Assessing the contagiousness of mass shootings with nonparametric Hawkes processes.
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ABSTRACT:
SUBMITTER: Boyd P
PROVIDER: S-EPMC8009418 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundSeveral past studies have found that media reports of suicides and homicides appear to subsequently increase the incidence of similar events in the community, apparently due to the coverage planting the seeds of ideation in at-risk individuals to commit similar acts.MethodsHere we explore whether or not contagion is evident in more high-profile incidents, such as school shootings and mass killings (incidents with four or more people killed). We fit a contagion model to recent data sets related to such incidents in the US, with terms that take into account the fact that a school shooting or mass murder may temporarily increase the probability of a similar event in the immediate future, by assuming an exponential decay in contagiousness after an event.ConclusionsWe find significant evidence that mass killings involving firearms are incented by similar events in the immediate past. On average, this temporary increase in probability lasts 13 days, and each incident incites at least 0.30 new incidents (p = 0.0015). We also find significant evidence of contagion in school shootings, for which an incident is contagious for an average of 13 days, and incites an average of at least 0.22 new incidents (p = 0.0001). All p-values are assessed based on a likelihood ratio test comparing the likelihood of a contagion model to that of a null model with no contagion. On average, mass killings involving firearms occur approximately every two weeks in the US, while school shootings occur on average monthly. We find that state prevalence of firearm ownership is significantly associated with the state incidence of mass killings with firearms, school shootings, and mass shootings.
| S-EPMC4489652 | biostudies-literature
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| S-EPMC10372703 | biostudies-literature
Project description:Developing new methods for modelling infectious diseases outbreaks is important for monitoring transmission and developing policy. In this paper we propose using semi-mechanistic Hawkes Processes for modelling malaria transmission in near-elimination settings. Hawkes Processes are well founded mathematical methods that enable us to combine the benefits of both statistical and mechanistic models to recreate and forecast disease transmission beyond just malaria outbreak scenarios. These methods have been successfully used in numerous applications such as social media and earthquake modelling, but are not yet widespread in epidemiology. By using domain-specific knowledge, we can both recreate transmission curves for malaria in China and Eswatini and disentangle the proportion of cases which are imported from those that are community based.
| S-EPMC8043404 | biostudies-literature
Project description: Not available
| S-EPMC8446816 | biostudies-literature
Project description:In this work, we propose nonparametric two-sample tests for population-averaged transition and state occupation probabilities for continuous-time and finite state space processes with clustered, right-censored, and/or left-truncated data. We consider settings where the two groups under comparison are independent or dependent, with or without complete cluster structure. The proposed tests do not impose assumptions regarding the structure of the within-cluster dependence and are applicable to settings with informative cluster size and/or non-Markov processes. The asymptotic properties of the tests are rigorously established using empirical process theory. Simulation studies show that the proposed tests work well even with a small number of clusters, and that they can be substantially more powerful compared to the only, to the best of our knowledge, previously proposed test for this problem. The tests are illustrated using data from a multicenter randomized controlled trial on metastatic squamous-cell carcinoma of the head and neck.
