Improving Structure MCMC for Bayesian Networks through Markov Blanket Resampling

Algorithms for inferring the structure of Bayesian networks from data have become an increasingly popular method for uncovering the direct and indirect influences among variables in complex systems. A Bayesian approach to structure learning uses posterior probabilities to quantify the strength with which the data and prior knowledge jointly support each possible graph feature. Existing Markov Chain Monte Carlo (MCMC) algorithms for estimating these posterior probabilities are slow in mixing and convergence, especially for large networks. We present a novel Markov blanket resampling (MBR) scheme that intermittently reconstructs the Markov blanket of nodes, thus allowing the sampler to more effectively traverse low-probability regions between local maxima. As we can derive the complementary forward and backward directions of the MBR proposal distribution, the Metropolis-Hastings algorithm can be used to account for any asymmetries in these proposals. Experiments across a range of network sizes show that the MBR scheme outperforms other state-of-the-art algorithms, both in terms of learning performance and convergence rate. In particular, MBR achieves better learning performance than the other algorithms when the number of observations is relatively small and faster convergence when the number of variables in the network is large

Novel Epidemiological tools to inform malaria control and elimination in Melanesia

Background: Malaria is a vector-borne parasitic disease that in 2017 was responsible for an estimated 219 million clinical cases of infection and an estimated 435000 deaths globally. Estimating the spatial distribution of malaria within endemic countries, and risk factors for transmission, is essential to the effective planning and allocation of malaria prevention interventions. The aims of this PhD thesis were to: 1) describe the epidemiology of malaria in Papua New Guinea (PNG) and summarise previous control strategies and outcomes in PNG over the last century; 2) compare the accuracy of multilevel generalised linear regression models (GLMs) with Bayesian decision network (BDN) models in the spatial prediction of prevalence of malaria in PNG; 3) predict the geographic niches of eight genotypes of Plasmodium falciparum in PNG to ascertain patterns of connectivity in the human population in terms of malaria transmission; and 4) examine the impact of human movement between high and low transmission intensity locations on malaria transmission using a mathematical model based on the example of two islands of Solomon Islands. Methods: Data for this research was obtained from published literature, a national malaria indicator survey conducted randomly selected villages in PNG in 2010 and 2011, and genotyped malaria indicator survey data collected in PNG and Solomon Islands between 2008 and 2009. Climate data at 1km resolution was obtained from the WorldClim and environmental remote sensing image data were obtained from Earthdata. Modelling approaches included: a comparison of GLMs with BDN models using point prevalence and ecological data to predict the spatial distribution of P. falciparum and P. vivax malaria in PNG; a Dirichlet regression model examining associations of P. falciparum genotype predominance with ecological covariates for the prediction of geographic niches of distinct parasite genotypes in PNG; and a Ross-Macdonald mathematical model using varying estimations of human migration rates and estimates of P. falciparum prevalence in two island of Solomon Islands for the estimation of the impact of human migration on malaria transmission. Results: In terms of P. falciparum and P. vivax spatial distribution in PNG, BDN models were found to have improved accuracy in spatial predictions when compared with generalised linear models. The predicted spatial distribution of P. falciparum and P. vivax based on BDN models followed a similar pattern to survey data with higher predicted prevalence on the islands to the East of PNG and northern coastline of the mainland, and lower predicted prevalence in the highlands and south coast. The results of the Dirichlet regression model identified geographic niches of eight distinct P. falciparum genotypes in PNG based on associations with population density, elevation, distance to the coastline, latitude and longitude, and their quadratic terms. The results of the mathematical model predicted that in the absence of sustained vector control post-elimination, resurgence of malaria may occur relatively quickly in low-transmission intensity locations where connectivity with high-transmission intensity locations exists due to human migration, such as in the islands of Solomon Islands. Conclusions: This PhD research provides a comprehensive review of literature on the control strategies for and challenges to, achieving goal of global malaria elimination, and a review of the current epidemiology of malaria, and major periods of malaria control in PNG. This thesis identifies novel epidemiological methods for improved prediction accuracy in the spatial distribution of malaria based on environmental and climate predictors, a method for inferring human connectivity in terms of malaria transmission in PNG using parasite genotype data and the application of a mathematical model to in examining the transmission dynamics of malaria transmission in two islands in Solomon Islands

