Search

In this paper, we tackle the problem of forecasting future pandemics by training models with a COVID-19 time series. We tested this approach by producing one model and using it to forecast a non-trained time series; however, we limited this paper to the eight states with the highest population density in Mexico. We propose a generalized pandemic forecasting framework that transforms the time series into a dataset via three different transformations using random forest and backward transformations. Additionally, we tested the impact of the horizon and dataset window sizes for the training phase. A Wilcoxon test showed that the best transformation technique statistically outperformed the other two transformations with 100% certainty. The best transformation included the accumulated efforts of the other two plus a normalization that helped rescale the non-trained time series, improving the sMAPE from the value of 25.48 attained for the second-best transformation to 13.53. The figures in the experimentation section show promising results regarding the possibility of forecasting the early stages of future pandemics with trained data from the COVID-19 time series. [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]

The new pandemic caused by the COVID-19 virus has generated an overload in the quality of medical care in clinical centers around the world. Causes that originate this fact include lack of medical personnel, infrastructure, medicines, among others. The rapid and exponential increase in the number of patients infected by COVID-19 has required an efficient and speedy prediction of possible infections and their consequences with the purpose of reducing the health care quality overload. Therefore, intelligent models are developed and employed to support medical personnel, allowing them to give a more effective diagnosis about the health status of patients infected by COVID-19. This paper aims to propose an alternative algorithmic analysis for predicting the health status of patients infected with COVID-19 in Mexico. Different prediction models such as KNN, logistic regression, random forests, ANN and majority vote were evaluated and compared. The models use risk factors as variables to predict the mortality of patients from COVID-19. The most successful scheme is the proposed ANN-based model, which obtained an accuracy of 90% and an F1 score of 89.64%. Data analysis reveals that pneumonia, advanced age and intubation requirement are the risk factors with the greatest influence on death caused by virus in Mexico. [ABSTRACT FROM AUTHOR]

Early in 2020, an unexpected and hazardous situation occurred threatening and challenging all of humankind. A new coronavirus called SARS-CoV-2 was first identified in Wuhan, China, and its related disease, called COVID-19, has induced one of the most dangerous crises at a global level since World War II. The ultra-fast transmission rate of the virus and the high mortality rate led the World Health Organization (WHO) to officially declare the situation a pandemic. Governments, for their part, were forced to implement unprecedented mobility restrictions and cease a large part of their economic activities. These facts triggered multiple reactions from people who expressed their feelings mainly through social networks (like Twitter), using them as vectors of information and opinion. In this paper, a study carried out in different Spanish speaking countries (Chile, Mexico, Peru, and Spain) is presented, which addresses the manner in which the evolution of the pandemic outbreak has affected the emotions expressed by individuals on Twitter over the last 13 months (from March 2020 to March 2021). We used a total of 3 million tweets to achieve this task. We made use of a well-known framework called EmoWeb to capture the dynamic variation in the sentimental value of pandemic-related words. The results reflect to what degree the pandemic and its derived problems have influenced and affected the population of the selected countries in different ways. The outcomes also illustrate the evolution over time of opinions published on Twitter regarding several topics related to COVID-19.

As a result of the COVID-19 pandemic, various joint efforts have been made to support the creation of vaccines. Different projects have been under development, of which some are in the clinical evaluation stage and others in are in phase III with positive results. The aim of this paper was to describe the current situation of the development and production of vaccines available to the population to facilitate future research and continue developing and proposing ideas for the benefit of the population. So, we carried out a systematic review using databases such as PubMed, ScienceDirect, SciELO, and MEDLINE, including keywords such as "vaccines," "COVID-19," and "SARS-CoV-2". We reviewed the development and production of the anti-COVID vaccine and its different platforms, the background leading to the massive development of these substances, and the most basic immune aspects for a better understanding of their physiological activity and the immune response in those who receive the vaccine. We also analyzed immunization effects in populations with any medical or physiological conditions (such as immunosuppression, people with comorbidities, and pregnancy), as well as the response to immunization with heterologous vaccines and the hybrid immunity (the combination of natural immunity to SARS-CoV-2 with immunity generated by the vaccine). Likewise, we address the current situation in Mexico and its role in managing the vaccination process against SARS-CoV-2 at the national and international levels. There are still many clinical and molecular aspects to be described, such as the duration of active immunity and the development of immunological memory, to mention some of the most important ones. However, due to the short time since the global vaccination roll-out and that it has been progressive (not counting children and people with medical conditions), it is premature to say whether a second vaccination schedule will be necessary for the near future. Thus, it is essential to continue with health measures. [ABSTRACT FROM AUTHOR]

