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Article ; Online: Epidemiological features of seasonal influenza transmission among 11 climate zones in Chinese Mainland.

Si, Xiaohan / Wang, Liping / Mengersen, Kerrie / Hu, Wenbiao

2024 Volume 13, Issue 1, Page(s) 4

Abstract: Background: Previous studies provided some evidence of meteorological factors influence seasonal influenza transmission patterns varying across regions and latitudes. However, research on seasonal influenza activities based on climate zones are still in ...

Abstract	Background: Previous studies provided some evidence of meteorological factors influence seasonal influenza transmission patterns varying across regions and latitudes. However, research on seasonal influenza activities based on climate zones are still in lack. This study aims to utilize the ecological-based Köppen Geiger climate zones classification system to compare the spatial and temporal epidemiological characteristics of seasonal influenza in Chinese Mainland and assess the feasibility of developing an early warning system. Methods: Weekly influenza cases number from 2014 to 2019 at the county and city level were sourced from China National Notifiable Infectious Disease Report Information System. Epidemic temporal indices, time series seasonality decomposition, spatial modelling theories including Moran's I and local indicators of spatial association were applied to identify the spatial and temporal patterns of influenza transmission. Results: All climate zones had peaks in Winter-Spring season. Arid, desert, cold (BWk) showed up the first peak. Only Tropical, savannah (Aw) and Temperate, dry winter with hot summer (Cwa) zones had unique summer peak. Temperate, no dry season and hot summer (Cfa) zone had highest average incidence rate (IR) at 1.047/100,000. The Global Moran's I showed that average IR had significant clustered trend (z = 53.69, P < 0.001), with local Moran's I identified high-high cluster in Cfa and Cwa. IR differed among three age groups between climate zones (0-14 years old: F = 26.80, P < 0.001; 15-64 years old: F = 25.04, P < 0.001; Above 65 years old: F = 5.27, P < 0.001). Age group 0-14 years had highest average IR in Cwa and Cfa (IR = 6.23 and 6.21) with unique dual peaks in winter and spring season showed by seasonality decomposition. Conclusions: Seasonal influenza exhibited distinct spatial and temporal patterns in different climate zones. Seasonal influenza primarily emerged in BWk, subsequently in Cfa and Cwa. Cfa, Cwa and BSk pose high risk for seasonal influenza epidemics. The research finds will provide scientific evidence for developing seasonal influenza early warning system based on climate zones.
MeSH term(s)	Adolescent ; Adult ; Aged ; Child ; Child, Preschool ; Humans ; Infant ; Infant, Newborn ; Middle Aged ; Young Adult ; China/epidemiology ; Climate ; Influenza, Human/epidemiology ; Influenza, Human/transmission ; Seasons
Language	English
Publishing date	2024-01-10
Publishing country	England
Document type	Journal Article
ZDB-ID	2689396-4
ISSN	2049-9957 ; 2049-9957
ISSN (online)	2049-9957
ISSN	2049-9957
DOI	10.1186/s40249-024-01173-9
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: SMOTE-CD: SMOTE for compositional data.

Nguyen, Teo / Mengersen, Kerrie / Sous, Damien / Liquet, Benoit

PloS one

2023 Volume 18, Issue 6, Page(s) e0287705

Abstract: Compositional data are a special kind of data, represented as a proportion carrying relative information. Although this type of data is widely spread, no solution exists to deal with the cases where the classes are not well balanced. After describing ... ...

