Real-time prediction of influenza outbreaks in Belgium

Seasonal influenza is a worldwide public health concern. Forecasting its dynamics can improve the management of public health regulations, resources and infrastructure, and eventually reduce mortality and the costs induced by influenza-related absenteism. In Belgium, a network of Sentinel General Practitioners (SGPs) is in place for the early detection of the seasonal influenza epidemic. This surveillance network reports the weekly incidence of influenza-like illness (ILI) cases, which makes it possible to detect the epidemic onset, as well as other characteristics of the epidemic season. In this paper, we present an approach for predicting the weekly ILI incidence in real-time by resorting to a dynamically calibrated compartmental model, which furthermore takes into account the dynamics of other influenza seasons. In order to validate the proposed approach, we used data collected by the Belgian SGPs for the influenza seasons 2010–2016. In spite of the great variability among different epidemic seasons, providing weekly predictions makes it possible to capture variations in the ILI incidence. The confidence region becomes more representative of the epidemic behavior as ILI data from more seasons become available. Since the SIR model is then calibrated dynamically every week, the predicted ILI curve gets rapidly tuned to the dynamics of the ongoing season. The results show that the proposed method can be used to characterize the overall behavior of an epidemic

Documenting and predicting topic changes in Computers in Biology and Medicine: A bibliometric keyword analysis from 1990 to 2017

The Computers in Biology and Medicine (CBM) journal promotes the use of com-puting machinery in the fields of bioscience and medicine. Since the first volume in 1970, the importance of computers in these fields has grown dramatically, this is evident in the diversification of topics and an increase in the publication rate. In this study, we quantify both change and diversification of topics covered in CBM. This is done by analysing the author supplied keywords, since they were electronically captured in 1990. The analysis starts by selecting 40 keywords, related to Medical (M) (7), Data (D)(10), Feature (F) (17) and Artificial Intelligence (AI) (6) methods. Automated keyword clustering shows the statistical connection between the selected keywords. We found that the three most popular topics in CBM are: Support Vector Machine (SVM), Elec-troencephalography (EEG) and IMAGE PROCESSING. In a separate analysis step, we bagged the selected keywords into sequential one year time slices and calculated the normalized appearance. The results were visualised with graphs that indicate the CBM topic changes. These graphs show that there was a transition from Artificial Neural Network (ANN) to SVM. In 2006 SVM replaced ANN as the most important AI algo-rithm. Our investigation helps the editorial board to manage and embrace topic change. Furthermore, our analysis is interesting for the general reader, as the results can help them to adjust their research directions

Modelagem espacialmente explícita em redes: compreendendo padrões e descrevendo processos

