Model shows screening for infectious disease is hard but Thompson sampling works well.
problem Optimal screening policy for infectious diseases is hard to find.
method Stochastic-control model with Thompson sampling for optimal performance.
result Thompson sampling provides optimal performance guarantees in screening for infectious diseases.
A framework for data-driven decision-making in infectious disease control.
problem Optimizing trade-offs between public health and economic impacts.
method Multi-objective model-based reinforcement learning.
result Pareto-optimal policies minimizing long-term costs.
PHIBP predicts infectious disease outbreaks in sparse data regions.
problem Predicting outbreaks in regions with no historical data.
method Poisson Hierarchical Indian Buffet Process (PHIBP) framework.
result PHIBP provides accurate outbreak predictions and meaningful insights in sparse data settings.
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.
problem Managing Black Sigatoka disease under climate change.
method Latent Neural ODEs to model infection dynamics.
result Superior generalization performance up to one month ahead.
Automates infectious disease policy-making via inference in epidemiological models.
problem Improving policy-making for infectious diseases during pandemics.
method Performing inference in existing epidemiological models using a probabilistic programming language.
result Automated inference leads to better disease progression outcomes and policy prescriptions.
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.
problem Limited data and simplistic models for accurate prediction.
method Combining SEIR and RNN on a graph structure with local and edge features.
result Improves prediction accuracy on state-level COVID-19 data.
Optimizes control of infectious disease spread using stochastic methods.
problem Optimizing control of highly infectious diseases like COVID-19.
method Reformulated Hamilton-Jacobi-Bellman equation as stochastic minimum principle, leading to forward-backward stochastic differential equations.
result Numerous numerical solutions presented under various scenarios.
Method predicts disease outbreaks using search logs, overcoming instability.
problem Predicting disease outbreaks from search logs is challenging due to short-term and long-term instability.
method Seasonal-adjustment method decomposes logs into seasonal, trend, and irregular components; feature selection method selects relevant search terms.
result Proposed method outperforms comparative methods in prediction accuracy for seven of ten diseases.
Simulation-based inference aids in predicting disease dynamics for health policy.
problem Predicting disease dynamics to inform public health interventions.
method Simulation-based inference using machine learning.
result Potential for efficient interpretable Bayesian inference in epidemiological models.
Model predicts COVID-19 spread with better accuracy than existing methods.
problem Limited daily samples in time for data-driven methods.
method Integrated spatiotemporal model combining epidemic differential equations and RNN.
result Model outperforms existing methods in forecasting cases.
Seq2Seq models speed up epidemic model predictions.
problem Complex epidemic models are computationally expensive.
method Used deep seq2seq models as surrogates for complex models.
result Surrogates predict scenarios up to several thousand times faster.
Noisy Pooled PCR tests large groups more efficiently.
problem Efficiently test large populations for viral infections.
method Converts group testing to a linear inverse problem with a message passing algorithm.
result Estimates patient illness status with fewer pooled measurements.
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.
problem Valuation of options on epidemic spread during an outbreak.
method Stochastic differential SIR model for epidemic dynamics.
result Parsimonious model for option valuation 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.
problem Limited accuracy in traditional models and lack of interpretability in deep learning models.
method CSTGNN integrates Spatio-Contact SIR model with Graph Neural Networks.
result Effective spatiotemporal epidemic forecasting with interpretability.
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.
problem Capturing the role of infection hotspots in disease transmission.
method Temporal point process modeling framework to represent visits and disease transmission.
result Estimation of transmission rates at sites and households using Bayesian optimization.
New bound for neural nets on non-iid data.
problem Generalization of deep nets for dependent data.
method Establishes a generalization bound for feed-forward neural networks on φ-mixing data. result Proves neural nets can generalize well 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.
problem Deciding how to prioritize vaccines for new variants with delayed feedback.
method Partial likelihood Thompson sampling, updating beliefs with partial likelihood.
result Method effectively prioritizes vaccines based on real-world data.
