1
|
Duarte I, Ribeiro MC, Pereira MJ, Leite PP, Peralta-Santos A, Azevedo L. Spatiotemporal evolution of COVID-19 in Portugal's Mainland with self-organizing maps. Int J Health Geogr 2023; 22:4. [PMID: 36710328 PMCID: PMC9884330 DOI: 10.1186/s12942-022-00322-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Accepted: 12/23/2022] [Indexed: 01/30/2023] Open
Abstract
BACKGROUND Self-Organizing Maps (SOM) are an unsupervised learning clustering and dimensionality reduction algorithm capable of mapping an initial complex high-dimensional data set into a low-dimensional domain, such as a two-dimensional grid of neurons. In the reduced space, the original complex patterns and their interactions can be better visualized, interpreted and understood. METHODS We use SOM to simultaneously couple the spatial and temporal domains of the COVID-19 evolution in the 278 municipalities of mainland Portugal during the first year of the pandemic. Temporal 14-days cumulative incidence time series along with socio-economic and demographic indicators per municipality were analyzed with SOM to identify regions of the country with similar behavior and infer the possible common origins of the incidence evolution. RESULTS The results show how neighbor municipalities tend to share a similar behavior of the disease, revealing the strong spatiotemporal relationship of the COVID-19 spreading beyond the administrative borders of each municipality. Additionally, we demonstrate how local socio-economic and demographic characteristics evolved as determinants of COVID-19 transmission, during the 1st wave school density per municipality was more relevant, where during 2nd wave jobs in the secondary sector and the deprivation score were more relevant. CONCLUSIONS The results show that SOM can be an effective tool to analysing the spatiotemporal behavior of COVID-19 and synthetize the history of the disease in mainland Portugal during the period in analysis. While SOM have been applied to diverse scientific fields, the application of SOM to study the spatiotemporal evolution of COVID-19 is still limited. This work illustrates how SOM can be used to describe the spatiotemporal behavior of epidemic events. While the example shown herein uses 14-days cumulative incidence curves, the same analysis can be performed using other relevant data such as mortality data, vaccination rates or even infection rates of other disease of infectious nature.
Collapse
Affiliation(s)
- Igor Duarte
- Formely: Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais, 1049-001 Lisbon, Portugal
| | - Manuel C. Ribeiro
- CERENA/DER, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais, 1049-001 Lisbon, Portugal
| | - Maria João Pereira
- CERENA/DER, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais, 1049-001 Lisbon, Portugal
| | - Pedro Pinto Leite
- Direção de Serviços de Informação e Análise, Direção-Geral da Saúde, Lisbon, Portugal
| | - André Peralta-Santos
- Direção de Serviços de Informação e Análise, Direção-Geral da Saúde, Lisbon, Portugal
- NOVA National School of Public Health, Public Health Research Centre, Universidade NOVA de Lisboa, Lisbon, Portugal
- Comprehensive Health Research Centre (CHRC), Universidade NOVA de Lisboa, Lisbon, Portugal
| | - Leonardo Azevedo
- CERENA/DER, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais, 1049-001 Lisbon, Portugal
| |
Collapse
|
2
|
Advancing our understanding of cultural heterogeneity with unsupervised machine learning. JOURNAL OF INTERNATIONAL MANAGEMENT 2021. [DOI: 10.1016/j.intman.2021.100885] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
|
3
|
Identifying Spatiotemporal Patterns in Land Use and Cover Samples from Satellite Image Time Series. REMOTE SENSING 2021. [DOI: 10.3390/rs13050974] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
The use of satellite image time series analysis and machine learning methods brings new opportunities and challenges for land use and cover changes (LUCC) mapping over large areas. One of these challenges is the need for samples that properly represent the high variability of land used and cover classes over large areas to train supervised machine learning methods and to produce accurate LUCC maps. This paper addresses this challenge and presents a method to identify spatiotemporal patterns in land use and cover samples to infer subclasses through the phenological and spectral information provided by satellite image time series. The proposed method uses self-organizing maps (SOMs) to reduce the data dimensionality creating primary clusters. From these primary clusters, it uses hierarchical clustering to create subclusters that recognize intra-class variability intrinsic to different regions and periods, mainly in large areas and multiple years. To show how the method works, we use MODIS image time series associated to samples of cropland and pasture classes over the Cerrado biome in Brazil. The results prove that the proposed method is suitable for identifying spatiotemporal patterns in land use and cover samples that can be used to infer subclasses, mainly for crop-types.
