Suicide as a contagion: modeling and forecasting emergent outbreaks

自杀作为一种传染病：建模和预测突发疫情

基本信息

批准号：
10297837
负责人：
KATHERINE MARGARET KEYES
金额：
$ 63.51万
依托单位：
COLUMBIA UNIVERSITY HEALTH SCIENCES
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-01-24 至 2024-11-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10297837
关键词：
Address Adolescent and Young Adult Age Algorithms Area Bayesian Analysis Bayesian Modeling Biological Models Cessation of life Cluster Analysis Communicable Diseases Communities Community based prevention Coupled Data Data Science Data Sources Detection Development Disease Outbreaks Disease model Elderly Environment Epidemic Epidemiologist Epidemiology Event Feeds Firearms Future Geographic Locations Geographic state Geography Goals Health Care Costs Hospitals Hot Spot Individual Intervention Length Life Expectancy Mental disorders Methods Modeling Non-linear Models Opioid Outcome Persons Prevention Process Property Psychiatric epidemiology Public Health Recording of previous events Registries Reporting Risk Risk Factors Role Schools Science Series Signal Transduction Space-Time Clustering Statistical Models Stream Suicide Suicide prevention System Temperature Testing Time Triad Acrylic Resin United States Violence Vital Statistics Weather Work age group base completed suicide contagion data modeling high risk ideation innovation mathematical model models and simulation novel response simulation suicidal suicidal behavior suicidal risk suicide model suicide rate surveillance data transmission process trend vector

Address Adolescent and Young Adult Age Algorithms Area Bayesian Analysis Bayesian Modeling Biological Models Cessation of life Cluster Analysis Communicable Diseases Communities Community based prevention Coupled Data Data Science Data Sources Detection Development Disease Outbreaks Disease model Elderly Environment Epidemic Epidemiologist Epidemiology Event Feeds Firearms Future Geographic Locations Geographic state Geography Goals Health Care Costs Hospitals Hot Spot Individual Intervention Length Life Expectancy Mental disorders Methods Modeling Non-linear Models Opioid Outcome Persons Prevention Process Property Psychiatric epidemiology Public Health Recording of previous events Registries Reporting Risk Risk Factors Role Schools Science Series Signal Transduction Space-Time Clustering Statistical Models Stream Suicide Suicide prevention System Temperature Testing Time Triad Acrylic Resin United States Violence Vital Statistics Weather Work age group base completed suicide contagion data modeling high risk ideation innovation mathematical model models and simulation novel response simulation suicidal suicidal behavior suicidal risk suicide model suicide rate surveillance data transmission process trend vector

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项目摘要

Abstract Suicide rates continue to increase in every age group in the United States and in almost every state—in 2016, 44,965 individuals in the US died by suicide. Developing scientifically rigorous surveillance, reporting, and forecasting systems for suicide is essential to craft appropriate public health responses. Here we will bring together geo-located data from Google Extended Trends, National Suicide Prevention Lifeline, Health Cost and Utilization Project, and vital statistics coupled with the National Violent Death Registry to build and validate statistical and mathematical models of queries, calls, attempts, and completions of suicide. Our models aim to address the epidemic by studying it as a communicable process. Our overarching goal is to provide an anticipatory warning system to inform school-based and community-based prevention and treatment capacity. Our first Aim is to estimate how suicidal queries, calls, attempts, and completions cluster in space and time. Temporal and spatial autocorrelation of suicidal behavior shares many features with communicable diseases that can be conceptualized in terms of the epidemiological triad—agent (media reporting, person-to-person transmission, lethal vectors), host (history of attempts and psychiatric disorder), and environment (weather, elevation, and temperature)—for which rigorous statistical models have been used to study geographic and temporal risk factors associated with infectious disease, even in the face of incomplete surveillance data. We will estimate the unique and shared autocorrelation of suicide queries, calls, attempts, and completions and test the extent to which autocorrelation varies by developmental stage (i.e., adolescents and young adults versus older adults). Our second Aim is to understand the dynamics of suicide risk across developmental stages through simulation of anomalous suicidal outbreaks using mathematical models that represent suicide as a contagion. The models will be coupled with Bayesian inference algorithms to enable simulation, optimization, and estimation of key epidemiological parameters that characterize system dynamics. This effort will bring together 4 data sources (queries, calls, attempts, and completions) to consider the system as a whole, rather than as separate streams of information. We will answer critical questions about the extent to which local and temporal anomalous increases in suicidal outcomes vary across events, as well as the force of contagious transmission, length of time of contagious suicidal crises, and contribution of lethal means. For our third Aim, we will use the model-inference framework to produce granular, local, 6-month predictions of suicide outbreak events. The generated forecasts will help inform when, where, and among whom we can expect suicide outbreaks to develop, and for how long unless prevention efforts are rapidly disseminated. This project brings together experts in mathematical modeling, communicable disease and suicide epidemiology, prevention, and intervention, who will apply state-of-the-art modeling approaches to suicide surveillance and forecasting.

抽象的在美国和几乎每个州的每个年龄段的自杀率继续增加 - 2016年，2016年，美国有44,965个人死于自杀。开发科学严格的监视，报告和预测自杀系统对于制定适当的公共卫生反应至关重要。我们将带来 Google扩展趋势，国家自杀预防生命线，健康成本和利用项目以及重要的统计数据，再加上国家暴力死亡注册表来建立和验证自杀的查询，呼叫，尝试和完成的统计和数学模型。我们的模型旨在通过将其作为传染过程研究来解决流行病。我们的总体目标是提供预期的警告系统，以告知基于学校和社区的预防和治疗能力。我们的第一个目的是估计自杀疑问，呼叫，尝试和完成如何在时空聚集。自杀行为的时间和空间自相关与传播疾病具有许多特征可以从流行病学三合会（媒体报道，人与人）来概念化传播，致命载体），宿主（尝试和精神障碍的历史）和环境（天气，高程和温度） - 因为哪些严格的统计模型已用于研究地理和即使在不完整的监视数据面前，与传染病相关的临时危险因素也是如此。我们将估算自杀查询，呼叫，尝试和完成的独特和共享的自相关以及测试自相关因发育阶段而变化的程度（即青少年和年轻人与老年人）。我们的第二个目的是了解发展中自杀风险的动态通过使用代表自杀的数学模型模拟异常自杀暴发的阶段作为一种传染。这些模型将与贝叶斯推理算法结合起来，以启用模拟，优化以及表征系统动力学的关键流行病学参数的估计。这项努力将汇总4个数据源（查询，呼叫，尝试和完成），以将系统视为一个整体，而不是作为单独的信息流。我们将回答有关程度的关键问题自杀结果的局部和临时异常增加在事件中各不相同，以及传染性传播，传染性自杀犯罪的时间长度以及致命手段的贡献。为我们第三目的，我们将使用模型推动框架来产生自杀的颗粒状，局部6个月的预测爆发事件。生成的森林将有助于告知我们何时，何处以及我们可以期望的森林自杀爆发要发育，除非预防努力迅速传播，否则需要多长时间。这个项目汇集了数学建模，可传播疾病和自杀流行病学专家，预防和干预，他们将采用最新的建模方法来自杀监视和预测。