COVID-19 is no longer a public health emergency of international concern, but long COVID's effects are yet to be fully understood. Hence, globally, SARS-CoV-2 is still a profound threat to public health and of perilous nature as a zoonotic disease. Timely vaccination provided to individuals worldwide during the pandemic phase was under a certain degree of control; however, few studies have reported the effectiveness of vaccines administered in Mexico, and its surveillance is paramount. Furthermore, an unknown proportion of Mexican individuals have not yet received any vaccine, and the circulation of the Omicron, Pirola, and FLiRT variants is ongoing. A cross-sectional serology survey study design was employed, involving 150 individuals from Southern Mexico (Oaxaca) whose humoral immune responses after vaccination were tested by an ELISA; the receptor-binding domain of the SARS-CoV-2 spike protein served as a recombinant antigen in the ELISA. One hundred thirty-nine out of 150 individuals (92.6%; 95%-CI = 87–95%) examined were positive for the ELISA, but in 11 individuals, the vaccines did not induce any immune response. Interestingly, the immune responses (antibody prevalence and levels) of females (58%) were higher (T= −2.21; p-value = 0.02) than those of males (41%). However, in this sample population of Southern Mexico, age, vaccine type, comorbidity, and body mass index did not have any effect (p > 0.05) after COVID-19 vaccination. Taking all results together, here, we present factors that affected immune responses of individuals during the first vaccination campaign in Oaxaca, Mexico; however, vaccine surveillance during the post-pandemic phase needs further investigation. [ABSTRACT FROM AUTHOR]

Amid the backdrop of the COVID-19 pandemic, the living conditions of the population were dramatically altered, with social distancing measures and the looming threat to public health leaving a profound impact on people's lives. This study aims to assess the influence of COVID-19 on subjective well-being and overall quality of life in Mexico. A structural model with latent variables was used. Data were extracted from the National Self-Reported Well-being Survey (SWLS) from October 2020 and January 2021, featuring a robust sample size of 3615 residents from urban areas in Mexico, all aged 18 and above. Findings revealed that around 38% of the variance in overall life satisfaction in October 2020 and January 2021 could be attributed to Personal well-being (0.231), Personal satisfaction (0.320), Satisfaction with the environment (0.076), and Negative emotional states (−0.116). In comparison, October 2019 to January 2020 saw a lower 20% explained variance, primarily associated with Personal well-being (0.184), Personal satisfaction (0.270), and Satisfaction with the environment (0.052). Reliability assessments, including Cronbach's Alpha coefficients, Rho_a, and Composite Reliability, all surpassed 0.70 for each subscale. In addition, our study confirmed convergent validity, as the Average Variance Extracted (AVE) consistently exceeded 0.50 across all subscales, while the discriminant coefficient exceeded 0.70. [ABSTRACT FROM AUTHOR]

Since the COVID-19 Pandemic began, there have been several efforts to create new technology to mitigate the impact of the COVID-19 Pandemic around the world. One of those efforts is to design a new task force, robots, to deal with fundamental goals such as public safety, clinical care, and continuity of work. However, those characteristics need new products based on features that create them more innovatively and creatively. Those products could be designed using the S4 concept (sensing, smart, sustainable, and social features) presented as a concept able to create a new generation of products. This paper presents a low-cost robot, Robocov, designed as a rapid response against the COVID-19 Pandemic at Tecnologico de Monterrey, Mexico, with implementations of artificial intelligence and the S4 concept for the design. Robocov can achieve numerous tasks using the S4 concept that provides flexibility in hardware and software. Thus, Robocov can impact positivity public safety, clinical care, continuity of work, quality of life, laboratory and supply chain automation, and non-hospital care. The mechanical structure and software development allow Robocov to complete support tasks effectively so Robocov can be integrated as a technological tool for achieving the new normality's required conditions according to government regulations. Besides, the reconfiguration of the robot for moving from one task (robot for disinfecting) to another one (robot for detecting face masks) is an easy endeavor that only one operator could do. Robocov is a teleoperated system that transmits information by cameras and an ultrasonic sensor to the operator. In addition, pre-recorded paths can be executed autonomously. In terms of communication channels, Robocov includes a speaker and microphone. Moreover, a machine learning algorithm for detecting face masks and social distance is incorporated using a pre-trained model for the classification process. One of the most important contributions of this paper is to show how a reconfigurable robot can be designed under the S3 concept and integrate AI methodologies. Besides, it is important that this paper does not show specific details about each subsystem in the robot. [ABSTRACT FROM AUTHOR]

Since December 2019, the novel coronavirus (SARS-CoV-2) and its associated illness COVID-19 have rapidly spread worldwide. The Mexican government has implemented public safety measures to minimize the spread of the virus. In this paper, we used statistical models in two stages to estimate the total number of coronavirus (COVID-19) cases per day at the state and national levels in Mexico. In this paper, we propose two types of models. First, a polynomial model of the growth for the first part of the outbreak until the inflection point of the pandemic curve and then a second nonlinear growth model used to estimate the middle and the end of the outbreak. Model selection was performed using Vuong's test. The proposed models showed overall fit similar to predictive models (e.g., time series and machine learning); however, the interpretation of parameters is simpler for decisionmakers, and the residuals follow the expected distribution when fitting the models without autocorrelation being an issue. [ABSTRACT FROM AUTHOR]

