[Quantitative evaluation of a SEIR model for forecasting COVID-19 cases]. / Evaluación cuantitativa de un modelo SEIR para predecir casos de COVID-19.

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.

Introducción: Los modelos epidemiológicos han sido ampliamente utilizados durante la pandemia de COVID-19, aunque la evaluación de su desempeño ha sido limitada. El objetivo del presente trabajo fue evaluar de forma retrospectiva un modelo SEIR para la predicción de casos a corto plazo (1 a 3 semanas), cuantificando su desempeño real y potencial, mediante la optimización de los parámetros del modelo. Métodos: Se realizaron proyecciones para cada día de la primera ola de casos (31 de julio de 2020 al 11 de marzo de 2021) en el municipio de General Pueyrredón (Argentina), cuantificando el desempeño del modelo en términos de incertidumbre, inexactitud e imprecisión. La evaluación se realizó con los parámetros originales del modelo (utilizados en proyecciones que fueron oportunamente publicadas), y luego variando distintos parámetros a fin de identificar valores óptimos. Resultados: El análisis del desempeño del modelo mostró que valores alternativos de algunos parámetros, y la corrección de los valores de entrada utilizando un filtro de "media móvil" para eliminar las variaciones semanales en los reportes de casos, habrían otorgado mejores resultados. El modelo con los parámetros optimizados logró disminuir desde casi 40% a menos de 15% la incertidumbre, con valores similares de inexactitud, y con una imprecisión levemente mayor. Discusión: Modelos epidemiológicos sencillos, sin grandes requerimientos para su implementación, pueden ser de utilidad para la toma de decisiones rápidas en localidades pequeñas o con recursos limitados, siempre y cuando se tenga en cuenta la importancia de su evaluación y la consideración de sus alcances y limitaciones.

Evaluación cuantitativa de un modelo SEIR para predecir casos de COVID-19 / Quantitative evaluation of a SEIR model for forecasting COVID-19 cases

Resumen Introducción : Los modelos epidemiológicos han sido ampliamente utilizados durante la pandemia de COVID-19, aunque la evaluación de su desempeño ha sido limitada. El objetivo del presente trabajo fue evaluar de forma retrospectiva un modelo SEIR para la predicción de casos a corto plazo (1 a 3 semanas), cuantificando su desempeño real y potencial, me diante la optimización de los parámetros del modelo. Métodos : Se realizaron proyecciones para cada día de la primera ola de casos (31 de julio de 2020 al 11 de marzo de 2021) en el municipio de General Pueyrredón (Argentina), cuantificando el desempeño del modelo en términos de incertidumbre, inexactitud e imprecisión. La evaluación se realizó con los parámetros originales del modelo (utilizados en proyecciones que fueron oportunamente publicadas), y luego variando distintos parámetros a fin de identificar valores óptimos. Resultados : El análisis del desempeño del modelo mostró que valores alternativos de algunos parámetros, y la corrección de los valores de entrada utilizando un filtro de "media móvil" para eliminar las variaciones semanales en los reportes de casos, habrían otorgado mejores resultados. El modelo con los parámetros opti mizados logró disminuir desde casi 40% a menos de 15% la incertidumbre, con valores similares de inexactitud, y con una imprecisión levemente mayor. Discusión : Modelos epidemiológicos sencillos, sin grandes requerimientos para su implementación, pue den ser de utilidad para la toma de decisiones rápi das en localidades pequeñas o con recursos limitados, siempre y cuando se tenga en cuenta la importancia de su evaluación y la consideración de sus alcances y limitaciones.

Abstract Introduction : Epidemiological models have been widely used during the COVID-19 pandemic, although performance evaluation has been limited. The objec tive of this work was to thoroughly evaluate a SEIR model used for the short-term (1 to 3 weeks) predic tion 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, inac curacy 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 "mov ing 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.

Open Science Drone Toolkit: Open source hardware and software for aerial data capture.