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.
problem Optimizing complex systems with heterogeneous components.
method Decomposed Gaussian Process (GP) regression and optimization algorithm.
result Proved lower variance and improved accuracy in subgroup feedback.
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.
problem Detecting close contacts to prevent SARS-CoV-2 spread.
method Machine learning approach using BLE data.
result Reliable detection of close contacts reduces SARS-CoV-2 risk.
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.
problem Comparing accuracy of mechanistic vs statistical models for short-term COVID-19 incidence forecasts.
method Empirical comparison of forecasts from mechanistic and statistical models using daily incidence data from six US states.
result Statistical models are at least as accurate as mechanistic models and better capture volatility.
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.
problem Balancing health and economic impacts of lockdowns during pandemics.
method Reinforcement learning to automatically compute lockdown policies.
result Algorithm learns optimal lockdown policies from disease and population data.
Paper models COVID-19 spread as spatio-temporal point processes.
problem Understanding complex spacetime propagation of COVID-19.
method Generative and intensity-free model using adversarial imitation learning.
result Imitation learning framework for scalable model inference.
Framework assesses variable importance for heterogeneous treatment effects.
problem High-risk domains need reliable methods to assess treatment effect heterogeneity.
method Inferential framework based on Shapley values and semiparametric theory.
result Valid inference on 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.
problem Improving diagnostic test sensitivity for bovine tuberculosis.
method Machine learning to assess risk landscapes and predict infection.
result Test sensitivity improved, detecting 240 more infected herds per year.
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.
problem Bayesian inference for ODE systems with non-analytical solutions is computationally expensive.
method Hybrid approach using ANN for tractable likelihood and Laplace approximation.
result Effective posterior inference with improved computational cost compared to traditional methods.
Study uses PPLs to model and forecast COVID-19 spread and policy interventions.
problem Forecast and control the spread of COVID-19 using policy interventions.
method Compartmental models, probabilistic programming, inference, greedy algorithm.
result Optimal series of policy interventions can control the infected population.
Study reveals hidden infections and infection dynamics from early data.
problem Understanding early infection dynamics and hidden infections in COVID-19.
method Data-driven machine learning analysis focusing on infection counts over time.
result Significant asymptomatic infections, 10-day lag, and strong infectious force.
Adaptive sequential testing optimizes epidemic control by learning optimal test strategies.
problem Optimizing test allocation in epidemics with network and temporal dependence.
method Adaptive sequential design with Online Super Learner for optimal test strategies.
result Superior performance in simulated university COVID-19 pandemic.
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.
problem Challenges in estimating parameters of SDEs with non-Gaussian noises.
method Introduces PEnet, a CNN-LSTM model for efficient parameter estimation.
result PEnet offers superior accuracy and adaptability for various SDE scenarios.
Proposes TPIS for early and low-cost TB vs. pneumonia diagnosis.
problem Challenges in differentiating TB from pneumonia.
method Two-step decision support system with stacked ensemble classifiers.
result TPIS outperforms other methods in early and final diagnosis.
Modeling influenza spread using feature engineering and international flow deconvolution.
problem Predicting and mitigating influenza spread through feature extraction and international flow analysis.
method Discrete Fourier Transform, matrix completion, SVM, autoencoders, PCA, deconvolution of international flow.
result Significant environmental and economic features are crucial to influenza mortality.
The paper proposes methods to infer from privacy-protected data using simulation-based techniques.
problem Valid statistical inference from privacy-protected data is computationally challenging.
method Simulation-based inference methods, including sequential Monte Carlo and neural conditional density estimators.
result Valid statistical inferences can be made from privacy-protected data.
BScNets expands graph learning to higher-order interactions.
problem Link prediction on graphs with higher-order interactions.
method Proposes a new SNN using block Hodge-Laplacian and block simplicial complexes.
result BScNets outperforms state-of-the-art models in link prediction.