Accurate forecasting remains a challenge, even with advanced techniques like deep learning (DL), ARIMA, and Holt–Winters (H&W), particularly for chaotic phenomena such as those observed in several areas, such as COVID-19, energy, and financial time series. Addressing this, we introduce a Forecasting Method with Filters and Residual Analysis (FMFRA), a hybrid methodology specifically applied to datasets of COVID-19 time series, which we selected for their complexity and exemplification of current forecasting challenges. FMFFRA consists of the following two approaches: FMFRA-DL, employing deep learning, and FMFRA-SSA, using singular spectrum analysis. This proposed method applies the following three phases: filtering, forecasting, and residual analysis. Initially, each time series is split into filtered and residual components. The second phase involves a simple fine-tuning for the filtered time series, while the third phase refines the forecasts and mitigates noise. FMFRA-DL is adept at forecasting complex series by distinguishing primary trends from insufficient relevant information. FMFRA-SSA is effective in data-scarce scenarios, enhancing forecasts through automated parameter search and residual analysis. Chosen for their geographical and substantial populations and chaotic dynamics, time series for Mexico, the United States, Colombia, and Brazil permitted a comparative perspective. FMFRA demonstrates its efficacy by improving the common forecasting performance measures of MAPE by 22.91%, DA by 13.19%, and RMSE by 25.24% compared to the second-best method, showcasing its potential for providing essential insights into various rapidly evolving domains. [ABSTRACT FROM AUTHOR]

A spatial survival analysis was performed to identify some of the factors that influence the survival of patients with COVID-19 in the states of Guerrero, México, and Chihuahua. The data that we analyzed correspond to the period from 28 February 2020 to 24 November 2021. A Cox proportional hazards frailty model and a Cox proportional hazards model were fitted. For both models, the estimation of the parameters was carried out using the Bayesian approach. According to the DIC, WAIC, and LPML criteria, the spatial model was better. The analysis showed that the spatial effect influences the survival times of patients with COVID-19. The spatial survival analysis also revealed that age, gender, and the presence of comorbidities, which vary between states, and the development of pneumonia increase the risk of death from COVID-19. [ABSTRACT FROM AUTHOR]

Disruptive events such as the coronavirus disease 2019 (COVID-19) pandemic have the potential to reshape even the most basic human systems and behaviors, including those related to food production, acquisition, and consumption. This paper provides an in-depth analysis of how the pandemic has changed the ratio of food-away-from-home (FAFH) expenditure to income in Mexico, as well as participation in this market. In 2020, household participation in FAFH expenditures declined in all income deciles and regions, but the impact on household shares is far from uniform. Using a detailed national database of household income and expenditures (N = 87,274), an Engel curve of the Working-Lesser functional form for FAFH including 19 independent variables was estimated using the Heckman method appropriate for censored response data. The results provide statistically significant estimates for income, which both increases the probability of participation in this expenditure and has a negative relationship with its budget share. The number of older adults and the exposure to food insecurity during the pandemic similarly limit participation and increase the budget share. In addition, remittances encourage participation and decrease the budget share. The corrected conditional income elasticity for FAFH is 0.4609; the sign and magnitude indicate that FAFH behaved as a necessary good in Mexico for the proportion of households that maintained spending during the lockdown conditions (about one-third of the sample). An increase or decrease in income will lead to a corresponding change in FAFH expenditure, but the change will be less than proportional. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has deeply influenced the economy of all nations, and Mexico is no exception. This effect is distinct for different groups of the population, with variable socio-economic and demographic characteristics, that live in regions of contrasting features. An efficient apparatus for measuring expenditure variations and tendency is the National Household Income and Expenditure Survey performed by the National Institute of Statistics and Geography in accordance with the international standardization established at the Canberra Conference; the latter permits, every two years, for different surveys to be comparable in time and space. Hence, in Mexico, there are three surveys for 2018, 2020, and 2022. The results of the comparison indicate that different groups of the Mexican population have experienced the pandemic with multiple impacts, apparently without large proportional variation regarding expenditure but with greater variation in time-use conditions. Moreover, it became apparent that even though the lower strata of Mexico's population have fewer proportional variations concerning income after the COVID-19 outburst, they must incorporate more family members into working activities to achieve a survival line; the latter results in the abandonment of school from various young household members that will negatively impact the quality of their lives in the long term. [ABSTRACT FROM AUTHOR]

