A Bayesian spatio-temporal nowcasting model for public health decision-making and surveillance

As COVID-19 spread through the United States in 2020, states began to set up alert systems to inform policy decisions and serve as risk communication tools for the general public. Many of these systems, like in Ohio, included indicators based on an assessment of trends in reported cases. However, when cases are indexed by date of disease onset, reporting delays complicate the interpretation of trends. Despite a foundation of statistical literature to address this problem, these methods have not been widely applied in practice. In this paper, we develop a Bayesian spatio-temporal nowcasting model for assessing trends in county-level COVID-19 cases in Ohio. We compare the performance of our model to the current approach used in Ohio and the approach that was recommended by the Centers for Disease Control and Prevention. We demonstrate gains in performance while still retaining interpretability using our model. In addition, we are able to fully account for uncertainty in both the time series of cases and in the reporting process. While we cannot eliminate all of the uncertainty in public health surveillance and subsequent decision-making, we must use approaches that embrace these challenges and deliver more accurate and honest assessments to policymakers

Knowledge-Driven Drug-Use NamedEntity Recognition with Distant Supervision

As Named Entity Recognition (NER) has been essential in identifying critical elements of unstructured content, generic NER tools remain limited in recognizing entities specific to a domain, such as drug use and public health. For such high-impact areas, accurately capturing relevant entities at a more granular level is critical, as this information influences real-world processes. On the other hand, training NER models for a specific domain without handcrafted features requires an extensive amount of labeled data, which is expensive in human effort and time. In this study, we employ distant supervision utilizing a domain-specific ontology to reduce the need for human labor and train models incorporating domain-specific (e.g., drug use) external knowledge to recognize domain specific entities. We capture entities related the drug use and their trends in government epidemiology reports, with an improvement of 8% in F1-score

Neonatal Mortality and Prevalence of Practices for Newborn Care in a Squatter Settlement of Karachi, Pakistan: A Cross-Sectional Study

Background: During the past two decades there has been a sustained decline in child and infant mortality, however neonatal mortality has remained relatively unchanged. Almost all neonatal deaths (99%) occur in developing countries, where the majority are delivered at homes. Evidence suggests that these deaths could be prevented by simple, inexpensive practices and interventions during the pregnancy, delivery and postnatal period. In Pakistan over the last decade extensive efforts have been made by the international donors and government to implement these practices. However, limited attempts have been made to explore if these efforts have made a difference at the grass root level. This study assessed the burden of neonatal mortality and prevalence of practices for newborn care in a squatter settlement of Karachi, Pakistan.Methodology/Principal Findings: A community based cross-sectional study was performed. A pre-tested structured questionnaire was administered to 565 women who had recently delivered. Information was collected on neonatal morbidity, mortality and practices of women regarding care during pregnancy, child birth and for newborn, till 28th day of birth. Although 70% of women mentioned receiving antenatal care by a skilled provider, only 54.5% had four or more visits. Tetanus toxoid was received by 79% of women while only 56% delivered at a health care facility by a skilled attendant. Newborn care practices like bathing the baby immediately after birth (56%), giving pre-lacteals (79.5%), late initiation of breast feeding (80.3%), application of substances on umbilical cord (58%) and body massage (89%) were common. Most neonates (81.1%) received BCG injection and polio drops after birth. Neonatal mortality rate was 27/1000 live births with the majority of deaths occurring during the first three days of life.Conclusion: Even after years of efforts by government and nongovernmental sector to reduce newborn morbidity and mortality, inadequate antenatal care, home deliveries and unhealthy newborn care practices are highly prevalent. This leads us to important questions of why practices and behaviors have not changed. Who is responsible and what strategies are needed to bring this change

Validation and integration in spread models of influenza: scientific insights and policy implications during influenza epidemics/pandemics

