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It is discussed the relevance of quantitative approaches, specifically mathematical modelling in epidemiology, in the public health decision-making process. This topic is discussed here based on the experience of various experts in mathematical epidemiology and public health. First, the definition of mathematical modelling is presented, especially in the context of epidemiology. Second, the different uses and socio-political implications, including empirical examples of recent experiences that have taken place at the international level are addressed. Finally, some general considerations regarding the challenges encountered in the use and application of mathematical modelling in epidemiology in the decision-making process at the local and national levels.

Se trata sobre la importancia de los abordajes cuantitativos, específicamente la formulación de modelos matemáticos en epidemiología, dentro del proceso de toma de decisiones en salud pública. Esta importante temática se analiza basándose en la experiencia de algunos expertos en epidemiología matemática y salud pública. En primer lugar, se presenta la definición de modelación matemática, particularmente dentro del contexto de la epidemiología. En segundo lugar, se abordan los diferentes usos y las implicaciones socio-políticas, incluyendo ejemplos de experiencias recientes que han ocurrido a nivel internacional. Finalmente, se hace referencia a ciertas consideraciones generales respecto a los retos que representa el uso y la aplicación de modelos matemáticos en epidemiología para el proceso de toma de decisiones a nivel local y nacional.

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BACKGROUND: The interaction between modelers and policymakers is becoming more common due to the increase in computing speed seen in recent decades. The recent pandemic caused by the SARS-CoV-2 virus was no exception. Thus, this study aims to identify and assess epidemiological mathematical models of SARS-CoV-2 applied to real-world data, including immunization for coronavirus 2019 (COVID-19). METHODOLOGY: PubMed, JSTOR, medRxiv, LILACS, EconLit, and other databases were searched for studies employing epidemiological mathematical models of SARS-CoV-2 applied to real-world data. We summarized the information qualitatively, and each article included was assessed for bias risk using the Joanna Briggs Institute (JBI) and PROBAST checklist tool. The PROSPERO registration number is CRD42022344542. FINDINGS: In total, 5646 articles were retrieved, of which 411 were included. Most of the information was published in 2021. The countries with the highest number of studies were the United States, Canada, China, and the United Kingdom; no studies were found in low-income countries. The SEIR model (susceptible, exposed, infectious, and recovered) was the most frequently used approach, followed by agent-based modeling. Moreover, the most commonly used software were R, Matlab, and Python, with the most recurring health outcomes being death and recovery. According to the JBI assessment, 61.4% of articles were considered to have a low risk of bias. INTERPRETATION: The utilization of mathematical models increased following the onset of the SARS-CoV-2 pandemic. Stakeholders have begun to incorporate these analytical tools more extensively into public policy, enabling the construction of various scenarios for public health. This contribution adds value to informed decision-making. Therefore, understanding their advancements, strengths, and limitations is essential.

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INTRODUCTION: The COVID-19 pandemic has negatively impacted mental health. Up to a quarter of the population has reported mental health disorders. This has been studied mainly from a nosological perspective, according to diagnostic criteria. Nevertheless, we did not find studies that have explored the daily expressions of the population. Our objective was to evaluate the perceptions of the COVID-19 pandemic and its repercussions on the emotional well-being of the Colombian population. METHODS: We performed a Twitter metrics and trend analysis. Initially, in the trend analysis, we calculated the average duration in hours of the 20 most popular trending topics of the day in Colombia and we grouped them into trends related to COVID-19 and unrelated trends. Subsequently, we identified dates of events associated with the pandemic relevant to the country, and they were related to the behaviour of the trends studied. Additionally, we did an exploratory analysis of these, selected the tweets with the greatest reach and categorised them in an inductive way to analyse them qualitatively. RESULTS: Issues not related to COVID-19 were more far-reaching than those related to coronavirus. However, a rise in these issues was seen on some dates consistent with important events in Colombia. We found expressions of approval and disapproval, solidarity and accusation. Inductively, we identified categories of informative tweets, humour, fear, stigma and discrimination, politics and entities, citizen complaints, and self-care and optimism. CONCLUSIONS: The impact of the COVID-19 pandemic generates different reactions in the population, which increasingly have more tools to express themselves and know the opinions of others. Social networks play a fundamental role in the communication of the population, so this content could serve as a public health surveillance tool and a useful and accessible means of communication in the management of health crises.