Book of Abstracts XVIII Congreso de Biometría CEBMADRID

Abstracts of the XVIII Congreso de Biometría CEBMADRID held from 25 to 27 May in MadridInteractive modelling and prediction of patient evolution via multistate models / Leire Garmendia Bergés, Jordi Cortés Martínez and Guadalupe Gómez Melis : This research was funded by the Ministerio de Ciencia e Innovación (Spain) [PID2019104830RBI00]; and the Generalitat de Catalunya (Spain) [2017SGR622 and 2020PANDE00148].Operating characteristics of a model-based approach to incorporate non-concurrent controls in platform trials / Pavla Krotka, Martin Posch, Marta Bofill Roig : EU-PEARL (EU Patient-cEntric clinicAl tRial pLatforms) project has received funding from the Innovative Medicines Initiative (IMI) 2 Joint Undertaking (JU) under grant agreement No 853966. This Joint Undertaking receives support from the European Union’s Horizon 2020 research and innovation programme and EFPIA and Children’s Tumor Foundation, Global Alliance for TB Drug Development non-profit organisation, Spring works Therapeutics Inc.Modeling COPD hospitalizations using variable domain functional regression / Pavel Hernández Amaro, María Durbán Reguera, María del Carmen Aguilera Morillo, Cristobal Esteban Gonzalez, Inma Arostegui : This work is supported by the grant ID2019-104901RB-I00 from the Spanish Ministry of Science, Innovation and Universities MCIN/AEI/10.13039/501100011033.Spatio-temporal quantile autoregression for detecting changes in daily temperature in northeastern Spain / Jorge Castillo-Mateo, Alan E. Gelfand, Jesús Asín, Ana C. Cebrián / Spatio-temporal quantile autoregression for detecting changes in daily temperature in northeastern Spain : This work was partially supported by the Ministerio de Ciencia e Innovación under Grant PID2020-116873GB-I00; Gobierno de Aragón under Research Group E46_20R: Modelos Estocásticos; and JC-M was supported by Gobierno de Aragón under Doctoral Scholarship ORDEN CUS/581/2020.Estimation of the area under the ROC curve with complex survey data / Amaia Iparragirre, Irantzu Barrio, Inmaculada Arostegui : This work was financially supported in part by IT1294-19, PID2020-115882RB-I00, KK-2020/00049. The work of AI was supported by PIF18/213.INLAMSM: Adjusting multivariate lattice models with R and INLA / Francisco Palmí Perales, Virgilio Gómez Rubio and Miguel Ángel Martínez Beneito : This work has been supported by grants PPIC-2014-001-P and SBPLY/17/180501/000491, funded by Consejería de Educación, Cultura y Deportes (Junta de Comunidades de Castilla-La Mancha, Spain) and FEDER, grant MTM2016-77501-P, funded by Ministerio de Economía y Competitividad (Spain), grant PID2019-106341GB-I00 from Ministerio de Ciencia e Innovación (Spain) and a grant to support research groups by the University of Castilla-La Mancha (Spain). F. Palmí-Perales has been supported by a Ph.D. scholarship awarded by the University of Castilla-La Mancha (Spain)

Quantifying spatio-temporal variation in malaria transmission in near elimination settings using individual level surveillance data

As countries move towards malaria elimination, tracking progress through quantifying changes in transmission over space and time is key. This information is necessary to effectively target resources to remaining ‘hotspots’ (high-risk locations) and ‘hotpops’ (high-risk populations) where transmission remains, decide if and when it is appropriate to scale back interventions, and to evaluate the success of existing interventions. However, as countries approach zero cases, it becomes difficult to measure transmission. Traditional metrics, such as the prevalence of parasites in the population, are no longer appropriate due to small numbers and increasingly focal distributions of cases over space and time. In order to address this, this thesis developed Bayesian network inference approaches to utilise information about the time and location of cases showing symptoms of malaria to jointly infer the likelihood that a) each observed case was linked to another by transmission and b) that a case was infected by an external, unobserved source. This information was used to calculate individual reproduction numbers for each reported case, or how many new cases of malaria are expected to have resulted from each case. In elimination settings, quantifying the distribution of individual reproduction numbers provides useful information about how quickly a disease may die out, and how the introduction of new cases through importation may affect ongoing transmission. These estimates were incorporated into additive regression models as well as geostatistical models to map how malaria transmission varied over space and time as well as considering timelines to elimination and the likelihood of resurgence of transmission once zero cases is achieved. This approach was applied to previously unanalysed individual-level datasets of malaria cases from China and El Salvador.Open Acces

Spatial epidemiological approaches to inform leptospirosis surveillance and control: a systematic review and critical appraisal of methods

Leptospirosis is a global zoonotic disease that the transmission is driven by complex geographical and temporal variation in demographics, animal hosts and socioecological factors. This results in complex challenges for the identification of high‐risk areas. Spatial and temporal epidemiological tools could be used to support leptospirosis control programs, but the adequacy of its application has not been evaluated. We searched literature in six databases including PubMed, Web of Science, EMBASE, Scopus, SciELO and Zoological Record to systematically review and critically assess the use of spatial and temporal analytical tools for leptospirosis and to provide general framework for its application in future studies. We reviewed 115 articles published between 1930 and October 2018 from 41 different countries. Of these, 65 (56.52%) articles were on human leptospirosis, 39 (33.91%) on animal leptospirosis and 11 (9.5%) used data from both human and animal leptospirosis. Spatial analytical (n = 106) tools were used to describe the distribution of incidence/prevalence at various geographical scales (96.5%) and to explored spatial patterns to detect clustering and hot spots (33%). A total of 51 studies modelled the relationships of various variables on the risk of human (n = 31), animal (n = 17) and both human and animal infection (n = 3). Among those modelling studies, few studies had generated spatially structured models and predictive maps of human (n = 2/31) and animal leptospirosis (n = 1/17). In addition, nine studies applied time‐series analytical tools to predict leptospirosis incidence. Spatial and temporal analytical tools have been greatly utilized to improve our understanding on leptospirosis epidemiology. Yet the quality of the epidemiological data, the selection of covariates and spatial analytical techniques should be carefully considered in future studies to improve usefulness of evidence as tools to support leptospirosis control. A general framework for the application of spatial analytical tools for leptospirosis was proposed

Characterizing personalized effects of family information on disease risk using graph representation learning

Family history is considered a risk factor for many diseases because it implicitly captures shared genetic, environmental and lifestyle factors. A nationwide electronic health record (EHR) system spanning multiple generations presents new opportunities for studying a connected network of medical histories for entire families. In this work we present a graph-based deep learning approach for learning explainable, supervised representations of how each family member's longitudinal medical history influences a patient's disease risk. We demonstrate that this approach is beneficial for predicting 10-year disease onset for 5 complex disease phenotypes, compared to clinically-inspired and deep learning baselines for a nationwide EHR system comprising 7 million individuals with up to third-degree relatives. Through the use of graph explainability techniques, we illustrate that a graph-based approach enables more personalized modeling of family information and disease risk by identifying important relatives and features for prediction