Abstract	Compositional data are a special kind of data, represented as a proportion carrying relative information. Although this type of data is widely spread, no solution exists to deal with the cases where the classes are not well balanced. After describing compositional data imbalance, this paper proposes an adaptation of the original Synthetic Minority Oversampling TEchnique (SMOTE) to deal with compositional data imbalance. The new approach, called SMOTE for Compositional Data (SMOTE-CD), generates synthetic examples by computing a linear combination of selected existing data points, using compositional data operations. The performance of the SMOTE-CD is tested with three different regressors (Gradient Boosting tree, Neural Networks, Dirichlet regressor) applied to two real datasets and to synthetic generated data, and the performance is evaluated using accuracy, cross-entropy, F1-score, R2 score and RMSE. The results show improvements across all metrics, but the impact of oversampling on performance varies depending on the model and the data. In some cases, oversampling may lead to a decrease in performance for the majority class. However, for the real data, the best performance across all models is achieved when oversampling is used. Notably, the F1-score is consistently increased with oversampling. Unlike the original technique, the performance is not improved when combining oversampling of the minority classes and undersampling of the majority class. The Python package smote-cd implements the method and is available online.
MeSH term(s)	Acclimatization ; Benchmarking ; Entropy ; Minority Groups ; Neural Networks, Computer
Language	English
Publishing date	2023-06-29
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2267670-3
ISSN	1932-6203 ; 1932-6203
ISSN (online)	1932-6203
ISSN	1932-6203
DOI	10.1371/journal.pone.0287705
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Article ; Online: Impacts of heatwaves on type 2 diabetes mortality in China: a comparative analysis between coastal and inland cities.

Zheng, Wenxiu / Chu, Jie / Bambrick, Hilary / Wang, Ning / Mengersen, Kerrie / Guo, Xiaolei / Hu, Wenbiao

International journal of biometeorology

2024

Abstract: The impacts of extreme temperatures on diabetes have been explored in previous studies. However, it is unknown whether the impacts of heatwaves appear variations between inland and coastal regions. This study aims to quantify the associations between ... ...

Abstract	The impacts of extreme temperatures on diabetes have been explored in previous studies. However, it is unknown whether the impacts of heatwaves appear variations between inland and coastal regions. This study aims to quantify the associations between heat exposure and type 2 diabetes mellitus (T2DM) deaths in two cities with different climate features in Shandong Province, China. We used a case-crossover design by quasi-Poisson generalized additive regression with a distributed lag model with lag 2 weeks, controlling for relative humidity, the concentration of air pollution particles with a diameter of 2.5 µm or less (PM
Language	English
Publishing date	2024-02-26
Publishing country	United States
Document type	Journal Article
ZDB-ID	280324-0
ISSN	1432-1254 ; 0020-7128
ISSN (online)	1432-1254
ISSN	0020-7128
DOI	10.1007/s00484-024-02638-0
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Article ; Online: Where are the vulnerable children? Identification and comparison of clusters of young children with health and developmental vulnerabilities across Queensland.

Draidi Areed, Wala / Price, Aiden / Arnett, Kathryn / Mengersen, Kerrie / Thompson, Helen

PloS one

2024 Volume 19, Issue 3, Page(s) e0298532

Abstract: This study aimed to better understand the vulnerability of children in their first year of school, aged between 5 years 5 months and 6 years 6 months, based on five health and development domains. Identification of subgroups of children within these ... ...

Abstract	This study aimed to better understand the vulnerability of children in their first year of school, aged between 5 years 5 months and 6 years 6 months, based on five health and development domains. Identification of subgroups of children within these domains can lead to more targeted policies to reduce these vulnerabilities. The focus of this study was to determine clusters of geographical regions with high and low proportions of vulnerable children in Queensland, Australia. This was achieved by carrying out a K-means analysis on data from the Australian Early Development Census and the Australian Bureau of Statistics. The clusters were then compared with respect to their geographic locations and risk factor profiles. The results are made publicly available via an interactive dashboard application developed in R Shiny.
MeSH term(s)	Child ; Humans ; Child, Preschool ; Infant ; Queensland/epidemiology ; Australia ; Risk Factors ; Vulnerable Populations ; Schools
Language	English
Publishing date	2024-03-15
Publishing country	United States
Document type	Journal Article
ZDB-ID	2267670-3
ISSN	1932-6203 ; 1932-6203
ISSN (online)	1932-6203
ISSN	1932-6203
DOI	10.1371/journal.pone.0298532
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Mapping the prevalence of cancer risk factors at the small area level in Australia.

