the model, it is found that the quarantine measures have had a negative effect on the trend weekly consumption of customers located in municipalities where some of the phases of the Plan have been applied. This effect is significantly different depending on each stage of the plan analysis, and statistically significant. It is also observed that, on average, the savings for a representative customer in the sample range from $ 8 to $ 6. 15 for some municipalities, and the sample costs range from $ 0. 02 to $ 0. 26 for others, depending on the municipality analyzed. The results can be expressed in a total of 2.95 tons less CO 2 emitted on a monthly basis. This shows a great impact in terms of sustainability and planning of energy demand and supply. • Quarantines by Covid 19 affected energy consumption, like a "Natural Experiment". • Using Panel Data of 400 thousand observations with geographic, temporal and Daylight Saving Time controls. • Difference-in-Difference-in-Difference method to quantify causal impact of quarantines on electric consumption. • Applying quarantines generated Energetic Savings from 8 cents to 6.15 dollars depending on client type. • An Event Study was designed to check the estimation results. [ABSTRACT FROM AUTHOR]

Simple Summary: Infectious diseases have been part of human history. Countless epidemics have produced high mortality rates in vulnerable populations. With the understanding of the spread of these types of diseases, population groups have been able to adapt and better cope with infections. Given the COVID-19 pandemic, one of the strategies used is the modeling of infectious diseases with the aim of establishing protection measures for people and stopping the spread of the epidemic. Our study evaluates protection strategies through infectious disease modeling with COVID-19 data in a commune in Chile. The results of the simulations indicate that the model generates important protection for the population by recognizing the super-propagating people (bridge nodes). This type of protection can be key in the fight against COVID-19. Among the diverse and important applications that networks currently have is the modeling of infectious diseases. Immunization, or the process of protecting nodes in the network, plays a key role in stopping diseases from spreading. Hence the importance of having tools or strategies that allow the solving of this challenge. In this paper, we evaluate the effectiveness of the DIL-W α ranking in immunizing nodes in an edge-weighted network with 3866 nodes and 6,841,470 edges. The network is obtained from a real database and the spread of COVID-19 was modeled with the classic SIR model. We apply the protection to the network, according to the importance ranking list produced by DIL-W α , considering different protection budgets. Furthermore, we consider three different values for α ; in this way, we compare how the protection performs according to the value of α. [ABSTRACT FROM AUTHOR]