scholarly journals Future Alcohol-Attributable Mortality in France Using a Novel Generalizable Age-Period-Cohort Projection Methodology

2020 ◽  
Author(s):  
Sergi Trias-Llimós ◽  
Anastasios Bardoutsos ◽  
Fanny Janssen
Keyword(s):  
Prediction Interval ◽  
Period Change ◽  
Attributable Mortality ◽  
Model Parameters ◽  
Baseline Scenario ◽  
Policy Measures ◽  
Cirrhosis Mortality ◽  
Cohort Change ◽  
Preventive Policy ◽  
Age And Sex

Abstract Aim To forecast age- and sex-specific alcohol-attributable mortality in France for the period 2015–2050 using a novel generalizable methodology that includes different scenarios regarding period and cohort change. Methods For the French national population aged 25–90 years (1979–2014), we estimated alcohol-attributable mortality by mortality from the main causes of death wholly attributable to alcohol, plus liver cirrhosis mortality. We modelled sex-specific alcohol-attributable mortality by adjusting for age, period and birth cohort. We forecasted the model parameters to obtain future age- and sex-specific alcohol-attributable mortality up until 2050 using a conventional baseline, scenario I (favourable period change) and scenario II (unfavourable cohort change). Results Alcohol-attributable mortality is clearly declining in France, with the decline decelerating from 1992 onwards. In 2014, the age-standardized alcohol-attributable mortality rates, in deaths per 100,000, were 34.7 among men and 9.9 among women. In 2050, the estimated rates are between 10.5 (prediction interval: 7.6–14.4; scenario I) and 17.6 (13.1–23.7; scenario II) among men, and between 1.1 (0.7–1.7; scenario I) and 1.8 (1.2–2.9; scenario II) among women; which implies declines of 58% for men and 84% for women (baseline). Conclusion Alcohol-attributable mortality in France is expected to further decline in the coming decades, accompanied by age pattern changes. However, France’s levels are not expected to reach the current lower levels in Italy and Spain for 15 years or more. Our results point to the value of implementing preventive policy measures that discourage alcohol consumption among people of all ages, but especially among adolescents.

scholarly journals Future alcohol-attributable mortality in France using a novel generalizable age-period-cohort projection methodology

2020 ◽  
Author(s):  
Sergi Trias-Llimós ◽  
Anastasios Bardoutsos ◽  
Fanny Janssen
Keyword(s):  
Public Health ◽  
Prediction Interval ◽  
Attributable Mortality ◽  
Public Health Issue ◽  
Model Parameters ◽  
Baseline Scenario ◽  
Birth Cohorts ◽  
Mortality Trends ◽  
Cohort Change ◽  
Age And Sex

Abstract Background: Alcohol is a major public health issue in Europe. Although future estimates of alcohol-attributable mortality can aid public health policy making, forecasts are scarce. Moreover, previous forecasts did not include the cohort dimension, despite the important role birth cohorts play in determining alcohol-attributable mortality trends. We forecast age- and sex-specific alcohol-attributable mortality in France for the period 2015-2050 using a novel generalizable methodology that includes different scenarios regarding period and cohort change. Within Western Europe, France has one of the highest levels of alcohol-attributable mortality.Methods: For the French national population aged 25-90 years (1979-2014), we estimated alcohol-attributable mortality by mortality from the main causes of death wholly-attributable to alcohol, plus liver cirrhosis mortality. We modelled sex-specific alcohol-attributable mortality by adjusting for age, period, and birth cohort. We forecasted the model parameters to obtain future age- and sex-specific alcohol-attributable mortality up until 2050 using a conventional baseline, scenario I (favourable period change) and scenario II (unfavourable cohort change). Results: Alcohol-attributable mortality is clearly declining in France, with the decline decelerating from 1992 onwards. In 2014, the age-standardized alcohol-attributable mortality rates, in deaths per 100,000, were 34.7 among men and 9.9 among women. In 2050, the estimated rates are between 10.5 (prediction interval: 7.6-14.4; scenario I) and 17.6 (13.1-23.7; scenario II) among men, and between 1.1 (0.7-1.7; scenario I) and 1.8 (1.2-2.9; scenario II) among women; which implies declines of 58% for men and 84% for women (baseline). The peak of the inverse u-shaped age pattern of alcohol-attributable mortality (currently at around age 65) is expected to shift towards older ages, and an additional hump in the age pattern is projected that moves towards higher ages over time, and is more extended in the cohort scenario.Conclusions: Alcohol-attributable mortality in France is expected to further decline in the coming decades, accompanied by age pattern changes. However, France’s levels are not expected to reach the current lower levels in Italy and Spain for 15 years or more. Our results point to the value of implementing preventive policy measures that discourage alcohol consumption among people of all ages, but especially among adolescents.

