Significance This paper compares the probabilistic accuracy of short-term forecasts of reported deaths due to COVID-19 during the first year and a half of the pandemic in the United States. Results show high variation in accuracy between and within stand-alone models and more consistent accuracy from an ensemble model that combined forecasts from all eligible models. This demonstrates that an ensemble model provided a reliable and comparatively accurate means of forecasting deaths during the COVID-19 pandemic that exceeded the performance of all of the models that contributed to it. This work strengthens the evidence base for synthesizing multiple models to support public-health action.

Daily urinary calcium excretion in renal stone-forming subjects is shown to vary directly with moderate changes in dietary sodium intake. The changes produced are sufficient to alter the basic diagnostic classification from 'hypercalciuric' to 'normocalciuric' because dietary sodium is reduced from 200 to 80 mM/day. Similar changes were observed in fasting morning 'spot' urine samples, resulting in alteration of diagnostic subclassification between so-called 'absorptive' and 'renal' categories, in the absence of demonstrable change in parathyroid function. Diagnostic and therapeutic studies in stone-forming subjects require control of both dietary calcium and dietary sodium if misinterpretations are to be avoided. Habitual high sodium intake may be an etiological factor in the generation of excessive excretion of calcium, sodium, and phosphate--the hypercalciuria syndrome.

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multi-model ensemble forecast that combined predictions from dozens of different research groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-week horizon 3-5 times larger than when predicting at a 1-week horizon. This project underscores the role that collaboration and active coordination between governmental public health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks. Significance Statement This paper compares the probabilistic accuracy of short-term forecasts of reported deaths due to COVID-19 during the first year and a half of the pandemic in the US. Results show high variation in accuracy between and within stand-alone models, and more consistent accuracy from an ensemble model that combined forecasts from all eligible models. This demonstrates that an ensemble model provided a reliable and comparatively accurate means of forecasting deaths during the COVID-19 pandemic that exceeded the performance of all of the models that contributed to it. This work strengthens the evidence base for synthesizing multiple models to support public health action.

1. Sixty healthy subjects aged 3-years (thirty men and thirty women) were randomly selected from electoral registers to participate in a dietary survey using the 7 d weighed-intake method during June-August 1985.2. Energy intake (MJ/d) was 123 for men and 8.4 for women. Fat contributed 36.0 and 39.1 % of the total energy intake of men and women respectively. When this was adjusted to exclude energy derived from alcoholic beverages, the corresponding values were 38.8 and 39.7 % respectively. The major sources of dietary fat (YO) were spreadable fats (28), meat (23), milk (12) and biscuits and cakes (11).3. The subjects were divided into low-and high-fat groups both on the relative intake of fat (< 35 % or > 40 Yo dietary energy from fat) and on the absolute intake of fat (> or < 120 g fat/d). By either criterion, high-fat consumers had lower than average intakes of low-fat, high-carbohydrate foods such as potatoes, bread, fruit and table sugar, and higher intakes of milk, butter and confectionery products. Meat intake was higher among highfat eaters only when a high-fat diet was defined as a percentage of energy.The Kilkenny Health Project is a community health programme which aims to alter the environment and behaviour of a defined population in order to modify risk factors for coronary heart disease. The 5-year programme began in 1985 with a baseline survey of 770 adults aged 35-64 years. The purpose of this survey was to estimate the knowledge, attitudes and behaviour relevant to the development of coronary heart disease and to measure the baseline levels of risk factors for this disease. While the baseline survey included a food-frequency questionnaire, it was felt that a more comprehensive analysis of eating habits was required from which a strategy for nutrition education could be developed. Accordingly, thirty males and thirty females in the age range 3 5 4 4 years were randomly selected to participate in a 7 d weighed-intake study of eating habits. This survey constitutes the basis of the present paper.