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Introducción: La pandemia de COVID-19 ha impactado negativamente en la salud mental. Hasta un cuarto de la población ha reportado alteraciones de salud mental. Esto se ha estudiado principalmente desde una perspectiva nosológica según criterios diagnósticos; sin embargo, no encontramos estudios que hayan explorado las expresiones cotidianas de la población. Nuestro objetivo es evaluar las percepciones y repercusiones en el bienestar emocional de la población colombiana por la pandemia de COVID-19. Métodos: Se realizó un análisis de métricas y tendencias en Twitter. Inicialmente, en el análisis de tendencias se calculó el promedio de duración en horas de los 20 temas tendencia del día más populares en Colombia y las agrupamos en relacionadas con la COVID-19 y no relacionadas. Después se identificaron fechas de acontecimientos asociados con la pandemia relevantes para el país, y se relacionaron con el comportamiento de las tendencias estudiadas. Además, se hizo un análisis exploratorio de estas, se seleccionaron los tweets con mayor alcance y se categorizaron de forma inductiva para analizarlos cualitativamente. Resultados: Los temas no relacionados con COVID-19 tuvieron mayor alcance que los relacionados con coronavirus. No obstante, se vio un alza de estos temas en algunas fechas concordantes con hechos importantes en Colombia. Se hallaron manifestaciones de aprobación y desaprobación, de solidaridad y de acusación. De manera inductiva, se identificaron categorías de tweets informativos, humor, miedo, estigma y discriminación, política y entidades, denuncia ciudadana, y autocuidado y optimismo. Conclusiones: El impacto de la pandemia de COVID-19 genera diferentes reacciones en la población, que cada vez tienen más herramientas para expresarse y conocer las opiniones de los demás. Las redes sociales tienen un papel primordial en la comunicación de la población, por lo que este contenido podría servir como herramienta de vigilancia en salud pública y medio de comunicación útil y accesible en el manejo de crisis sanitarias.

Introduction: The COVID-19 pandemic has negatively impacted mental health. Up to a quarter of the population has reported mental health disorders. This has been studied mainly from a nosological perspective, according to diagnostic criteria. Nevertheless, we did not find studies that have explored the daily expressions of the population. Our objective was to evaluate the perceptions of the COVID-19 pandemic and its repercussions on the emotional well-being of the Colombian population. Methods: We performed a Twitter metrics and trend analysis. Initially, in the trend analysis, we calculated the average duration in hours of the 20 most popular trending topics of the day in Colombia and we grouped them into trends related to COVID-19 and unrelated trends. Subsequently, we identified dates of events associated with the pandemic relevant to the country, and they were related to the behaviour of the trends studied. Additionally, we did an exploratory analysis of these, selected the tweets with the greatest reach and categorized them in an inductive way to analyse them qualitatively. Results: Issues not related to COVID-19 were more far-reaching than those related to coronavirus. However, a rise in these issues was seen on some dates consistent with important events in Colombia. We found expressions of approval and disapproval, solidarity and accusation. Inductively, we identified categories of informative tweets, humour, fear, stigma and discrimination, politics and entities, citizen complaints, and self-care and optimism. Conclusions: The impact of the COVID-19 pandemic generates different reactions in the population, which increasingly have more tools to express themselves and know the opinions of others. Social networks play a fundamental role in the communication of the population, so this content could serve as a public health surveillance tool and a useful and accessible means of communication in the management of health crises.

