A Mathematical Model of Terrorism

We will discuss the outcomes of a mathematical model of terrorism based on a system of equations. The mathematical tools will be omitted to make the presentation accessible to a general audience. The model makes simple but reasonable assumptions about the interaction between members of the terrorist organization. The organization is divided into two populations, the leaders and non-leaders. Although the model is simplified to avoid mathematical complications, the graphs obtained from the solutions to the equations are easily interpreted, providing useful information. The insights obtained from the model are of interest to policy makers. For instance, we discuss counter-terrorism measures and the decline of the organization, conditions for defeat of the organization, and the relationship between the decline in the strength of the organization and the decline of leaders and non-leaders

Early mobilisation in critically ill COVID-19 patients: a subanalysis of the ESICM-initiated UNITE-COVID observational study

Background Early mobilisation (EM) is an intervention that may improve the outcome of critically ill patients. There is limited data on EM in COVID-19 patients and its use during the first pandemic wave. Methods This is a pre-planned subanalysis of the ESICM UNITE-COVID, an international multicenter observational study involving critically ill COVID-19 patients in the ICU between February 15th and May 15th, 2020. We analysed variables associated with the initiation of EM (within 72 h of ICU admission) and explored the impact of EM on mortality, ICU and hospital length of stay, as well as discharge location. Statistical analyses were done using (generalised) linear mixed-effect models and ANOVAs. Results Mobilisation data from 4190 patients from 280 ICUs in 45 countries were analysed. 1114 (26.6%) of these patients received mobilisation within 72 h after ICU admission; 3076 (73.4%) did not. In our analysis of factors associated with EM, mechanical ventilation at admission (OR 0.29; 95% CI 0.25, 0.35; p = 0.001), higher age (OR 0.99; 95% CI 0.98, 1.00; p ≤ 0.001), pre-existing asthma (OR 0.84; 95% CI 0.73, 0.98; p = 0.028), and pre-existing kidney disease (OR 0.84; 95% CI 0.71, 0.99; p = 0.036) were negatively associated with the initiation of EM. EM was associated with a higher chance of being discharged home (OR 1.31; 95% CI 1.08, 1.58; p = 0.007) but was not associated with length of stay in ICU (adj. difference 0.91 days; 95% CI − 0.47, 1.37, p = 0.34) and hospital (adj. difference 1.4 days; 95% CI − 0.62, 2.35, p = 0.24) or mortality (OR 0.88; 95% CI 0.7, 1.09, p = 0.24) when adjusted for covariates. Conclusions Our findings demonstrate that a quarter of COVID-19 patients received EM. There was no association found between EM in COVID-19 patients' ICU and hospital length of stay or mortality. However, EM in COVID-19 patients was associated with increased odds of being discharged home rather than to a care facility. Trial registration ClinicalTrials.gov: NCT04836065 (retrospectively registered April 8th 2021)

A Mathematical Model of Terrorism

We will discuss the outcomes of a mathematical model of terrorism based on a system of equations. The mathematical tools will be omitted to make the presentation accessible to a general audience. The model makes simple but reasonable assumptions about the interaction between members of the terrorist organization. The organization is divided into two populations, the leaders and non-leaders. Although the model is simplified to avoid mathematical complications, the graphs obtained from the solutions to the equations are easily interpreted, providing useful information. The insights obtained from the model are of interest to policy makers. For instance, we discuss counter-terrorism measures and the decline of the organization, conditions for defeat of the organization, and the relationship between the decline in the strength of the organization and the decline of leaders and non-leaders