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Project description:BackgroundGranular data on gun sales has been historically difficult to obtain. In 2016, California (CA) made monthly data from 1996 to 2015 publically available. Control charts are a method to analyze how a process changes over time in response to nonroutine events. We utilized this technique to study the impact of US mass shootings on CA gun sales.MethodsMonthly gun sales were provided by the CA Department of Justice and monthly fatalities from the CDC Wonder Death Certificate Registry. Mass shooting events were obtained from after-action reports, news media, and court proceedings. Time-ordered data were analyzed with control charts with 95% confidence intervals (upper control limit, lower control limit) using QiMacros.ResultsIndividual gun sales of 9,917,811 occurred in CA with a median monthly rate of 41,324 (range, 20,057-132,903). A median of 263 people lost their lives monthly from firearms (124 homicides, 128 suicides), totaling 53,975 fatalities from 1999 to 2015. Fifteen of 21 current deadliest mass shootings occurred during this study period with 40% from 2012 to 2015. Also, 36 school shootings occurred during the study (mean, 5 deaths; range, 0-33; 6 injuries; range, 0-23) with 31% in 2012 to 2015 at rate of 3 events/year versus 1.4 events/year in the 17 prior years (p < 0.05). Sales were generally consistent from 1996 to 2011 (except post-Columbine, Col). Starting in 2011, sales exceeded the 95% predicted upper control limit every single month. Before October 2011, there was no statistically significant sustained effect of mass shootings on sales (except Col); however, since a statistically significant proportional spike in sales occurred in the months immediately following every single deadliest mass shooting event. Every year since 2012, CA has strengthened gun laws in response to mass shootings yet sales have risen immediately preceding enactment of these laws each January.ConclusionGun sales are more frequent since 2012, with an additional increase following both mass shootings and legislative changes enacted in response to these shootings.Level of evidenceEpidemiology, level III.
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Project description:Frequently, clinical trials and observational studies involve complex event history data with multiple events. When the observations are independent, the analysis of such studies can be based on standard methods for multistate models. However, the independence assumption is often violated, such as in multicenter studies, which makes standard methods improper. This work addresses the issue of nonparametric estimation and two-sample testing for the population-averaged transition and state occupation probabilities under general multistate models with cluster-correlated, right-censored, and/or left-truncated observations. The proposed methods do not impose assumptions regarding the within-cluster dependence, allow for informative cluster size, and are applicable to both Markov and non-Markov processes. Using empirical process theory, the estimators are shown to be uniformly consistent and to converge weakly to tight Gaussian processes. Closed-form variance estimators are derived, rigorous methodology for the calculation of simultaneous confidence bands is proposed, and the asymptotic properties of the nonparametric tests are established. Furthermore, I provide theoretical arguments for the validity of the nonparametric cluster bootstrap, which can be readily implemented in practice regardless of how complex the underlying multistate model is. Simulation studies show that the performance of the proposed methods is good, and that methods that ignore the within-cluster dependence can lead to invalid inferences. Finally, the methods are illustrated using data from a multicenter randomized controlled trial.
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Project description:This paper proposes nonparametric two-sample tests for the direct comparison of the probabilities of a particular transition between states of a continuous time non-homogeneous Markov process with a finite state space. The proposed tests are a linear nonparametric test, an L 2-norm-based test and a Kolmogorov-Smirnov-type test. Significance level assessment is based on rigorous procedures, which are justified through the use of modern empirical process theory. Moreover, the L 2-norm and the Kolmogorov-Smirnov-type tests are shown to be consistent for every fixed alternative hypothesis. The proposed tests are also extended to more complex situations such as cases with incompletely observed absorbing states and non-Markov processes. Simulation studies show that the test statistics perform well even with small sample sizes. Finally, the proposed tests are applied to data on the treatment of early breast cancer from the European Organization for Research and Treatment of Cancer (EORTC) trial 10854, under an illness-death model.
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Project description:This paper deals with the issue of nonparametric estimation of the transition probability matrix of a non-homogeneous Markov process with finite state space and partially observed absorbing state. We impose a missing at random assumption and propose a computationally efficient nonparametric maximum pseudolikelihood estimator (NPMPLE). The estimator depends on a parametric model that is used to estimate the probability of each absorbing state for the missing observations based, potentially, on auxiliary data. For the latter model we propose a formal goodness-of-fit test based on a residual process. Using modern empirical process theory we show that the estimator is uniformly consistent and converges weakly to a tight mean-zero Gaussian random field. We also provide methodology for simultaneous confidence band construction. Simulation studies show that the NPMPLE works well with small sample sizes and that it is robust against some degree of misspecification of the parametric model for the missing absorbing states. The method is illustrated using HIV data from sub-Saharan Africa to estimate the transition probabilities of death and disengagement from HIV care.
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Project description: Not available
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