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

Xiaohan Si / Liping Wang / Kerrie Mengersen / Wenbiao Hu

Infectious Diseases of Poverty, Vol 13, Iss 1, Pp 1-

2024 Volume 15

Abstract: 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 ... ...

Abstract	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 ...
Keywords	Seasonal influenza ; Köppen Geiger climate zones classification system ; Chinese Mainland ; Seasonality decomposition ; Local indicators of spatial association ; Infectious and parasitic diseases ; RC109-216 ; Public aspects of medicine ; RA1-1270
Subject code	550
Language	English
Publishing date	2024-01-01T00:00:00Z
Publisher	BMC
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: SMOTE-CD

Teo Nguyen / Kerrie Mengersen / Damien Sous / Benoit Liquet

PLoS ONE, Vol 18, Iss 6, p e

SMOTE for compositional data.

2023 Volume 0287705

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.
Keywords	Medicine ; R ; Science ; Q
Subject code	780
Language	English
Publishing date	2023-01-01T00:00:00Z
Publisher	Public Library of Science (PLoS)
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Virtual Reef Diver

Julie Vercelloni / Edgar Santos-Fernández / Kerrie Mengersen

Citizen Science: Theory and Practice, Vol 8, Iss 1, Pp 28-

Enabling People to Help Protect the Great Barrier Reef

2023 Volume 28

Abstract: Two Sustainable Development Goals are focused directly on combating the impacts of climate change on coral reef communities: Goal 13, Climate Action (Take urgent action to combat climate change and its impacts) and Goal 14, Life Below Water (Conserve and ...

Abstract	Two Sustainable Development Goals are focused directly on combating the impacts of climate change on coral reef communities: Goal 13, Climate Action (Take urgent action to combat climate change and its impacts) and Goal 14, Life Below Water (Conserve and sustainably use the oceans, seas and marine resources for sustainable development). Citizen science (CS) features prominently in a range of programs that have been developed to address the SDGs. One such program is Virtual Reef Diver, which is designed to help monitor the health of the Great Barrier Reef in Australia. This program engages citizen scientists in two ways. Scuba-divers are asked to take geo-coded underwater images of the reef and upload them to an online virtual reef. Members of the public across the world are then asked to classify these images with respect to key reef indicators such as coral. Through the lens of a Virtual Reef Diver event held as part of Australia’s National Science Week 2021, we describe important features of this program that positively address common concerns about CS data, including the scientific trustworthiness of the data, the ability to incorporate these data with other more traditional data sources, and the quantifiable improvement in information about reef health using these data for management decisions. This demonstrates the important role that citizen science can play in achieving the SDGs by supporting the development of global policies for coral reef conservation.
Keywords	citizen science ; sustainable development goals ; great barrier reef ; reef health ; disturbances ; management ; Science ; Q
Subject code	333
Language	English
Publishing date	2023-06-01T00:00:00Z
Publisher	Ubiquity Press
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Spatial statistical machine learning models to assess the relationship between development vulnerabilities and educational factors in children in Queensland, Australia

Wala Draidi Areed / Aiden Price / Kathryn Arnett / Kerrie Mengersen

BMC Public Health, Vol 22, Iss 1, Pp 1-

2022 Volume 12

Abstract: Abstract Background The health and development of children during their first year of full time school is known to impact their social, emotional, and academic capabilities throughout and beyond early education. Physical health, motor development, social ...

