scholarly journals Estimating Under Reporting of Leprosy in Brazil using a Bayesian Approach

2020 ◽  
Author(s):  
Guilherme L Oliveira ◽  
Juliane Fonseca Oliveira ◽  
Roberto F S Andrade ◽  
Joilda S Nery ◽  
Julia M Pescarini ◽  
...  
Keyword(s):  
Health Problem ◽  
Health Resources ◽  
Bayesian Hierarchical Model ◽  
Incidence Rates ◽  
Grouped Data ◽  
Bayesian Hierarchical ◽  
Effective Interventions ◽  
True Number ◽  
Important Health ◽  
Resouce Allocation

Leprosy remains an important health problem in Brazil - the country register the second largest number of new leprosy cases each year, accounting for 14% of the world's new cases in 2019. Although there was increasing advances in leprosy surveillance worldwide, the true number of leprosy cases is expected to be much larger than the reported. Leprosy underreporting impair planning effective interventions and thoughful decisions about the distribution of financial and health resources. In this study, we estimated leprosy underreporting for each Brazilian microregion in order to guide effective interventions and resouce allocation to improve leprosy detection in the country. We extracted the number of new cases of leprosy from 2007 to 2015 and population and socioeconomic information from the 2010 Census for each Brazilian municipality and grouped data in microregions. We applied a Bayesian hierarchical model to obtain the best explicative model for leprosy underreporing using Grade 2 of leprosy-related disabilities as a proxy to explain the incidence rates. Then, we estimated the number of missing leprosy cases (underreported cases) and the corrected leprosy incidence rates for each Brazilian microrregion.

scholarly journals Estimating underreporting of leprosy in Brazil using a Bayesian approach

2021 ◽  
Vol 15 (8) ◽  
pp. e0009700
Author(s):  
Guilherme L. de Oliveira ◽  
Juliane F. Oliveira ◽  
Júlia M. Pescarini ◽  
Roberto F. S. Andrade ◽  
Joilda S. Nery ◽  
...  
Keyword(s):  
Delayed Diagnosis ◽  
Health Resources ◽  
Incidence Rates ◽  
Surveillance Program ◽  
Middle Income ◽  
Bayesian Hierarchical ◽  
Effective Interventions ◽  
Efficient Resource ◽  
Leprosy Case ◽  
Using Data

Background Leprosy remains concentrated among the poorest communities in low-and middle-income countries and it is one of the primary infectious causes of disability. Although there have been increasing advances in leprosy surveillance worldwide, leprosy underreporting is still common and can hinder decision-making regarding the distribution of financial and health resources and thereby limit the effectiveness of interventions. In this study, we estimated the proportion of unreported cases of leprosy in Brazilian microregions. Methodology/Principal findings Using data collected between 2007 to 2015 from each of the 557 Brazilian microregions, we applied a Bayesian hierarchical model that used the presence of grade 2 leprosy-related physical disabilities as a direct indicator of delayed diagnosis and a proxy for the effectiveness of local leprosy surveillance program. We also analyzed some relevant factors that influence spatial variability in the observed mean incidence rate in the Brazilian microregions, highlighting the importance of socioeconomic factors and how they affect the levels of underreporting. We corrected leprosy incidence rates for each Brazilian microregion and estimated that, on average, 33,252 (9.6%) new leprosy cases went unreported in the country between 2007 to 2015, with this proportion varying from 8.4% to 14.1% across the Brazilian States. Conclusions/Significance The magnitude and distribution of leprosy underreporting were adequately explained by a model using Grade 2 disability as a marker for the ability of the system to detect new missing cases. The percentage of missed cases was significant, and efforts are warranted to improve leprosy case detection. Our estimates in Brazilian microregions can be used to guide effective interventions, efficient resource allocation, and target actions to mitigate transmission.

scholarly journals A Bayesian Approach to Real-Time Monitoring and Forecasting of Chinese Foodborne Diseases

2018 ◽  
Vol 15 (8) ◽  
pp. 1740 ◽  
Author(s):  
Xueli Wang ◽  
Moqin Zhou ◽  
Jinzhu Jia ◽  
Zhi Geng ◽  
Gexin Xiao
Keyword(s):  
Public Health ◽  
Hierarchical Model ◽  
Bayesian Hierarchical Model ◽  
Hunan Province ◽  
Foodborne Diseases ◽  
Foodborne Disease ◽  
Bayesian Hierarchical ◽  
True Number ◽  
Number Of Patients ◽  
Reporting Delays

