INDEMICS: An Interactive High-Performance Computing Framework for Data Intensive Epidemic Modeling

We describe the design and prototype implementation of Indemics (_Interactive; Epi_demic; _Simulation;)—a modeling environment utilizing high-performance computing technologies for supporting complex epidemic simulations. Indemics can support policy analysts and epidemiologists interested in planning and control of pandemics. Indemics goes beyond traditional epidemic simulations by providing a simple and powerful way to represent and analyze policy-based as well as individual-based adaptive interventions. Users can also stop the simulation at any point, assess the state of the simulated system, and add additional interventions. Indemics is available to end-users via a web-based interface. Detailed performance analysis shows that Indemics greatly enhances the capability and productivity of simulating complex intervention strategies with a marginal decrease in performance. We also demonstrate how Indemics was applied in some real case studies where complex interventions were implemented

A Survey of the Individual-Based Model applied in Biomedical and Epidemiology

Individual-based model (IBM) has been used to simulate and to design control strategies for dynamic systems that are subject to stochasticity and heterogeneity, such as infectious diseases. In the IBM, an individual is represented by a set of specific characteristics that may change dynamically over time. This feature allows a more realistic analysis of the spread of an epidemic. This paper presents a literature survey of IBM applied to biomedical and epidemiology research. The main goal is to present existing techniques, advantages and future perspectives in the development of the model. We evaluated 89 articles, which mostly analyze interventions aimed at endemic infections. In addition to the review, an overview of IBM is presented as an alternative to complement or replace compartmental models, such as the SIR (Susceptible-Infected-Recovered) model. Numerical simulations also illustrate the capabilities of IBM, as well as some limitations regarding the effects of discretization. We show that similar side-effects of discretization scheme for compartmental models may also occur in IBM, which requires careful attention

Bayesian Calibration to Address the Challenge of Antimicrobial Resistance: A Review

Antimicrobial resistance (AMR) emerges when disease-causing microorganisms develop the ability to withstand the effects of antimicrobial therapy. This phenomenon is often fueled by the human-to-human transmission of pathogens and the overuse of antibiotics. Over the past 50 years, increased computational power has facilitated the application of Bayesian inference algorithms. In this comprehensive review, the basic theory of Markov Chain Monte Carlo (MCMC) and Sequential Monte Carlo (SMC) methods are explained. These inference algorithms are instrumental in calibrating complex statistical models to the vast amounts of AMR-related data. Popular statistical models include hierarchical and mixture models as well as discrete and stochastic epidemiological compartmental and agent based models. Studies encompassed multi-drug resistance, economic implications of vaccines, and modeling AMR in vitro as well as within specific populations. We describe how combining these topics in a coherent framework can result in an effective antimicrobial stewardship. We also outline recent advancements in the methodology of Bayesian inference algorithms and provide insights into their prospective applicability for modeling AMR in the future

Algorithms and Software for the Analysis of Large Complex Networks

The work presented intersects three main areas, namely graph algorithmics, network science and applied software engineering. Each computational method discussed relates to one of the main tasks of data analysis: to extract structural features from network data, such as methods for community detection; or to transform network data, such as methods to sparsify a network and reduce its size while keeping essential properties; or to realistically model networks through generative models

Simulation Intelligence: Towards a New Generation of Scientific Methods

The original "Seven Motifs" set forth a roadmap of essential methods for the field of scientific computing, where a motif is an algorithmic method that captures a pattern of computation and data movement. We present the "Nine Motifs of Simulation Intelligence", a roadmap for the development and integration of the essential algorithms necessary for a merger of scientific computing, scientific simulation, and artificial intelligence. We call this merger simulation intelligence (SI), for short. We argue the motifs of simulation intelligence are interconnected and interdependent, much like the components within the layers of an operating system. Using this metaphor, we explore the nature of each layer of the simulation intelligence operating system stack (SI-stack) and the motifs therein: (1) Multi-physics and multi-scale modeling; (2) Surrogate modeling and emulation; (3) Simulation-based inference; (4) Causal modeling and inference; (5) Agent-based modeling; (6) Probabilistic programming; (7) Differentiable programming; (8) Open-ended optimization; (9) Machine programming. We believe coordinated efforts between motifs offers immense opportunity to accelerate scientific discovery, from solving inverse problems in synthetic biology and climate science, to directing nuclear energy experiments and predicting emergent behavior in socioeconomic settings. We elaborate on each layer of the SI-stack, detailing the state-of-art methods, presenting examples to highlight challenges and opportunities, and advocating for specific ways to advance the motifs and the synergies from their combinations. Advancing and integrating these technologies can enable a robust and efficient hypothesis-simulation-analysis type of scientific method, which we introduce with several use-cases for human-machine teaming and automated science