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Background: Chagas disease (CD) is a neglected tropical disease, endemic in Latin America, but due to migration and environmental changes it has become a global public health issue. Objectives: To assess the global prevalence and disability-adjusted life years due to CD using findings from the Global Burden of Disease Study 2019. Methods: The Global Burden of Disease data was obtained from the Global Burden of Disease Collaborative Network; results were provided by the Institute for Health Metrics and Evaluation. The prevalence and disability-adjusted life-years (DALYs) were described at a global, regional, and national level, including data from 1990 to 2019. Results: Globally, CD prevalence decreased by 11.3% during the study period, from 7,292,889 cases estimated in 1990 to 6,469,283 in 2019. Moreover, the global DALY rate of CD decreased by 23.7% during the evaluated period, from 360,872 in 1990 to 275,377 in 2019. In addition, significant differences in the burden by sex, being men the most affected, age, with the elderly having the highest burden of the disease, and sociodemographic index (SDI), with countries with the lowest SDI values having the highest prevalence of the disease, were observed. Finally, the prevalence trends have followed different patterns according to the region, with a sustained decrease in Latin America, compared to an increasing trend in North America and Europe until 2010. Conclusion: The global burden of CD has changed in recent decades, with a sustained decline in the number of cases. Although the majority of cases remain concentrated in Latin America, the increase observed in countries in North America and Europe highlights the importance of screening at-risk populations and raising awareness of this neglected tropical disease.

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[EXTRACT]. Este editorial es parte del suplemento conjunto del American Journal of Public Health y la Revista Panamericana de Salud Pública y que espera contribuir a arrojar la luz sobre la preparación ante emergencias en América Latina y sobre su experiencia con la pandemia de COVID-19, y que contribuya a encontrar soluciones a sus complejos desafíos.

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INTRODUCTION: The COVID-19 pandemic has negatively impacted mental health. Up to a quarter of the population has reported mental health disorders. This has been studied mainly from a nosological perspective, according to diagnostic criteria. Nevertheless, we did not find studies that have explored the daily expressions of the population. Our objective was to evaluate the perceptions of the COVID-19 pandemic and its repercussions on the emotional well-being of the Colombian population. METHODS: We performed a Twitter metrics and trend analysis. Initially, in the trend analysis, we calculated the average duration in hours of the 20 most popular trending topics of the day in Colombia and we grouped them into trends related to COVID-19 and unrelated trends. Subsequently, we identified dates of events associated with the pandemic relevant to the country, and they were related to the behaviour of the trends studied. Additionally, we did an exploratory analysis of these, selected the tweets with the greatest reach and categorised them in an inductive way to analyse them qualitatively. RESULTS: Issues not related to COVID-19 were more far-reaching than those related to coronavirus. However, a rise in these issues was seen on some dates consistent with important events in Colombia. We found expressions of approval and disapproval, solidarity and accusation. Inductively, we identified categories of informative tweets, humour, fear, stigma and discrimination, politics and entities, citizen complaints, and self-care and optimism. CONCLUSIONS: The impact of the COVID-19 pandemic generates different reactions in the population, which increasingly have more tools to express themselves and know the opinions of others. Social networks play a fundamental role in the communication of the population, so this content could serve as a public health surveillance tool and a useful and accessible means of communication in the management of health crises.

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In May 2020, Latin America became the epicenter of the COVID-19 pandemic, a region already afflicted by social disparities, poor healthcare access, inadequate nutrition and a large prevalence of noncommunicable chronic diseases. Obesity and its comorbidities are increasingly prevalent in Latin America, with a more rapid growth in individuals with lower income, and currently a disease associated with COVID-19 severity, complications and death. In this document, the Latin American Association of Obesity Societies and collaborators present a review of the burden of two pandemics in Latin America, discuss possible mechanisms that explain their relationship with each other and provide public health and individual recommendations, as well as questions for future studies.