Abstract	Abstract Background The health and development of children during their first year of full time school is known to impact their social, emotional, and academic capabilities throughout and beyond early education. Physical health, motor development, social and emotional well-being, learning styles, language and communication, cognitive skills, and general knowledge are all considered to be important aspects of a child’s health and development. It is important for many organisations and governmental agencies to continually improve their understanding of the factors which determine or influence development vulnerabilities among children. This article studies the relationships between development vulnerabilities and educational factors among children in Queensland, Australia. Methods Spatial statistical machine learning models are reviewed and compared in the context of a study of geographic variation in the association between development vulnerabilities and attendance at preschool among children in Queensland, Australia. A new spatial random forest (SRF) model is suggested that can explain more of the spatial variation in data than other approaches. Results In the case study, spatial models were shown to provide a better fit compared to models that ignored the spatial variation in the data. The SRF model was shown to be the only model which can explain all of the spatial variation in each of the development vulnerabilities considered in the case study. The spatial analysis revealed that the attendance at preschool factor has a strong influence on the physical health domain vulnerability and emotional maturity vulnerability among children in their first year of school. Conclusion This study confirmed that it is important to take into account the spatial nature of data when fitting statistical machine learning models. A new spatial random forest model was introduced and was shown to explain more of the spatial variation and provide a better model fit in the case study of development vulnerabilities among children in ...
Keywords	Statistical machine learning methods ; Spatial random forest ; Deveplomental vulnerabilities ; Public aspects of medicine ; RA1-1270
Subject code	370
Language	English
Publishing date	2022-11-01T00:00:00Z
Publisher	BMC
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Assessing the accuracy of record linkages with Markov chain based Monte Carlo simulation approach

Shovanur Haque / Kerrie Mengersen / Steven Stern

Journal of Big Data, Vol 8, Iss 1, Pp 1-

2021 Volume 25

Abstract: Abstract Record linkage is the process of finding matches and linking records from different data sources so that the linked records belong to the same entity. There is an increasing number of applications of record linkage in statistical, health, ... ...

Abstract	Abstract Record linkage is the process of finding matches and linking records from different data sources so that the linked records belong to the same entity. There is an increasing number of applications of record linkage in statistical, health, government and business organisations to link administrative, survey, population census and other files to create a complete set of information for more complete and comprehensive analysis. To make valid inferences using a linked file, it has become increasingly important to have effective and efficient methods for linking data from different sources. Therefore, it becomes necessary to assess the ability of a linking method to achieve high accuracy or to compare between methods with respect to accuracy. This motivates the development of a method for assessing the linking process and facilitating decisions about which linking method is likely to be more accurate for a particular linking task. This paper proposes a Markov Chain based Monte Carlo simulation approach, MaCSim for assessing a linking method and illustrates the utility of the approach using a realistic synthetic dataset received from the Australian Bureau of Statistics to avoid privacy issues associated with using real personal information. A linking method applied by MaCSim is also defined. To assess the defined linking method, correct re-link proportions for each record are calculated using our developed simulation approach. The accuracy is determined for a number of simulated datasets. The analyses indicated promising performance of the proposed method MaCSim of the assessment of accuracy of the linkages. The computational aspects of the methodology are also investigated to assess its feasibility for practical use.
Keywords	Record linkage ; Linkage accuracy ; Linking method ; Markov chain Monte Carlo ; Simulation ; Blocking ; Computer engineering. Computer hardware ; TK7885-7895 ; Information technology ; T58.5-58.64 ; Electronic computers. Computer science ; QA75.5-76.95
Subject code	310
Language	English
Publishing date	2021-01-01T00:00:00Z
Publisher	SpringerOpen
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Bayesian mixture models and their Big Data implementations with application to invasive species presence-only data

Insha Ullah / Kerrie Mengersen

Journal of Big Data, Vol 6, Iss 1, Pp 1-

2019 Volume 25

Abstract: Abstract Due to their conceptual simplicity and flexibility, non-parametric mixture models are widely used to identify latent clusters in data. However, when it comes to Big Data, such as Landsat imagery, such model fitting is computationally prohibitive. ...

