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1. Second‐order preserving point process permutations

   https://doi.org/10.1002/sta4.558  

   Mohler, George; Mateu, Jorge (March 2023, Stat)

   While random permutations of point processes are useful for generating counterfactuals in bivariate interaction tests, such permutations require that the underlying intensity be separable. In many real‐world datasets where clustering or inhibition is present, such an assumption does not hold. Here, we introduce a simple combinatorial optimization algorithm that generates second‐order preserving (SOP) point process permutations, for example, permutations of the times of events such that the function of the permuted process matches the function of the data. We apply the algorithm to synthetic data generated by a self‐exciting Hawkes process and a self‐avoiding point process, along with data from Los Angeles on earthquakes and arsons and data from Indianapolis on law enforcement drug seizures and overdoses. In all cases, we are able to generate a diverse sample of permuted point processes where the distribution of the functions closely matches that of the data. We then show how SOP point process permutations can be used in two applications: (1) bivariate Knox tests and (2) data augmentation to improve deep learning‐based space‐time forecasts. 

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2. 14-Carbon demonstrates that some illegal ivory is being taken from government stockpiles

   https://doi.org/10.1073/pnas.2211550119  

   Cerling, Thure E.; Brown, John E.; Hoareau, Yves; Kahumbu, Paula; Odhacha, Tobias; Southon, John R.; Wasser, Samuel K. (November 2022, Proceedings of the National Academy of Sciences)

   The 14-carbon in animal tissues records the time that the tissues are formed; since the 1960s, using the “bomb curve” for 14 C, the age of animal death can be determined accurately. Using animal tissue samples of known collection and formation dates for calibration, we determine the age of ivory samples from four ivory seizures made by law enforcement agencies between 2017 and 2019. The 14 C measurements from these seizures show that most ivory in the illegal wildlife trade is from animals from recent poaching activities. However, one seizure has a large fraction of ivory that is more than 30 y old, consistent with markings on the tusks indicating they were derived from a government stockpile. 

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3. A Spatio-temporal Cluster-aware Supervised Learning Framework for Predicting County-level Drug Overdose Deaths

   https://doi.org/10.1609/aaai.v39i27.35015  

   Feng, Zixuan; Ye, Qing; Xie, Weijun; Chen, Qiushi (April 2025, Proceedings of the AAAI Conference on Artificial Intelligence)

   The soaring drug overdose crisis in the United States has claimed more than half a million lives in the past decade and remains a major public health threat. The ability to predict drug overdose deaths at the county level can help local communities develop action plans in response to emerging changes. Applying off-the-shelf machine learning algorithms for prediction can be challenging due to the heterogeneous risk profiles of the counties and suppressed data in common publicly available data sources. To fill these gaps, we develop a cluster-aware supervised learning (CASL) framework to enhance the prediction of county-level drug overdose deaths. This CASL model simultaneously clusters counties into groups based on geographical and socioeconomic characteristics and minimizes the loss function that accounts for suppressed values and cluster-specific regularization. Our computational study uses real-world data from 2010 to 2021, focusing on the ten states most severely impacted by the drug overdose crisis. The results demonstrate that our proposed CASL framework significantly outperforms state-of-the-art methods by achieving a superior balance in prediction accuracy for both unsuppressed and suppressed observations. The proposed model also identifies different clusters of counties, capturing heterogeneous patterns of overdose mortality among counties of diverse characteristics. 

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4. Modeling and forecasting age-specific drug overdose mortality in the United States

   https://doi.org/10.1140/epjs/s11734-023-00801-z  

   Böttcher, Lucas; Chou, Tom; D’Orsogna, Maria R. (April 2023, The European Physical Journal Special Topics)

   Abstract Drug overdose deaths continue to increase in the United States for all major drug categories. Over the past two decades the total number of overdose fatalities has increased more than fivefold; since 2013 the surge in overdose rates is primarily driven by fentanyl and methamphetamines. Different drug categories and factors such as age, gender, and ethnicity are associated with different overdose mortality characteristics that may also change in time. For example, the average age at death from a drug overdose has decreased from 1940 to 1990 while the overall mortality rate has steadily increased. To provide insight into the population-level dynamics of drug overdose mortality, we develop an age-structured model for drug addiction. Using an augmented ensemble Kalman filter (EnKF), we show through a simple example how our model can be combined with synthetic observation data to estimate mortality rate and an age-distribution parameter. Finally, we use an EnKF to combine our model with observation data on overdose fatalities in the United States from 1999 to 2020 to forecast the evolution of overdose trends and estimate model parameters. 

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5. Spatiotemporal Analysis of Fentanyl-Associated Overdose Deaths in Chicago, IL, USA

   https://doi.org/10.1007/s11524-025-00986-9  

   Kang, Hyojung; Janakos, Kaylee; Varga, Csaba (June 2025, Journal of Urban Health)

   Abstract Overdose deaths involving fentanyl represent a major public health crisis in the USA. This study investigates the spatiotemporal dynamics of fentanyl-involved deaths before, during, and after the COVID-19 pandemic and examines how sociodemographic factors influence these deaths across geographic regions. Using a retrospective ecological approach, we analyzed data on ZIP code-level fentanyl-related deaths in Cook County, IL, between 2018 and 2023, obtained from the Medical Examiner’s Office and linked with sociodemographic data from the American Community Survey. We first mapped area-level death rates to assess their distribution and then conducted global and local clustering analyses to identify spatial autocorrelations and the locations of high- or low-death-rate areas. A geographically weighted Poisson regression (GWPR) model evaluated the associations between area-level fentanyl-related death rates and the area-level proportion of young adults, males, and individuals with at least a college degree, disability rate, and poverty rate. Spatial analyses found stronger spatial autocorrelations during (2020–2021) and after (2022–2023) the pandemic. Initially, high death rates were concentrated in the downtown area of Chicago, and they expanded to the surrounding areas during and after the pandemic. The GWPR model revealed that an increase in the area-level proportions of poverty, disability, and young adult residents increased the fentanyl-related death rates in most of the areas. Our findings highlight the urgent need to address the evolving dynamics of fentanyl-related overdoses through tailored public health interventions that account for the unique socioeconomic determinants of different regions. Importantly, a comprehensive approach to addressing differences in overdose death rates and their risk factors will be crucial to mitigating this public health crisis. 

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