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Actualmente, el mundo se enfrenta a la pandemia generada por el SARS-CoV-2, infección para la cual no hay medidas farmacológicas de prevención ni tratamiento. Hasta el momento, ha dejado más de 4'880.000 casos confirmados y 322.000 muertes. Se han propuesto diferentes estrategias para el control de la enfermedad que implican la participación de diferentes sectores de la sociedad con acciones guiadas por lineamientos jurídicos y basados en medidas de salud pública, entre ellas, la contención, la mitigación, el aislamiento físico y la cuarentena. Dado que se trata de una situación de dimensión poblacional, la información tiene un papel fundamental; sin embargo, la proliferación de términos nuevos, muchas veces usados erróneamente, causa confusión y desinformación y, en consecuencia, limitan la participación ciudanía. En ese contexto, en el presente documento se hizo una revisión de los términos utilizados en epidemias y pandemias de enfermedades infecciosas, con énfasis en la COVID-19, para facilitar al público general la comprensión de los términos relevantes sobre el comportamiento de los agentes patógenos y de su ciclo epidémico y pandémico, así como los criterios para la adopción de las decisiones pertinentes en salud pública. Se aspira a que el glosario resultante ayude al uso correcto de los términos y a homogenizar la información.

Currently, the world is facing the pandemic generated by SARS-CoV-2. There are no no pharmacological measures for the prevention or treatment of this infection and, so far, it has caused more than 4'880.000 confirmed cases and 322.000 deaths. The different strategies for the control of the disease that have been proposed involve the participation of different actors. Such participation, guided by legal guidelines based on public health measures, include containment, mitigation, physical isolation, and quarantine. As this is a population-based problem, information plays a primary role; however, the many new terms hat have arisen and their misuse confuse and, therefore, misinform thus limiting citizen participation. For this reason, we conducted a review of the terms used in epidemics and pandemics of infectious diseases, particularly COVID-19. We considered and differentiated the relevant terms to facilitate the understanding of pathogen's behavior and epidemic and pandemic cycles, as well as the criteria for public health decision-making for the general public. This glossary should facilitate the use of the terms and standardize the information.

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RESUMEN Objetivo Modelar el curso de la pandemia COVID-19 en Chile y proyectar la demanda de recursos hospitalarios y letalidad en escenarios simulados: primero, recurriendo a distintas medidas de mitigación para contener la propagación en un mes -desde el 14 de abril hasta el 14 de mayo del 2020- y, segundo, en el supuesto contagio del 70% de la población, según edad, sin límite de tiempo. Métodos Utilizamos como base el número de contagios confirmados con SARS-CoV-2 en Chile hasta el 14 de abril del 2020 (8 273 casos, 94 muertes). Para los distintos escenarios, asumimos un número reproductivo básico que va desde R0=2,5 hasta R0=1,5. La proyección de la demanda hospitalaria y letalidad por edad se fundamentaron en reportes italianos y británicos. Resultados Estimamos que para el 14 de mayo del 2020 habría en Chile 2 019 775 contagiados y 15 068 fallecidos en ausencia de medidas de mitigación (R0=2,5). Al implementar medidas que reduzcan R0 a 1,5 (detección temprana y aislamiento de casos, cuarentena y distanciamiento social de mayores de 70 años), el número de contagios y letalidad disminuirían a 94 235 y 703 respectivamente. Sin embargo, la demanda hospitalaria aún sobrepasaría la capacidad de respuesta. La población de mayor riesgo la componen los mayores de 60 años. Conclusión Encontramos evidencia a favor de las medidas de mitigación implementadas por el Gobierno chileno. Sin embargo, medidas más estrictas son necesarias para no colapsar el sistema sanitario, que cuenta con menos recursos hospitalarios que los proyectados. Es esencial aumentar la capacidad hospitalaria en términos de equipamiento y entrenamiento del personal de salud.(AU)