Abstract	Abstract Due to their conceptual simplicity and flexibility, non-parametric mixture models are widely used to identify latent clusters in data. However, when it comes to Big Data, such as Landsat imagery, such model fitting is computationally prohibitive. To overcome this issue, we fit Bayesian non-parametric models to pre-smoothed data, thereby reducing the computational time from days to minutes, while disregarding little of the useful information. Tree based clustering is used to partition the clusters into smaller and smaller clusters in order to identify clusters of high, medium and low interest. The tree-based clustering method is applied to Landsat images from the Brisbane region, which were the actual sources of motivation for development of the method. The images are taken as a part of the red imported fire-ant eradication program that was launched in September 2001 and which is funded by all Australian states and territories, along with the federal government. To satisfy budgetary constraints, modelling is performed to estimate the risk of fire-ant incursion in each cluster so that the eradication program focuses on high risk clusters. The likelihood of containment is successfully derived by combining the fieldwork survey data with the results obtained from the proposed method.
Keywords	Dirichlet process mixture models ; k-means clustering ; Tree-based clustering ; Satellite imagery data ; Fire-ants habitat ; One-class support vector machine ; Computer engineering. Computer hardware ; TK7885-7895 ; Information technology ; T58.5-58.64 ; Electronic computers. Computer science ; QA75.5-76.95
Subject code	310 ; 006
Language	English
Publishing date	2019-03-01T00:00:00Z
Publisher	SpringerOpen
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Mapping of Coral Reefs with Multispectral Satellites

Teo Nguyen / Benoît Liquet / Kerrie Mengersen / Damien Sous

Remote Sensing, Vol 13, Iss 4470, p

A Review of Recent Papers

2021 Volume 4470

Abstract: Coral reefs are an essential source of marine biodiversity, but they are declining at an alarming rate under the combined effects of global change and human pressure. A precise mapping of coral reef habitat with high spatial and time resolutions has ... ...

Abstract	Coral reefs are an essential source of marine biodiversity, but they are declining at an alarming rate under the combined effects of global change and human pressure. A precise mapping of coral reef habitat with high spatial and time resolutions has become a necessary step for monitoring their health and evolution. This mapping can be achieved remotely thanks to satellite imagery coupled with machine-learning algorithms. In this paper, we review the different satellites used in recent literature, as well as the most common and efficient machine-learning methods. To account for the recent explosion of published research on coral reel mapping, we especially focus on the papers published between 2018 and 2020. Our review study indicates that object-based methods provide more accurate results than pixel-based ones, and that the most accurate methods are Support Vector Machine and Random Forest. We emphasize that the satellites with the highest spatial resolution provide the best images for benthic habitat mapping. We also highlight that preprocessing steps (water column correction, sunglint removal, etc.) and additional inputs (bathymetry data, aerial photographs, etc.) can significantly improve the mapping accuracy.
Keywords	coral mapping ; coral reefs ; machine learning ; remote sensing ; satellite imagery ; Science ; Q
Subject code	333
Language	English
Publishing date	2021-11-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Statistical Machine Learning Methods and Remote Sensing for Sustainable Development Goals

Jacinta Holloway / Kerrie Mengersen

Remote Sensing, Vol 10, Iss 9, p

A Review

2018 Volume 1365

Abstract: Interest in statistical analysis of remote sensing data to produce measurements of environment, agriculture, and sustainable development is established and continues to increase, and this is leading to a growing interaction between the earth science and ... ...

Abstract	Interest in statistical analysis of remote sensing data to produce measurements of environment, agriculture, and sustainable development is established and continues to increase, and this is leading to a growing interaction between the earth science and statistical domains. With this in mind, we reviewed the literature on statistical machine learning methods commonly applied to remote sensing data. We focus particularly on applications related to the United Nations World Bank Sustainable Development Goals, including agriculture (food security), forests (life on land), and water (water quality). We provide a review of useful statistical machine learning methods, how they work in a remote sensing context, and examples of their application to these types of data in the literature. Rather than prescribing particular methods for specific applications, we provide guidance, examples, and case studies from the literature for the remote sensing practitioner and applied statistician. In the supplementary material, we also describe the necessary steps pre and post analysis for remote sensing data; the pre-processing and evaluation steps.
Keywords	machine learning ; statistical methods ; remote sensing ; satellite imagery ; big data ; agriculture ; sustainable development ; Science ; Q
Subject code	710
Language	English
Publishing date	2018-08-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Estimating a novel stochastic model for within-field disease dynamics of banana bunchy top virus via approximate Bayesian computation.

