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.
Study of influenza A virus spread using mathematical equations.
problem Understanding the spread of influenza A virus infection.
method Mathematical model and analysis of dynamical system.
result Surface trajectories and asymptotic behavior of the system.
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.
Novel method combines neural network features with survival models for ICU infections.
problem Improving predictive models of ICU infections while maintaining interpretability.
method Semi-parametric approach combining low-resolution and high-resolution data.
result Improved predictive power with interpretability maintained.
Many works have been proposed in the literature to capture the dynamics of diffusion in networks. While some of them define graphical markovian models to extract temporal relationships between node infections in networks, others consider diffusion episodes as sequences of infections via recurrent neural models. In this…
Although timely sepsis diagnosis and prompt interventions in Intensive Care Unit (ICU) patients are associated with reduced mortality, early clinical recognition is frequently impeded by non-specific signs of infection and failure to detect signs of sepsis-induced organ dysfunction in a constellation of dynamically cha…
A new neural network approach for diffusion on networks.
problem Inference and estimation of diffusion on network structures.
method Neural mean-field dynamics derived from Mori-Zwanzig formalism, approximated by learnable time convolution operators.
result Significantly outperforms existing approaches in accuracy and efficiency.
A new model characterizes undocumented and asymptomatic infections to quantify COVID-19 uncertainties.
problem Quantifying uncertainties in COVID-19 infections and contagion.
method SUDR model: characterizes undocumented and documented infections, captures probabilistic density, and incorporates Bayesian inference.
result Demonstrates deeper understanding of COVID-19 uncertainties compared to classic models.
The study examines how model predictions hold up under model extensions.
problem Model predictions may not be robust under model extensions, limiting their applicability.
method The study uses causal ordering to assess robustness of qualitative model predictions and characterizes model extensions that preserve predictions.
result Conditions and techniques are provided to assess robustness of model predictions under model extensions.
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.
Satellite constructions on a knot can be thought of as taking some strands of a knot and then tying in another knot. Using satellite constructions one can construct many distinct isotopy classes of knots. Pushing this further one can construct distinct concordance classes of knots which preserve some algebraic invarian…
This study proposes a method to predict ICU infections from imbalanced data using clustering-based undersampling and ensemble classifiers.
problem Predicting healthcare-associated infections in ICU patients from imbalanced data.
method Clustering-based undersampling strategy combined with ensemble classifiers.
result The proposed method outperforms other resampling techniques in predicting ICU infections.
The present paper provides a multi-period contagion model in the credit risk field. Our model is an extension of Davis and Lo's infectious default model. We consider an economy of n firms which may default directly or may be infected by other defaulting firms (a domino effect being also possible). The spontaneous defau…
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.
Bayesian methods improve group testing for identifying infected patients.
problem Identifying infected patients from group testing results with false positives.
method Bayesian inference and belief propagation algorithm, combined with expectation-maximization method.
result True-positive rate improved by considering credible intervals.
CRISP predicts individual-level COVID-19 risk based on contact data.
problem Estimating individual-level infection risk during the pandemic.
method Probabilistic graphical model using SEIR framework with contact data.
result Model accurately predicts infection spread and recovery times.
Deep learning identifies transcriptomic patterns and cell types associated with SARS-CoV-2 infection and COVID-19 severity.
problem Understanding how SARS-CoV-2 varies in infecting and causing severe COVID-19.
method Developed a new approach to generating self-supervised edge features, using Graph Attention Networks (GAT) and Set Transformer.
result Achieved state-of-the-art performance in predicting disease state of individual cells using single-cell RNA sequencing data.
Paper proposes a new method for more accurate group testing of infected patients.
problem Identifying infected patients efficiently with reduced tests and corrected errors.
method Adaptive design of pools based on Bayesian posterior prediction using belief propagation algorithm.
result The proposed method results in more accurate identification of infected patients.