Influenza presents many challenges to society, leading to severe impacts in terms of social, economic and health-care costs. To minimize these impacts, models for the spatial spread of influenza help us prepare and plan for epidemic/pandemic events. These models also increase our scientific understanding about the epidemic process and identify optimal mitigation strategies during such events. Given the human experience with past pandemics and severe seasonal epidemics, modeling studies will continue to be a useful tool for policy-makers in reducing the burden of influenza on society. I highlight two avenues of research which may enhance our understanding of the epidemic process and improve the use of models for setting and implementing policy.Validation remains limited and predictive validation is almost non-existent in complex simulation models of influenza spread. This is a serious concern because policy-makers use predictions from such models as inputs for making important decisions. Current models of influenza spread are coming under increased scrutiny for their lack of predictive ability, but it seems that no one has actually evaluated their predictive ability in the first place. To fill this gap in knowledge, I demonstrate the process of predictive validation by generalizing an individual-based model for the spread of influenza to the urban area of Montreal, Canada. Using this model and extensive data on several past epidemics, I show that the reliability and timing of several epidemic metrics depends on two important factors: the method of forecasting and the type of the epidemic metric which we want to forecast.Predictors of health disparities are not included in current models of influenza spread. This is despite an extensive literature showing that these predictors are related to burden of influenza in vulnerable subpopulations of society. Through formulating two different integrated models, I illustrate novel approaches to address this limitation. In the first model, I integrate social deprivation within an individual-based model for the spread of influenza. Using this model, I examine hypotheses about the relationship between social deprivation and influenza burden. In the second model, I integrate socioeconomic information in a metapopulation model. I develop a novel social-attributes gravity model to describe local-scale contact processes. I perform a theoretical analysis of this model to show the consequences of local-scale heterogeneity, in contact and susceptibility, on large-scale epidemic patterns. For both models, I show their practical application through evaluating vaccination strategies which make use of never-before-available data within complex and dynamic models of influenza spread.L'influenza présente de nombreux défis pour la société, entre autres des conséquences sociales, économiques et sanitaires. Afin de minimiser les impacts de la propagation spatiale de l'influenza, certains modèles sont développés pour aider à préparer et planifier des épidémies et pandémies. Ces modèles augmentent aussi notre compréhension scientifique des processus d'épidémie et identifient les stratégies optimales d'atténuation de ces évènements. Étant donné l'expérience précédente des humains lors de pandémies et les dynamiques saisonnières de celles-ci, les études de modélisation continueront d'être un outil utile pour les politiciens afin de réduire le fardeau de l'influenza pour la société. Ici, je souligne deux axes de recherche qui peut améliorer notre compréhension du processus de l'épidémie et améliorer l'utilisation de modèles pour l'élaboration des politiques.La validation des modèles demeure limitée et la validation prévisible n'existe pas dans de modèles complexes de la propagation de l'influenza. Ce manque de validation est une grande préoccupation car les politiciens utilisent ces prévisions pour faire des décisions importantes. Les modèles actuels de la propagation de la grippe sont soumis à une surveillance accrue pour leur manque de capacité prédictive, mais il semble que personne ne sont effectivement évalué leur capacité prédictive en premier lieu. Pour combler cette lacune dans les connaissances. Je démontre le processus de validation prévisible en généralisant le modèle courant, basé sur l'individu dans la région urbaine de Montréal, Canada. J'utilise un grand jeu de données comportant plusieurs épidémies en plus de perturbations réelles pour démontrer que la méthode de prévision et la métrique du type d'épidémie peuvent avoir de grands enjeux sur le temps de détection et la fiabilité lorsque de telles estimées sont possibles.Les disparités de santé ne sont pas incluses dans les modèles courant de la répartition de l'influenza malgré le fait que la littérature démontre que les prédictions de celles-ci sont reliées au fardeau de l'influenza. Par la formulation de deux modèles intégraux différents, je démontre une nouvelle approche qui adresse cette limitation. Dans le premier modèle, j'intègre la privation sociale dans un modèle basé sur l'individu. En utilisant ce modèle, j'examine les hypothèses concernant le lien entre la privation sociale et le fardeau de l'influenza. Dans le deuxième modèle, j'intègre de l'information socioéconomique dans un modèle de métapopulations. Je développe un nouveau modèle gravitationnel d'attributs sociaux pour décrire l'état local des processus de contact. J'effectue une analyse théorique pour démontrer les conséquences de l'hétérogénéité à l'échelle locale, du contact et de la susceptibilité sur les patrons épidémiques à grande échelle. Pour les deux modèles, je démontre leur application pratique par rapport à l'évaluation des stratégies de vaccination. Ces stratégies utilisent des jeux de données complexes, jamais utilisés auparavant, et des modèles dynamiques de propagation de l'influenza

