Model Learning from Published Aggregated Data

Author(s):  
Janusz Wojtusiak ◽  
Ancha Baranova
2020 ◽  
Vol 36 (3) ◽  
pp. 500-509
Author(s):  
Hannah G. Bosley ◽  
Devon B. Sandel ◽  
Aaron J. Fisher

Abstract. Generalized anxiety disorder (GAD) is associated with worry and emotion regulation difficulties. The contrast-avoidance model suggests that individuals with GAD use worry to regulate emotion: by worrying, they maintain a constant state of negative affect (NA), avoiding a feared sudden shift into NA. We tested an extension of this model to positive affect (PA). During a week-long ecological momentary assessment (EMA) period, 96 undergraduates with a GAD analog provided four daily measurements of worry, dampening (i.e., PA suppression), and PA. We hypothesized a time-lagged mediation relationship in which higher worry predicts later dampening, and dampening predicts subsequently lower PA. A lag-2 structural equation model was fit to the group-aggregated data and to each individual time-series to test this hypothesis. Although worry and PA were negatively correlated in 87 participants, our model was not supported at the nomothetic level. However, idiographically, our model was well-fit for about a third (38.5%) of participants. We then used automatic search as an idiographic exploratory procedure to detect other time-lagged relationships between these constructs. While 46 individuals exhibited some cross-lagged relationships, no clear pattern emerged across participants. An alternative hypothesis about the speed of the relationship between variables is discussed using contemporaneous correlations of worry, dampening, and PA. Findings suggest heterogeneity in the function of worry as a regulatory strategy, and the importance of temporal scale for detection of time-lagged effects.


Author(s):  
Dace Zavadska ◽  
Zane Odzelevica

Aggregated data on TBE cases in Latvia are available from 1955, but serological testing for TBE began in the 1970’s.


2021 ◽  
Author(s):  
Su-Jeong Park ◽  
Soon-Seo Park ◽  
Han-Lim Choi ◽  
Kyeong-Soo An ◽  
Young-Gon Kim

2015 ◽  
Vol 7 (1) ◽  
pp. 29-38
Author(s):  
Esti Munafiah ◽  
Agus Basir Ali Akbar S

The objective of this study is to see learning process using LCC model for chemistry course.  The study used classroom action research with three cycles each of which implements planning, acting, observing and reflection.  Subject of the study was 40 students of grade 8E of MTsN Blitar in the academic year 2009/2010. The findings of the study are as follows:  (1) Cycle I:  students participation 62.5%, mean score of worksheet 60, mean score of quiz 41,7, and mastery learning 3 students; (2) Cycle II: students participation 86.6%, mean score of worksheet 81, mean score of quiz 72.38, and mastery learning 26 students; (3) Cycle III:  students participation 100%, mean score of worksheet 89, mean score of quiz 72.44, and mastery learning 39 students.


Author(s):  
Manju Rahi ◽  
Payal Das ◽  
Amit Sharma

Abstract Malaria surveillance is weak in high malaria burden countries. Surveillance is considered as one of the core interventions for malaria elimination. Impressive reductions in malaria-associated morbidity and mortality have been achieved across the globe, but sustained efforts need to be bolstered up to achieve malaria elimination in endemic countries like India. Poor surveillance data become a hindrance in assessing the progress achieved towards malaria elimination and in channelizing focused interventions to the hotspots. A major obstacle in strengthening India’s reporting systems is that the surveillance data are captured in a fragmented manner by multiple players, in silos, and is distributed across geographic regions. In addition, the data are not reported in near real-time. Furthermore, multiplicity of malaria data resources limits interoperability between them. Here, we deliberate on the acute need of updating India’s surveillance systems from the use of aggregated data to near real-time case-based surveillance. This will help in identifying the drivers of malaria transmission in any locale and therefore will facilitate formulation of appropriate interventional responses rapidly.


Author(s):  
Scott A. McDonald ◽  
Fuminari Miura ◽  
Eric R. A. Vos ◽  
Michiel van Boven ◽  
Hester E. de Melker ◽  
...  

Abstract Background The proportion of SARS-CoV-2 positive persons who are asymptomatic—and whether this proportion is age-dependent—are still open research questions. Because an unknown proportion of reported symptoms among SARS-CoV-2 positives will be attributable to another infection or affliction, the observed, or 'crude' proportion without symptoms may underestimate the proportion of persons without symptoms that are caused by SARS-CoV-2 infection. Methods Based on two rounds of a large population-based serological study comprising test results on seropositivity and self-reported symptom history conducted in April/May and June/July 2020 in the Netherlands (n = 7517), we estimated the proportion of reported symptoms among those persons infected with SARS-CoV-2 that is attributable to this infection, where the set of relevant symptoms fulfills the ECDC case definition of COVID-19, using inferential methods for the attributable risk (AR). Generalised additive regression modelling was used to estimate the age-dependent relative risk (RR) of reported symptoms, and the AR and asymptomatic proportion (AP) were calculated from the fitted RR. Results Using age-aggregated data, the 'crude' AP was 37% but the model-estimated AP was 65% (95% CI 63–68%). The estimated AP varied with age, from 74% (95% CI 65–90%) for < 20 years, to 61% (95% CI 57–65%) for the 50–59 years age-group. Conclusion Whereas the 'crude' AP represents a lower bound for the proportion of persons infected with SARS-CoV-2 without COVID-19 symptoms, the AP as estimated via an attributable risk approach represents an upper bound. Age-specific AP estimates can inform the implementation of public health actions such as targetted virological testing and therefore enhance containment strategies.


2021 ◽  
Vol 15 (6) ◽  
pp. 1-20
Author(s):  
Dongsheng Li ◽  
Haodong Liu ◽  
Chao Chen ◽  
Yingying Zhao ◽  
Stephen M. Chu ◽  
...  

In collaborative filtering (CF) algorithms, the optimal models are usually learned by globally minimizing the empirical risks averaged over all the observed data. However, the global models are often obtained via a performance tradeoff among users/items, i.e., not all users/items are perfectly fitted by the global models due to the hard non-convex optimization problems in CF algorithms. Ensemble learning can address this issue by learning multiple diverse models but usually suffer from efficiency issue on large datasets or complex algorithms. In this article, we keep the intermediate models obtained during global model learning as the snapshot models, and then adaptively combine the snapshot models for individual user-item pairs using a memory network-based method. Empirical studies on three real-world datasets show that the proposed method can extensively and significantly improve the accuracy (up to 15.9% relatively) when applied to a variety of existing collaborative filtering methods.


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