The objective was to evaluate the Modified Early Warning Score in patients hospitalized for COVID-19 plus chronic disease. Methods: Retrospective observational study, 430 hospitalized patients with COVID-19 and chronic disease. Instrument, Modified Early Warning Score (MEWS). Data analysis, with Cox and logistic regression, to predict survival and risk. Results: Of 430 patients, 58.6% survived, and 41.4% did not. The risk was: low 53.5%, medium 23.7%, and high 22.8%. The MEWS score was similar between survivors 3.02, p 0.373 (95% CI: −0.225–0.597) and non-survivors 3.20 (95% CI: −0.224–0.597). There is a linear relationship between MEWS and mortality risk R 0.920, ANOVA 0.000, constant 4.713, and coefficient 4.406. The Cox Regression p 0.011, with a risk of deterioration of 0.325, with a positive coefficient, the higher the risk, the higher the mortality, while the invasive mechanical ventilation coefficient was negative −0.757. By providing oxygen and ventilation, mortality is lower. Conclusions: The predictive value of the modified early warning score in patients hospitalized for COVID-19 and chronic disease is not predictive with the MEWS scale. Additional assessment is required to prevent complications, especially when patients are assessed as low-risk. [ABSTRACT FROM AUTHOR]

The pandemic caused by COVID-19 led schools to continue their work by relying on digital technologies. Changes in this matter are observed within three dimensions in the theoretical and conceptual background: (a) the influence of ITC in education, (b) the macrosocial changes in the educational systems and public policy derived from the COVID-19 pandemic, and (c) the impact of the COVID-19 pandemic in higher education and its role for the future. The general objective of this research was to characterize the educational practices executed by the university community (students, professors, and managers) during the emerging remote classes derived from the pandemic at the Universidad Autonoma Metropolitana, Cuajimalpa Campus, a public educational institution in Mexico (through an explanation for each educational actor profile). As specific research objectives, this paper: (a) examines whether the professors and students had enough digital technology to continue with the classes, (b) defines the obstacles they had in the use of said digital technology, and (c) recognizes the existence of innovative educational practices and determines whether stated learning was achieved in educational programs. For this purpose, a mixed methodology was chosen, comprising the application of surveys to students and professors and semi-structured interviews with managers, professors, and students. It was found that there was innovation in the area of resources (material–economic dimension) and in the area of relationships (socio-political dimension), while the discursive dimension (cultural-discursive dimension) was negatively impacted. Based on the above, we conclude that the school culture of the UAM-C is solid and that it has the necessary technological resources to continue with the teaching–learning process. The educational practice was transformed, which resulted in advantages and disadvantages, but despite these situations, most students developed their learning. [ABSTRACT FROM AUTHOR]

The implementation and validation of anti-SARS-CoV-2 IgG serological assays are reported in this paper. S1 and RBD proteins were used to coat ELISA plates, and several secondary antibodies served as reporters. The assays were initially validated with 50 RT-PCR positive COVID-19 sera, which showed high IgG titers of mainly IgG1 isotype, followed by IgG3. Low or no IgG2 and IgG4 titers were detected. Then, the RBD/IgG assay was further validated with 887 serum samples from RT-PCR positive COVID-19 individuals collected at different times, including 7, 14, 21, and 40 days after the onset of symptoms. Most of the sera were IgG positive at day 40, with seroconversion happening after 14–21 days. A third party conducted an additional performance test of the RBD/IgG assay with 406 sera, including 149 RT-PCR positive COVID-19 samples, 229 RT-PCR negative COVID-19 individuals, and 28 sera from individuals with other viral infections not related to SARS-CoV-2. The sensitivity of the assay was 99.33%, with a specificity of 97.82%. All the sera collected from individuals with infectious diseases other than COVID-19 were negative. Given the robustness of this RBD/IgG assay, it received approval from the sanitary authority in Mexico (COFEPRIS) for production and commercialization under the name UDISTEST-V2G®. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic accelerated and, somehow, forced the process of digital transformation within the higher education sector. Due to the COVID-19 pandemic, online modes of course delivery have become the only available way of teaching in almost all parts of the world. We conducted a study in Mexico to know about students' preferences for these forced online class schedules, exams and assignments, and online teaching styles during this health crisis. Furthermore, this research sought to know about the impact of this forced digitalization on students' mental health and happiness. To conduct a conjoint analysis, we collected survey-based data from 219 undergraduate Mexican students. The most preferred attributes for students for online classes were "having a short online class (50 min)", "possibility to have 70% pre-exam assignments and 30% final exam", and "having a humorous professor in online classes". In terms of students' mental health, the prevalence of "moderate" anxiety and depression was 21.8%, and the prevalence of "severe" anxiety and depression was 14.9%. In terms of happiness, male students and students with shorter online classes per day felt a higher level of happiness. [ABSTRACT FROM AUTHOR]

