Dynamics of COVID-19 in Amazonia: A history of government denialism and the risk of a third wave.

The city of Manaus (the capital of Brazil's state of Amazonas) has become a key location for understanding the dynamics of the global pandemic of COVID-19. Different groups of scientists have foreseen different scenarios, such as the second wave or that Manaus could escape such a wave by having reached herd immunity. Here we test five hypotheses that explain the second wave of COVID-19 in Manaus: 1) The greater transmissibility of the Amazonian (gamma or P.1) variant is responsible for the second wave; 2) SARS-CoV-2 infection levels during the first wave were overestimated by those foreseeing herd immunity, and the population remained below this threshold when the second wave began at the beginning of December 2020; 3) Antibodies acquired from infection by one lineage do not confer immunity against other lineages; 4) Loss of immunity has generated a feedback phenomenon among infected people, which could generate future waves, and 5) A combination of the foregoing hypotheses. We also evaluated the possibility of a third wave in Manaus despite advances in vaccination, the new wave being due to the introduction of the delta variant in the region and the loss of immunity from natural contact with the virus. We developed a multi-strain SEIRS (Susceptible-Exposed-Infected-Removed-Susceptible) model and fed it with data for Manaus on mobility, COVID-19 hospitalizations, numbers of cases and deaths. Our model contemplated the current vaccination rates for all vaccines applied in Manaus and the individual protection rates already known for each vaccine. Our results indicate that the SARS-CoV-2 gamma (P.1) strain that originated in the Amazon region is not the cause of the second wave of COVID-19 in Manaus, but rather this strain originated during the second wave and became predominant in January 2021. Our multi-strain SEIRS model indicates that neither the doubled transmission rate of the gamma variant nor the loss of immunity alone is sufficient to explain the sudden rise of hospitalizations in late December 2020. Our results also indicate that the most plausible explanation for the current second wave is a SARS-CoV-2 infection level at around 50% of the population in early December 2020, together with loss of population immunity and early relaxation of restrictive measures. The most-plausible model indicates that contact with one strain does not provide protection against other strains and that the gamma variant has a transmissibility rate twice that of the original SARS-CoV-2 strain. Our model also shows that, despite the advance of vaccination, and even if future vaccination advances at a steady pace, the introduction of the delta variant or other new variants could cause a new wave of COVID-19.

Brazil's COVID-19 Epicenter in Manaus: How Much of the Population Has Already Been Exposed and Are Vulnerable to SARS-CoV-2?

Is Brazil's COVID-19 epicenter really approaching herd immunity? A recent study estimated that in October 2020 three-quarters of the population of Manaus (the capital of the largest state in the Brazilian Amazon) had contact with SARS-CoV-2. We show that 46% of the Manaus population having had contact with SARS-CoV-2 at that time is a more plausible estimate, and that Amazonia is still far from herd immunity. The second wave of COVID-19 is now evident in Manaus. We predict that the pandemic of COVID-19 will continue throughout 2021, given the duration of naturally acquired immunity of only 240 days and the slow pace of vaccination. Manaus has a large percentage of the population that is susceptible (35 to 45% as of May 17, 2021). Against this backdrop, measures to restrict urban mobility and social isolation are still necessary, such as the closure of schools and universities, since the resumption of these activities in 2020 due to the low attack rates of SARS-CoV-2 was the main trigger for the second wave in Manaus.

Dynamics of COVID-19 in Amazonia: a history of government denialism and the risk of a third wave