In contrast to established approaches that analyze networks based on their structural properties, networks can also be studied by investigating the patterns that are evolved by a discrete dynamical system built upon them, such as cellular automata (CAs). Combined with networks these tools can be used to map the relationship between the network architecture and its impact on the patterns evolved by the governing spatially discrete dynamical system. This thesis focuses on the investigation of discrete spatially explicit models (SEMs), among which are CAs, for network analysis and characterization. The relationship between network architecture and its dynamic aspects concerning pattern formation is studied. Additionally, this work aims at the development of evolutionary methods that can be employed for extracting features from such patterns and then be used as network descriptors. In order to achieve this goal, methods that integrate the network structure with the SEMs were proposed, implemented and analyzed. The proposed family of network automata is characterized by birth-survival dynamics that results in different categories of spatio-temporal patterns. Such patterns were quantitatively assessed and used to characterize different network topologies and perform classification tasks in the context of pattern recognition. Inspired by the classic Life-like CA, the proposed Life-like Network Automata (LLNA) illustrate how such tasks can be performed in real-world applications. In addition, the rock-paper-scissors (RPS) model, normally implemented on square lattices, was investigated by defining it on networks. The obtained results confirm the potential of the proposed quantitative analysis of the spatio-temporal patterns for network classification. This quantitative analysis was performed for a set of different pattern recognition tasks and for the majority of them, the classification performance improved. In addition, the reliability of LLNA as a general tool for pattern recognition applications was demonstrated in a diverse scope of classification tasks. The applicability of structural network descriptors was also highlighted in the context of shape characterization in computer vision. Through the proposed approach, the link between these network descriptors and the shape properties, such as angle and curvature, was illustrated. Moreover, when chosen adequately, the network descriptors led to a better classification performance for different shape recognition tasks. Regarding the RPS model, we demonstrated that the presence of long-range correlations in some networks directly influence the RPS dynamics. Finally, it was shown how a commuter network can be used to predict influenza outbreaks. All the proposed methods use different aspects of network analysis and contribute to the study of CAs and other SEMs on irregular tessellations, in contrast to the commonly used regular topologies. In addition, new insights were obtained concerning pattern recognition in networks through the use of spatio-temporal patterns as network descriptors.Em contraste às abordagens clássicas que analisam redes com base em suas propriedades estruturais, as redes também podem ser estudadas investigando-se os padrões desenvolvidos por um sistema dinâmico discreto construído sobre essas redes, como os autômatos celulares (CAs). Combinadas às redes, essas ferramentas podem ser usadas para se mapear a relação entre a arquitetura da rede e seu impacto nos padrões obtidos pelo sistema dinâmico subjacente. Esta tese está focada na investigação de modelos discretos espacialmente explícitos (SEMs), entre os quais os CAs, para análise e caracterização de redes. A relação entre a arquitetura da rede e seu aspecto dinâmico em relação à formação de padrões é investigada. Além disso, este trabalho visa o desenvolvimento de métodos evolutivos que podem ser usados para extrair características de tais padrões para, então, serem usados como descritores de redes. Para atingir este objetivo, métodos que integram a estrutura da rede com os SEMs foram propostos, implementados e analisados. A família de redes-autômatos proposta é caracterizada por uma dinâmica de nascimento-sobrevivência que resulta em diferentes categorias de padrões espaço-temporais. Tais padrões foram avaliados quantitativamente e utilizados para caracterizar diferentes topologias de redes e realizar tarefas de classificação no contexto do reconhecimento de padrões. Inspirados pelo clássico Life-Like CA, a rede-autômato proposta, Life-like (LLNA), ilustra como tais tarefas podem ser realizadas em aplicações mais realistas. Além disso, o modelo de rock-paper-scissors (RPS), normalmente implementado em reticulados quadrados, foi investigado usando-se redes como tesselações. Os resultados obtidos confirmam o potencial da análise quantitativa proposta dos padrões espaço-temporais para classificação de redes. Essa análise quantitativa foi realizada para um conjunto de tarefas de reconhecimento de padrões, e, para a maioria dessas tarefas, o desempenho da classificação melhorou. Além disso, a confiabilidade do LLNA como uma ferramenta genérica para reconhecimento de padrões foi demonstrada para várias tarefas de classificação de diferentes escopos. A aplicabilidade de descritores estruturais de redes também foi destacada no contexto de caracterização de formas em visão computacional. Através da abordagem proposta, a ligação entre esses descritores de rede e as propriedades da forma, como ângulo e curvatura, foi ilustrada. Além disso, quando escolhidos adequadamente, os descritores de rede levam a um melhor desempenho de classificação para diferentes tarefas de categorização de formas. No que diz respeito ao modelo RPS, demonstramos que a presença de correlações de longo alcance nas redes afeta diretamente a dinâmica do modelo. Finalmente, foi apresentado como uma rede de transporte pode ser usada para prever surtos de gripe. Todos os métodos propostos utilizam diferentes aspectos da análise de redes e contribuem para o estudo de CAs e outras SEMs em tesselações irregulares, uma vez que estes modelos são geralmente descritos em topologias regulares. Além disso, uma nova metodologia foi proposta em relação ao reconhecimento de padrões em redes através do uso de padrões espaço-temporais como descritores da rede

Modelagem espacialmente explícita em redes: compreendendo padrões e descrevendo processos