Abhishek Varghese / Christopher Drovandi / Antonietta Mira / Kerrie Mengersen

PLoS Computational Biology, Vol 16, Iss 5, p e

2020 Volume 1007878

Abstract: The Banana Bunchy Top Virus (BBTV) is one of the most economically important vector-borne banana diseases throughout the Asia-Pacific Basin and presents a significant challenge to the agricultural sector. Current models of BBTV are largely deterministic, ...

Abstract	The Banana Bunchy Top Virus (BBTV) is one of the most economically important vector-borne banana diseases throughout the Asia-Pacific Basin and presents a significant challenge to the agricultural sector. Current models of BBTV are largely deterministic, limited by an incomplete understanding of interactions in complex natural systems, and the appropriate identification of parameters. A stochastic network-based Susceptible-Infected-Susceptible model has been created which simulates the spread of BBTV across the subsections of a banana plantation, parameterising nodal recovery, neighbouring and distant infectivity across summer and winter. Findings from posterior results achieved through Markov Chain Monte Carlo approach to approximate Bayesian computation suggest seasonality in all parameters, which are influenced by correlated changes in inspection accuracy, temperatures and aphid activity. This paper demonstrates how the model may be used for monitoring and forecasting of various disease management strategies to support policy-level decision making.
Keywords	Biology (General) ; QH301-705.5
Language	English
Publishing date	2020-05-01T00:00:00Z
Publisher	Public Library of Science (PLoS)
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Evaluating the impact of a small number of areas on spatial estimation

Aswi Aswi / Susanna Cramb / Earl Duncan / Kerrie Mengersen

International Journal of Health Geographics, Vol 19, Iss 1, Pp 1-

2020 Volume 14

Abstract: Abstract Background There is an expanding literature on different representations of spatial random effects for different types of spatial correlation structure within the conditional autoregressive class of priors for Bayesian spatial models. However, ... ...

Abstract	Abstract Background There is an expanding literature on different representations of spatial random effects for different types of spatial correlation structure within the conditional autoregressive class of priors for Bayesian spatial models. However, little is known about the impact of these different priors when the number of areas is small. This paper aimed to investigate this problem both in the context of a case study of spatial analysis of dengue fever and more generally through a simulation study. Methods Both the simulation study and the case study considered count data aggregated to a small area level in a region. Five different conditional autoregressive priors for a simple Bayesian Poisson model were considered: independent, Besag-York-Mollié, Leroux, and two variants of a localised clustering model. Data were simulated with eight different sizes of areal grids, ranging from 4 to 2500 areas, and two different levels of both spatial autocorrelation and disease counts. Model goodness-of-fit measures and model estimates were compared. A case study involving dengue fever cases in 14 local areas in Makassar, Indonesia, was also considered. Results The simulation study showed that model performance varied under different scenarios. When areas had low autocorrelation and high counts, and the number of areas was at most 25, the BYM, Leroux and localised $$G = 2$$ G = 2 models performed similarly and better than the independent and localised $$G = 3$$ G = 3 models. However, when the number of areas were at least 100, all models performed differently, and the Leroux model performed the best. Overall, the Leroux model performed the best for every scenario especially when there were at least 16 areas. Based on the case study, the comparative performance of spatial models may also vary for a small number of areas, especially when the data have a relatively large mean and variance over areas. In this case, the localised model with G = 3 was a better choice. Conclusion Detecting spatial patterns can be difficult ...
Keywords	Bayesian spatial estimation ; Conditional autoregressive (CAR) ; Few areas ; Computer applications to medicine. Medical informatics ; R858-859.7
Subject code	310
Language	English
Publishing date	2020-09-01T00:00:00Z
Publisher	BMC
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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