Causal Inference in Disease Spread across a Heterogeneous Social System

Diffusion processes are governed by external triggers and internal dynamics in complex systems. Timely and cost-effective control of infectious disease spread critically relies on uncovering the underlying diffusion mechanisms, which is challenging due to invisible causality between events and their time-evolving intensity. We infer causal relationships between infections and quantify the reflexivity of a meta-population, the level of feedback on event occurrences by its internal dynamics (likelihood of a regional outbreak triggered by previous cases). These are enabled by our new proposed model, the Latent Influence Point Process (LIPP) which models disease spread by incorporating macro-level internal dynamics of meta-populations based on human mobility. We analyse 15-year dengue cases in Queensland, Australia. From our causal inference, outbreaks are more likely driven by statewide global diffusion over time, leading to complex behavior of disease spread. In terms of reflexivity, precursory growth and symmetric decline in populous regions is attributed to slow but persistent feedback on preceding outbreaks via inter-group dynamics, while abrupt growth but sharp decline in peripheral areas is led by rapid but inconstant feedback via intra-group dynamics. Our proposed model reveals probabilistic causal relationships between discrete events based on intra- and inter-group dynamics and also covers direct and indirect diffusion processes (contact-based and vector-borne disease transmissions).Comment: arXiv admin note: substantial text overlap with arXiv:1711.0635

The temporal event-based model: Learning event timelines in progressive diseases

Timelines of events, such as symptom appearance or a change in biomarker value, provide powerful signatures that characterise progressive diseases. Understanding and predicting the timing of events is important for clinical trials targeting individuals early in the disease course when putative treatments are likely to have the strongest effect. However, previous models of disease progression cannot estimate the time between events and provide only an ordering in which they change. Here, we introduce the temporal event-based model (TEBM), a new probabilistic model for inferring timelines of biomarker events from sparse and irregularly sampled datasets. We demonstrate the power of the TEBM in two neurodegenerative conditions: Alzheimer's disease (AD) and Huntington's disease (HD). In both diseases, the TEBM not only recapitulates current understanding of event orderings but also provides unique new ranges of timescales between consecutive events. We reproduce and validate these findings using external datasets in both diseases. We also demonstrate that the TEBM improves over current models; provides unique stratification capabilities; and enriches simulated clinical trials to achieve a power of 80% with less than half the cohort size compared with random selection. The application of the TEBM naturally extends to a wide range of progressive conditions

DPVis: Visual Analytics with Hidden Markov Models for Disease Progression Pathways

Clinical researchers use disease progression models to understand patient status and characterize progression patterns from longitudinal health records. One approach for disease progression modeling is to describe patient status using a small number of states that represent distinctive distributions over a set of observed measures. Hidden Markov models (HMMs) and its variants are a class of models that both discover these states and make inferences of health states for patients. Despite the advantages of using the algorithms for discovering interesting patterns, it still remains challenging for medical experts to interpret model outputs, understand complex modeling parameters, and clinically make sense of the patterns. To tackle these problems, we conducted a design study with clinical scientists, statisticians, and visualization experts, with the goal to investigate disease progression pathways of chronic diseases, namely type 1 diabetes (T1D), Huntington's disease, Parkinson's disease, and chronic obstructive pulmonary disease (COPD). As a result, we introduce DPVis which seamlessly integrates model parameters and outcomes of HMMs into interpretable and interactive visualizations. In this study, we demonstrate that DPVis is successful in evaluating disease progression models, visually summarizing disease states, interactively exploring disease progression patterns, and building, analyzing, and comparing clinically relevant patient subgroups.Comment: to appear at IEEE Transactions on Visualization and Computer Graphic

Zero-Shot On-the-Fly Event Schema Induction

What are the events involved in a pandemic outbreak? What steps should be taken when planning a wedding? The answers to these questions can be found by collecting many documents on the complex event of interest, extracting relevant information, and analyzing it. We present a new approach in which large language models are utilized to generate source documents that allow predicting, given a high-level event definition, the specific events, arguments, and relations between them to construct a schema that describes the complex event in its entirety. Using our model, complete schemas on any topic can be generated on-the-fly without any manual data collection, i.e., in a zero-shot manner. Moreover, we develop efficient methods to extract pertinent information from texts and demonstrate in a series of experiments that these schemas are considered to be more complete than human-curated ones in the majority of examined scenarios. Finally, we show that this framework is comparable in performance with previous supervised schema induction methods that rely on collecting real texts while being more general and flexible without the need for a predefined ontology

Cost-effectiveness of First-line Chronic Lymphocytic Leukemia Treatments When Full-dose Fludarabine Is Unsuitable