Mental health problems are one of the various ills that afflict the world's population. Early diagnosis and medical care are public health problems addressed from various perspectives. Among the mental illnesses that most afflict the population is depression; its early diagnosis is vitally important, as it can trigger more severe illnesses, such as suicidal ideation. Due to the lack of homogeneity in current diagnostic tools, the community has focused on using AI tools for opportune diagnosis. Unfortunately, there is a lack of data that allows the use of IA tools for the Spanish language. Our work has a cross-lingual scheme to address this issue, allowing us to identify Spanish and English texts. The experiments demonstrated the methodology's effectiveness with an F1-score of 0.95. With this methodology, we propose a method to solve a classification problem for depression tweets (or short texts) by reusing English language databases with insufficient data to generate a classification model, such as in the Spanish language. We also validated the information obtained with public data to analyze the behavior of depression in Mexico during the COVID-19 pandemic. Our results show that the use of these methodologies can serve as support, not only in the diagnosis of depression, but also in the construction of different language databases that allow the creation of more efficient diagnostic tools. [ABSTRACT FROM AUTHOR]

We aimed to estimate the lead time and infection prevalence from SARS-CoV-2 wastewater (WW) monitoring compared with clinical surveillance data in Mexico to generate evidence about the feasibility of a large-scale WW surveillance system. We selected 10 WW treatment plants (WWTP) and 5 COVID-19 hospitals in major urban conglomerates in Mexico and collected biweekly 24-h flow-adjusted composite samples during October–November 2020. We concentrated WW samples by polyethylene glycol precipitation and employed quantitative PCR (RT-qPCR) assays, targeting the nucleoprotein (N1 and N2) genes. We detected and quantified SARS-CoV-2 RNA in 88% and 58% of the raw WW samples from WWTPs and COVID-19 hospitals, respectively. The WW RNA daily loads lead the active cases by more than one month in large and medium WWTP sites. WW estimated that cases were 2 to 20-fold higher than registered active cases. Developing a continuous monitoring surveillance system for SARS-CoV-2 community transmission through WW is feasible, informative, and recognizes three main challenges: (1) WW system data (catchment area, population served), (2) capacity to maintain the cold-chain and process samples, and (3) supplies and personnel to ensure standardized procedures. [ABSTRACT FROM AUTHOR]

This study measured the prevalence of cases of domestic violence against women and some associated factors during the COVID-19 pandemic in Mexico. Data were collected through a remote survey during 2020. The sample included 47,819 women aged 15 years and older. Jointpoint regression and logistic regression models were used. The prevalence of violence was 11.5%, which decreased in July and subsequently increased. The associated factors were being unemployed (OR = 2.01; 95%CI 1.89–2.16); being partially and totally quarantined (OR = 1.58; 95%CI 1.43–1.75 and OR = 1.47; 95%CI 1.32–1.63); being a caregiver of children; being a caregiver of elderly and/or suffering from a chronic illness (OR = 1.27; 95%CI 1.19–1.36; OR = 1.42; 95%CI 1.33–1.53; OR = 1.59; 95%CI 1.47–1.73); losing a family member to COVID-19 (OR = 1.26; 95%CI 1.13–1.41); and binge drinking (OR = 1.94; 95%CI 1.78–2.12). The confinement measures increased gender inequalities, economic problems and workload which further evidenced violence against women. [ABSTRACT FROM AUTHOR]

Association rule mining plays a crucial role in the medical area in discovering interesting relationships among the attributes of a data set. Traditional association rule mining algorithms such as Apriori, FP growth, or Eclat require considerable computational resources and generate large volumes of rules. Moreover, these techniques depend on user-defined thresholds which can inadvertently cause the algorithm to omit some interesting rules. In order to solve such challenges, we propose an evolutionary multi-objective algorithm based on NSGA-II to guide the mining process in a data set composed of 15.5 million records with official data describing the COVID-19 pandemic in Mexico. We tested different scenarios optimizing classical and causal estimation measures in four waves, defined as the periods of time where the number of people with COVID-19 increased. The proposed contributions generate, recombine, and evaluate patterns, focusing on recovering promising high-quality rules with actionable cause–effect relationships among the attributes to identify which groups are more susceptible to disease or what combinations of conditions are necessary to receive certain types of medical care. [ABSTRACT FROM AUTHOR]

In this paper, a multiple ensemble neural network model with fuzzy response aggregation for the COVID-19 time series is presented. Ensemble neural networks are composed of a set of modules, which are used to produce several predictions under different conditions. The modules are simple neural networks. Fuzzy logic is then used to aggregate the responses of several predictor modules, in this way, improving the final prediction by combining the outputs of the modules in an intelligent way. Fuzzy logic handles the uncertainty in the process of making a final decision about the prediction. The complete model was tested for the case of predicting the COVID-19 time series in Mexico, at the level of the states and the whole country. The simulation results of the multiple ensemble neural network models with fuzzy response integration show very good predicted values in the validation data set. In fact, the prediction errors of the multiple ensemble neural networks are significantly lower than using traditional monolithic neural networks, in this way showing the advantages of the proposed approach. [ABSTRACT FROM AUTHOR]

