A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Model shows screening for infectious disease is hard but Thompson sampling works well.
A framework for data-driven decision-making in infectious disease control.
PHIBP predicts infectious disease outbreaks in sparse data regions.
In retrospective assessments, internet news reports have been shown to capture early reports of unknown infectious disease transmission prior to official laboratory confirmation. In general, media interest and reporting peaks and wanes during the course of an outbreak. In this study, we quantify the extent to which med…
New model predicts banana disease risk from climate data.
Automates infectious disease policy-making via inference in epidemiological models.
The early detection of infectious disease outbreaks is a crucial task to protect population health. To this end, public health surveillance systems have been established to systematically collect and analyse infectious disease data. A variety of statistical tools are available, which detect potential outbreaks as abber…
Hybrid model improves COVID-19 case forecasting accuracy.
Optimizes control of infectious disease spread using stochastic methods.
Method predicts disease outbreaks using search logs, overcoming instability.
Simulation-based inference aids in predicting disease dynamics for health policy.
Model predicts COVID-19 spread with better accuracy than existing methods.
Seq2Seq models speed up epidemic model predictions.
Noisy Pooled PCR tests large groups more efficiently.
Maps of infectious disease---charting spatial variations in the force of infection, degree of endemicity, and the burden on human health---provide an essential evidence base to support planning towards global health targets. Contemporary disease mapping efforts have embraced statistical modelling approaches to properly…
Model for valuing options on epidemic spread.
Simulations of infectious disease spread have long been used to understand how epidemics evolve and how to effectively treat them. However, comparatively little attention has been paid to understanding the fairness implications of different treatment strategies -- that is, how might such strategies distribute the expec…
Unified model forecasts epidemics with spatial and temporal dynamics.
Accurate and reliable predictions of infectious disease dynamics can be valuable to public health organizations that plan interventions to decrease or prevent disease transmission. A great variety of models have been developed for this task, using different model structures, covariates, and targets for prediction. Expe…
Modeling infection hotspots to quantify effects of contact tracing and testing.
New bound for neural nets on non-iid data.
Market trade-routes can support infectious-disease transmission, impacting biological populations and even disrupting causal trade. Epidemiological models increasingly account for reductions in infectious contact, such as risk-aversion behaviour in response to pathogen outbreaks. However, market dynamics clearly differ…
New method targets vaccines for new variants using Thompson sampling.
Due to globalization, geographic boundaries no longer serve as effective shields for the spread of infectious diseases. In order to aid bio-surveillance analysts in disease tracking, recent research has been devoted to developing information retrieval and analysis methods utilizing the vast corpora of publicly availabl…
New method learns from subgroup feedback in complex systems.
Machine learning has been an emerging tool for various aspects of infectious diseases including tuberculosis surveillance and detection. However, WHO provided no recommendations on using computer-aided tuberculosis detection software because of the small number of studies, methodological limitations, and limited genera…
New method detects close contacts to prevent SARS-CoV-2 spread.
Scientific investigations that incorporate next generation sequencing involve analyses of high-dimensional data where the need to organize, collate and interpret the outcomes are pressingly important. Currently, data can be collected at the microbiome level leading to the possibility of personalized medicine whereby tr…
Statistical models outperform mechanistic models in short-term COVID-19 incidence forecasts.
It is the main purpose of this paper to introduce a graph-valued stochastic process in order to model the spread of a communicable infectious disease. The major novelty of the SIR model we promote lies in the fact that the social network on which the epidemics is taking place is not specified in advance but evolves thr…
Algorithm optimizes lockdown policies balancing health and economy.
Paper models COVID-19 spread as spatio-temporal point processes.
Framework assesses variable importance for heterogeneous treatment effects.
Epidemiology simulations have become a fundamental tool in the fight against the epidemics of various infectious diseases like AIDS and malaria. However, the complicated and stochastic nature of these simulators can mean their output is difficult to interpret, which reduces their usefulness to policymakers. In this pap…
Artificial intelligence has provided us with an exploration of a whole new research era. As more data and better computational power become available, the approach is being implemented in various fields. The demand for it in health informatics is also increasing, and we can expect to see the potential benefits of its a…
Machine learning improves diagnostic test accuracy for bovine tuberculosis.
Predicting epidemic dynamics is of great value in understanding and controlling diffusion processes, such as infectious disease spread and information propagation. This task is intractable, especially when surveillance resources are very limited. To address the challenge, we study the problem of active surveillance, i.…
A new method combines ANN and Laplace for fast Bayesian inference in ODE models.
Study uses PPLs to model and forecast COVID-19 spread and policy interventions.
Study reveals hidden infections and infection dynamics from early data.
Adaptive sequential testing optimizes epidemic control by learning optimal test strategies.
In this article, we focus on the analysis of the potential factors driving the spread of influenza, and possible policies to mitigate the adverse effects of the disease. To be precise, we first invoke discrete Fourier transform (DFT) to conclude a yearly periodic regional structure in the influenza activity, thus safel…
Contagions such as the spread of popular news stories, or infectious diseases, propagate in cascades over dynamic networks with unobservable topologies. However, "social signals" such as product purchase time, or blog entry timestamps are measurable, and implicitly depend on the underlying topology, making it possible …
In this paper, we propose a two-sector Markovian infectious model, which is an extension of Greenwood's model. The central idea of this model is that the causality of defaults of two sectors is in both direction, which enrich dependence dynamics. The Bayesian Information Criterion is adopted to compare the proposed mod…
Study improves parameter estimation for SDEs driven by Levy noise.
The paper proposes methods to infer from privacy-protected data using simulation-based techniques.
Proposes TPIS for early and low-cost TB vs. pneumonia diagnosis.
BScNets expands graph learning to higher-order interactions.