Hogg, James / Cameron, Jessica / Cramb, Susanna / Baade, Peter / Mengersen, Kerrie

International journal of health geographics

2023 Volume 22, Issue 1, Page(s) 37

Abstract: Background: Cancer is a significant health issue globally and it is well known that cancer risk varies geographically. However in many countries there are no small area-level data on cancer risk factors with high resolution and complete reach, which ... ...

Abstract	Background: Cancer is a significant health issue globally and it is well known that cancer risk varies geographically. However in many countries there are no small area-level data on cancer risk factors with high resolution and complete reach, which hinders the development of targeted prevention strategies. Methods: Using Australia as a case study, the 2017-2018 National Health Survey was used to generate prevalence estimates for 2221 small areas across Australia for eight cancer risk factor measures covering smoking, alcohol, physical activity, diet and weight. Utilising a recently developed Bayesian two-stage small area estimation methodology, the model incorporated survey-only covariates, spatial smoothing and hierarchical modelling techniques, along with a vast array of small area-level auxiliary data, including census, remoteness, and socioeconomic data. The models borrowed strength from previously published cancer risk estimates provided by the Social Health Atlases of Australia. Estimates were internally and externally validated. Results: We illustrated that in 2017-2018 health behaviours across Australia exhibited more spatial disparities than previously realised by improving the reach and resolution of formerly published cancer risk factors. The derived estimates revealed higher prevalence of unhealthy behaviours in more remote areas, and areas of lower socioeconomic status; a trend that aligned well with previous work. Conclusions: Our study addresses the gaps in small area level cancer risk factor estimates in Australia. The new estimates provide improved spatial resolution and reach and will enable more targeted cancer prevention strategies at the small area level. Furthermore, by including the results in the next release of the Australian Cancer Atlas, which currently provides small area level estimates of cancer incidence and relative survival, this work will help to provide a more comprehensive picture of cancer in Australia by supporting policy makers, researchers, and the general public in understanding the spatial distribution of cancer risk factors. The methodology applied in this work is generalisable to other small area estimation applications and has been shown to perform well when the survey data are sparse.
MeSH term(s)	Humans ; Australia/epidemiology ; Prevalence ; Bayes Theorem ; Risk Factors ; Neoplasms/diagnosis ; Neoplasms/epidemiology
Language	English
Publishing date	2023-12-19
Publishing country	England
Document type	Journal Article
ZDB-ID	2091613-9
ISSN	1476-072X ; 1476-072X
ISSN (online)	1476-072X
ISSN	1476-072X
DOI	10.1186/s12942-023-00352-5
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Article ; Online: Comparing Bayesian spatial models: Goodness-of-smoothing criteria for assessing under- and over-smoothing.

Duncan, Earl W / Mengersen, Kerrie L

PloS one

2020 Volume 15, Issue 5, Page(s) e0233019

Abstract: Background: Many methods of spatial smoothing have been developed, for both point data as well as areal data. In Bayesian spatial models, this is achieved by purposefully designed prior(s) or smoothing functions which smooth estimates towards a local or ...