In this article we connect theoretically the concepts of structural vulnerabilities, recursive crises, and disasters through the linking-up of the COVID-19 pandemic with extreme hydrometeorological events in three municipalities in southern Yucatan, Mexico. The main research goal was to show the effects in productive and commercial systems in beekeeper and farmer households and their coping strategies to highlight the inter-relationships between historical vulnerabilities, crises, and disasters. The methodological approach included ethnographic fieldwork, 101 semi-structured interviews, and five focal groups. In the results, we reconstruct the agro-productive and commercial vulnerabilities built up since 1960 and contextualize the health and hydrometeorological crisis to show how some 87% of households suffered severe consequences to their incomes. The prices of main products (maize, fruit, honey) reached historically low levels as a result of conditions within local markets during the crisis. Half of the households surveyed had to make use of savings and more than 60% received no support from government or from development agencies. We conclude by pointing out the need for accompanying the design and implementation of community mitigation plans, which should take as a starting point the recovery of knowledge and local organization in order to demand from government co-managed, preventive programs, and capacities that would enable communities to confront increasing negative consequences in situations of global climate change and market instabilities in local peasant contexts. Our study aims to reach policy-makers, social organizations, and communities in order to highlight the importance of developing joint capabilities to respond to growing environmental, economic, and health vulnerabilities. [ABSTRACT FROM AUTHOR]

The World Health Organization labelled the new COVID-19 breakout a public health crisis of worldwide concern on 30 January 2020, and it was named the new global pandemic in March 2020. It has had catastrophic consequences on the world economy and well-being of people and has put a tremendous strain on already-scarce healthcare systems globally, particularly in underdeveloped countries. Over 11 billion vaccine doses have already been administered worldwide, and the benefits of these vaccinations will take some time to appear. Today, the only practical approach to diagnosing COVID-19 is through the RT-PCR and RAT tests, which have sometimes been known to give unreliable results. Timely diagnosis and implementation of precautionary measures will likely improve the survival outcome and decrease the fatality rates. In this study, we propose an innovative way to predict COVID-19 with the help of alternative non-clinical methods such as supervised machine learning models to identify the patients at risk based on their characteristic parameters and underlying comorbidities. Medical records of patients from Mexico admitted between 23 January 2020 and 26 March 2022, were chosen for this purpose. Among several supervised machine learning approaches tested, the XGBoost model achieved the best results with an accuracy of 92%. It is an easy, non-invasive, inexpensive, instant and accurate way of forecasting those at risk of contracting the virus. However, it is pretty early to deduce that this method can be used as an alternative in the clinical diagnosis of coronavirus cases. [ABSTRACT FROM AUTHOR]

The lockdown measures implemented due to the SARS-CoV-2 pandemic to reduce the epidemic curve, in most cases, have had a positive impact on air quality indices. Our study describes the changes in the concentration levels of PM 2.5 and PM 10 during the lockdown and post-lockdown in Victoria, Mexico, considering the following periods: before the lockdown (BL) from 16 February to 14 March, during the lockdown (DL) from 15 March to 2 May, and in the partial lockdown (PL) from 3 May to 6 June. When comparing the DL period of 2019 and 2020, we document a reduction in the average concentration of PM 2.5 and PM 10 of −55.56% and −55.17%, respectively. Moreover, we note a decrease of −53.57% for PM 2.5 and −51.61% for PM 10 in the PL period. When contrasting the average concentration between the DL periods of 2020 and 2021, an increase of 91.67% for PM 2.5 and 100.00% for PM 10 was identified. Furthermore, in the PL periods of 2020 and 2021, an increase of 38.46% and 31.33% was observed for PM 2.5 and PM 10 , respectively. On the other hand, when comparing the concentrations of PM 2.5 in the three periods of 2020, we found a decrease between BL and DL of −50.00%, between BL and PL a decrease of −45.83%, and an increase of 8.33% between DL and PL. In the case of PM 10 , a decrease of −48.00% between BL and DL, −40.00% between BL and PL, and an increase of 15.38% between the DL and PL periods were observed. In addition, we performed a non-parametric statistical analysis, where a significant statistical difference was found between the DL-2020 and DL-2019 pairs ( x 2 = 1.204) and between the DL-2021 and DL-2019 pairs ( x 2 = 0.372), with a p < 0.000 for PM 2.5 , and the contrast between pairs of PM 10 (DL) showed a significant difference between all pairs with p < 0.01 . [ABSTRACT FROM AUTHOR]