scholarly journals High COVID-19 transmission potential associated with re-opening universities can be mitigated with layered interventions

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Ellen Brooks-Pollock ◽  
Hannah Christensen ◽  
Adam Trickey ◽  
Gibran Hemani ◽  
Emily Nixon ◽  
...  
Keyword(s):  
Prediction Interval ◽  
Mitigation Strategies ◽  
Control Measures ◽  
Transmission Model ◽  
Model Parameters ◽  
Infection Rates ◽  
First Year Students ◽  
Transmission Potential ◽  
Plausible Model ◽  
Effective Option

AbstractControlling COVID-19 transmission in universities poses challenges due to the complex social networks and potential for asymptomatic spread. We developed a stochastic transmission model based on realistic mixing patterns and evaluated alternative mitigation strategies. We predict, for plausible model parameters, that if asymptomatic cases are half as infectious as symptomatic cases, then 15% (98% Prediction Interval: 6–35%) of students could be infected during the first term without additional control measures. First year students are the main drivers of transmission with the highest infection rates, largely due to communal residences. In isolation, reducing face-to-face teaching is the most effective intervention considered, however layering multiple interventions could reduce infection rates by 75%. Fortnightly or more frequent mass testing is required to impact transmission and was not the most effective option considered. Our findings suggest that additional outbreak control measures should be considered for university settings.

scholarly journals Estimating the Potential Public Health Impact of Prohibiting Characterizing Flavors in Cigars throughout the US

2019 ◽  
Vol 16 (18) ◽  
pp. 3234
Author(s):  
Brian L. Rostron ◽  
Catherine G. Corey ◽  
Enver Holder-Hayes ◽  
Bridget K. Ambrose
Keyword(s):  
Public Health ◽  
Smoking Prevalence ◽  
Prediction Interval ◽  
Health Benefits ◽  
Health Impact ◽  
Attributable Mortality ◽  
The Us ◽  
Cigar Smoking ◽  
Potential Public Health ◽  
Over Time

Flavored cigar use is common among cigar smokers, particularly those at younger ages. Several US localities have implemented policies restricting the sale of flavored tobacco products, including cigars. We estimated the population health benefits of removal of flavored cigars throughout the US in terms of reductions in cigar smoking-attributable mortality due to increased cessation and reductions in cigar smoking prevalence due to decreased initiation and continuing use. Monte Carlo simulation was used to estimate possible ranges for these values. We used published estimates of cigar use and attributable mortality in the US, as well as prior study conclusions on the effect of local and national flavor restriction policies. We estimated that removal of flavored cigars would result in approximately 800 (90% prediction interval = 400–1200) fewer cigar smoking-attributable deaths in the US each year and 112,000 fewer cigar smokers (90% prediction interval = 76,000–139,000) in each cohort of 18 year olds. The removal of characterizing flavors in cigars sold in the US is thus projected to have substantial public health benefits over time.

scholarly journals Multi-Objective Interval Prediction of Load Based on the Conditional Copula Function

2019 ◽  
Vol 9 (5) ◽  
pp. 955 ◽  
Author(s):  
Gang Zhang ◽  
Zhixuan Li ◽  
Jinwang Hou ◽  
Kaoshe Zhang ◽  
Fuchao Liu ◽  
...  
Keyword(s):  
Prediction Accuracy ◽  
Prediction Interval ◽  
Prediction Method ◽  
Copula Function ◽  
Solution Set ◽  
Shaanxi Province ◽  
Model Parameters ◽  
Multi Objective ◽  
Interval Prediction ◽  
Conditional Copula

Compared with the point prediction, the interval prediction of the load could more effectively guarantee the safe operation of the power system. In view of the problem that the correlation between adjacent load data is not fully utilized so that the prediction accuracy is reduced, this paper proposes the conditional copula function interval prediction method, which could make full use of the correlation relationship between adjacent load data so as to obtain the interval prediction result. At the same time, there are the different prediction results of the method under different parameters, and the evaluation results of the two accuracy evaluation indicators containing PICP (prediction interval coverage probability) and the PIAW (prediction interval average width) are inconsistent, the above result that the optimal parameters and prediction results cannot be obtained, therefore, the NSGA-II (Non-dominated Sorting Genetic Algorithm-II) multi-objective optimization algorithm is proposed to seek out the optimal solution set, and by evaluating the solution set, obtain the optimal prediction model parameters and the corresponding prediction results. Finally, the proposed method is applied to the three regions of Shaanxi Province, China to conduct ultra-short-term load prediction, and compare it with the commonly used load interval prediction method such as Gaussian process regression (GPR) algorithm, artificial neural network (ANN), extreme learning machine (ELM) and others, and the results show that the proposed method always has better prediction accuracy when applying it to different regions.