Abstract	Background: Many methods of spatial smoothing have been developed, for both point data as well as areal data. In Bayesian spatial models, this is achieved by purposefully designed prior(s) or smoothing functions which smooth estimates towards a local or global mean. Smoothing is important for several reasons, not least of all because it increases predictive robustness and reduces uncertainty of the estimates. Despite the benefits of smoothing, this attribute is all but ignored when it comes to model selection. Traditional goodness-of-fit measures focus on model fit and model parsimony, but neglect "goodness-of-smoothing", and are therefore not necessarily good indicators of model performance. Comparing spatial models while taking into account the degree of spatial smoothing is not straightforward because smoothing and model fit can be viewed as opposing goals. Over- and under-smoothing of spatial data are genuine concerns, but have received very little attention in the literature. Methods: This paper demonstrates the problem with spatial model selection based solely on goodness-of-fit by proposing several methods for quantifying the degree of smoothing. Several commonly used spatial models are fit to real data, and subsequently compared using the goodness-of-fit and goodness-of-smoothing statistics. Results: The proposed goodness-of-smoothing statistics show substantial agreement in the task of model selection, and tend to avoid models that over- or under-smooth. Conversely, the traditional goodness-of-fit criteria often don't agree, and can lead to poor model choice. In particular, the well-known deviance information criterion tended to select under-smoothed models. Conclusions: Some of the goodness-of-smoothing methods may be improved with modifications and better guidelines for their interpretation. However, these proposed goodness-of-smoothing methods offer researchers a solution to spatial model selection which is easy to implement. Moreover, they highlight the danger in relying on goodness-of-fit measures when comparing spatial models.
MeSH term(s)	Bayes Theorem ; Humans ; Infant ; Models, Statistical ; Neoplasms/epidemiology ; Spatial Analysis ; Sudden Infant Death/epidemiology
Language	English
Publishing date	2020-05-20
Publishing country	United States
Document type	Comparative Study ; Journal Article ; Research Support, Non-U.S. Gov't
ISSN	1932-6203
ISSN (online)	1932-6203
DOI	10.1371/journal.pone.0233019
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Book ; Online: Deep Generative Models, Synthetic Tabular Data, and Differential Privacy

Hassan, Conor / Salomone, Robert / Mengersen, Kerrie

An Overview and Synthesis

2023

Abstract: This article provides a comprehensive synthesis of the recent developments in synthetic data generation via deep generative models, focusing on tabular datasets. We specifically outline the importance of synthetic data generation in the context of ... ...

Abstract	This article provides a comprehensive synthesis of the recent developments in synthetic data generation via deep generative models, focusing on tabular datasets. We specifically outline the importance of synthetic data generation in the context of privacy-sensitive data. Additionally, we highlight the advantages of using deep generative models over other methods and provide a detailed explanation of the underlying concepts, including unsupervised learning, neural networks, and generative models. The paper covers the challenges and considerations involved in using deep generative models for tabular datasets, such as data normalization, privacy concerns, and model evaluation. This review provides a valuable resource for researchers and practitioners interested in synthetic data generation and its applications.
Keywords	Computer Science - Machine Learning ; Statistics - Applications ; Statistics - Computation ; Statistics - Machine Learning
Subject code	006
Publishing date	2023-07-28
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: A new method to explicitly estimate the shift of optimum along gradients in multispecies studies

Mourguiart, Bastien / Liquet, Benoît / Mengersen, Kerrie / Couturier, Thibaut / Mansons, Jérôme / Braud, Yoan / Besnard, Aurélien

Journal of Biogeography. 2023 May, v. 50, no. 5 p.1000-1011

2023

Abstract: AIM: Optimum shifts in species–environment relationships are intensively studied in a wide range of ecological topics, including climate change and species invasion. Numerous statistical methods are used to study optimum shifts, but, to our knowledge, ... ...

