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Abstract Most of the current public health surveillance methods used in epidemiological studies to identify hotspots of diseases assume that the regional disease case counts are independently distributed and they lack the ability of adjusting for confounding covariates. This article proposes a new approach that uses a simultaneous autoregressive (SAR) model, a popular spatial regression approach, within the classical space‐time cumulative sum (CUSUM) framework for detecting changes in the spatial distribution of count data while accounting for risk factors and spatial correlation. We develop expressions for the likelihood ratio test monitoring statistics based on a SAR model with covariates, leading to the proposed space‐time CUSUM test statistic. The effectiveness of the proposed monitoring approach in detecting and identifying step shifts is studied by simulation of various shift scenarios in regional counts. A case study for monitoring regional COVID‐19 infection counts while adjusting for social vulnerability, often correlated with a community's susceptibility towards disease infection, is presented to illustrate the application of the proposed methodology in public health surveillance. 

The growing complexity of natural disasters, intensified by climate change, has amplified the challenges of managing emergency shelter demand. Accurate shelter demand forecasting is crucial to optimize resource allocation, prevent overcrowding, and ensure evacuee safety, particularly during concurrent disasters like hurricanes and pandemics. Real-time decision-making during evacuations remains a significant challenge due to dynamic evacuation behaviors and evolving disaster conditions. This study introduces a spatiotemporal modeling framework that leverages connected vehicle data to predict shelter demand using data collected during Hurricane Sally (September 2020) across Santa Rosa, Escambia, and Okaloosa counties in Florida, USA. Using Generalized Additive Models (GAMs) with spatial and temporal smoothing, integrated with GIS tools, the framework captures non-linear evacuation patterns and predicts shelter demand. The GAM outperformed the baseline Generalized Linear Model (GLM), achieving a Root Mean Square Error (RMSE) of 6.7791 and a correlation coefficient (CORR) of 0.8593 for shelters on training data, compared to the GLM’s RMSE of 12.9735 and CORR of 0.1760. For lodging facilities, the GAM achieved an RMSE of 4.0368 and CORR of 0.5485, improving upon the GLM’s RMSE of 4.6103 and CORR of 0.2897. While test data showed moderate declines in performance, the GAM consistently offered more accurate and interpretable results across both facility types. This integration of connected vehicle data with spatiotemporal modeling enables real-time insights into evacuation dynamics. Visualization outputs, like spatial heat maps, provide actionable data for emergency planners to allocate resources efficiently, enhancing disaster resilience and public safety during complex emergencies. 

Research on hurricane impacts in Florida’s coastal regions has been extensive, yet there remains a gap in comparing the effects and potential damage of different hurricanes within the same geographical area. Additionally, there is a need for reliable discussions on how variations in storm surges during these events influence evacuation accessibility to hurricane shelters. This is especially significant for rural areas with a vast number of aging populations, whose evacuation may require extra attention due to their special needs (i.e., access and functional needs). Therefore, this study aims to address this gap by conducting a comparative assessment of storm surge impacts on the evacuation accessibility of southwest Florida communities (e.g., Lee and Collier Counties) affected by two significant hurricanes: Irma in 2017 and Ian in 2022. Utilizing the floating catchment area method and examining Replica’s OD Matrix data with Geographical Information Systems (GISs)-based technical tools, this research seeks to provide insights into the effectiveness of evacuation plans and identify areas that need enhancements for special needs sheltering. By highlighting the differential impacts of storm surges on evacuation accessibility between these two hurricanes, this assessment contributes to refining disaster risk reduction strategies and has the potential to inform decision-making processes for mitigating the impacts of future coastal hazards. 