In contrast to established approaches that analyze networks based on their structural properties, networks can also be studied by investigating the patterns that are evolved by a discrete dynamical system built upon them, such as cellular automata (CAs). Combined with networks these tools can be used to map the relationship between the network architecture and its impact on the patterns evolved by the governing spatially discrete dynamical system. This thesis focuses on the investigation of discrete spatially explicit models (SEMs), among which are CAs, for network analysis and characterization. The relationship between network architecture and its dynamic aspects concerning pattern formation is studied. Additionally, this work aims at the development of evolutionary methods that can be employed for extracting features from such patterns and then be used as network descriptors. In order to achieve this goal, methods that integrate the network structure with the SEMs were proposed, implemented and analyzed. The proposed family of network automata is characterized by birth-survival dynamics that results in different categories of spatio-temporal patterns. Such patterns were quantitatively assessed and used to characterize different network topologies and perform classification tasks in the context of pattern recognition. Inspired by the classic Life-like CA, the proposed Life-like Network Automata (LLNA) illustrate how such tasks can be performed in real-world applications. In addition, the rock-paper-scissors (RPS) model, normally implemented on square lattices, was investigated by defining it on networks. The obtained results confirm the potential of the proposed quantitative analysis of the spatio-temporal patterns for network classification. This quantitative analysis was performed for a set of different pattern recognition tasks and for the majority of them, the classification performance improved. In addition, the reliability of LLNA as a general tool for pattern recognition applications was demonstrated in a diverse scope of classification tasks. The applicability of structural network descriptors was also highlighted in the context of shape characterization in computer vision. Through the proposed approach, the link between these network descriptors and the shape properties, such as angle and curvature, was illustrated. Moreover, when chosen adequately, the network descriptors led to a better classification performance for different shape recognition tasks. Regarding the RPS model, we demonstrated that the presence of long-range correlations in some networks directly influence the RPS dynamics. Finally, it was shown how a commuter network can be used to predict influenza outbreaks. All the proposed methods use different aspects of network analysis and contribute to the study of CAs and other SEMs on irregular tessellations, in contrast to the commonly used regular topologies. In addition, new insights were obtained concerning pattern recognition in networks through the use of spatio-temporal patterns as network descriptors.Em contraste às abordagens clássicas que analisam redes com base em suas propriedades estruturais, as redes também podem ser estudadas investigando-se os padrões desenvolvidos por um sistema dinâmico discreto construído sobre essas redes, como os autômatos celulares (CAs). Combinadas às redes, essas ferramentas podem ser usadas para se mapear a relação entre a arquitetura da rede e seu impacto nos padrões obtidos pelo sistema dinâmico subjacente. Esta tese está focada na investigação de modelos discretos espacialmente explícitos (SEMs), entre os quais os CAs, para análise e caracterização de redes. A relação entre a arquitetura da rede e seu aspecto dinâmico em relação à formação de padrões é investigada. Além disso, este trabalho visa o desenvolvimento de métodos evolutivos que podem ser usados para extrair características de tais padrões para, então, serem usados como descritores de redes. Para atingir este objetivo, métodos que integram a estrutura da rede com os SEMs foram propostos, implementados e analisados. A família de redes-autômatos proposta é caracterizada por uma dinâmica de nascimento-sobrevivência que resulta em diferentes categorias de padrões espaço-temporais. Tais padrões foram avaliados quantitativamente e utilizados para caracterizar diferentes topologias de redes e realizar tarefas de classificação no contexto do reconhecimento de padrões. Inspirados pelo clássico Life-Like CA, a rede-autômato proposta, Life-like (LLNA), ilustra como tais tarefas podem ser realizadas em aplicações mais realistas. Além disso, o modelo de rock-paper-scissors (RPS), normalmente implementado em reticulados quadrados, foi investigado usando-se redes como tesselações. Os resultados obtidos confirmam o potencial da análise quantitativa proposta dos padrões espaço-temporais para classificação de redes. Essa análise quantitativa foi realizada para um conjunto de tarefas de reconhecimento de padrões, e, para a maioria dessas tarefas, o desempenho da classificação melhorou. Além disso, a confiabilidade do LLNA como uma ferramenta genérica para reconhecimento de padrões foi demonstrada para várias tarefas de classificação de diferentes escopos. A aplicabilidade de descritores estruturais de redes também foi destacada no contexto de caracterização de formas em visão computacional. Através da abordagem proposta, a ligação entre esses descritores de rede e as propriedades da forma, como ângulo e curvatura, foi ilustrada. Além disso, quando escolhidos adequadamente, os descritores de rede levam a um melhor desempenho de classificação para diferentes tarefas de categorização de formas. No que diz respeito ao modelo RPS, demonstramos que a presença de correlações de longo alcance nas redes afeta diretamente a dinâmica do modelo. Finalmente, foi apresentado como uma rede de transporte pode ser usada para prever surtos de gripe. Todos os métodos propostos utilizam diferentes aspectos da análise de redes e contribuem para o estudo de CAs e outras SEMs em tesselações irregulares, uma vez que estes modelos são geralmente descritos em topologias regulares. Além disso, uma nova metodologia foi proposta em relação ao reconhecimento de padrões em redes através do uso de padrões espaço-temporais como descritores da rede