The city of Manaus (the capital of Brazil’s state of Amazonas) has become a key location for understanding the dynamics of the global pandemic of COVID-19. Different groups of scientists have foreseen different scenarios, such as the second wave or that Manaus could escape such a wave by having reached herd immunity. Here we test five hypotheses that explain the second wave of COVID-19 in Manaus: 1) The greater transmissibility of the Amazonian (gamma or P.1) variant is responsible for the second wave; 2) SARS-CoV-2 infection levels during the first wave were overestimated by those foreseeing herd immunity, and the population remained below this threshold when the second wave began at the beginning of December 2020; 3) Antibodies acquired from infection by one lineage do not confer immunity against other lineages; 4) Loss of immunity has generated a feedback phenomenon among infected people, which could generate future waves, and 5) A combination of the foregoing hypotheses. We also evaluated the possibility of a third wave in Manaus despite advances in vaccination, the new wave being due to the introduction of the delta variant in the region and the loss of immunity from natural contact with the virus. We developed a multi-strain SEIRS (Susceptible-Exposed-Infected-Removed-Susceptible) model and fed it with data for Manaus on mobility, COVID-19 hospitalizations, numbers of cases and deaths. Our model contemplated the current vaccination rates for all vaccines applied in Manaus and the individual protection rates already known for each vaccine. Our results indicate that the SARS-CoV-2 gamma (P.1) strain that originated in the Amazon region is not the cause of the second wave of COVID-19 in Manaus, but rather this strain originated during the second wave and became predominant in January 2021. Our multi-strain SEIRS model indicates that neither the doubled transmission rate of the gamma variant nor the loss of immunity alone is sufficient to explain the sudden rise of hospitalizations in late December 2020. Our results also indicate that the most plausible explanation for the current second wave is a SARS-CoV-2 infection level at around 50% of the population in early December 2020, together with loss of population immunity and early relaxation of restrictive measures. The most-plausible model indicates that contact with one strain does not provide protection against other strains and that the gamma variant has a transmissibility rate twice that of the original SARS-CoV-2 strain. Our model also shows that, despite the advance of vaccination, and even if future vaccination advances at a steady pace, the introduction of the delta variant or other new variants could cause a new wave of COVID-19.

Brazil's planned exploitation of Amazonian indigenous lands for commercial agriculture increases risk of new pandemics.

We report the emergence of a new production chain for commercial food that aims to maximize profit to the detriment of the environment and traditional communities in the Amazonian region. In addition, the combination of environmental impact and the raising of confined animals (including pigs and poultry), in locations where the animals may have contact with other diseases carries the danger of generating a new pandemic of worldwide proportions.

How Brazil's President turned the country into a global epicenter of COVID-19.

In this manuscript, we point out that the federal government headed by President Bolsonaro has pursued a political agenda that contributed to the spread of COVID-19, transforming the country into a major repository for SARS-CoV-2 and its variants, thus representing a risk for worldwide containment efforts. Furthermore his actions are also weakening democratic institutions, which could counter his political agenda, effectively facilitating the spread of COVID-19. Thus, the perpetuation of the COVID-19 pandemic in Brazil is due to human behaviour factors, especially high-level public decision makers.

The First Case of Immunity Loss and SARS-CoV-2 Reinfection by the Same Virus Lineage in Amazonia.

We report the first confirmed record of a SARS-CoV-2 immunity loss and reinfection for the Amazon region and for Brazil by the same virus lineage. The patient presented an asymptomatic condition the first time and an aggravated one after reinfection. We raise the possibility of a recessive genotype in the Amazonian population that does not generate an immune memory response to SARS-CoV-2.

Brazil’s COVID-19 epicenter in Manaus: how much of the population has already been exposed and are vulnerable to SARS-CoV-2?

Is Brazil’s COVID-19 epicenter really approaching herd immunity? A recent study estimated that in October 2020 three-quarters of the population of Manaus (the capital of the largest state in the Brazilian Amazon) had contact with SARS-CoV-2. We show that 46% of the Manaus population having had contact with SARS-CoV-2 at that time is a more plausible estimate, and that Amazonia is still far from herd immunity. The second wave of COVID-19 is now evident in Manaus. We predict that the pandemic of COVID-19 will continue throughout 2021, given the duration of naturally acquired immunity of only 240 days and the slow pace of vaccination. Manaus has a large percentage of the population that is susceptible (35 to 45% as of May 17, 2021). Against this backdrop, measures to restrict urban mobility and social isolation are still necessary, such as the closure of schools and universities, since the resumption of these activities in 2020 due to the low attack rates of SARS-CoV-2 was the main trigger for the second wave in Manaus.

How Brazil’s President turned the country into a global epicenter of COVID-19

In this manuscript, we point out that the federal government headed by President Bolsonaro has pursued a political agenda that contributed to the spread of COVID-19, transforming the country into a major repository for SARS-CoV-2 and its variants, thus representing a risk for worldwide containment efforts. Furthermore his actions are also weakening democratic institutions, which could counter his political agenda, effectively facilitating the spread of COVID-19. Thus, the perpetuation of the COVID-19 pandemic in Brazil is due to human behaviour factors, especially high-level public decision makers.

