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Objective. To assess how relevant the flow of people between communities is, compared to vaccination and type of vector, on the spread and potential outbreaks of yellow fever in a disease-free host community. Methods. Using a SEIRV-SEI model for humans and vectors, we applied numerical simulations to the scenarios: (1) migration from an endemic community to a disease-free host community, comparing the performance of Haemagogus janthinomys and Aedes aegypti as vectors; (2) migration through a transit community located on a migratory route, where the disease is endemic, to a disease-free one; and (3) effects of different vaccination rates in the host community, considering the vaccination of migrants upon arrival. Results. Results show no remarkable differences between scenarios 1 and 2. The type of vector and vaccination coverage in the host community are more relevant for the occurrence of outbreaks than migration rates, with H. janthinomys being more effective than A. aegypti. Conclusions. With vaccination being more determinant for a potential outbreak than migration rates, vaccinating migrants on arrival may be one of the most effective measures against yellow fever. Furthermore, H. janthinomys is a more competent vector than A. aegypti at similar densities, but the presence of A. aegypti is a warning to maintain vaccination above recommended levels.

OBJECTIVE: To develop a Charlson-like comorbidity index based on clinical conditions and weights of the original Charlson comorbidity index. METHODS: Clinical conditions and weights were adapted from the International Classification of Diseases, 10th revision and applied to a single hospital admission diagnosis. The study included 3,733 patients over 18 years of age who were admitted to a public general hospital in the city of Rio de Janeiro, southeast Brazil, between Jan 2001 and Jan 2003. The index distribution was analyzed by gender, type of admission, blood transfusion, intensive care unit admission, age and length of hospital stay. Two logistic regression models were developed to predict in-hospital mortality including: a) the aforementioned variables and the risk-adjustment index (full model); and b) the risk-adjustment index and patient's age (reduced model). RESULTS: Of all patients analyzed, 22.3% had risk scores >1, and their mortality rate was 4.5% (66.0% of them had scores >1). Except for gender and type of admission, all variables were retained in the logistic regression. The models including the developed risk index had an area under the receiver operating characteristic curve of 0.86 (full model), and 0.76 (reduced model). Each unit increase in the risk score was associated with nearly 50% increase in the odds of in-hospital death. CONCLUSIONS: The risk index developed was able to effectively discriminate the odds of in-hospital death which can be useful when limited information is available from hospital databases.OBJETIVO: Desarrollar un índice de co-morbilidad a partir de las condiciones clínicas y de los pesos de índice original de co-morbilidad de Charlson. MÉTODOS: Las condiciones clínicas y pesos del índice de Charlson fueron adaptados según la Clasificación Internacional de Enfermedades - 10a Revisión, y aplicados al diagnóstico principal de internación hospitalaria. Fueron estudiados 3.733 pacientes arriba de 18 años hospitalizados en el hospital general público del municipio de Rio de Janeiro (sudeste de Brasil), de 2001-2003. La distribución del índice fue de acuerdo con el género, tipo de admisión, presencia de transfusión de sangre, admisión a la unidad de terapia intensiva, edad y tiempo de internación. Dos modelos de regresión logística fueron desarrollados con el objetivo de prevenir la mortalidad hospitalaria: a) con las variables arriba y el índice de co-morbilidad (modelo completo); y b) conteniendo solo el índice y la edad de los pacientes (modelo reducido). RESULTADOS: Dentro del total de pacientes analizados, 22,3% tuvieron puntajes >1 para el índice y su taza de mortalidad fue 4,5% (66,0% de los cuales con puntajes >1). A excepción del género y del tipo de admisión todas las variables fueron retenidas en la regresión. Los modelos tuvieron una área bajo la curva característica ROC igual a 0,86 (modelo completo) y 0,76 (modelo reducido). Cada aumento de una unidad en los puntajes del índice fue asociado con un aumento de casi 50% en la probabilidad de mortalidad hospitalaria. CONCLUSIONES: El índice desarrollado puede discriminar probabilidades de mortalidad con una eficacia aceptable, el que puede ser útil al ser usado con bancos de datos hospitalarios con información limitada.OBJETIVO: Desenvolver um índice de co-morbidade a partir das condições clínicas e dos pesos do índice de co-morbidade de Charlson. MÉTODOS: As condições clínicas e pesos do índice de Charlson foram adaptados segundo a Classificação Internacional de Doenças 10a Revisão, e aplicados ao diagnóstico principal de internação hospitalar. Foram estudados 3.733 pacientes acima de 18 anos hospitalizados em hospital geral público do município do Rio de Janeiro, RJ, 2001-2003. A distribuição do índice foi de acordo com o gênero, tipo da admissão, presença de transfusão de sangue, admissão à unidade de terapia intensiva, idade e tempo de internação. Dois modelos de regressão logística foram desenvolvidos com o objetivo de prever a mortalidade hospitalar desses pacientes: a) com as variáveis acima e o índice de co-morbidade (modelo completo); e b) contendo apenas o índice e a idade dos pacientes (modelo reduzido). RESULTADOS: Dentre o total de pacientes analisados, 22,3% possuíam escores >1 para o índice e sua taxa de mortalidade foi 4,5% (66,0% dos quais com escores >1). Exceto gênero e do tipo de admissão, todas as variáveis foram retidas na regressão. Os modelos tiveram uma área sob a curva característica ROC igual a 0,86 (modelo completo) e 0,76 (modelo reduzido). Cada aumento de uma unidade nos escores do índice foi associado com um aumento de quase 50% na probabilidade de mortalidade hospitalar. CONCLUSÕES: O índice desenvolvido pôde discriminar probabilidades de mortalidade com uma eficácia aceitável, o que pode ser útil ao lidar-se com bancos de dados hospitalares com informação limitada.

