Modelo matemático optimizado para la predicción y planificación de la asistencia sanitaria por la COVID-19 / Mathematical model optimized for prediction and health care planning for COVID-19

Objetivo: La pandemia de la COVID-19 ha supuesto una amenaza de colapso de los servicios hospitalarios y de unidades de cuidado intensivo (UCI), así como una reducción de la dinámica asistencial de pacientes afectados por otras patologías. El objetivo fue desarrollar un modelo matemático diseñado para optimizar las predicciones relacionadas con las necesidades de hospitalización e ingresos en UCI por la COVID-19. Diseño: Estudio prospectivo. Ámbito: Provincia de Granada (España). Pacientes: Pacientes de COVID-19 hospitalizados, ingresados en UCI, recuperados y fallecidos desde el 15 de marzo hasta el 22 de septiembre del 2020. Intervenciones: Desarrollo de un modelo matemático tipo susceptible, expuesto, infectado y recuperado (SEIR) capaz de predecir la evolución de la pandemia, considerando las medidas de salud pública establecidas. Variables de interés: Número de pacientes infectados por SARS-CoV-2, hospitalizados e ingresados en UCI por la COVID-19.Resultados: A partir de los datos registrados, hemos podido desarrollar un modelo matemático que refleja el flujo de la población entre los diferentes grupos de interés en relación con la COVID-19. Esta herramienta permite analizar diferentes escenarios basados en medidas de restricción socio-sanitarias y pronosticar el número de infectados, hospitalizados e ingresados en UCI. Conclusiones: El modelo matemático es capaz de proporcionar predicciones sobre la evolución de la COVID-19 con suficiente antelación como para poder conjugar los picos de prevalencia y de necesidades de asistencia hospitalaria y de UCI, con la aparición de ventanas temporales que posibiliten la atención de enfermos no-COVID (AU)

Objective: The COVID-19 pandemic has threatened to collapse hospital and ICU services, and it has affected the care programs for non-COVID patients. The objective was to develop a mathematical model designed to optimize predictions related to the need for hospitalization and ICU admission by COVID-19 patients. Design: Prospective study. Setting: Province of Granada (Spain). Population: COVID-19 patients hospitalized, admitted to ICU, recovered and died from March 15 to September 22, 2020. Study variables: The number of patients infected with SARS-CoV-2 and hospitalized or admitted to ICU for COVID-19. Results: The data reported by hospitals was used to develop a mathematical model that reflects the flow of the population among the different interest groups in relation to COVID-19. This tool allows to analyse different scenarios based on socio-health restriction measures, and to forecast the number of people infected, hospitalized and admitted to the ICU. Conclusions:The mathematical model is capable of providing predictions on the evolution of the COVID-19 sufficiently in advance as to anticipate the peaks of prevalence and hospital and ICU care demands, and also the appearance of periods in which the care for non-COVID patients could be intensified (AU)

Mathematical model optimized for prediction and health care planning for COVID-19.

OBJECTIVE: The COVID-19 pandemic has threatened to collapse hospital and ICU services, and it has affected the care programs for non-COVID patients. The objective was to develop a mathematical model designed to optimize predictions related to the need for hospitalization and ICU admission by COVID-19 patients. DESIGN: Prospective study. SETTING: Province of Granada (Spain). POPULATION: COVID-19 patients hospitalized, admitted to ICU, recovered and died from March 15 to September 22, 2020. STUDY VARIABLES: The number of patients infected with SARS-CoV-2 and hospitalized or admitted to ICU for COVID-19. RESULTS: The data reported by hospitals was used to develop a mathematical model that reflects the flow of the population among the different interest groups in relation to COVID-19. This tool allows to analyse different scenarios based on socio-health restriction measures, and to forecast the number of people infected, hospitalized and admitted to the ICU. CONCLUSIONS: The mathematical model is capable of providing predictions on the evolution of the COVID-19 sufficiently in advance as to anticipate the peaks of prevalence and hospital and ICU care demands, and also the appearance of periods in which the care for non-COVID patients could be intensified.