Collapse
|
4
|
Brock J, Lange M, Tratalos JA, More SJ, Graham DA, Guelbenzu-Gonzalo M, Thulke HH. Combining expert knowledge and machine-learning to classify herd types in livestock systems. Sci Rep 2021; 11:2989. [PMID: 33542295 PMCID: PMC7862359 DOI: 10.1038/s41598-021-82373-3] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2020] [Accepted: 01/13/2021] [Indexed: 11/20/2022] Open
Abstract
A detailed understanding of herd types is needed for animal disease control and surveillance activities, to inform epidemiological study design and interpretation, and to guide effective policy decision-making. In this paper, we present a new approach to classify herd types in livestock systems by combining expert knowledge and a machine-learning algorithm called self-organising-maps (SOMs). This approach is applied to the cattle sector in Ireland, where a detailed understanding of herd types can assist with on-going discussions on control and surveillance for endemic cattle diseases. To our knowledge, this is the first time that the SOM algorithm has been used to differentiate livestock systems. In compliance with European Union (EU) requirements, relevant data in the Irish livestock register includes the birth, movements and disposal of each individual bovine, and also the sex and breed of each bovine and its dam. In total, 17 herd types were identified in Ireland using 9 variables. We provide a data-driven classification tree using decisions derived from the Irish livestock registration data. Because of the visual capabilities of the SOM algorithm, the interpretation of results is relatively straightforward and we believe our approach, with adaptation, can be used to classify herd type in any other livestock system.
Collapse
Affiliation(s)
- Jonas Brock
- Department of Ecological Modelling, PG Ecological Epidemiology, Helmholtz Centre for Environmental Research GmbH-UFZ, Leipzig, Germany. .,Animal Health Ireland, Carrick-on-Shannon, Co., Leitrim, Ireland.
| | - Martin Lange
- Department of Ecological Modelling, PG Ecological Epidemiology, Helmholtz Centre for Environmental Research GmbH-UFZ, Leipzig, Germany
| | - Jamie A Tratalos
- Centre for Veterinary Epidemiology and Risk Analysis, UCD School of Veterinary Medicine, University College Dublin, Dublin, D04 W6F6, Ireland
| | - Simon J More
- Centre for Veterinary Epidemiology and Risk Analysis, UCD School of Veterinary Medicine, University College Dublin, Dublin, D04 W6F6, Ireland
| | - David A Graham
- Animal Health Ireland, Carrick-on-Shannon, Co., Leitrim, Ireland
| | | | - Hans-Hermann Thulke
- Department of Ecological Modelling, PG Ecological Epidemiology, Helmholtz Centre for Environmental Research GmbH-UFZ, Leipzig, Germany
| |
Collapse
|
5
|
Han L, Yang G, Dai H, Yang H, Xu B, Li H, Long H, Li Z, Yang X, Zhao C. Combining self-organizing maps and biplot analysis to preselect maize phenotypic components based on UAV high-throughput phenotyping platform. PLANT METHODS 2019; 15:57. [PMID: 31149023 PMCID: PMC6537385 DOI: 10.1186/s13007-019-0444-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/29/2019] [Accepted: 05/22/2019] [Indexed: 05/31/2023]
Abstract
BACKGROUND With environmental deterioration, natural resource scarcity, and rapid population growth, mankind is facing severe global food security problems. To meet future needs, it is necessary to accelerate progress in breeding for new varieties with high yield and strong resistance. However, the traditional phenotypic screening methods have some disadvantages, such as destructive, inefficient, low-dimensional, labor-intensive and cumbersome, which seriously hinder the development of field breeding. Breeders urgently need a high-throughput technique for acquiring and evaluating phenotypic data that can efficiently screen out excellent phenotypic traits from large-scale genotype populations. RESULTS In the present study, we used an unmanned aerial vehicle (UAV) high-throughput phenotyping (HTP) platform to collect RGB and multispectral images for a breeding program and acquired multiple phenotypic components (or traits), such as plant height, normalized difference vegetation index, biomass accumulation, plant-height growth rate, lodging, and leaf color. By implementing self-organizing maps and principal components analysis biplots to establish phenotypic map and similarity, we proposed an UAV-assisted HTP framework for preselecting maize (Zee mays L.) phenotypic components (or traits). CONCLUSIONS This framework gives breeders additional information to allow them to quickly identify and preselect plants that have genotypes conferring desirable phenotypic components out of thousands of field plots. The present study also demonstrates that remote sensing is a powerful tool with which to acquire abundant phenotypic components. By using these rich phenotypic components, breeders should be able to more effectively identify and select superior genotypes.