Detection of malware-infected computers and detection of malicious web domains based on their encrypted HTTPS traffic are challenging problems, because only addresses, timestamps, and data volumes are observable. The detection problems are coupled, because infected clients tend to interact with malicious domains. Traff…
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.
A time-dependent SIR model predicts COVID-19 spread and herd immunity.
problem Modeling and predicting COVID-19 spread and herd immunity.
method Time-dependent SIR model with 2 types of infected persons (detectable and undetectable).
result The model accurately predicts the turning point and herd immunity threshold.
Predicting fine-grained interests of users with temporal behavior is important to personalization and information filtering applications. However, existing interest prediction methods are incapable of capturing the subtle degreed user interests towards particular items, and the internal time-varying drifting attention …
New Milnor's invariant condition for topologically slice links.
problem Conditions for topologically slice links involving Milnor's invariant.
method Different Milnor's invariant condition to handle cases not covered by previous theorem.
result New sufficient conditions for topologically slice links.
LSTM models predict low likelihood of another COVID-19 wave in India.
problem Inaccurate and unreliable COVID-19 infection forecasting models due to data limitations and model complexity.
method Application of LSTM, bidirectional LSTM, and encoder-decoder LSTM models for multi-step infection forecasting.
result Predictions indicate low likelihood of another wave in October and November 2021.
Budney recently constructed an operad that encodes splicing of knots. He further showed that the space of (long) knots is generated over this operad by the space of torus knots and hyperbolic knots, thus generalizing the satellite decomposition of knots from isotopy classes to the level of the space of knots. Infection…
We consider the problem of learning the weighted edges of a graph by observing the noisy times of infection for multiple epidemic cascades on this graph. Past work has considered this problem when the cascade information, i.e., infection times, are known exactly. Though the noisy setting is well motivated by many epide…
The global crisis of 2008 provoked a heightened interest among scientists to study the phenomenon, its propagation and negative consequences. The process of modelling the spread of a virus is commonly used in epidemiology. Conceptually, the spread of a disease among a population is similar to the contagion process in e…
Study tackles causal effects of close contact on MRSA infections from entangled treatment data.
problem Estimating causal effects of close contact on MRSA infections from observational data with entangled treatments.
method NEAT method that models treatment assignment mechanism and mitigates confounding biases.
result NEAT method effectively estimates causal effects from entangled treatment data.
Machine learning detects and diagnoses coughs for respiratory infections.
problem Detecting and diagnosing coughs for respiratory infections.
method Convolutional Neural Networks (CNNs) to detect cough events and diagnose three illnesses.
result Accuracy over 89% for detection and diagnosis.
Acute respiratory infections have epidemic and pandemic potential and thus are being studied worldwide, albeit in many different contexts and study formats. Predicting infection from symptom data is critical, though using symptom data from varied studies in aggregate is challenging because the data is collected in diff…
New group testing method uses Belief Propagation for accurate screening.
problem Efficiently identifying infected samples in large groups with minimal tests.
method Belief Propagation algorithm for inference in group testing schemes.
result Significantly increased accuracy of infection identification with fewer tests.
Paper uses UKS to improve BLE RSSI for proximity inference in mobile phone apps.
problem Improper BLE RSSI for accurate proximity inference during pandemics.
method Single-dimensional Unscented Kalman Smoother (UKS) with Gaussian process observation transforms.
result UKS outperforms traditional methods in predicting infection risk from BLE RSSI.
Bayesian optimization improves classifier selection for acute infection and mortality.
problem Improving accuracy of acute infection and mortality prediction.
method Comparison of hyperparameter optimization methods (grid search, random sampling, Bayesian optimization).
result Bayesian optimization outperforms grid search or random sampling for in-hospital mortality classifiers.
Epidemiological model updates infection rates in China, US, Italy.
problem Evaluate policy interventions for mitigating the 2019-nCov pandemic.
method Custom SITR model with variational data assimilation for real-time updates.
result Model robust to initial conditions, infers infection numbers and parameters.