Social deprivation and burden of influenza: Testing hypotheses and gaining insights from a simulation model for the spread of influenza

Factors associated with the burden of influenza among vulnerable populations have mainly been identified using statistical methodologies. Complex simulation models provide mechanistic explanations, in terms of spatial heterogeneity and contact rates, while controlling other factors and may be used to better understand statistical patterns and, ultimately, design optimal population-level interventions. We extended a sophisticated simulation model, which was applied to forecast epidemics and validated for predictive ability, to identify mechanisms for the empirical relationship between social deprivation and the burden of influenza. Our modeled scenarios and associated epidemic metrics systematically assessed whether neighborhood composition and/or spatial arrangement could qualitatively replicate this empirical relationship. We further used the model to determine consequences of local-scale heterogeneities on larger scale disease spread. Our findings indicated that both neighborhood composition and spatial arrangement were critical to qualitatively match the empirical relationship of interest. Also, when social deprivation was fully included in the model, we observed lower age-based attack rates and greater delay in epidemic peak week in the most socially deprived neighborhoods. Insights from simulation models complement current understandings from statistical-based association studies. Additional insights from our study are: (1) heterogeneous spatial arrangement of neighborhoods is a necessary condition for simulating observed disparities in the burden of influenza and (2) unmeasured factors may lead to a better quantitative match between simulated and observed rate ratio in the burden of influenza between the most and least socially deprived populations

Integrating Data, Biology, and Decision Models for Invasive Species Management: Application to Leafy Spurge (Euphorbia esula)

Invasive species are a major cause of environmental change and are often costly to control. Decision theory should offer managers guidance to formulate the optimal allocation of resources. Unfortunately, current decision theory models typically do not consider invasion dynamics and do not make full use of the best models of biological spread and best biological data from theoretical models. We developed a decision theory model that integrated population dynamics, spread, uncertainty, and changes in management policies. We applied this model to leafy spurge (Euphorbia esula), a high-priority invasive weed in North America. We used field data to construct a biological model that included stochastic population dynamics and spatial spread and integrated it with decision theory using stochastic dynamic programming (SDP). The SDP model considered three control strategies: no control, biological control, and herbicide control. Solutions from the SDP model determined the optimal strategy to apply at a given state for any time horizon. The optimal strategy depended on the area and density of leafy spurge and varied with the time horizon; therefore, dynamic control is important in management programs. Biological control was consistently indicated as the optimal strategy for all time horizons. Herbicide control was the optimal strategy for small areas with high-density infestation for long time horizons. We conclude that dynamic control, forecasting, and the time horizon are important considerations for invasive species managers who are under financial, logistical, and time constraints

Understanding the spatiotemporal evolution of opioid overdose events using a regionalized sequence alignment analysis

Background: Opioid overdose events and deaths have become a serious public health crisis in the United States, and understanding the spatiotemporal evolution of the disease occurrences is crucial for developing effective prevention strategies, informing health systems policy and planning, and guiding local responses. However, current research lacks the capability to observe the dynamics of the opioid crisis at a fine spatial-temporal resolution over a long period, leading to ineffective policies and interventions at the local level. Methods: This paper proposes a novel regionalized sequential alignment analysis using opioid overdose events data to assess the spatiotemporal similarity of opioid overdose evolutionary trajectories within regions that share similar socioeconomic status. The model synthesizes the shape and correlation of space-time trajectories to assist space-time pattern mining in different neighborhoods, identifying trajectories that exhibit similar spatiotemporal characteristics for further analysis. Results: By adopting this methodology, we can better understand the spatiotemporal evolution of opioid overdose events and identify regions with similar patterns of evolution. This enables policymakers and health researchers to develop effective interventions and policies to address the opioid crisis at the local level. Conclusions: The proposed methodology provides a new framework for understanding the spatiotemporal evolution of opioid overdose events, enabling policymakers and health researchers to develop effective interventions and policies to address this growing public health crisis