Purpose: The cost-effectiveness of first-line chronic lymphocytic leukemia treatments was assessed among patients unsuitable for full doses of fludarabine. Methods: The study's key outcome was the life-time incremental cost-effectiveness ratio (ICER) (euro/quality-adjusted life-year [QALY] gained) with an annual 3% discounting. A probabilistic Markov model with 3 health states (progression-free, progression, and death) was developed. Survival time was modeled based on age-matched clinical data by using appropriate survival distributions. Each health state was assigned an EuroQoL-5D-3L quality-of-life estimate and Finnish payer costs according to treatment received, and Binet stage of disease; severe adverse events and treatment inconvenience were also included. Six approaches considered the risk and value of key outcomes: cost-effectiveness efficiency frontiers; Bayesian treatment ranking (BTR) rated the lowest ICERs and best QALY gains; the cost-effectiveness acceptability frontier demonstrated optimal treatment; expected value of perfect information; and the cost-benefit assessment (CBA), a type of clinical value analysis, increased the clinical interpretation and appeal of modeled outcomes by including both relative and absolute (impact investment [benefit obtained with a fixed limited budget]) benefit assessments. Findings: The ICERs compared with chlorambucil varied from (sic)29,334 with obinutuzumab + chlorambucil to (sic)82,159 with ofatumumab + chlorambucil. Based on the BTR of ICERs versus chlorambucil, obinutuzumab + chlorambucil was the most cost-effective with 93% probability; rituximab + chlorambucil was the second most cost-effective (73%); and rituximab + bendamustine was the third most cost-effective (65%). The ICERs of obinutuzumab + chlorambucil were (sic)20,038, (sic)11,556, and (sic)15,586 compared with rituximab + chlorambucil, rituximab + bendamustine, and ofatumumab + chlorambucil. Obinutuzumab + chlorambucil was the most cost-effective treatment, with 54% and 99% probability at (sic)30,000 and (sic)50,000/ QALY gained, respectively. The corresponding expected values of perfect information were (sic)1438 and (sic)44 per patient. Based on the BTR of QALYs gained, obinutuzumab + chlorambucil was the most effective, with 100% probability; rituximab + chlorambucil was the second most effective (56%); and rituximab + bendamustine was the third most effective treatment (81%). Results were robust in sensitivity analyses. For obinutuzumab + chlorambucil, the CBA demonstrated the best clinical value to cost-effectiveness relation and the longest time progression-free with a limited budget. Implications: The mean results were sensitive to large changes in time horizon, indirect comparison hazard ratios, survival distributions, and discounting; however, obinutuzumab + chlorambucil provided considerable effectiveness and best value for money among chronic lymphocytic leukemia patients unsuitable to receive full doses of fludarabine. In this case, CBA concurred with the key outcome of the study. However, the CBA cannot fully substitute the key outcome, and further cost-effectiveness studies with different cancer types are needed to assess the validity of a limited CBA. (C) 2016 The Authors. Published by Elsevier HS Journals, Inc.Peer reviewe

Analyzing the effect of APOE on Alzheimer's disease progression using an event-based model for stratified populations

Alzheimer's disease (AD) is the most common form of dementia and is phenotypically heterogeneous. APOE is a triallelic gene which correlates with phenotypic heterogeneity in AD. In this work, we determined the effect of APOE alleles on the disease progression timeline of AD using a discriminative event-based model (DEBM). Since DEBM is a data-driven model, stratification into smaller disease subgroups would lead to more inaccurate models as compared to fitting the model on the entire dataset. Hence our secondary aim is to propose and evaluate novel approaches in which we split the different steps of DEBM into group-aspecific and group-specific parts, where the entire dataset is used to train the group-aspecific parts and only the data from a specific group is used to train the group-specific parts of the DEBM. We performed simulation experiments to benchmark the accuracy of the proposed approaches and to select the optimal approach. Subsequently, the chosen approach was applied to the baseline data of 417 cognitively normal, 235 mild cognitively impaired who convert to AD within 3 years, and 342 AD patients from the Alzheimers Disease Neuroimaging Initiative (ADNI) dataset to gain new insights into the effect of APOE carriership on the disease progression timeline of AD. In the ε4 carrier group, the model predicted with high confidence that CSF Amyloidβ42 and the cognitive score of Alzheimer's Disease Assessment Scale (ADAS) are early biomarkers. Hippocampus was the earliest volumetric biomarker to become abnormal, closely followed by the CSF Phosphorylated Tau181 (PTAU) biomarker. In the homozygous ε3 carrier group, the model predicted a similar ordering among CSF biomarkers. However, the volume of the fusiform gyrus was identified as one of the earliest volumetric biomarker. While the findings in the ε4 carrier and the homozygous ε3 carrier groups fit the current understanding of progression of AD, the finding in the ε2 carrier group did not. The model predicted, with relatively low confidence, CSF Neurogranin as one of the earliest biomarkers along with cognitive score of Mini-Mental State Examination (MMSE). Amyloid β42 was found to become abnormal after PTAU. The presented models could aid understanding of the disease, and in selecting homogeneous group of presymptomatic subjects at-risk of developing symptoms for clinical trials