Introduction: Epidemiological models have been widely used during the COVID-19 pandemic, although performance evaluation has been limited. The objective of this work was to thoroughly evaluate a SEIR model used for the short-term (1 to 3 weeks) prediction of cases, quantifying its actual past performance, and its potential performance by optimizing the model parameters., Methods: Daily case forecasts were obtained for the first wave of cases (July 31, 2020 to March 11, 2021) in the district of General Pueyrredón (Argentina), quantifying the model performance in terms of uncertainty, inaccuracy and imprecision. The evaluation was carried out with the original parameters of the model (used in the forecasts that were published), and also varying different parameters in order to identify optimal values., Results: The analysis of the model performance showed that alternative values of some parameters, and the correction of the input values using a "moving average" filter to eliminate the weekly variations in the case reports, would have yielded better results. The model with the optimized parameters was able to reduce the uncertainty from almost 40% to less than 15%, with similar values of inaccuracy, and with slightly greater imprecision., Discussion: Simple epidemiological models, without large requirements for their implementation, can be very useful for making quick decisions in small cities or cities with limited resources, as long as the importance of their evaluation is taken into account and their scope and limitations are considered.

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.

Objective: To develop a prognostic SIR model of the COVID-19 pandemic in Colombia., Materials and Methods: A SIR model with a deterministic approach was used to forecast the development of the COVID-19 pandemic in Colombia. The states considered were susceptible (S), infectious (i) and recovered or deceased (R). Population data were obtained from the National Administrative Department of Statistics (DANE) - Population Projections 2018-2020, released in January 2020-, and data on daily confirmed cases of COVID-19 from the National Institute of Health. Different models were proposed varying the basic reproduction number (R0)., Results: Based on the cases reported by the Ministry of Health, 4 simulated environments were created in an epidemiological SIR model. The time series was extended until May 30, the probable date when 99% of the population will be infected. R0=2 is the basic reproduction number and the closest approximation to the behavior of the pandemic during the first 15 days since the first case report; the worst scenario would occur in the first week of April with R0=3., Conclusions: Further mitigation and suppression measures are necessary in the containment and sustained transmission phases, such as increased diagnostic capacity through testing and disinfection of populated areas and homes in isolation.