Unified functional network and nonlinear time series analysis for complex systems science: The pyunicorn package

We introduce the \texttt{pyunicorn} (Pythonic unified complex network and recurrence analysis toolbox) open source software package for applying and combining modern methods of data analysis and modeling from complex network theory and nonlinear time series analysis. \texttt{pyunicorn} is a fully object-oriented and easily parallelizable package written in the language Python. It allows for the construction of functional networks such as climate networks in climatology or functional brain networks in neuroscience representing the structure of statistical interrelationships in large data sets of time series and, subsequently, investigating this structure using advanced methods of complex network theory such as measures and models for spatial networks, networks of interacting networks, node-weighted statistics or network surrogates. Additionally, \texttt{pyunicorn} provides insights into the nonlinear dynamics of complex systems as recorded in uni- and multivariate time series from a non-traditional perspective by means of recurrence quantification analysis (RQA), recurrence networks, visibility graphs and construction of surrogate time series. The range of possible applications of the library is outlined, drawing on several examples mainly from the field of climatology.Comment: 28 pages, 17 figure

Characterizing Hospital Workers' Willingness to Respond to a Radiological Event

Terrorist use of a radiological dispersal device (RDD, or "dirty bomb"), which combines a conventional explosive device with radiological materials, is among the National Planning Scenarios of the United States government. Understanding employee willingness to respond is critical for planning experts. Previous research has demonstrated that perception of threat and efficacy is key in the assessing willingness to respond to a RDD event.An anonymous online survey was used to evaluate the willingness of hospital employees to respond to a RDD event. Agreement with a series of belief statements was assessed, following a methodology validated in previous work. The survey was available online to all 18,612 employees of the Johns Hopkins Hospital from January to March 2009.Surveys were completed by 3426 employees (18.4%), whose demographic distribution was similar to overall hospital staff. 39% of hospital workers were not willing to respond to a RDD scenario if asked but not required to do so. Only 11% more were willing if required. Workers who were hesitant to agree to work additional hours when required were 20 times less likely to report during a RDD emergency. Respondents who perceived their peers as likely to report to work in a RDD emergency were 17 times more likely to respond during a RDD event if asked. Only 27.9% of the hospital employees with a perception of low efficacy declared willingness to respond to a severe RDD event. Perception of threat had little impact on willingness to respond among hospital workers.Radiological scenarios such as RDDs are among the most dreaded emergency events yet studied. Several attitudinal indicators can help to identify hospital employees unlikely to respond. These risk-perception modifiers must then be addressed through training to enable effective hospital response to a RDD event

Extracorporeal Membrane Oxygenation for Severe Acute Respiratory Distress Syndrome associated with COVID-19: An Emulated Target Trial Analysis.

RATIONALE: Whether COVID patients may benefit from extracorporeal membrane oxygenation (ECMO) compared with conventional invasive mechanical ventilation (IMV) remains unknown. OBJECTIVES: To estimate the effect of ECMO on 90-Day mortality vs IMV only Methods: Among 4,244 critically ill adult patients with COVID-19 included in a multicenter cohort study, we emulated a target trial comparing the treatment strategies of initiating ECMO vs. no ECMO within 7 days of IMV in patients with severe acute respiratory distress syndrome (PaO2/FiO2 <80 or PaCO2 ≥60 mmHg). We controlled for confounding using a multivariable Cox model based on predefined variables. MAIN RESULTS: 1,235 patients met the full eligibility criteria for the emulated trial, among whom 164 patients initiated ECMO. The ECMO strategy had a higher survival probability at Day-7 from the onset of eligibility criteria (87% vs 83%, risk difference: 4%, 95% CI 0;9%) which decreased during follow-up (survival at Day-90: 63% vs 65%, risk difference: -2%, 95% CI -10;5%). However, ECMO was associated with higher survival when performed in high-volume ECMO centers or in regions where a specific ECMO network organization was set up to handle high demand, and when initiated within the first 4 days of MV and in profoundly hypoxemic patients. CONCLUSIONS: In an emulated trial based on a nationwide COVID-19 cohort, we found differential survival over time of an ECMO compared with a no-ECMO strategy. However, ECMO was consistently associated with better outcomes when performed in high-volume centers and in regions with ECMO capacities specifically organized to handle high demand. This article is open access and distributed under the terms of the Creative Commons Attribution Non-Commercial No Derivatives License 4.0 (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Effect of angiotensin-converting enzyme inhibitor and angiotensin receptor blocker initiation on organ support-free days in patients hospitalized with COVID-19