Abstract	AIM: Optimum shifts in species–environment relationships are intensively studied in a wide range of ecological topics, including climate change and species invasion. Numerous statistical methods are used to study optimum shifts, but, to our knowledge, none explicitly estimate it. We extended an existing model to explicitly estimate optimum shifts for multiple species having symmetrical response curves. We called this new Bayesian hierarchical model the Explicit Hierarchical Model of Optimum Shifts (EHMOS). LOCATION: All locations. TAXON: All taxa. METHODS: In a simulation study, we compared the accuracy of EHMOS to a mean comparison method and a Bayesian generalized linear mixed model (GLMM). Specifically, we tested if the accuracy of the methods was sensitive to (1) sampling design, (2) species optimum position and (3) species ecological specialization. In addition, we compared the three methods using a real dataset of investigated optimum shifts in 24 Orthopteran species between two time periods along an elevation gradient. RESULTS: Of all the simulated scenarios, EHMOS was the most accurate method. GLMM was the most sensitive method to species optimum position, providing unreliable estimates in the presence of marginal species, that is, species with an optimum close to a sampling boundary. The mean comparison method was also sensitive to species optimum position and ecological specialization, especially in an unbalanced sampling design, with high negative bias and low interval coverage compared to EHMOS. The case study results obtained with EHMOS were consistent with what is expected considering ongoing climate change, with mostly upward shifts, which further improved confidence in the accuracy of the EHMOS method. MAIN CONCLUSIONS: Explicit Hierarchical Model of Optimum Shifts could be used for a wide range of topics and extended to produce new insights, especially in climate change studies. Explicit estimation of optimum shifts notably allows investigation of ecological assumptions that could explain interspecific variability of these shifts.
Keywords	Bayesian theory ; Orthoptera ; altitude ; biogeography ; case studies ; climate change ; data collection ; interspecific variation ; statistical models
Language	English
Dates of publication	2023-05
Size	p. 1000-1011.
Publishing place	John Wiley & Sons, Ltd
Document type	Article ; Online
Note	JOURNAL ARTICLE
ZDB-ID	188963-1
ISSN	0305-0270
ISSN	0305-0270
DOI	10.1111/jbi.14570
Database	NAL-Catalogue (AGRICOLA)

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Book ; Online: Piecewise Deterministic Markov Processes for Bayesian Neural Networks

Goan, Ethan / Perrin, Dimitri / Mengersen, Kerrie / Fookes, Clinton

2023

Abstract: Inference on modern Bayesian Neural Networks (BNNs) often relies on a variational inference treatment, imposing violated assumptions of independence and the form of the posterior. Traditional MCMC approaches avoid these assumptions at the cost of ... ...

Abstract	Inference on modern Bayesian Neural Networks (BNNs) often relies on a variational inference treatment, imposing violated assumptions of independence and the form of the posterior. Traditional MCMC approaches avoid these assumptions at the cost of increased computation due to its incompatibility to subsampling of the likelihood. New Piecewise Deterministic Markov Process (PDMP) samplers permit subsampling, though introduce a model specific inhomogenous Poisson Process (IPPs) which is difficult to sample from. This work introduces a new generic and adaptive thinning scheme for sampling from these IPPs, and demonstrates how this approach can accelerate the application of PDMPs for inference in BNNs. Experimentation illustrates how inference with these methods is computationally feasible, can improve predictive accuracy, MCMC mixing performance, and provide informative uncertainty measurements when compared against other approximate inference schemes. Comment: Includes correction to software and corrigendum note
Keywords	Statistics - Machine Learning ; Computer Science - Machine Learning ; Statistics - Other Statistics
Subject code	310
Publishing date	2023-02-17
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Federated Variational Inference Methods for Structured Latent Variable Models

Hassan, Conor / Salomone, Robert / Mengersen, Kerrie

2023

Abstract: Federated learning methods enable model training across distributed data sources without data leaving their original locations and have gained increasing interest in various fields. However, existing approaches are limited, excluding many structured ... ...

Abstract	Federated learning methods enable model training across distributed data sources without data leaving their original locations and have gained increasing interest in various fields. However, existing approaches are limited, excluding many structured probabilistic models. We present a general and elegant solution based on structured variational inference, widely used in Bayesian machine learning, adapted for the federated setting. Additionally, we provide a communication-efficient variant analogous to the canonical FedAvg algorithm. The proposed algorithms' effectiveness is demonstrated, and their performance is compared with hierarchical Bayesian neural networks and topic models.
Keywords	Statistics - Machine Learning ; Computer Science - Machine Learning ; Statistics - Computation ; Statistics - Methodology
Publishing date	2023-02-07
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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