ABSTRACT Objetive To model disease progression, healthcare demand and case fatality rate attributed to COVID-19 pandemic that may occur in Chile in 1-month time, by simulating different scenarios according to diverse mitigation measures hypothetically implemented. Furthermore, we aimed to estimate the same outcomes assuming that 70% of the population will be infected by SARS-CoV-2, with no time limit assumption. Methods We based on the number of confirmed COVID-19 cases in Chile up to April 14th 2020 (8 273 cases and 94 deaths). For the simulated scenarios we assumed basic reproduction numbers ranging from R0=2.5 to R0=1.5. The estimation of the number of patients that would require intensive care and the age-specific case fatality rate were based on data provided by the Imperial College of London and the Instituto Superiore di Sanità en Italia. Results If no mitigation measures were applied (R0=2.5), by May 25, Chile would have 2 019 775 cases and 15 068 deaths. If mitigations measures were implemented to decrease R0 to 1.5 (early detection of cases, quarantine, social distancing of elderly), the number of cases and deaths would importantly decrease. Nonetheless, the demand for in-hospital care including intensive care would exceed the available resources. Our age-specific analysis showed that population over 60 years are at higher risk of needing intensive care and death. Conclusion Our evidence supports the mitigation measures implemented by the Chilean government. Nevertheless, more stringent measures are needed to prevent the health care system's collapse due to shortfall of resources to confront the COVID-19 pandemic.(AU)

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BACKGROUND: Chronic Chagas cardiomyopathy (CCM) is characterized by a unique type of cardiac involvement. Few studies have characterized echocardiographic (Echo) transitions from the indeterminate Chagas disease (ChD) form to CCM. The objective of this study was to identify the best cutoffs in multiple Echo parameters, speckle tracking, and N-terminal pro B-type natriuretic peptide (NT-proBNP) to distinguish patients without CCM (stage A) vs patients with myocardial involvement (stages B, C, or D). METHODS: Cross-sectional study conducted in 273 consecutive patients with different CCM stages. Echo parameters, NT-proBNP, and other clinical variables were measured. Logistic regression models (dichotomized in stage A versus B, C, and D) adjusted for age, sex, body mass index, and NT-proBNP were performed. RESULTS: Left ventricular global longitudinal strain (LV-GLS), mitral flow E velocity, LV mass index, and NT-proBNP identified early changes that differentiated stages A vs B, C, and D. The LV-GLS with a cutoff -20.5% showed the highest performance (AUC 92.99%; accuracy 84.56% and negative predictive value (NPV) 88.82%), which improved when it was additionally adjusted by NT-proBNP with a cutoff -20.0% (AUC 94.30%; accuracy 88.42% and NPV 93.55%). CONCLUSIONS: Our findings suggest that Echo parameters and NT-proBNP may be used as diagnostic variables in detecting the onset of myocardial alterations in patients with the indeterminate stage of ChD. LV-GLS was the more accurate measurement regarding stage A differentiation from the stages B, C, and D. Prospective longitudinal studies are needed to validate these findings.

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RESUMEN Objetivo Este estudio tiene como primer objetivo: realizar predicciones del curso de la infección en el horizonte temporal desde marzo 18 a abril 18 del 2020, según diferentes medidas de aislamiento aplicadas. Las predicciones incluyen, población total contagiada, mortalidad y necesidad de recursos hospitalarios. Segundo objetivo: modelar la mortalidad y la necesidad de recursos hospitalarios, estratificando por edad el escenario de contagio del 70% de la población. Métodos Para el primer objetivo, nos basamos en el número de casos confirmados en el país hasta marzo 18, 2020 (n=93). Como suposiciones para el modelo, incluimos un índice de contagio R0=2,5 y el índice de casos reales por cada caso confirmado. Para la proporción de pacientes que necesitarían cuidados intensivos u otros cuidados intrahospitalarios, nos basamos en datos aportados por el Imperial College of London. Para el segundo objetivo usamos como tasa de mortalidad por edad, datos aportados por el Instituto Superiore di Sanità en Italia. Resultados Basándonos en los 93 casos reportados al 18 de marzo, si no se aplicase ninguna medida de mitigación, para el 18 de abril el país tendría un total de 613 037 casos. Medidas de mitigación que reduzcan el R0 en un 10%, generan una reducción del 50% del número de casos. Sin embargo, a pesar de reducirse los casos a la mitad, todavía habría un déficit en el número de camas requeridas y sólo uno de cada dos pacientes tendría acceso a dicho recurso. Conclusión En nuestro modelo encontramos que las medidas de mitigación que han sido implementadas hasta la fecha por el gobierno colombiano, se fundamentan en evidencia suficiente para pensar que es posible reducir significativamente el número de casos contagiados y con esto, el número de pacientes que requerirán manejo hospitalario.(AU)