The first case of immunity loss and SARS-CoV-2 reinfection by the same virus lineage in Amazonia

We report the first confirmed record of a SARS-CoV-2 immunity loss and reinfection for the Amazon region and for Brazil by the same virus lineage. The patient presented an asymptomatic condition the first time and an aggravated one after reinfection. We raise the possibility of a recessive genotype in the Amazonian population that does not generate an immune memory response to SARS-CoV-2.

Vertical social distancing policy is ineffective to contain the COVID-19 pandemic.

Considering numerical simulations, this study shows that the so-called vertical social distancing health policy is ineffective to contain the COVID-19 pandemic. We present the SEIR-Net model, for a network of social group interactions, as a development of the classic mathematical model of SEIR epidemics (Susceptible-Exposed-Infected (symptomatic and asymptomatic)-Removed). In the SEIR-Net model, we can simulate social contacts between groups divided by age groups and analyze different strategies of social distancing. In the vertical distancing policy, only older people are distanced, whereas in the horizontal distancing policy all age groups adhere to social distancing. These two scenarios are compared to a control scenario in which no intervention is made to distance people. The vertical distancing scenario is almost as bad as the control, both in terms of people infected and in the acceleration of cases. On the other hand, horizontal distancing, if applied with the same intensity in all age groups, significantly reduces the total infected people "flattening the disease growth curve". Our analysis considers the city of Belo Horizonte, Minas Gerais State, Brazil, but similar conclusions apply to other cities as well. Code implementation of the model in R-language is provided in the supplementary material.

Vertical social distancing policy is ineffective to contain the COVID-19 pandemic / A política de distanciamento social vertical é ineficaz para conter a pandemia da COVID-19 / La política de aislamiento social vertical es ineficaz para contener la pandemia de COVID-19

Abstract: Considering numerical simulations, this study shows that the so-called vertical social distancing health policy is ineffective to contain the COVID-19 pandemic. We present the SEIR-Net model, for a network of social group interactions, as a development of the classic mathematical model of SEIR epidemics (Susceptible-Exposed-Infected (symptomatic and asymptomatic)-Removed). In the SEIR-Net model, we can simulate social contacts between groups divided by age groups and analyze different strategies of social distancing. In the vertical distancing policy, only older people are distanced, whereas in the horizontal distancing policy all age groups adhere to social distancing. These two scenarios are compared to a control scenario in which no intervention is made to distance people. The vertical distancing scenario is almost as bad as the control, both in terms of people infected and in the acceleration of cases. On the other hand, horizontal distancing, if applied with the same intensity in all age groups, significantly reduces the total infected people "flattening the disease growth curve". Our analysis considers the city of Belo Horizonte, Minas Gerais State, Brazil, but similar conclusions apply to other cities as well. Code implementation of the model in R-language is provided in the supplementary material.

Resumo: O artigo demonstra através de simulações numéricas que a política do chamado distanciamento social vertical é ineficaz para conter a pandemia da COVID-19. Os autores apresentam o modelo SEIR-Net para uma rede de interações entre grupos sociais, enquanto desdobramento do modelo matemático clássico para epidemias, chamado SEIR (Suscetíveis-Expostos-Infectados (sintomáticos e assintomáticos)-Removidos). No modelo SEIR-Net, pode-se simular contatos sociais entre grupos, divididos por faixas etárias, e analisar diferentes estratégias de distanciamento social. Na política de distanciamento vertical, apenas os idosos ficam distanciados, ao contrário da política de distanciamento horizontal, em que todas as faixas etárias aderem ao distanciamento. O artigo compara esses dois cenários a um cenário controle, sem nenhuma intervenção para distanciar as pessoas umas das outras. O cenário do distanciamento vertical é quase tão ruim quanto aquele sem nenhum distanciamento, em termos tanto do número de infectados quanto da aceleração do número de casos. Por outro lado, o distanciamento horizontal, desde que aplicado com a mesma intensidade a todos os grupos etários, reduz significativamente o número total de infectados e "achata a curva de crescimento da doença". Nossa análise foi feita no Município de Belo Horizonte, Minas Gerais, Brasil, mas conclusões semelhantes se aplicam igualmente a outras cidades. O material suplementar do artigo fornece detalhes sobre a implementação do código do modelo na linguagem R.