We give an explicit construction of linearly independent families of knots arbitrarily deep in the (n)-solvable filtration of the knot concordance group using the ρ^1-invariant. A difference between previous constructions of infinite rank subgroups in the concordance group and ours is that the deepest infecting knots i…
Study uses machine learning to optimize antibiotic therapy for MRSA skin infections.
problem Optimizing antibiotic choice for MRSA skin infections due to reduced treatment options and side effects.
method Propensity score matching, machine learning models (SVM, RF, LASSO), counterfactual analysis.
result RF model shows stronger treatment heterogeneity and potential for therapy change.
Study phase transitions in identifying infected individuals using group testing.
problem Identifying a set of k infected individuals from a population using pooled tests.
method Two random assignment designs (constant-column and Bernoulli) and polynomial-time inference procedures.
result Sharp phase transitions in statistical and computational limits for detection and recovery problems.
Backdoor attacks are found to be effective against robust machine learning models trained with PGD.
problem Injecting and defending against backdoor attacks in robust machine learning models.
method Study and detection of backdoor attacks on PGD-trained robust models using feature clustering.
result AEGIS effectively detects PGD-trained robust DNNs infected with backdoors with 91.6% accuracy.
Dynamic treatment strategies on networks amplify policy impact through spillovers.
problem Effective dynamic treatment allocation in network settings.
method Q-Ising, a three-stage pipeline integrating Bayesian dynamic Ising model, treatment adoption histories, and offline reinforcement learning.
result Adaptive targeting outperforms static centrality benchmarks in Indian village microfinance networks and synthetic data.
Random forest models predict CLABSI risk in hospital admissions, with static models performing similarly to dynamic ones.
problem Predicting CLABSI risk in hospital admissions using EHR data with competing risks.
method Comparison of static and dynamic random forest models for binary, multinomial, survival, and competing risks outcomes.
result Static and dynamic random forest models perform similarly in predicting CLABSI risk, with multinomial models having the lowest computation times.
Information diffusion and virus propagation are fundamental processes taking place in networks. While it is often possible to directly observe when nodes become infected with a virus or adopt the information, observing individual transmissions (i.e., who infects whom, or who influences whom) is typically very difficult…
Researchers use clustering to differentiate COVID-19 lung scans.
problem Identifying infected individuals with COVID-19.
method Applied unsupervised clustering techniques using PCA, K-Means++, and RCC.
result KM++ and RCC algorithms improved in clustering COVID-19 lung scans.
Reducing the incidence of surgical site infections (SSIs) is one of the objectives of the French nosocomial infection control program. Manual monitoring of SSIs is carried out each year by the hospital hygiene team and surgeons at the University Hospital of Bordeaux. Our goal was to develop an automatic detection algor…
Can evolving networks be inferred and modeled without directly observing their nodes and edges? In many applications, the edges of a dynamic network might not be observed, but one can observe the dynamics of stochastic cascading processes (e.g., information diffusion, virus propagation) occurring over the unobserved ne…
Model quantifies cyber-attacks' impact on firms and insurers.
problem Impact of cyber-attacks on firms' revenues and insurers' portfolios.
method Stochastic SIR model coupled with granular firm growth model.
result Predicts insurer needs to compensate up to two days of revenue in a 100-day incident.
Paper derives policy rules from observational data for hepatitis C treatment.
problem Improving treatment guidelines for HIV/HCV co-infected patients.
method Weighted K-means algorithm for estimating CATEs, decision tree implementation.
result Identifies a subgroup with high spontaneous HCV clearance rate.
We consider the problem of estimating the latent structure of a social network based on the observed information diffusion events, or cascades, where the observations for a given cascade consist of only the timestamps of infection for infected nodes but not the source of the infection. Most of the existing work on this…
Various obstructions to knot concordance have been found using Casson-Gordon invariants, higher-order Alexander polynomials, as well as von-Neumann rho-invariants. Examples have been produced using (iterated) doubling operations K=R(c,J), and considering these as parametrized by invariants of the base knot J and doubli…