Imitation learning for combinatorial optimization and contact tracing

The field of Imitation Learning (IL) has seen significant progress in recent years as researchers have applied this machine learning technique to various domains such as robotics, self-driving cars, healthcare, and game playing. Each domain has contributed to the advancement of the field by developing and applying new methods to solve the unique problems specific to their domain. In this thesis, we focus on IL in two domains that pose their own unique challenges. The first application involves learning to imitate a highly accurate heuristic for mixed-integer linear programming (MILP) solvers, which although precise, is not practical due to its computational inefficiency. The second application involves the development of an IL framework to accurately predict the infectiousness of individuals through a smartphone application utilizing the newly developed Proactive Contact Tracing (PCT) framework, which overcomes the limitations of conventional contact tracing methods. We design our IL frameworks based on the dynamics of a manageable environment (e.g., simulator), with the goal of transferring the learned models to larger, unseen environments. The development of these frameworks requires the consideration and resolution of several challenges. These challenges include incorporating domain-specific inductive biases, ensuring the robustness of models against distribution shifts, and designing models that are lightweight and suitable for deployment. By addressing these challenges, we hope to contribute not only to the advancement of IL, but also to the domains in which it is applied, bringing new and improved solutions to these respective fields. Specifically, to imitate the expert heuristic of MILP solvers, we identified and addressed two key shortcomings of the existing IL framework. First, the proposed Graph Neural Networks (GNNs) are computationally expensive but highly accurate and their runtime performance degrades in the absence of GPUs. This setting may arise since MILP solvers are CPU--only. To address this, we proposed novel architectures that trade-off the expressivity of GNNs with inexpensive computations of multi-linear perceptrons, along with training protocols that make the models robust to distribution shifts. The models trained using these techniques resulted in up to 26% improvement in runtime. The second issue is the inability to capture the dependence between observations to train GNNs. Our research revealed a ``lookback'' phenomenon that occurs frequently in the expert heuristic, where the best decision at the child node is often the second-best at the parent node. To incorporate this phenomenon in the loss function, we proposed a new loss function that imitates this heuristic more accurately, resulting in models with up to 15% improvement in running time. Finally, during the COVID-19 pandemic, nations around the world faced a dilemma of whether to open up the economy or prioritize saving lives. In response, digital contact tracing applications emerged. However, to avoid violating user privacy, most apps relied on a quarantine-or-not interface with limited intelligence on the level of risk of the notification recipient. This approach led to alert fatigue, making users less likely to follow recommendations. To overcome these issues while maintaining user privacy and sophisticated risk estimation models, we proposed the Proactive Contact Tracing (PCT) framework. Our framework repurposes user communication to carry information about estimated risk in "risk messages". These messages, along with personal information (e.g., medical history or symptoms), are used in a risk estimation model to output risk messages sent to other users. Depending on estimated risk, graded notifications (e.g., exercise caution or avoid unnecessary behavior) are shown to the users. Using an agent-based model (ABM) and a simple interpretable rule-based model, we demonstrated that the rule-based PCT has a better economic-public health trade-off than the existing apps. In follow-up work, we turned to deep learning to design a risk estimation model. While reinforcement learning would have been ideal, the computationally expensive ABM precludes its use. Instead, we employed an imitation learning framework to train deep learning models, specifically, we proposed several variants of set transformers. We also used domain randomization, collecting observations using several random instantiations of the ABM, to ensure that models were robust to assumptions baked into the ABM. Furthermore, we used iterative training to ensure the models remained robust to auto-induced distribution shifts. Overall, we showed that a deep learning-based PCT outperforms rule-based PCT. To finalize our proposal, we suggest an iterative procedure for app deployment and ABM calibration to bridge the gap from the ABM to real-world deployment

Optimizing Non-pharmaceutical Interventions Using Multi-coaffiliation Networks

Computational modeling is of fundamental significance in mapping possible disease spread, and designing strategies for its mitigation. Conventional contact networks implement the simulation of interactions as random occurrences, presenting public health bodies with a difficult trade off between a realistic model granularity and robust design of intervention strategies. Recently, researchers have been investigating the use of agent-based models (ABMs) to embrace the complexity of real world interactions. At the same time, theoretical approaches provide epidemiologists with general optimization models in which demographics are intrinsically simplified. The emerging study of affiliation networks and co-affiliation networks provide an alternative to such trade off. Co-affiliation networks maintain the realism innate to ABMs while reducing the complexity of contact networks into distinctively smaller k-partite graphs, were each partition represent a dimension of the social model. This dissertation studies the optimization of intervention strategies for infectious diseases, mainly distributed in school systems. First, concepts of synthetic populations and affiliation networks are extended to propose a modified algorithm for the synthetic reconstruction of populations. Second, the definition of multi-coaffiliation networks is presented as the main social model in which risk is quantified and evaluated, thereby obtaining vulnerability indications for each school in the system. Finally, maximization of the mitigation coverage and minimization of the overall cost of intervention strategies are proposed and compared, based on centrality measures

High-Performance Modelling and Simulation for Big Data Applications

This open access book was prepared as a Final Publication of the COST Action IC1406 “High-Performance Modelling and Simulation for Big Data Applications (cHiPSet)“ project. Long considered important pillars of the scientific method, Modelling and Simulation have evolved from traditional discrete numerical methods to complex data-intensive continuous analytical optimisations. Resolution, scale, and accuracy have become essential to predict and analyse natural and complex systems in science and engineering. When their level of abstraction raises to have a better discernment of the domain at hand, their representation gets increasingly demanding for computational and data resources. On the other hand, High Performance Computing typically entails the effective use of parallel and distributed processing units coupled with efficient storage, communication and visualisation systems to underpin complex data-intensive applications in distinct scientific and technical domains. It is then arguably required to have a seamless interaction of High Performance Computing with Modelling and Simulation in order to store, compute, analyse, and visualise large data sets in science and engineering. Funded by the European Commission, cHiPSet has provided a dynamic trans-European forum for their members and distinguished guests to openly discuss novel perspectives and topics of interests for these two communities. This cHiPSet compendium presents a set of selected case studies related to healthcare, biological data, computational advertising, multimedia, finance, bioinformatics, and telecommunications