ABSTRACT Introduction First case of COVID-19 in Colombia was diagnosed on March 6th. Two weeks later, cases have rapidly increased, leading the government to establish some mitigation measures. Objectives The first objective is to estimate and model the number of cases, use of hospital resources and mortality by using different R0 scenarios in a 1-month scenario (from March 18 to April 18, 2020), based on the different isolation measures applied. This work also aims to model, without establishing a time horizon, the same outcomes given the assumption that eventually 70% of the population will be infected. Materials and Methods Data on the number of confirmed cases in the country as of March 18, 2020 (n=93) were taken as the basis for the achievement of the first objective. An initial transmission rate of R0= 2.5 and a factor of 27 for undetected infections per each confirmed case were taken as assumptions for the model. The proportion of patients who may need intensive care or other in-hospital care was based on data from the Imperial College of London. On the other hand, an age-specific mortality rate provided by the Instituto Superiore di Sanità in Italy was used for the second objective. Results Based on the 93 cases reported as of March 18, if no mitigation measures were applied, by April 18, the country would have 613 037 cases. Mitigation measures that reduce R0 by 10% generate a 50% reduction in the number of cases. However, despite halving the number of cases, there would still be a shortfall in the number of beds required and only one in two patients would have access to this resource. Conclusion This model found that the mitigation measures implemented to date by the Colombian government and analyzed in this article are based on sufficient evidence and will help to slow the spread of SARS-CoV-2 in Colombia. Although a time horizon of one month was used for this model, it is plausible to believe that, if the current measures are sustained, the mitigation effect will also be sustained over time.(AU)

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INTRODUCTION: First case of COVID-19 in Colombia was diagnosed on March 6th. Two weeks later, cases have rapidly increased, leading the government to establish some mitigation measures. OBJECTIVES: The first objective is to estimate and model the number of cases, use of hospital resources and mortality by using different R0 scenarios in a 1-month scenario (from March 18 to April 18, 2020), based on the different isolation measures applied. This work also aims to model, without establishing a time horizon, the same outcomes given the assumption that eventually 70% of the population will be infected. MATERIALS AND METHODS: Data on the number of confirmed cases in the country as of March 18, 2020 (n=93) were taken as the basis for the achievement of the first objective. An initial transmission rate of R0= 2.5 and a factor of 27 for undetected infections per each confirmed case were taken as assumptions for the model. The proportion of patients who may need intensive care or other in-hospital care was based on data from the Imperial College of London. On the other hand, an age-specific mortality rate provided by the Instituto Superiore di Sanità in Italy was used for the second objective. RESULTS: Based on the 93 cases reported as of March 18, if no mitigation measures were applied, by April 18, the country would have 613 037 cases. Mitigation measures that reduce R0 by 10% generate a 50% reduction in the number of cases. However, despite halving the number of cases, there would still be a shortfall in the number of beds required and only one in two patients would have access to this resource. CONCLUSION: This model found that the mitigation measures implemented to date by the Colombian government and analyzed in this article are based on sufficient evidence and will help to slow the spread of SARS-CoV-2 in Colombia. Although a time horizon of one month was used for this model, it is plausible to believe that, if the current measures are sustained, the mitigation effect will also be sustained over time.

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