IMPORTANCE Overactivation of the renin-angiotensin system (RAS) may contribute to poor clinical outcomes in patients with COVID-19. Objective To determine whether angiotensin-converting enzyme (ACE) inhibitor or angiotensin receptor blocker (ARB) initiation improves outcomes in patients hospitalized for COVID-19. DESIGN, SETTING, AND PARTICIPANTS In an ongoing, adaptive platform randomized clinical trial, 721 critically ill and 58 non–critically ill hospitalized adults were randomized to receive an RAS inhibitor or control between March 16, 2021, and February 25, 2022, at 69 sites in 7 countries (final follow-up on June 1, 2022). INTERVENTIONS Patients were randomized to receive open-label initiation of an ACE inhibitor (n = 257), ARB (n = 248), ARB in combination with DMX-200 (a chemokine receptor-2 inhibitor; n = 10), or no RAS inhibitor (control; n = 264) for up to 10 days. MAIN OUTCOMES AND MEASURES The primary outcome was organ support–free days, a composite of hospital survival and days alive without cardiovascular or respiratory organ support through 21 days. The primary analysis was a bayesian cumulative logistic model. Odds ratios (ORs) greater than 1 represent improved outcomes. RESULTS On February 25, 2022, enrollment was discontinued due to safety concerns. Among 679 critically ill patients with available primary outcome data, the median age was 56 years and 239 participants (35.2%) were women. Median (IQR) organ support–free days among critically ill patients was 10 (–1 to 16) in the ACE inhibitor group (n = 231), 8 (–1 to 17) in the ARB group (n = 217), and 12 (0 to 17) in the control group (n = 231) (median adjusted odds ratios of 0.77 [95% bayesian credible interval, 0.58-1.06] for improvement for ACE inhibitor and 0.76 [95% credible interval, 0.56-1.05] for ARB compared with control). The posterior probabilities that ACE inhibitors and ARBs worsened organ support–free days compared with control were 94.9% and 95.4%, respectively. Hospital survival occurred in 166 of 231 critically ill participants (71.9%) in the ACE inhibitor group, 152 of 217 (70.0%) in the ARB group, and 182 of 231 (78.8%) in the control group (posterior probabilities that ACE inhibitor and ARB worsened hospital survival compared with control were 95.3% and 98.1%, respectively). CONCLUSIONS AND RELEVANCE In this trial, among critically ill adults with COVID-19, initiation of an ACE inhibitor or ARB did not improve, and likely worsened, clinical outcomes. TRIAL REGISTRATION ClinicalTrials.gov Identifier: NCT0273570

Appendix A. Simulation study for uncertain species analysis.

Simulation study for uncertain species analysis

Data from: How much is new information worth? Evaluating the financial benefit of resolving management uncertainty.

1. Conservation decision-makers face a trade-off between spending limited funds on direct management action, or gaining new information in an attempt to improve management performance in the future. Value-of-information analysis can help to resolve this trade-off by evaluating how much management performance could improve if new information was gained. Value-of-information analysis has been used extensively in other disciplines, but there are only a few examples where it has informed conservation planning, none of which have used it to evaluate the financial value of gaining new information. 2. We address this gap by applying value-of-information analysis to the management of a declining koala Phascolarctos cinereus population. Decision-makers responsible for managing this population face uncertainty about survival and fecundity rates, and how habitat cover affects mortality threats. The value of gaining new information about these uncertainties was calculated using a deterministic matrix model of the koala population to find the expected population growth rate if koala mortality threats were optimally managed under alternative model hypotheses, which represented the uncertainties faced by koala managers. 3. Gaining new information about survival and fecundity rates and the effect of habitat cover on mortality threats will do little to improve koala management. Across a range of management budgets, no more than 1·7% of the budget should be spent on resolving these uncertainties. 4. The value of information was low because optimal management decisions were not sensitive to the uncertainties we considered. Decisions were instead driven by a substantial difference in the cost efficiency of management actions. The value of information was up to forty times higher when the cost efficiencies of different koala management actions were similar. 5. Synthesis and applications. This study evaluates the ecological and financial benefits of gaining new information to inform a conservation problem. We also theoretically demonstrate that the value of reducing uncertainty is highest when it is not clear which management action is the most cost efficient. This study will help expand the use of value-of-information analyses in conservation by providing a cost efficiency metric by which to evaluate research or monitoring

Appendix A. Modeling of capture–mark–recapture (CMR) data for joint detection-CMR modeling of occupancy and abundance.

