Suicide as a contagion: modeling and forecasting emergent outbreaks

摘要 美国每个年龄段和几乎每个州的自杀率都在持续上升--2016年， 美国有44，965人死于自杀。制定科学严谨的监测、报告和 自杀预测系统对于制定适当的公共卫生对策至关重要。在这里，我们将带来 Google Extended Trends、National Suicide Prevention Lifeline、Health Cost和 利用项目和生命统计数据，再加上国家暴力死亡登记册，以建立和验证 查询、呼叫、尝试和完成自杀的统计和数学模型。我们的模型旨在 通过将其作为一个传染过程进行研究来解决这一流行病。我们的首要目标是提供一个 预期预警系统，为学校和社区的预防和治疗能力提供信息。 我们的第一个目标是估计自杀查询，呼叫，尝试和完成如何在空间和时间上聚集。 自杀行为的时空自相关性与传染病有许多共同特征 这可以用流行病学的三合会媒介（媒体报道，人与人之间的传播）来概念化 传播，致命媒介），宿主（尝试和精神障碍的历史），和环境（天气， 海拔和温度）-严格的统计模型已被用于研究地理和 与传染病相关的暂时性风险因素，即使在监测数据不完整的情况下。我们 将估计自杀查询、呼叫、尝试和完成的独特和共享自相关， 测试自相关随发育阶段变化的程度（即，青少年和年轻人 与老年人相比）。我们的第二个目标是了解自杀风险在发展过程中的动态变化。 通过使用代表自杀的数学模型模拟异常自杀爆发的阶段 作为传染病。这些模型将与贝叶斯推理算法相结合，以实现模拟， 优化和估计表征系统动态的关键流行病学参数。这一努力 将汇集4个数据源（查询、调用、尝试和完成），将系统视为 整体，而不是作为单独的信息流。我们将回答一些关键问题， 自杀结局的局部和时间异常增加在不同事件中有所不同， 传染性传播、传染性自杀危机的持续时间以及致命手段的作用。为我们 第三个目标，我们将使用模型推理框架来产生粒度的、局部的、6个月的自杀预测 爆发事件。生成的预测将有助于告知我们何时，何地以及我们可以预期的人 自杀事件爆发的可能性，以及除非迅速开展预防工作，否则会持续多久。这个项目 汇集了数学建模、传染病和自杀流行病学方面的专家， 预防和干预，世卫组织将采用最先进的建模方法进行自杀监测， 预测。

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.