Foodborne diseases have a big impact on public health and are often underreported. This is because a lot of patients delay treatment when they suffer from foodborne diseases. In Hunan Province (China), a total of 21,226 confirmed foodborne disease cases were reported from 1 March 2015 to 28 February 2016 by the Foodborne Surveillance Database (FSD) of the China National Centre for Food Safety Risk Assessment (CFSA). The purpose of this study was to make use of the daily number of visiting patients to forecast the daily true number of patients. Our main contribution is that we take the reporting delays into consideration and apply a Bayesian hierarchical model for this forecast problem. The data shows that there were 21,226 confirmed cases reported among 21,866 visiting patients, a proportion as high as 97%. Given this observation, the Bayesian hierarchical model was established to predict the daily true number of patients using the number of visiting patients. We propose several scoring rules to assess the performance of different nowcasting procedures. We conclude that Bayesian nowcasting with consideration of right truncation of the reporting delays has a good performance for short-term forecasting, and could effectively predict the epidemic trends of foodborne diseases. Meanwhile, this approach could provide a methodological basis for future foodborne disease monitoring and control strategies, which are crucial for public health.

scholarly journals Measuring spatial allocative efficiency in basketball

2020 ◽  
Vol 16 (4) ◽  
pp. 271-289
Author(s):  
Nathan Sandholtz ◽  
Jacob Mortensen ◽  
Luke Bornn
Keyword(s):  
Spatial Distribution ◽  
Hierarchical Model ◽  
Opportunity Cost ◽  
Allocative Efficiency ◽  
Bayesian Hierarchical Model ◽  
National Basketball Association ◽  
Spatial Context ◽  
Bayesian Hierarchical ◽  
The Impact

AbstractEvery shot in basketball has an opportunity cost; one player’s shot eliminates all potential opportunities from their teammates for that play. For this reason, player-shot efficiency should ultimately be considered relative to the lineup. This aspect of efficiency—the optimal way to allocate shots within a lineup—is the focus of our paper. Allocative efficiency should be considered in a spatial context since the distribution of shot attempts within a lineup is highly dependent on court location. We propose a new metric for spatial allocative efficiency by comparing a player’s field goal percentage (FG%) to their field goal attempt (FGA) rate in context of both their four teammates on the court and the spatial distribution of their shots. Leveraging publicly available data provided by the National Basketball Association (NBA), we estimate player FG% at every location in the offensive half court using a Bayesian hierarchical model. Then, by ordering a lineup’s estimated FG%s and pairing these rankings with the lineup’s empirical FGA rate rankings, we detect areas where the lineup exhibits inefficient shot allocation. Lastly, we analyze the impact that sub-optimal shot allocation has on a team’s overall offensive potential, demonstrating that inefficient shot allocation correlates with reduced scoring.

A point-based Bayesian hierarchical model to predict the outcome of tennis matches

2019 ◽  
Vol 15 (4) ◽  
pp. 313-325 ◽  
Author(s):  
Martin Ingram
Keyword(s):  
Random Walk ◽  
Hierarchical Model ◽  
Bayesian Hierarchical Model ◽  
Bayesian Hierarchical ◽  
Gaussian Random Walk ◽  
Over Time ◽  
Modelling Approach ◽  
Independent And Identically Distributed

Abstract A well-established assumption in tennis is that point outcomes on each player’s serve in a match are independent and identically distributed (iid). With this assumption, it is enough to specify the serve probabilities for both players to derive a wide variety of event distributions, such as the expected winner and number of sets, and number of games. However, models using this assumption, which we will refer to as “point-based”, have typically performed worse than other models in the literature at predicting the match winner. This paper presents a point-based Bayesian hierarchical model for predicting the outcome of tennis matches. The model predicts the probability of winning a point on serve given surface, tournament and match date. Each player is given a serve and return skill which is assumed to follow a Gaussian random walk over time. In addition, each player’s skill varies by surface, and tournaments are given tournament-specific intercepts. When evaluated on the ATP’s 2014 season, the model outperforms other point-based models, predicting match outcomes with greater accuracy (68.8% vs. 66.3%) and lower log loss (0.592 vs. 0.641). The results are competitive with approaches modelling the match outcome directly, demonstrating the forecasting potential of the point-based modelling approach.

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