scholarly journals Combined effects of predicted climate and land use changes on future hydrological droughts in the Luanhe River basin, China

2021 ◽  
Author(s):  
Xu Chen ◽  
Ruiguang Han ◽  
Yongjie Wang
Keyword(s):  
Climate Change ◽  
Land Use ◽  
Land Use Change ◽  
River Basin ◽  
Emission Scenario ◽  
Land Use Changes ◽  
Model Parameters ◽  
Baseline Scenario ◽  
Luanhe River Basin ◽  
Hydrological Droughts

Abstract Drought can be impacted by both climate change and land use change in different ways. Thus, in order to predict future drought conditions, hydrological simulations as an ideal means, can be used to account for both projected climate change and projected land use change. In this study, projected climate and land use changes were integrated with the SWAT (Soil and Water Assessment Tool) model to estimate the combined impact of climate and land use projections on hydrological droughts in the Luanhe River basin. We presented that the measured runoff and the remote sensing inversion of soil water content were simultaneously used to validate the model to ensure the reliability of model parameters. Following the calibration and validation, the SWAT model was forced with downscaled precipitation and temperature outputs from a suite of nine Global Climate Models (GCMs) based on the CMIP5, corresponding to three different representative concentration pathways (RCP 2.6, RCP 4.5 and 8.5) for three distinct time periods: 2011–2040, 2041–2070 and 2071–2100, referred to as early-century, mid-century and late-century, respectively, and the land use predicted by CA-Markov model in the same future periods. Hydrological droughts were quantified using the Standardized Runoff Index (SRI). Compared to the baseline scenario (1961–1990), mild drought occurred more frequently during the next three periods (except the 2080s under the RCP2.6 emission scenario). Under the RCP8.5 emission scenario, the probability of severe drought or above occurring in the 2080s increased, the duration prolonged and the severity increased. Under the RCP2.6 scenario, the upper central region of the Luanhe river in the 2020s and upper reaches of the Luanhe river in the 2080s, were more likely to suffer extreme drought events. And under the RCP8.5 scenario, the middle and lower Luanhe river in the 2080s, were more likely to suffer these conditions.

scholarly journals The Impact of COVID-19 Vaccination on California's Return to Normalcy

2021 ◽  
Author(s):  
Maria L Daza-Torres ◽  
Yury Elena Garcia Puerta ◽  
Alec J Schmidt ◽  
James L Sharpnack ◽  
Bradley H Pollock ◽  
...  
Keyword(s):  
Transmission Rate ◽  
Realistic Model ◽  
Vaccine Uptake ◽  
Model Parameters ◽  
Baseline Scenario ◽  
Effective Population ◽  
Vaccination Rates ◽  
Population Sizes ◽  
Incident Cases ◽  
The Impact

SARS-CoV-2 has infected nearly 3.7 million and killed 61,722 Californians, as of May 22, 2021. Non-pharmaceutical interventions have been instrumental in mitigating the spread of the coronavirus. However, as we ease restrictions, widespread implementation of COVID-19 vaccines is essential to prevent its resurgence. In this work, we addressed the adequacy and deficiency of vaccine uptake within California and the possibility and severity of resurgence of COVID-19 as restrictions are lifted given the current vaccination rates. We implemented a real-time Bayesian data assimilation approach to provide projections of incident cases and deaths in California following the reopening of its economy on June 15, 2021. We implemented scenarios that vary vaccine uptake prior to reopening, and transmission rates and effective population sizes following the reopening. For comparison purposes, we adopted a baseline scenario using the current vaccination rates, which projects a total 11,429 cases and 429 deaths in a 15-day period after reopening. We used posterior estimates based on CA historical data to provide realistic model parameters after reopening. When the transmission rate is increased after reopening, we projected an increase in cases by 21.8% and deaths by 4.4% above the baseline after reopening. When the effective population is increased after reopening, we observed an increase in cases by 51.8% and deaths by 12.3% above baseline. A 30% reduction in vaccine uptake alone has the potential to increase cases and deaths by 35% and 21.6%, respectively. Conversely, increasing vaccine uptake by 30% could decrease cases and deaths by 26.1% and 17.9%, respectively. As California unfolds its plan to reopen its economy on June 15, 2021, it is critical that social distancing and public behavior changes continue to be promoted, particularly in communities with low vaccine uptake. The Centers of Disease Control's (CDC) recommendation to ease mask-wearing for fully vaccinated individuals despite major inequities in vaccine uptake in counties across the state highlights some of the logistical challenges that society faces as we enthusiastically phase out of this pandemic.