Despite the increased access to scientific publications and data as a result of open science initiatives, access to scientific tools remains limited. Uncrewed aerial vehicles (UAVs, or drones) can be a powerful tool for research in disciplines such as agriculture and environmental sciences, but their use in research is currently dominated by proprietary, closed source tools. The objective of this work was to collect, curate, organize and test a set of open source tools for aerial data capture for research purposes. The Open Science Drone Toolkit was built through a collaborative and iterative process by more than 100 people in five countries, and comprises an open-hardware autonomous drone and off-the-shelf hardware, open-source software, and guides and protocols that enable the user to perform all the necessary tasks to obtain aerial data. Data obtained with this toolkit over a wheat field was compared to data from satellite imagery and a commercial hand-held sensor, finding a high correlation for both instruments. Our results demonstrate the possibility of capturing research-grade aerial data using affordable, accessible, and customizable open source software and hardware, and using open workflows.

Biomass Allocation Patterns Are Linked to Genotypic Differences in Whole-Plant Transpiration Efficiency in Sunflower.

Increased transpiration efficiency (the ratio of biomass to water transpired, TE) could lead to increased drought tolerance under some water deficit scenarios. Intrinsic (i.e., leaf-level) TE is usually considered as the primary source of variation in whole-plant TE, but empirical data usually contradict this assumption. Sunflower has a significant variability in TE, but a better knowledge of the effect of leaf and plant-level traits could be helpful to obtain more efficient genotypes for water use. The objective of this study was, therefore, to assess if genotypic variation in whole-plant TE is better related to leaf- or plant-level traits. Three experiments were conducted, aimed at verifying the existence of variability in whole-plant TE and whole-plant and leaf-level traits, and to assess their correlation. Sunflower public inbred lines and a segregating population of recombinant inbred lines were grown under controlled conditions and subjected to well-watered and water-deficit treatments. Significant genotypic variation was found for TE and related traits. These differences in whole-plant transpiration efficiency, both between genotypes and between plants within each genotype, showed no association to leaf-level traits, but were significantly and negatively correlated to biomass allocation to leaves and to the ratio of leaf area to total biomass. These associations are likely of a physiological origin, and not only a consequence of genetic linkage in the studied population. These results suggest that genotypic variation for biomass allocation could be potentially exploited as a source for increased transpiration efficiency in sunflower breeding programmes. It is also suggested that phenotyping for TE in this species should not be restricted to leaf-level measurements, but also include measurements of plant-level traits, especially those related to biomass allocation between photosynthetic and non-photosynthetic organs.

Genetic variability for leaf growth rate and duration under water deficit in sunflower: analysis of responses at cell, organ, and plant level.

Plants under water deficit reduce leaf growth, thereby reducing transpiration rate at the expense of reduced photosynthesis. The objective of this work was to analyse the response of leaf growth to water deficit in several sunflower genotypes in order to identify and quantitatively describe sources of genetic variability for this trait that could be used to develop crop varieties adapted to specific scenarios. The genetic variability of the response of leaf growth to water deficit was assessed among 18 sunflower (Helianthus annuus L.) inbred lines representing a broad range of genetic diversity. Plants were subjected to long-term, constant-level, water-deficit treatments, and the response to water deficit quantified by means of growth models at cell-, leaf-, and plant-scale. Significant variation among lines was found for the response of leaf expansion rate and of leaf growth duration, with an equal contribution of these responses to the variability in the reduction of leaf area. Increased leaf growth duration under water deficit is usually suggested to be caused by changes in the activity of cell-wall enzymes, but the present results suggest that the duration of epidermal cell division plays a key role in this response. Intrinsic genotypic responses of rate and duration at a cellular scale were linked to genotypic differences in whole-plant leaf area profile to water deficit. The results suggest that rate and duration responses are the result of different physiological mechanisms, and therefore capable of being combined to increase the variability in leaf area response to water deficit.