Demostramos mediante simulaciones numéricas que la denominada política de salud de aislamiento social vertical es ineficaz para contener la pandemia de COVID-19. Presentamos el modelo SEIR-Net para interacciones de grupo en una red social, como una evolución del clásico modelo matemático SEIR epidemics (Susceptibles-Expuestos-Infectados (sintomáticos y asintomáticos)-Removidos). En el modelo SEIR-Net, podemos simular contactos sociales entre grupos divididos por grupos de edad y analizar diferentes estrategias de distanciamiento social. En la política de aislamiento vertical, solamente se aísla a los ancianos, frente a la política de aislamiento horizontal, donde todos los grupos de edad se adhieren al aislamiento social. Estos dos escenarios se compararon a un escenario de control, en el que no se realiza ninguna intervención para aislar a la gente. El escenario de aislamiento vertical es casi tan malo, como el escenario donde no se aplica ningún tipo de aislamiento, tanto en términos del número de infectados, como en la aceleración del número de casos. Por otro lado, el aislamiento horizontal, si se aplica con la misma intensidad en todos los grupos de edad, reduce significativamente el número total de infectados y "aplana la curva de crecimiento de la enfermedad". Nuestro análisis se realiza en la municipalidad de Belo Horizonte, Minas Gerais, Brasil, pero conclusiones similares se pueden aplicar también a otras ciudades. En el material complementario se facilita la implementación del código del modelo en R-language.

Data-driven inference for the spatial scan statistic.

BACKGROUND: Kulldorff's spatial scan statistic for aggregated area maps searches for clusters of cases without specifying their size (number of areas) or geographic location in advance. Their statistical significance is tested while adjusting for the multiple testing inherent in such a procedure. However, as is shown in this work, this adjustment is not done in an even manner for all possible cluster sizes. RESULTS: A modification is proposed to the usual inference test of the spatial scan statistic, incorporating additional information about the size of the most likely cluster found. A new interpretation of the results of the spatial scan statistic is done, posing a modified inference question: what is the probability that the null hypothesis is rejected for the original observed cases map with a most likely cluster of size k, taking into account only those most likely clusters of size k found under null hypothesis for comparison? This question is especially important when the p-value computed by the usual inference process is near the alpha significance level, regarding the correctness of the decision based in this inference. CONCLUSIONS: A practical procedure is provided to make more accurate inferences about the most likely cluster found by the spatial scan statistic.

Cursos de graduação em medicina: uma análise a partir do sinaes / Undergraduate medical courses in Brazil: an analysis based on the sinaes system

Estudo descritivo analítico dos indicadores do Sistema Nacional de Avaliação da Educação Superior-Sinaes/Inep/MEC. Existem evidências de problemas metodológicos nos instrumentos do Exame Nacional de Desempenho dos Estudantes (Enade). Houve variação das matrizes de referência em 2004 e 2007, mudanças nas fórmulas e multicolinearidade no cálculo do Conceito Preliminar e do nível de dificuldade das provas, respectivamente 65 por cento em 2004 e 35 por cento em 2007. Nos dois períodos, quase metade das questões avaliou a capacidade de memorização (47 por cento e 42 por cento). A análise Cart apontou condição sociocultural, rede pública de ensino e percepção de alta oferta de formação complementar pela instituição como fatores associados ao melhor desempenho do concluinte. Tanto a nota no componente específico do concluinte como a diferença entre a nota de formação geral do concluinte e do ingressante são indicadores de forte discriminação de desempenho. Propõe-se um indicador multicritério dessas duas quantidades, que foi capaz de discriminar as instituições bem conceituadas da área médica. Matrizes de referência compatíveis e robustas são necessárias para o monitoramento de cursos que formem profissionais de excelência e que estejam conscientes de suas responsabilidades sociais.

