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Projects such as the European Covid-19 Forecast Hub publish forecasts on the national level for new deaths, new cases, and hospital admissions, but not direct measurements of hospital strain like critical care bed occupancy at the sub-national level, which is of particular interest to health professionals for planning purposes. We present a sub-national French framework for forecasting hospital strain based on a non-Markovian compartmental model, its associated online visualisation tool and a retrospective evaluation of the real-time forecasts it provided from January to December 2021 by comparing to three baselines derived from standard statistical forecasting methods (a naive model, auto-regression, and an ensemble of exponential smoothing and ARIMA). In terms of median absolute error for forecasting critical care unit occupancy at the two-week horizon, our model only outperformed the naive baseline for 4 out of 14 geographical units and underperformed compared to the ensemble baseline for 5 of them at the 90% confidence level (n = 38). However, for the same level at the 4 week horizon, our model was never statistically outperformed for any unit despite outperforming the baselines 10 times spanning 7 out of 14 geographical units. This implies modest forecasting utility for longer horizons which may justify the application of non-Markovian compartmental models in the context of hospital-strain surveillance for future pandemics. Author summary: The US and European Covid-19 Forecast Hubs focus on metrics such as deaths, new cases, and hospital admissions, but do not offer measurements of hospital strain like critical care bed occupancy, which was essential for the provisioning of healthcare resources during the COVID-19 pandemic. Furthermore, forecasting support was only guaranteed on the national level leaving many countries to look elsewhere for valuable sub-national forecasts. In France statistical modelling approaches were proposed to anticipate hospital stain at the sub-national level but these were limited by a two-week forecast horizon. We present a sub-national French modelling framework and online application for anticipating hospital strain at the four-week horizon that can account for abrupt changes in key epidemiological parameters. It was the only publicly available real-time non-Markovian mechanistic model for the French epidemic when implemented in January 2021 and, to our knowledge, it still was at the time it stopped in early 2022. Further adaptations of this surveillance system can serve as an anticipation tool for hospital strain across sub-national localities to aid in the prevention of short-noticed ward closures and patient transfers. [ABSTRACT FROM AUTHOR]

A simple design-based approach to estimating net change of a forest attribute such as timber volume is to observe the change directly on the plot level and then make that the response variable of interest using established estimation techniques such as multiphase regression estimators. This direct approach is only possible for inventories with permanent plots and is constrained to estimating net change over time periods matching the duration of the remeasurement cycle. Indirect estimation involves applying one of the aforementioned techniques to estimate the state at two time points and taking their difference. Indirect methods, although less common, are not necessarily constrained to permanent plots and can estimate net change over any desired time span for annual designs. This article compares design-based direct and indirect regression estimators under the Monte Carlo approach and illustrates their performances with data from the Swiss National Forest Inventory. The major finding is that direct estimation should be preferred whenever change is observable directly on the plot level but that multiphase indirect estimation can still improve precision when direct estimation is not possible such as for inventories employing only temporary plots. Key words: design based, Monte Carlo approach, three-phase regression estimation, direct and indirect net change estimation, forest inventory. Une methode simple pour estimer la variation nette d'un attribut comme le materiel sur pied est d'observer directement cette variation au niveau de la placette, puis de s'en servir comme variable cible dans le cadre d'estimateurs multiphases. Cette approche directe n'est possible qu'avec des placettes permanentes et ou l'intervalle entre deux mesures correspond au cycle complet d'inventaire. L'approche indirecte consiste a estimer l'etat de l'attribut en deux instants donnes et de prendre leur difference comme estimateur de la variation nette. Cette technique est aussi applicable avec des placettes temporaires et permet dans le cadre d'inventaires annuels d'estimer la variation sur une periode arbitraire. Cet article decrit les methodes directes et indirectes dans le cadre d'estimateurs par regression et de l'inference sous le plan de sondage dans le formalisme Monte-Carlo. L'article illustre les performances de plusieurs estimateurs avec des donnees de l'inventaire Forestier National Suisse. Le resultat principal est que la methode directe est preferable pour autant qu'elle soit possible, dans le cas contraire la precision de l'estimation peut etre neanmoins amelioree en utilisant les estimateurs par regression multiphases. Mots-cles: inference sous le plan de sondage, formalisme Monte-Carlo, estimateur par regression a trois phases, estimation directe et indirecte de la variation nette, inventaire forestier., 1. Introduction In 2009, the Swiss National Forest Inventory (NFI) changed from a periodic design, conducted approximately every 10 years based on two-phase sampling with manually interpreted aerial stereophotographs on [...]