A Method for Processing and Computational Analysis of histopathological images to support the diagnosis of Cervical Cancer

A histopatologia é considerada um dos recursos diagnósticos mais importantes na prática médica e caracteriza-se pelo estudo das alterações estruturais e morfológicas das células e dos tecidos causadas por doenças. Atualmente, o principal método utilizado no diagnóstico histopatológico de imagens microscópicas, obtidas por meio de amostras em exames convencionais, é a avaliação visual do patologista, a qual se baseia na experiência do mesmo. O uso de técnicas de processamento computacional de imagens possibilita a identificação de elementos estruturais e a determinação de características inerentes, subsidiando o estudo da organização estrutural das células e de suas variações patológicas. A utilização de métodos computacionais no auxílio ao diagnóstico visa diminuir a subjetividade do processo de avaliação e classificação realizado pelo médico. Diferentes características dos tecidos podem ser mapeadas por meio de métricas específicas que poderão ser utilizadas em sistemas de reconhecimento de padrões. Dentro desta perspectiva, o objetivo geral deste trabalho inclui a proposta, a implementação e a avaliação de um método para a identificação e a análise de estruturas histológicas, a ser utilizado para a análise de lesões neoplásicas do colo do útero (NICs) a partir de amostras histopatológicas. Este trabalho foi desenvolvido em colaboração com uma equipe de patologistas, especialistas do domínio. As imagens microscópicas digitalizadas foram adquiridas a partir de lâminas previamente fixadas, contendo amostras de biópsias. Para segmentação dos núcleos celulares, foi implementado um pipeline de operadores morfológicos. Métodos de segmentação baseados em cor também foram testados e comparados à abordagem morfológica. Foi proposta e implementada uma abordagem baseada em camadas para representação do tecido, adotando-se a Triangulação de Delaunay (TD) como modelo de grafo de vizinhança. A TD apresenta algumas propriedades particulares que permitem a extração de métricas específicas. Foram utilizados algoritmos de agrupamento e morfologia de grafos, adotando-se critérios de semelhança e relações de adjacência entre os triângulos da rede, a fim de se obter a fronteira entre as camadas histológicas do tecido epitelial de forma automática. As seguintes métricas foram extraídas dos agrupamentos resultantes: grau médio, entropia e taxa de ocupação dos triângulos da rede. Finalmente, foi projetado um classificador estatístico levando-se em consideração os diferentes agrupamentos que poderiam ser obtidos a partir das imagens de treinamento. Valores de acurácia, sensitividade e especificidade foram utilizadas para avaliação dos resultados obtidos. Foi implementada validação cruzada em todos os experimentos realizados e foi utilizado um total de 116 imagens. Primeiro, foi avaliado a acurácia da metodologia proposta na determinação correta da presença de anomalia no tecido, para isto, todas as imagens que apresentavam NICs foram agrupadas em uma mesma classe. A maior taxa de acurácia obtida neste experimento foi de 88%. Em uma segunda etapa, foram realizadas avaliações entre as seguintes classes: Normal e NIC-I; NIC-I e NIC-II, e, NIC-II e NIC-III, obtendo-se taxas de acurácia máximas de 73%, 77% e 86%, respectivamente. Além disso, foi verificada também, a acurácia na discriminação entre os três tipos de NICs e regiões normais, obtendo-se acurácia de 64%. As taxas de ocupação relativas aos agrupamentos representativos das camadas basais e superficiais, foram os atributos que levaram às maiores taxas de acurácia. Os resultados obtidos permitem verificar a adequação do método proposto na representação e análise do processo de evolução das NICs no tecido