This descriptive study of the indicators from the National System for the Evaluation of Higher Education-Sinaes/Inep/MEC. There is evidence of methodological problems in the construction of the instruments for the National Examination of Students' Performance (Enade). There was a variation in the reference matrices in 2004 and 2007, changes in the formulas, and multi-colinearity in calculation of the Preliminary Classification. In 2004, 65 percent of the questions were classified as "difficult", as compared to only 35 percent in 2007..Nearly half of the questions evaluated the capacity for memorization (47 percent and 42 percent). Cart analysis pointed to the following factors associated with better performance on the graduating student's: higher socioeconomic status, public institution, and high supply of complementary training by the institution. Both the graduating student's specific component grade and the difference between them and first-year student's overall classification appear as robust performance indicators. This study proposes a multiple-criteria indicator which proved capable of discriminating between highly rated institutions in the medical field. Consistent and robust reference matrices are necessary for monitoring courses that train excellent professionals who are aware of their social responsibilities.

Voronoi distance based prospective space-time scans for point data sets: a dengue fever cluster analysis in a southeast Brazilian town.

BACKGROUND: The Prospective Space-Time scan statistic (PST) is widely used for the evaluation of space-time clusters of point event data. Usually a window of cylindrical shape is employed, with a circular or elliptical base in the space domain. Recently, the concept of Minimum Spanning Tree (MST) was applied to specify the set of potential clusters, through the Density-Equalizing Euclidean MST (DEEMST) method, for the detection of arbitrarily shaped clusters. The original map is cartogram transformed, such that the control points are spread uniformly. That method is quite effective, but the cartogram construction is computationally expensive and complicated. RESULTS: A fast method for the detection and inference of point data set space-time disease clusters is presented, the Voronoi Based Scan (VBScan). A Voronoi diagram is built for points representing population individuals (cases and controls). The number of Voronoi cells boundaries intercepted by the line segment joining two cases points defines the Voronoi distance between those points. That distance is used to approximate the density of the heterogeneous population and build the Voronoi distance MST linking the cases. The successive removal of edges from the Voronoi distance MST generates sub-trees which are the potential space-time clusters. Finally, those clusters are evaluated through the scan statistic. Monte Carlo replications of the original data are used to evaluate the significance of the clusters. An application for dengue fever in a small Brazilian city is presented. CONCLUSIONS: The ability to promptly detect space-time clusters of disease outbreaks, when the number of individuals is large, was shown to be feasible, due to the reduced computational load of VBScan. Instead of changing the map, VBScan modifies the metric used to define the distance between cases, without requiring the cartogram construction. Numerical simulations showed that VBScan has higher power of detection, sensitivity and positive predicted value than the Elliptic PST. Furthermore, as VBScan also incorporates topological information from the point neighborhood structure, in addition to the usual geometric information, it is more robust than purely geometric methods such as the elliptic scan. Those advantages were illustrated in a real setting for dengue fever space-time clusters.

Nonparametric intensity bounds for the delineation of spatial clusters.

BACKGROUND: There is considerable uncertainty in the disease rate estimation for aggregated area maps, especially for small population areas. As a consequence the delineation of local clustering is subject to substantial variation. Consider the most likely disease cluster produced by any given method, like SaTScan, for the detection and inference of spatial clusters in a map divided into areas; if this cluster is found to be statistically significant, what could be said of the external areas adjacent to the cluster? Do we have enough information to exclude them from a health program of prevention? Do all the areas inside the cluster have the same importance from a practitioner perspective? RESULTS: We propose a method to measure the plausibility of each area being part of a possible localized anomaly in the map. In this work we assess the problem of finding error bounds for the delineation of spatial clusters in maps of areas with known populations and observed number of cases. A given map with the vector of real data (the number of observed cases for each area) shall be considered as just one of the possible realizations of the random variable vector with an unknown expected number of cases. The method is tested in numerical simulations and applied for three different real data maps for sharply and diffusely delineated clusters. The intensity bounds found by the method reflect the degree of geographic focus of the detected clusters. CONCLUSIONS: Our technique is able to delineate irregularly shaped and multiple clusters, making use of simple tools like the circular scan. Intensity bounds for the delineation of spatial clusters are obtained and indicate the plausibility of each area belonging to the real cluster. This tool employs simple mathematical concepts and interpreting the intensity function is very intuitive in terms of the importance of each area in delineating the possible anomalies of the map of rates. The Monte Carlo simulation requires an effort similar to the circular scan algorithm, and therefore it is quite fast. We hope that this tool should be useful in public health decision making of which areas should be prioritized.