scholarly journals Assessing the Peach Fruit Refractometric Index at Harvest with a Simple Model Based on Fruit Growth

2008 ◽  
Vol 133 (2) ◽  
pp. 178-187 ◽  
Author(s):  
Isabelle Grechi ◽  
Nadine Hilgert ◽  
Michel Génard ◽  
Françoise Lescourret
Keyword(s):  
Consumer Satisfaction ◽  
Prunus Persica ◽  
Mean Squared Error ◽  
Prediction Interval ◽  
Sugar Content ◽  
Model Parameters ◽  
Data Sets ◽  
Peach Fruit ◽  
Crop Models ◽  
Growing Conditions

Whereas quality is an increasingly important aspect of peach fruit [Prunus persica (L.) Batsch] production at this time, it is still not adequately addressed in crop models. Our objective was to develop a model to assess an essential trait of peach fruit quality (the refractometric index at harvest) to include it in existing crop models and to address the issue of quality in programs dealing with the improvement of crop management. The model predicts the fruit refractometric index, an indicator of sugar content (the most decisive parameter in consumer satisfaction) commonly used by the fruit industry. The model was simple enough so that it could be easily linked to carbon-based crop models. It was calibrated and tested using several independent data sets representing many growing conditions. To account for the effect of uncertainty in input and model parameters, the output of the model was qualified by a prediction interval. Results indicated that the model accurately predicted refractometric indices under 12% (relative root mean squared error values of 0.09 and 0.12 for two data sets), which corresponds to the fruit industry's range of interest. Prediction intervals revealed that the uncertainty in model parameters has moderate effects, whereas the uncertainty of the model input has important effects.

A non-conventional hybrid numerical approach with multi-dimensional random sampling for cocaine abuse in Spain

2018 ◽  
Vol 11 (08) ◽  
pp. 1850110 ◽  
Author(s):  
Maha A. Mohammed ◽  
N. F. M. Noor ◽  
A. I. N. Ibrahim ◽  
Z. Siri
Keyword(s):  
Finite Difference ◽  
Random Sampling ◽  
Cocaine Abuse ◽  
Prediction Interval ◽  
Latin Hypercube Sampling ◽  
Numerical Approach ◽  
Model Parameters ◽  
Latin Hypercube ◽  
Statistical Probability ◽  
Statistical Estimations

This paper introduces a non-conventional approach with multi-dimensional random sampling to solve a cocaine abuse model with statistical probability. The mean Latin hypercube finite difference (MLHFD) method is proposed for the first time via hybrid integration of the classical numerical finite difference (FD) formula with Latin hypercube sampling (LHS) technique to create a random distribution for the model parameters which are dependent on time [Formula: see text]. The LHS technique gives advantage to MLHFD method to produce fast variation of the parameters’ values via number of multidimensional simulations (100, 1000 and 5000). The generated Latin hypercube sample which is random or non-deterministic in nature is further integrated with the FD method to complete one cycle of LHS-FD simulation iteration. This process is repeated until [Formula: see text] final iterations of LHS-FD are obtained. The means of these [Formula: see text] final solutions (MLHFD solutions) are tabulated, graphed and analyzed. The numerical simulation results of MLHFD for the SEIR model are presented side-by-side with deterministic solutions obtained from the classical FD scheme and homotopy analysis method with Pade approximation (HAM-Pade). The present MLHFD results are also compared with the previous non-deterministic statistical estimations from 1995 to 2015. Good agreement between the two is perceived with small errors. MLHFD method can be used to predict future behavior, range and prediction interval for the epidemic model solutions. The expected profiles of the cocaine abuse subpopulations are projected until the year 2045. Both the statistical estimations and the deterministic results of FD and HAM-Pade are found to be within the MLHFD prediction intervals for all the years and for all the subpopulations considered.

scholarly journals Modelled impacts of mitigation measures on greenhouse gas emissions from Finnish agriculture up to 2020

