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Dynamics, Optimal Control, and Spillover Risk of the 2026 Bundibugyo Ebola Outbreak in the Democratic Republic of the Congo

This study utilizes an SEIDR model combined with optimal control theory and mobility data to analyze the 2026 Bundibugyo Ebola outbreak in the DRC, identifying a basic reproduction number of 1.83, determining that 58.12% public adherence to protective behaviors could halt transmission, and demonstrating that a time-varying control strategy could shorten the epidemic by seven months while significantly reducing cross-border spillover risks to neighboring African nations, France, and Canada.

Tianmu Chen, Jiahui Li, Shengjie Lai, Yanhua Su, Qiuping Chen, Jia Rui, Zeyu Zhao2026-09-22

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Marginal Mean Weighting Through Stratification for Time-Varying Multivalued Treatments: A Monte Carlo Comparison with Inverse-Probability-Weighted Marginal Structural Models

This Monte Carlo study demonstrates that Marginal Mean Weighting Through Stratification (MMWS) generally offers a more stable and robust alternative to Inverse Probability Weighting (IPTW) for time-varying multivalued treatments by reducing root mean squared error, particularly under model misspecification, though it may still struggle with bias and coverage when positivity assumptions are violated.

Ariel Linden2026-09-22

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A Dynamical Approach to Extracting Language Style Patterns using Recurrence Quantification

This paper introduces recurrence-based language style matching (rbLSM), a novel framework that utilizes recurrence quantification analysis to overcome the limitations of traditional global estimates by dynamically capturing turn-level linguistic synchrony patterns and predicting positive emotional outcomes in conversations.

Aaron Necaise, Jingjing Han, Mary Jean Amon2026-09-22

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Food-Assistance Modality, Geographic Patterning, and Child Nutrition in Rohingya Camps: A Reanalysis of Public Replication Data

This reanalysis of public data on Rohingya camps reveals that while food-assistance modalities are strongly patterned by geography and institutional factors, the released dataset lacks the necessary design and allocation details to support causal claims about program effectiveness, limiting its utility to descriptive reassessment.

Nerob Ahmed2026-09-22

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Data Driven Options Pricing using ANN: Evidence from the CRR Model

This paper demonstrates that artificial neural networks trained on Cox-Ross-Rubinstein model data serve as a fast, accurate, and versatile surrogate for pricing various option types and computing Greeks, significantly reducing computational costs for real-time risk management and implied volatility surface construction.

Arun Kumar, Pawan Kumar, Jitendra Kumar2026-09-21

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Clustering-Informed Inverse Probability Weighting Strategies for Causal Effect Estimation in Observational Studies

This paper evaluates and compares standard inverse probability weighting against two clustering-informed strategies for causal effect estimation, demonstrating that while both cluster-based approaches improve robustness to omitted-covariate misspecification, their relative performance in terms of bias, mean squared error, and coverage depends on the underlying subgroup structure and sample size.

Ruohui Chen, Scott Zuo, Whitney Stevens, Seth Pollack, Wenna Xi, Lucia Petito, Lihui Zhao, Hui Zhao2026-09-21

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Power-Optimal Covariate Adjustment for Switchback Experiments

This paper proposes a power-optimal CUPAC methodology for switchback experiments with unequal cluster sizes that improves statistical power by specifically balancing the prediction of noise between and within randomization units, rather than merely targeting overall predictive accuracy.

Sergei Pankratev2026-09-21

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Psychometric evaluation of a disaster preparedness perception instrument among paramedic students in Saudi Arabia: A Factor Analytic Study

This study psychometrically evaluated the Disaster Preparedness Evaluation Tool (DPET) among 152 Saudi paramedic students, refining the original 36-item instrument into a robust, four-factor, 24-item structure with strong internal consistency to better assess disaster preparedness in Emergency Medical Services populations.

Abdullah Alruwaili, Abdulhadi Al Ruwaithi2026-09-21

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Decomposing Uncertainty in Spatial Accessibility Estimates of Urban Park Using Uncertainty Analysis and Global Sensitivity Analysis (UA-GSA)

This study proposes an uncertainty and global sensitivity analysis (UA-GSA) framework to evaluate how uncertainties in demand, supply, and travel impedance affect 2SFCA-based urban park accessibility estimates in Seoul, revealing that while spatial patterns remain consistent, the dominant sources of uncertainty vary significantly across space and time.

Huiji Jeong, Jeon-Young Kang, Alexander Michels, Dong Liu, Jared Aldstadt2026-09-21

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Scalable Statistical Computation for Large-Scale Data: Distributed, Subsampling, and Minibatch Approaches

This study quantitatively evaluates distributed computing, subsampling, and minibatch optimization for large-scale statistical analysis, finding that while distributed methods boost power at high cost and subsampling saves resources with scalability limits, minibatch optimization offers the best overall balance of speed, resource efficiency, and accuracy.

Nadia Naqvi2026-09-21

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