Penalized likelihood and multi-objective spatial scans for the detection and inference of irregular clusters.

BACKGROUND: Irregularly shaped spatial clusters are difficult to delineate. A cluster found by an algorithm often spreads through large portions of the map, impacting its geographical meaning. Penalized likelihood methods for Kulldorff's spatial scan statistics have been used to control the excessive freedom of the shape of clusters. Penalty functions based on cluster geometry and non-connectivity have been proposed recently. Another approach involves the use of a multi-objective algorithm to maximize two objectives: the spatial scan statistics and the geometric penalty function. RESULTS & DISCUSSION: We present a novel scan statistic algorithm employing a function based on the graph topology to penalize the presence of under-populated disconnection nodes in candidate clusters, the disconnection nodes cohesion function. A disconnection node is defined as a region within a cluster, such that its removal disconnects the cluster. By applying this function, the most geographically meaningful clusters are sifted through the immense set of possible irregularly shaped candidate cluster solutions. To evaluate the statistical significance of solutions for multi-objective scans, a statistical approach based on the concept of attainment function is used. In this paper we compared different penalized likelihoods employing the geometric and non-connectivity regularity functions and the novel disconnection nodes cohesion function. We also build multi-objective scans using those three functions and compare them with the previous penalized likelihood scans. An application is presented using comprehensive state-wide data for Chagas' disease in puerperal women in Minas Gerais state, Brazil. CONCLUSIONS: We show that, compared to the other single-objective algorithms, multi-objective scans present better performance, regarding power, sensitivity and positive predicted value. The multi-objective non-connectivity scan is faster and better suited for the detection of moderately irregularly shaped clusters. The multi-objective cohesion scan is most effective for the detection of highly irregularly shaped clusters.

Multivariate scan statistics for disease surveillance.

In disease surveillance, there are often many different data sets or data groupings for which we wish to do surveillance. If each data set is analysed separately rather than combined, the statistical power to detect an outbreak that is present in all data sets may suffer due to low numbers in each. On the other hand, if the data sets are added by taking the sum of the counts, then a signal that is primarily present in one data set may be hidden due to random noise in the other data sets. In this paper, we present an extension of the spatial and space-time scan statistic that simultaneously incorporates multiple data sets into a single likelihood function, so that a signal is generated whether it occurs in only one or in multiple data sets. This is done by defining the combined log likelihood as the sum of the individual log likelihoods for those data sets for which the observed case count is more than the expected. We also present another extension, where the concept of combining likelihoods from different data sets is used to adjust for covariates. Using data from the National Bioterrorism Syndromic Surveillance Demonstration Project, we illustrate the new method using physician telephone calls, regular physician visits and urgent care visits by Harvard Pilgrim Health Care members cared for by Harvard Vanguard Medical Associates, a large multi-specialty group practice in Massachusetts. For upper and lower gastrointestinal (GI) illness, there were on average 20 telephone calls, nine urgent care visits and 22 regular physician visits per day. The strongest signal was generated by a single data set and due to a familial outbreak of pinworm disease. The second and third strongest signals were generated by the combined strength of two of the three data sets.

A workflow spatial scan statistic.

We propose a modification of the spatial scan statistic that takes account of workflow, which is the movement of individuals between home and work. The objective is to detect clusters of disease in situations where exposure occurs in the workplace, but only home address is available for analysis. In these situations, application of the usual spatial scan statistic does not account for possible differences between home and work address, thereby reducing the power of detection. We describe an extension to the usual spatial scan statistic that uses workflow data to search for disease clusters resulting from workplace exposure. We also present results from simulations that demonstrate the increased power of the workflow scan statistic over the usual scan statistic for detecting clusters arising from exposures in the workplace.

An elliptic spatial scan statistic.

The spatial scan statistic is commonly used for geographical disease cluster detection, cluster evaluation and disease surveillance. The most commonly used shape of the scanning window is circular. In this paper we explore an elliptic version of the spatial scan statistic, using a scanning window of variable location, shape (eccentricity), angle and size, and with and without an eccentricity penalty. The method is applied to breast cancer mortality data from Northeastern United States and female oral cancer mortality in the United States. Power comparisons are made with the circular scan statistic.