public health officials

Abstract: Confirmation of overdose deaths in the United States is often delayed due to the complexities of certifying drug-involved deaths. Death investigation reports represent a rich but underutilized data source that could be used to predict whether a death is a suspected overdose (i.e., a death preliminarily identified as an overdose prior to the final cause and manner of death determination). We present an automated framework that classifies suspected overdoses from death investigation reports, while producing supporting explanations guided by expert knowledge. Through an expert-guided design process, we investigate 1) what type of explanations are best for model prediction performance, and 2) which architecture is better suited for this task: pipeline models that rationalize first and then predict, or self-rationalizing models that jointly predict and rationalize? Our experiments show that a pipeline model with explanations grounded in the input death investigation reports outperforms the other settings. Our best model also outperforms a rule-based system currently in deployment by 15%, while allowing expert interventions for final decisions.

Abstract: Source detection in hypergraphs plays an important role in mitigating the spread of harmful rumors and ensuring social stability. Current methods frequently overlook the causal entanglement problem in observations, thereby limiting the performance of source inference. In this work, we propose a source detection approach in Hypergraphs via Causal Disentanglement (HyperCD). To decouple causal propagation semantics from confounding noise, we propose the causal disentanglement module. A dual-branch variational encoder is utilized to disentangle snapshot embeddings into causal and confounding latent spaces, ensuring that the inference module relies solely on causal propagation signals. To further enforce this separation, an adversarial inference loss term is introduced to guarantee statistical independence between the decoupled branches. Moreover, to capture diffusion dynamics in hypergraphs, an attention-based hypergraph aggregation module is designed, which comprises the node-to-hyperedge propagation and hyperedge-to-node propagation processes. Extensive experiments on six datasets demonstrate the superiority of our HyperCD in effectively mitigating the causal entanglement problem.

Abstract: High-resolution socioeconomic variables are important for applications such as urban planning, public health, disaster response, and resource allocation. In practice, however, these variables are often observed only at a coarse spatial resolution. We introduce Puffin, a probabilistic framework for statistical disaggregation that raises the resolution of coarse totals using high-resolution satellite embeddings as covariates. Instead of predicting a single value for each fine-resolution subregion, Puffin learns a probability distribution and is trained through an aggregation-aware likelihood. At inference, Puffin conditions these predictions on the observed regional total and splits it among the subregions. The resulting fine-scale estimates are consistent with the observed aggregate and come with calibrated uncertainty, without requiring fine-resolution labels for training. We evaluate Puffin on German and US census, employment, and election data across population, jobs, and other count variables, and study when statistical disaggregation succeeds or fails across regions, countries, and targets.

Abstract: Despite being an emerging credible means of using artificial intelligence (AI) for improving disease surveillance via early detection of outbreaks, epidemics prediction and evidence-based decision-making, there continue to be challenges in the use of AI tools in many low- and middle-income countries (LMICs) due to various factors including fragmented health information system, digital inequality, governance problems and institutional incapability. Most of the research conducted so far has concentrated on the efficacy and accuracy of AI in terms of predicting outbreaks, with little focus on other conditions necessary for its deployment. This study adopts a qualitative problem-discovery research design, integrating thematic analysis with philosophical analysis to examine the structural and normative barriers surrounding AI implementation in disease surveillance. The analysis identifies four interrelated dimensions of implementation readiness: epistemic adequacy, distributive justice, ethics of governance, and institutional legitimacy. These dimensions provide a framework for understanding how limitations in knowledge integration, unequal digital infrastructure, privacy and accountability concerns, and deficits in institutional and public trust can constrain the practical adoption of AI-enabled surveillance. Rather than proposing another predictive model, this study develops a theoretical framework that conceptualizes AI implementation in disease surveillance as simultaneously a socio-technical and normative process. The framework provides a foundation for subsequent empirical investigation and offers a structured perspective for designing more context-sensitive, ethically grounded, and institutionally sustainable AI-enabled disease surveillance systems in resource-constrained healthcare settings.

Abstract: We develop a framework to use multiparameter persistent homology (PH) to examine access to heterogeneous-quality resources and exposure to heterogeneous-severity nuisances in a geographic region. Persistent homology, which is a type of topological data analysis {(TDA)}, has been employed previously to examine resource coverage. Unlike prior approaches, which used one-parameter PH to study resource coverage and nuisance exposure, our method accounts for heterogeneous-quality resources. Our framework, which employs a computationally-efficient approximation of multiparameter PH, allows one to study access to any resource ({or} exposure of any nuisance) using any notion of quality (or severity). Using the city of Chicago as an example region, we employ our framework to detect clusters of poor access to public parks, overexposure to landfills, and both underexposure and overexposure to pubs and bars.