Southeastern United States frequently experience tornadoes, necessitating rapid response and recovery efforts by state and federal agencies. Accurate information about the extent and severity of tornado-induced damage, especially debris volume and locations, is crucial for these efforts. This study, therefore, focuses on post-tornado debris assessment in Leon County, Florida, which was hit by two EF-2 and an EF-1 tornadoes in May 2024. Using satellite imagery from the Planetscope satellite and Geographic Information Systems (GIS), a macro-level evaluation of tornado debris impact was conducted, particularly on roadways and impacted communities. The proposed approach includes an evaluation of the overall post-tornado debris impact across the entire county and its population, and a detailed analysis of debris impact on roadways and its effect on accessibility. Spectral indices from satellite images, specifically the Normalized Difference Vegetation Index (NDVI), were utilized to derive assessment parameters. By comparing NDVI values from pre- and post-tornado images, we analyzed changes in vegetation and debris accumulation along roadway segments leading to possible roadway closures. This integrated method provides critical insights for enhancing disaster response and recovery operations in tornado-prone regions. Findings indicate that high volumes of vegetative debris were present in the south-central parts of the county, which is occupied by the highest population of county residents. The roadway segments in this region also recorded highest debris volumes, which is a critical information for agencies that need to know highly impacted locations. Comparing the results to ground truth damage data, the accuracy recorded was 74%. 

This project presents methodology to analyze high resolution connected vehicle data (CVD) for understanding how movements of populations during pandemic-hurricanes impact disease spread and devising better plans for safe sheltering and evacuations of vulnerable populations. The dataset contains historical vehicular movement data of for Florida Panhandle Counties of Calhoun, Escambia, Liberty, Gadsden, Jackson, Santa Rosa, Washington and Bay that are impacted by Hurricane Sally which made landfall on September 16, 2020. This coincided with the first phase of the Covid-19 pandemic. The datasets used for this study consist of GPS movement data, shelter and lodging facility wait time, and vehicle count data for 44 shelters and 123 lodging facilities in Florida’s Santa Rosa, Escambia, and Okaloosa counties from 01 to 30 September 2020. The dataset has been used in the following publications: Tsekeni, D.E., Alisan, O., Yang, J., Vanli, O. A., Ozguven, E.E., (2025) “Spatiotemporal modeling of connected vehicle data: An application to non-congregate shelter planning during hurricane-pandemics”, Applied Sciences, 15, 3185. DOI: 10.3390/app15063185. Tsekeni, D.E., Vanli, O. A., (2025) “Time Series Segmentation of Movement Network Data for Endemic-Epidemic Modeling of Infectious Diseases”, IISE Transactions on Healthcare Systems Engineering, (Submitted, May 2025) 

Traffic crashes significantly contribute to global fatalities, particularly in urban areas, highlighting the need to evaluate the relationship between urban environments and traffic safety. This study extends former spatial modeling frameworks by drawing paths between global models, including spatial lag (SLM), and spatial error (SEM), and local models, including geographically weighted regression (GWR), multi-scale geographically weighted regression (MGWR), and multi-scale geographically weighted regression with spatially lagged dependent variable (MGWRL). Utilizing the proposed framework, this study analyzes severe traffic crashes in relation to urban built environments using various spatial regression models within Leon County, Florida. According to the results, SLM outperforms OLS, SEM, and GWR models. Local models with lagged dependent variables outperform both the global and generic versions of the local models in all performance measures, whereas MGWR and MGWRL outperform GWR and GWRL. Local models performed better than global models, showing spatial non-stationarity; so, the relationship between the dependent and independent variables varies over space. The better performance of models with lagged dependent variables signifies that the spatial distribution of severe crashes is correlated. Finally, the better performance of multi-scale local models than classical local models indicates varying influences of independent variables with different bandwidths. According to the MGWRL model, census block groups close to the urban area with higher population, higher education level, and lower car ownership rates have lower crash rates. On the contrary, motor vehicle percentage for commuting is found to have a negative association with severe crash rate, which suggests the locality of the mentioned associations. 

This paper reviews a set of Bayesian model updating methodologies for quantification of uncertainty in multi-modal models for estimating failure probabilities in rare hazard events. Specifically, a two-stage Bayesian regression model is proposed to fuse an analytical capacity model with experimentally observed capacity data to predict failure probability of residential building roof systems under severe wind loading. The ultimate goals are to construct fragility models accounting for uncertainties due to model inadequacy (epistemic uncertainty) and lack of experimental data (aleatory uncertainty) in estimating failure (exceedance) probabilities and number of damaged buildings in building portfolios. The proposed approach is illustrated on a case study involving a sample residential building portfolio under scenario hurricanes to compare the exceedance probability and aggregate expected loss to determine the most cost-effective wind mitigation options.