Collapse
Affiliation(s)
- Liang Han
- Key Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture, Beijing Research Center for Information Technology in Agriculture, Beijing, 100097 China
- 2College of Architecture and Geomatics Engineering, Shanxi Datong University, Datong, 037003 China
- 4College of Geoscience and Surveying Engineering, China University of Mining and Technology (Beijing), Beijing, 100083 China
| | - Guijun Yang
- Key Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture, Beijing Research Center for Information Technology in Agriculture, Beijing, 100097 China
| | - Huayang Dai
- 4College of Geoscience and Surveying Engineering, China University of Mining and Technology (Beijing), Beijing, 100083 China
| | - Hao Yang
- Key Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture, Beijing Research Center for Information Technology in Agriculture, Beijing, 100097 China
- 3National Engineering Research Center for Information Technology in Agriculture, Beijing, 100097 China
| | - Bo Xu
- Key Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture, Beijing Research Center for Information Technology in Agriculture, Beijing, 100097 China
| | - Heli Li
- 3National Engineering Research Center for Information Technology in Agriculture, Beijing, 100097 China
| | - Huiling Long
- Key Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture, Beijing Research Center for Information Technology in Agriculture, Beijing, 100097 China
- 3National Engineering Research Center for Information Technology in Agriculture, Beijing, 100097 China
| | - Zhenhai Li
- 3National Engineering Research Center for Information Technology in Agriculture, Beijing, 100097 China
| | - Xiaodong Yang
- Key Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture, Beijing Research Center for Information Technology in Agriculture, Beijing, 100097 China
- 3National Engineering Research Center for Information Technology in Agriculture, Beijing, 100097 China
| | - Chunjiang Zhao
- Key Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture, Beijing Research Center for Information Technology in Agriculture, Beijing, 100097 China
- 3National Engineering Research Center for Information Technology in Agriculture, Beijing, 100097 China
| |
Collapse
|
6
|
Gandía-Aguiló V, Cibrián R, Soria E, Serrano AJ, Aguiló L, Paredes V, Gandía JL. Use of self-organizing maps for analyzing the behavior of canines displaced towards midline under interceptive treatment. Med Oral Patol Oral Cir Bucal 2017; 22:e233-e241. [PMID: 28160587 PMCID: PMC5359714 DOI: 10.4317/medoral.21509] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2016] [Accepted: 10/01/2016] [Indexed: 11/16/2022] Open
Abstract
Background Displaced maxillary permanent canine is one of the more frequent findings in canine eruption process and it’s easy to be outlined and early diagnosed by means of x-ray images. Late diagnosis frequently needs surgery to rescue the impacted permanent canine.
In many cases, interceptive treatment to redirect canine eruption is needed. However, some patients treated by interceptive means end up requiring fenestration to orthodontically guide the canine to its normal occlusal position.
It would be interesting, therefore, to discover the dental characteristics of patients who will need additional surgical treatment to interceptive treatment. Material and Methods To study the dental characteristics associated with canine impaction, conventional statistics have traditionally been used. This approach, although serving to illustrate many features of this problem, has not provided a satisfactory response or not provided an overall idea of the characteristics of these types of patients, each one of them with their own particular set of variables.
Faced with this situation, and in order to analyze the problem of impaction despite interceptive treatment, we have used an alternative method for representing the variables that have an influence on this syndrome. This method is known as Self-Organizing Maps (SOM), a method used for analyzing problems with multiple variables. Results We analyzed 78 patients with a PMC angulation higher than 100º. All of them were subject to interceptive treatment and in 21 cases it was necessary to undertake the above-mentioned fenestration to achieve the final eruption of the canine. Conclusions In this study, we describe the process of debugging variables and selecting the appropriate number of cells in SOM so as to adequately visualize the problem posed and the dental characteristics of patients with regard to a greater or lesser probability of the need for fenestration. Key words:Interceptive orthodontic treatment, altered eruption, impacted canines, neuronal networks, self-organizing maps.
Collapse
Affiliation(s)
- V Gandía-Aguiló
- Avenida Maria Cristina n 12- 2 , CP: 46001, Valencia, Spain,
| | | | | | | | | | | | | |
Collapse
|