Mathematical model optimized for prediction and health care planning for COVID-19. / Modelo matemático optimizado para la predicción y planificación de la asistencia sanitaria por la COVID-19.

OBJECTIVE: The COVID-19 pandemic has threatened to collapse hospital and ICU services, and it has affected the care programs for non-COVID patients. The objective was to develop a mathematical model designed to optimize predictions related to the need for hospitalization and ICU admission by COVID-19 patients. DESIGN: Prospective study. SETTING: Province of Granada (Spain). POPULATION: COVID-19 patients hospitalized, admitted to ICU, recovered and died from March 15 to September 22, 2020. STUDY VARIABLES: The number of patients infected with SARS-CoV-2 and hospitalized or admitted to ICU for COVID-19. RESULTS: The data reported by hospitals was used to develop a mathematical model that reflects the flow of the population among the different interest groups in relation to COVID-19. This tool allows to analyse different scenarios based on socio-health restriction measures, and to forecast the number of people infected, hospitalized and admitted to the ICU. CONCLUSIONS: The mathematical model is capable of providing predictions on the evolution of the COVID-19 sufficiently in advance as to anticipate the peaks of prevalence and hospital and ICU care demands, and also the appearance of periods in which the care for non-COVID patients could be intensified.

Review of the Philippine taxa formerly assigned to the genus Amphicnemis Selys, II. Genus Sangabasis with descriptions of eight new species (Odonata: Coenagrionidae).

The Philippine genus Sangabasis Villanueva is reviewed. Eight new species are described: S. bukid sp. nov., S. bulba sp. nov., S. cahilogi sp. nov., S. carmelae sp. nov., S. feliculoi sp. nov., S. hamis sp. nov., S. janvantoli sp. nov., and S. zamboanga sp. nov.

Analysing the effect of public health campaigns on reducing excess weight: a modelling approach for the Spanish Autonomous Region of the Community of Valencia.

Excess weight is fast becoming a serious health concern in the developed and developing world. The concern of the public health sector has lead to the development of public health campaigns, focusing on two-fold goals: to inform the public as to the health risks inherent in being overweight, and the benefits of a change in nutritional behaviour. Recent studies indicate that the effects of the average public health campaign on the target community is around 5%. In this study we aim to quantify the effect of different public health campaigns on lifestyle behaviour in the target populations in order to bring about weightloss in a significant number of people over the next few years. This study is based on recent works that consider excess weight as a consequence of the transmission of unhealthy lifestyles from one individual to another. Following this point of view, first a mathematical model is presented. Then, policies based on public health campaigns addressed to stop people gaining weight (prevention; this type of policy acts on individuals in order to maintain their weight and to stop an increase in weight) and, policies addressed to overweight individuals to reduce their weight (treatment; these campaigns act on overweight and/or obese individuals in order to reduce their weight) are simulated in order to evaluate their effectiveness. The study concludes that combination of preventive plus treatment campaigns are more effective than considering them separately.

Mathematical modelling of respiratory syncytial virus (RSV): vaccination strategies and budget applications.

We propose an age-structured mathematical model for respiratory syncytial virus in which children aged <1 year are especially considered. Real data on hospitalized children in the Spanish region of Valencia were used in order to determine some seasonal parameters of the model. Weekly predictions of the number of children aged <1 year that will be hospitalized in the following years in Valencia are presented using this model. Results are applied to estimate the regional cost of paediatric hospitalizations and to perform a cost-effectiveness analysis of possible vaccination strategies.

CASANDRA: a prototype implementation of a system of network progressive transmission of medical digital images.

In this paper, a prototype for progressive transmission of medical digital 2D images through the network, called CASANDRA, is presented. The prototype consists of the server part and the client part. In the server part, the images are acquired, stored, computed their wavelet transform and the wavelet coefficients stored, then transmitted progressively, when required, via TCP to the client. In the client part, with the inverse wavelet transform, the received wavelet coefficients are used to build successive improved reconstructions of the image. This prototype has been implemented and is being tested in the Radiotherapy Service of the Valencia University Hospital (Valencia, Spain).