Modeling of capture–mark–recapture (CMR) data for joint detection-CMR modeling of occupancy and abundance

Supplement 2. Python and WinBUGS model code for simulation study.

<h2>File List</h2><blockquote> <p><a href="bugs_sim.py">bugs_sim.py</a> -- Python code to simulate data for input into WINBUGS. Program calls WINBUGS in a DOS command and sends summary output to a storage file.</p> <p><a href="def_sim.py">def_sim.py</a> -- Python code (called by bugs_sim.py) specifying input parameters for simulations.</p> <p><a href="oarunscript.txt">oarunscript.txt</a> -- WINBUGS script file for batch execution.</p> <p><a href="oa_model.txt">oa_model.txt</a> -- WINBUGS model file for batch execution.</p> <p><a href="oa_data.txt">oa_data.txt</a> -- WINBUGS data file (replaced at each simulation iteration).</p> <p><a href="oa_inits.txt">oa_inits.txt</a> -- WINBUGS initial parameter value file (replaced at each simulation iteration).</p> </blockquote><h2>Description</h2><blockquote> <p>To execute program in WINDOWS operating systems, place all files in /Program Files/ WINBUGS14 and open the file "bugs_sim.py". This file references the "def_sim.py" and simulates data and initial values, replacing oa_data.txt and oa_intis.txt. The program then executes WINBUGS as a DOS command, with parameters contained in "oa_runscript.txt", using the simulated model specified in "oa_model.txt" and current data and initial values. Summary output is sent to two comma-delimited files: "sim_params.csv" and "sim_summary.csv", which keep track of parameter values and model estimates, respectively, for each simulation, and from which estimator performance (e.g., bias, MSE, and interval coverage) can be evaluated.</p> <p><i>Note</i>: Execution of "bugs_sim.py" and "def_sim.py" requires installation of Python; we recommend ActiveState ActivePython 2.5 (http://www.activestate.com), and numpy numeric Python (http://www.scipy.org/Download). Installation of pymc (http://pymc.googlecode.com/files/pymc-2.0.win32-py2.5.exe) is required to access the likelihood objects in "bugs_sim.py"; alternatively users may code their own likelihood functions directly in Python. </p> </blockquote

Estimates of Southern White-tailed Ptarmigan daily nest survival from multiple sites in the Southern Rocky Mountains of Colorado

Estimating vital rates of avian species is important to understand population dynamics and develop potential conservation strategies that target rates for management. Avian species have reduced potential for high annual fecundity in alpine ecosystems due to a short breeding window and harsh weather conditions. We located nests from Southern White-tailed Ptarmigan ( Lagopus leucura altipetens ) across six study sites in the Southern Rocky Mountains of Colorado to estimate daily nest survival from 2013–2017. We used a known-fate hierarchical nest survival model and fit several covariates, including environmental conditions representing daily weather events and shrub cover, to describe variation in daily survival and derive estimates of nest success. We located and monitored 198 nests from 128 radio-marked ptarmigan hens. The mean nest success estimated as a derived parameter from daily nest survival was 45.6% (95% credible interval [CI]: 31.2–59.6%) and ranged from 40.3% to 50.3% across sites. Variation in daily nest survival was poorly described by the covariates we fit (95% CI of most slope coefficients overlapped 0), although there was some support for a negative effect of relative elevation (nests at lower elevations within a site survived at higher rates) and a positive effect of nest age (older nests survived at higher rates). We examined how variation in nest success was likely to influence the finite rate of population growth using a simple simulation with an age-transition matrix parameterized with previously reported fecundity and survival estimates. We found that the finite growth rate was predicted to increase 18.7% when evaluated from the lower to upper 95% CI estimated values of nest success, conditional on the other vital rates used in our simulation. We discuss the broader implications of these findings in the context of managing for nest survival of Southern White-tailed Ptarmigan