epitelial do colo uterino.Histopathology is considered one of the most important diagnostic tools in medical routine and is characterized by the study of structural and morphological changes of the cells in biological tissues caused by diseases. Currently, the visual assessment of the pathologist is the main method used in the histopathological diagnosis of microscopic images obtained from biopsy samples. This diagnosis is usually based on the experience of the pathologist. The use of computational techniques in the processing of these images allows the identification of structural elements and the determination of inherent characteristics, supporting the study of the structural organization of tissues and their pathological changes. Also, the use of computational methods to improve diagnosis aims to reduce the subjectivity of the evaluation made by the physician. Besides, different tissue characteristics can be mapped through specific metrics that can be used in pattern recognition systems. Within this perspective, the overall objective of this work includes the proposal, the implementation and the evaluation of a methodology for the identification and analysis of histological structures. This methodology includes the specification of a method for the analysis of cervical intraepithelial neoplasias (CINs) from histopathological samples. This work was developed in collaboration with a team of pathologists. Microscopic images were acquired from blades previously stained, containing samples of biopsy examinations. For the segmentation of cell nuclei, a pipeline of morphological operators were implemented. Segmentation techniques based on color were also tested and compared to the morphological approach. For the representation of the tissue architecture an approach based on the tissue layers was proposed and implemented adopting the Delaunay Triangulation (DT) as neighborhood graph. The DT has some special properties that allow the extraction of specific metrics. Clustering algorithms and graph morphology were used in order to automatically obtain the boundary between the histological layers of the epithelial tissue. For this purpose, similarity criteria and adjacency relations between the triangles of the network were explored. The following metrics were extracted from the resulting clusters: mean degree, entropy and the occupation rate of the clusters. Finally, a statistical classifier was designed taking into account the different combinations of clusters that could be obtained from the training process. Values of accuracy, sensitivity and specificity were used to evaluate the results. All the experiments were taken in a cross-validation process (5-fold) and a total of 116 images were used. First, it was evaluated the accuracy in determining the correct presence of abnormalities in the tissue. For this, all images presenting CINs were grouped in the same class. The highest accuracy rate obtained for this evaluation was 88%. In a second step, the discrimination between the following classes were analyzed: Normal/CIN 1; CIN 1/CIN 2, and, CIN 2/CIN 3, which represents the histological grading of the CINs. In a similar way, the highest accuracy rates obtained were 73%, 77% and 86%, respectively. In addition, it was also calculated the accuracy rate in discriminating between the four classes analyzed in this work: the three types of CINs and the normal region. In this last case, it was obtained a rate of 64%.The occupation rate for the basal and superficial layers were the attributes that led to the highest accuracy rates. The results obtained shows the adequacy of the proposed method in the representation and classification of the CINs evolution in the cervical epithelial tissue

Spatially explicit modeling on networks : understanding patterns & describing processes