Background and Objectives: An association between high red blood cell distribution width (RDW) and mortality has been found in several diseases, including infection and sepsis. Some studies have aimed at determining the association of elevated RDW with adverse prognosis in COVID-19, but its usefulness has not been well established. The objective of this study was to determine the accuracy of the RDW, measured at hospital admission and discharge, for predicting death in patients with COVID-19. Materials andMethods: An observational, retrospective, longitudinal, and analytical study was conducted in two different COVID-19 reference centers in the state of Guanajuato, Mexico. A total of 323 patients hospitalized by COVID-19 were included. Results: We found higher RDW levels at the time of hospital admission in the non-survivors group compared to levels in survivors (median = 13.6 vs. 13.0, p < 0.001). Final RDW levels were even higher in the deceased group when compared with those of survivors (median = 14.6 [IQR, 12.67–15.6] vs. 12.9 [IQR, 12.2–13.5], p < 0.001). For patients who died, an RDW > 14.5% was more common at the time of death than for patients who survived at the time of discharge (81 vs. 13 patients, p < 0.001; RR = 2.3, 95% CI 1.89–2.81). Conclusions: The RDW is an accessible and economical parameter that, together with other characteristics of the presentation and evolution of patients with COVID-19, can be helpful in determining the prognosis. An RDW that increases during hospitalization could be a more important mortality predictor than the RDW at hospital admission. [ABSTRACT FROM AUTHOR]

Background: The current pandemic caused by SARS-CoV-2 is an acute illness of global concern. SARS-CoV-2 is an infectious disease caused by a recently discovered coronavirus. Most people who get sick from COVID-19 experience either mild, moderate, or severe symptoms. In order to help make quick decisions regarding treatment and isolation needs, it is useful to determine which significant variables indicate infection cases in the population served by the Tijuana General Hospital (Hospital General de Tijuana). An Artificial Intelligence (Machine Learning) mathematical model was developed in order to identify early-stage significant variables in COVID-19 patients. Methods: The individual characteristics of the study subjects included age, gender, age group, symptoms, comorbidities, diagnosis, and outcomes. A mathematical model that uses supervised learning algorithms, allowing the identification of the significant variables that predict the diagnosis of COVID-19 with high precision, was developed. Results: Automatic algorithms were used to analyze the data: for Systolic Arterial Hypertension (SAH), the Logistic Regression algorithm showed results of 91.0% in area under ROC (AUC), 80% accuracy (CA), 80% F1 and 80% Recall, and 80.1% precision for the selected variables, while for Diabetes Mellitus (DM) with the Logistic Regression algorithm it obtained 91.2% AUC, 89.2% accuracy, 88.8% F1, 89.7% precision, and 89.2% recall for the selected variables. The neural network algorithm showed better results for patients with Obesity, obtaining 83.4% AUC, 91.4% accuracy, 89.9% F1, 90.6% precision, and 91.4% recall. Conclusions: Statistical analyses revealed that the significant predictive symptoms in patients with SAH, DM, and Obesity were more substantial in fatigue and myalgias/arthralgias. In contrast, the third dominant symptom in people with SAH and DM was odynophagia. [ABSTRACT FROM AUTHOR]

Background: The potential impact of the COVID-19 pandemic on weight, shape-related appearance behaviors (body image dissatisfaction), and bulimic symptoms in nonclinical participants is poorly evaluated. This study aimed to identify the relationship between labor status, confinement degree due to COVID-19, dissatisfaction with body image, and anxiety and to discover its effect on bulimic behavior in Mexican adults. Methods: A cross-sectional study was conducted with a non-probabilistic sample of 276 participants via an online survey. Questions addressed their anxiety, body image dissatisfaction, and bulimic behavior. Results: The path analysis showed direct effects on the confinement degree (β = −0.157); of the labor situation (β = −0.147) and gender (β = 0.129) on anxiety; of dissatisfaction on bulimic behavior (β = 0.443) and anxiety about bulimic behavior (β = 0.184); and dissatisfaction (β = 0.085). Conclusions: The confinement, gender, and labor status are predictors of anxiety, while anxiety and body dissatisfaction directly influence bulimic behavior. [ABSTRACT FROM AUTHOR]

Background: The outbreak of SARS-CoV-2 abruptly disseminated in early 2020, overcoming the capacity of health systems to respond the pandemic. It was not until the vaccines were launched worldwide that an increase in survival was observed. The objectives of this study were to analyse the characteristics of survivors and their relationship with comorbidities. We had access to a database containing information on 16,747 hospitalized patients from Mexico, all infected with SARS-CoV-2, as part of a regular follow-up. The descriptive analysis looked for clusters of either success or failure. We categorized the samples into no comorbidities, or one and up to five coexisting with the infection. We performed a logistic regression test to ascertain what factors were more influential in survival. The main variable of interest was survival associated with multimorbidity factors. The database hosted information on hospitalized patients from Mexico between March 2020 through to April 2021. Categories 2 and 3 had the largest number of patients. Survival rates were higher in categories 0 (64.8%), 1 (57.5%) and 2 (51.6%). In total, 1741 (10.5%) patients were allocated to an ICU unit. Mechanical ventilators were used on 1415 patients, corresponding to 8.76%. Survival was recorded in 9575 patients, accounting for 57.2% of the sample population. Patients without comorbidities, younger people and women were more likely to survive. [ABSTRACT FROM AUTHOR]