This paper compares design-based properties of the classical two-phase regression estimator with several nonparametric kernel-based estimators of which k nearest neighbors (kNN) is a special case. Metrics are based on the Euclidean distance applied to either a multidimensional space of explanatory variables or to a one-dimensional space of predictions obtained from a linear model. The main concepts of kernel-based regression estimators are reformulated in the design-based Monte Carlo approach to forest inventory. The results, based on a case study of a forest inventory in Switzerland and extensive simulations, suggest that the commonly used analytical external variance formula may systematically underestimate the true variance for a variety of kernel-based estimators including kNN but that it is still adequate for the classical regression estimator. Although using a bootstrap variance can help to correct this underestimation, it was also found that the bootstrap variance estimates could be unstable if the optimal bandwidth is recalculated in each bootstrap sample. These findings suggest that if the model captures the main features of the underlying process, then it is advisable to use the classical regression estimator, because it performs at least as well as the other techniques and is by far simpler to implement. Key words: two-phase forest inventory, kNN, regression estimator, kernel regression, design based. Cet article compare plusieurs estimateurs non parametriques bases soit sur des noyaux, soit sur la methode des proches voisins (kNN), en utilisant des metriques euclidiennes dans l'espace multidimensionel des variables explicatives ou l'espace unidimensionel des predictions obtenues par un modele lineaire. En particulier on considere de nouveaux estimateurs obtenus par l'estimation de l'esperance conditionnelle de la variable reponse donne sa prediction, et ce avec plans de sondage a deux phases pour inventaires forestiers, en utilisant le formalisme Monte Carlo et d'inference statistique uniquement sous le plan (design-based model-assisted). Une etude de cas et une simulation montrent que la formule usuelle de la variance externe sous-estime, parfois largement, la variance empirique pour tous les estimateurs excepte l'estimateur classique par regression. La technique d'estimation de la variance par re-echantillonage permet de remedier a ce defaut mais elle requiert un choix judicieux entre diverses options possibles. Les resultats suggerent que si le modele lineaire est adequat l'estimateur classique est au moins aussi performant que les autres et bien plus simple a mettre en oeuvre. Il semblerait que ce soit le cas pour l'estimation du materiel sur pied en utilisant comme variables explicatives les caracteristiques d'ecrivant la canopee accessibles aujourd'hui par les techniques de teledetection comme LiDAR. Mots-cles: inventaire forestier a deux phases, methode des k plus proches voisins, estimateur par regression, estimateur par noyau, inference sous le plan de sondage., 1. Introduction Design-based inference has long been a standard for estimation in forest inventory programs. This is largely due to the fact that one does not need to believe that [...]

In 2009, the Swiss National Forest Inventory (NFI) turned from a periodic into an annual measurement design in which only one-ninth of the overall sample of permanent plots is measured every year. The reduction in sample size due to the implementation of the annual design results in an unacceptably large increase in variance when using the standard simple random sampling estimator. Thus, a flexible estimation procedure using two- and three-phase regression estimators is presented with a special focus on utilizing updating techniques to account for disturbances and growth and is applied to the second and third Swiss NFIs. The first phase consists of a dense sample of systematically distributed plots on a 500 m x 500 m grid for which auxiliary variables are obtained through the interpretation of aerial photographs. The second phase is an eightfold looser subgrid with terrestrial plot data collected from the past inventory, and the third and final phase consists of the three most recent annual subgrids with the current state of the target variable (stem volume). The proposed three-phase estimators reduce the increase in variance from 294% to 145% compared with the estimator based on the full periodic sample while remaining unbiased. Key words: national forest inventories, three-phase regression, annual (paneled) inventory design, Swiss National Forest Inventory, remote sensing and previous measurement. L'inventaire Forestier National Suisse a change en 2009 d'un systeme periodique (inventaire complet chaque 10 ans) a un releve continu annuel par placettes permanentes sur un neuvieme de l'echantillon total. La taille reduite de l'echantillon annuel conduit a une erreur inacceptablement grande de l'estimateur base sur l'echantillonnage aleatoire simple. Pour remedier a ce probleme nous proposons des estimateurs par regression utilisant un plan de sondage a deux ou trois phase et tres flexibles. La variable cible consideree dans cet article est le volume sur pied. La premiere phase repose sur un reseau dense de photographies aeriennes (500 m x 500 m), dont l'interpretation permet d'obtenir des variables explicatives sur la canopee (en particulier concernant les interventions), la deuxieme phase consiste en placettes terrestres sur un sous-reseau (un huitieme) de la premiere phase, dont les valeurs actualisees servent de variable explicative tres performante, la troisieme et derniere phase consiste des placettes avec les trois plus recentes donnees annuelles. La technique proposee permet d'obtenir des estimateurs sans biais et de reduire l'augmentation de la variance due au changement de systeme de 294 a 145 %. Mots-cles: Inventaire forestier, estimation par regression avec plan d'echantillonnage a trois phases, Inventaire Forestier National Suisse avec releve annuel, teledetection, actualisation de mesures anterieures., 1. Introduction The fourth Swiss National Forest Inventory (NFI) is the first Swiss NFI to implement an annual measurement design in which one-ninth of its permanent remeasured terrestrial plots are [...]