In contrast to established approaches that analyze networks based on their structural properties, networks can also be studied by investigating the patterns that are evolved by a discrete dynamical system built upon them, such as cellular automata (CAs). Combined with networks these tools can be used to map the relationship between the network architecture and its impact on the patterns evolved by the governing spatially discrete dynamical system. This thesis focuses on the investigation of discrete spatially explicit models (SEMs), among which are CAs, for network analysis and characterization. The relationship between network architecture and its dynamic aspects concerning pattern formation is studied. Additionally, this work aims at the development of evolutionary methods that can be employed for extracting features from such patterns and then be used as network descriptors. In order to achieve this goal, methods that integrate the network structure with the SEMs were proposed, implemented and analyzed. The proposed family of network automata is characterized by birth-survival dynamics that results in different categories of spatio-temporal patterns. Such patterns were quantitatively assessed and used to characterize different network topologies and perform classification tasks in the context of pattern recognition. Inspired by the classic Life-like CA, the proposed Life-like Network Automata (LLNA) illustrate how such tasks can be performed in real-world applications. In addition, the rock-paper-scissors (RPS) model, normally implemented on square lattices, was investigated by defining it on networks. The obtained results confirm the potential of the proposed quantitative analysis of the spatio-temporal patterns for network classification. This quantitative analysis was performed for a set of different pattern recognition tasks and for the majority of them, the classification performance improved. In addition, the reliability of LLNA as a general tool for pattern recognition applications was demonstrated in a diverse scope of classification tasks. The applicability of structural network descriptors was also highlighted in the context of shape characterization in computer vision. Through the proposed approach, the link between these network descriptors and the shape properties, such as angle and curvature, was illustrated. Moreover, when chosen adequately, the network descriptors led to a better classification performance for different shape recognition tasks. Regarding the RPS model, we demonstrated that the presence of long-range correlations in some networks directly influence the RPS dynamics. Finally, it was shown how a commuter network can be used to predict influenza outbreaks. All the proposed methods use different aspects of network analysis and contribute to the study of CAs and other SEMs on irregular tessellations, in contrast to the commonly used regular topologies. In addition, new insights were obtained concerning pattern recognition in networks through the use of spatio-temporal patterns as network descriptors

What is the ocular phenotype associated with a single exon 78 deletion in Duchenne muscular dystrophy?

International audienc

Mobility and the spatial spread of SARS-COV-2 in Belgium

We analyse and mutually compare time series of covid-19-related data and mobility data across Belgium's 43 arrondissements (NUTS 3). In this way, we reach three conclusions. First, we could detect a decrease in mobility during high-incidence stages of the pandemic. This is expressed as a sizeable change in the average amount of time spent outside one's home arrondissement, investigated over five distinct periods, and in more detail using an inter-arrondissement "connectivity index"(CI). Second, we analyse spatio-temporal covid-19-related hospitalisation time series, after smoothing them using a generalise additive mixed model (GAMM). We confirm that some arrondissements are ahead of others and morphologically dissimilar to others, in terms of epidemiological progression. The tools used to quantify this are time-lagged cross-correlation (TLCC) and dynamic time warping (DTW), respectively. Third, we demonstrate that an arrondissement's CI with one of the three identified first-outbreak arrondissements is correlated to a substantial local excess mortality some five to six weeks after the first outbreak. More generally, we couple results leading to the first and second conclusion, in order to demonstrate an overall correlation between CI values on the one hand, and TLCC and DTW values on the other. We conclude that there is a strong correlation between physical movement of people and viral spread in the early stage of the sars-cov-2 epidemic in Belgium, though its strength weakens as the virus spreads

Influence of topology on the dynamics of in silico ecosystems with non-hierarchical competition

The extinction of ecosystems and the mechanisms that support or limit species coexistence have long been studied by scientists. It has been shown that competition and cyclic dominance among species promote species coexistence, such as in the classic Rock-Paper-Scissors (RPS) game. However, individuals’ mobility and the underlying topology that defines the neighbourhood relations between individuals also play an important role in maintaining biodiversity. Typically, square grids are used for simulating such interactions. However, these constrain the individuals’ spatial degrees of freedom. In this work, we investigate the effect of the underlying topology on the RPS dynamics. For that purpose, we considered networks with varying node degree distributions and generated according to different theoretical models. We analyzed the time to the first extinction and the patchiness of the in silico ecosystem over time. In general, we observed a distinct large effect of the network topology on the RPS dynamics. Moreover, leaving regular networks aside, the probability of extinction is very high for some network models due to their inherent long-range connections. On the other hand, spatial arrangements characterized by nearest neighbors interactions have fewer long-range correlations, which is essential for biodiversity