The main expected result of a vaccine against viruses is the ability to produce neutralizing antibodies. Currently, several vaccines against SARS-CoV-2 are being applied to prevent mortal complications, being Pfizer-BioNTech (BNT162b2) one of the first to be authorized in the USA and Mexico (11 December 2020). This study evaluated the efficacy of this vaccine on antibody production with neutralizing capacity and its side effects in healthcare workers with and without prior SARS-CoV-2 infection and in a group of unvaccinated individuals with prior COVID-19. The main findings are the production of 100% neutralizing antibodies in both groups after the second dose, well-tolerated adverse effects, the possible presence of immunosenescence, and finally, we support that a single dose of this vaccine in individuals with prior COVID-19 would be sufficient to achieve an immunization comparable to people without prior COVID-19 with a complete vaccination program (2 doses). [ABSTRACT FROM AUTHOR]

International food trade is an integral part of the food system, and the COVID-19 pandemic has exposed the fragility of external food supplies. Based on the perspective of cereals trade networks (CTN), the pandemic risk is combined with the trade intensity between countries, and an assessment model of cereals external supply risk is constructed that includes external dependence index (EDI), import concentration, and risk of COVID-19 from import countries index (RICI). The results show that: (1) the global main CTN have typical scale-free characteristics, and seven communities are detected under the influence of the core countries; (2) about 60%, 50%, and 70% of countries face risks of medium and above (high and very high) external dependence, concentration of imports, and COVID-19 in the country of origin, respectively. Under the influence of the pandemic, the risk of global external cereal supply index (RECSI) has increased by 65%, and the USA-CAN communities show the highest risk index; (3) the countries with a very high risk are mainly the Pacific island countries and the Latin American and African countries. In addition, Japan, Mexico, South Korea, and 80% of the net food-importing developing countries are at high or very high RECSI levels. Approximately 50% of countries belong to the compound risk type, and many export countries belong to the RICI risk type; (4) global external food supply is subjected to multiple potential threats such as trade interruption, "price crisis", and "payment dilemma". The geographical proximity of community members and the geographical proximity of the pandemic risk is superimposed, increasing the regional risk of external food supply; and (5) this study confirms that the food-exporting countries should avoid the adoption of food export restriction measures and can prevent potential external supply risks from the dimensions of maintaining global food liquidity and promoting diversification of import sources. We believe that our assessment model of cereals external supply risk comprises a useful method for investigations regarding the international CTN or global food crisis under the background of the pandemic. [ABSTRACT FROM AUTHOR]

Today, the world is experiencing the COVID-19 health contingency, which prevents people from being exposed to one another and restricts physical contact. Under this context, the use of technology has become an essential tool to face the challenges of daily life, and virtual reality can be an alternative in the development of solutions that effectively support the acquisition of learning skills and knowledge transmission through the execution of tasks designed by multi-disciplinary groups. In addition, it can encourage the user to continue with the acquisition of learning skills in a friendly and fun way in a health and education context. This work proposes the use of virtual reality environments as an alternative to support the learning process in children with special educational needs such as Attention Deficit Hyperactivity Disorder (ADHD) and other associated disorders that occur in basic education. These proposed reality environments are designed under the Lean UX process model and their contents are designed according to expert therapeutic guidelines. As a result of this proposal, a case study is presented in which the user experience is evaluated through the use of an interactive environment to support the special educational needs of elementary school children attending an educational institution in Mexico. [ABSTRACT FROM AUTHOR]

The new coronavirus that was first identified in December 2019 in Wuhan China, now called SARS-CoV-2, which causes the disease called COVID-19, has spread from China to the entire world in a few months. Due to its contagious potential (R0: 5.7) and because there is still no effective treatment to stop the infection, and a vaccine for prevention it is not yet available to the general population, COVID-19 is currently considered a global health problem. The need to implement sensitive methods for the identification of individuals with COVID-19 has led to the development of different molecular and immunological tests. The importance of a timely and accurate diagnosis is essential to determine the course of the pandemic. The interpretation of the results obtained by each test as well as the factors that affect these results have not been fully described. In this review, we describe and analyze the different SARS-CoV-2 detection methods that have been performed in Mexico and are available worldwide, outlining their strengths and weaknesses. Further, a broader perspective of the correct use and interpretation of the results obtained with these diagnostic tools is proposed to improve the containment strategy and identify the true impact of the pandemic. [ABSTRACT FROM AUTHOR]