2009 ◽  
Vol 18 (3-4) ◽  
pp. 477-493 ◽  
Author(s):  
K. REGINA ◽  
H. LEHTONEN ◽  
J. NOUSIAINEN
Keyword(s):  
Greenhouse Gas Emissions ◽  
Greenhouse Gas ◽  
Biogas Production ◽  
Greenhouse Gas Mitigation ◽  
Mitigation Measures ◽  
Organic Soils ◽  
Baseline Scenario ◽  
Emission Scenarios ◽  
Gas Emissions ◽  
Policy Measures

Emission scenarios based on integrated quantitative modelling are a valuable tool in planning strategies for greenhouse gas mitigation. By estimating the potential of individual mitigation measures to reduce greenhouse gas emissions, resources can be targeted to the most promising policy measures. This paper reports two agricultural emission scenarios for Finland up to year 2020, one baseline scenario (Scenario 1) based on the projected agricultural production levels determined by markets and agricultural policy and one with selected mitigation measures included (Scenario 2). Measures selected for the analysis consisted of 1) keeping agricultural area at the current level, 2) decreasing the proportion of organic soils, 3) increasing the proportion of grass cultivation on organic soils and 4) supporting biogas production on farms. Starting from 2005, the emissions of nitrous oxide and methane from agriculture would decrease 2.3% in Scenario 1 by 2020 whereas the respective decrease would be 11.5% in Scenario 2. According to the results, mitigation measures targeted to cultivation of organic soils have the largest potential to reduce the emissions. Such measures would include reducing the area of cultivated organic soils and increasing the proportion of perennial crops on the remaining area.

scholarly journals Simulation-based Estimation of the Spread of COVID-19 in Iran

2020 ◽  
Author(s):  
Navid Ghaffarzadegan ◽  
Hazhir Rahmandad
Keyword(s):  
The State ◽  
Seasonal Effects ◽  
Series Data ◽  
Model Parameters ◽  
Official Statistics ◽  
Social Distancing ◽  
Death Toll ◽  
Order Of Magnitude ◽  
Policy Measures ◽  
Official Data

SummaryBackgroundThe COVID-19 disease has turned into a global pandemic with unprecedented challenges for the global community. Understanding the state of the disease and planning for future trajectories relies heavily on data on the spread and mortality. Yet official data coming from various countries are highly unreliable: symptoms similar to common cold in majority of cases and limited screening resources and delayed testing procedures may contribute to under-estimation of the burden of disease. Anecdotal and more limited data are available, but few have systematically combined those with official statistics into a coherent view of the epidemic. This study is a modeling-in-real-time of the emerging outbreak for understanding the state of the disease. Our focus is on the case of the spread of disease in Iran, as one of the epicenters of the disease in the first months of 2020.MethodWe develop a simple dynamic model of the epidemic to provide a more reliable picture of the state of the disease based on existing data. Building on the generic SEIR (Susceptible, Exposed, Infected, and Recovered) framework we incorporate two behavioral and logistical considerations. First we capture the endogenous changes in contact rate (average contact per person) as more death are reported. As a result the reproduction number changes endogenously in the model. Second we differentiate reported and true cases by including simple formulations for how only a fraction of cases might be diagnosed, and how that fraction changes in response to epidemic’s progression. In estimating the model we use both the official data as well as the discovered infected travelers and unofficial medical community estimates and triangulate these sources to build a more complete picture. Calibration is completed by forming a likelihood function for observing the actual time series data conditional on model parameters, and conducting a Markov Chain Monte Carlo simulations. The model is used to estimate current “true” cases of infection and death. We analyze the future trajectory of the disease under six conditions related to the seasonal effects and policy measures targeting social distancing.FindingsThe model closely replicates the past data but also shows the true number of cases is likely far larger. We estimate about 493,000 current infected cases (90% CI: 271K-810K) as of March 20th, 2020. Our estimate for cumulative cases of infection until that date is 916,000 (90% CI: 508K, 1.5M), and for total death is 15,485 (90% CI: 8.4K, 25.8K). These numbers are significantly (more than one order of magnitude) higher than official statistics. The trajectory of the epidemic until the end of June could take various paths depending on the impact of seasonality and policies targeting social distancing. In the most optimistic scenario for seasonal effects, depending on policy measures, 1.6 million Iranians (90% CI: 0.9M-2.6M) are likely to get infected, and death toll will reach about 58,000 cases (90% CI: 32K-97K), while in the more pessimistic scenarios, death toll may exceed 103,000 cases (90% CI: 56K-172K).ImplicationOur results suggest that the number of cases and deaths may be over an order of magnitude larger than official statistics in Iran. Absent extended testing capacity other countries may face a significant under-count of existing cases and thus be caught off guard about the actual toll of the epidemic.

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