In the context of Poisson sampling, numerous adjustments to classical estimators have been proposed that are in-tended to compensate for inflated variance due to random sample size. However, such adjustments have never been applied to extensive forest inventories. This work investigates the performances of four estimators for the timber volume in one-phase two-stage forest inventories, where trees in the first stage are selected, at the plot level, by concentric circles or angle-count methods and a subset thereof are selected by Poisson sampling for further measurements to get a better estimation. The original two-stage estimator is the sum of two components: the first is the mean of Horwitz--Thompson estimators using simple volume approximations, based on diameter and species alone, of all first-stage trees in each inventory plot, and the second is the mean of Horwitz--Thompson estimators based on the differences between the simple volume approximations and refined volume determinations based on further diameter and height measurements on the second-stage trees within each inventory plot. This two-stage estimator is particularly useful because it provides unbiased estimates even if the simple prediction model is not correct, which is particularly important for small area estimation. The other three estimators rely on adjustments of the second component of the original estimator that are adapted from estimators proposed in the literature by L.R. Grosenbaugh and C.-E. Sandal. It turns out that these adjustments introduce a negligible bias and that the original simple estimator performs just as well or even better than the new estimators with respect to the variance. Resume: Plusieurs ajustements des estimateurs classiques ont ete proposes dans le cadre d'echantillonage de Poisson afin de compenser l'augmentation de la variance due a la taille aleatoire de l'echantillon. Toutefois, aucun de ces ajustements n'ont ete consideres dans le cadre des inventaires forestiers extensifs. Cet article analyse les performances de divers estima-teurs dans le contexte d'inventaires forestiers a une phase et deux degres, dans lesquels les arbres sont echantillonnes, au ni-veau de la placette, par cercles concentriques ou comptage angulaire pour le premier degre et par un tirage de Poisson pour le deuxiee degre. L'estimateur originel est la somme de deux composantes: la premiee est la moyenne d'estimateurs Hor-witz--ompson bases sur des approximations simples du volume n'utilisant que le diamere et l'espee de tous les arbres du premier degre dans chaque placette d'inventaire, et la deuxieme est la moyenne d'estimateurs Horwitz--ompson utili-sant les differences entre approximations simples et approximations affinees requerant des mesures supplementaires de dia-mere et de hauteur sur les arbres du second degre dans chaque placette. Cet estimateur permet d'obtenir des estimations sans biais, mee si le modee approximatif est incorrect, ce qui est particulieement utile pour les estimations locales. Les trois autres estimateurs s'inspirent d'ajustements proposes dans la litterature par L.R. Grosenbaugh et C.E. Sandal. Il s'a-vee que ces ajustements introduisent un biais negligeable mais que les performances, en terme de variance, de l'estimateur originel sont aussi bonnes, voire meilleures, que celles des nouveaux estimateurs., Introduction In forest inventory and other fields of application of sampling techniques, costs to measure the response variable [Y.sub.i] are high. A commonly used approach, when obtaining [Y.sub.i] be-comes prohibitively [...]

Presents correspondence between medical practitioners as of November 9, 2002. Need for alcohol-based hand gels and hand hygiene in hospitals; Consideration of the postmortem diagnosis of testicular cancer; Use of galantamine to treat vascular dementia; Others.