The impact of the COVID-19 pandemic on wildlife-aircraft collisions at US airports.

Exploiting unprecedented reductions in aircraft movements caused by the COVID-19 pandemic, we investigated the relationship between air traffic volume and the frequency of wildlife-aircraft collisions, or wildlife strikes, at the 50 largest airports in the United States. During the COVID-19 months of 2020 (March-December), both air traffic volume and the absolute number of wildlife strikes were reduced. The net effect of these two movements, however, was an increase in the wildlife strike rate from May 2020-September 2020. This increase was found to be most pronounced at airports with larger relative declines in air traffic volume. We concluded that the observed increase in the wildlife strike rate was, at least in part, generated by risk-enhancing changes in wildlife abundance and behavior within the airport environment. That is, wildlife became more abundant and active at airports in response to declines in air traffic volume.

Optimizing management of invasions in an uncertain world using dynamic spatial models.

Dispersal drives invasion dynamics of nonnative species and pathogens. Applying knowledge of dispersal to optimize the management of invasions can mean the difference between a failed and a successful control program and dramatically improve the return on investment of control efforts. A common approach to identifying optimal management solutions for invasions is to optimize dynamic spatial models that incorporate dispersal. Optimizing these spatial models can be very challenging because the interaction of time, space, and uncertainty rapidly amplifies the number of dimensions being considered. Addressing such problems requires advances in and the integration of techniques from multiple fields, including ecology, decision analysis, bioeconomics, natural resource management, and optimization. By synthesizing recent advances from these diverse fields, we provide a workflow for applying ecological theory to advance optimal management science and highlight priorities for optimizing the control of invasions. One of the striking gaps we identify is the extremely limited consideration of dispersal uncertainty in optimal management frameworks, even though dispersal estimates are highly uncertain and greatly influence invasion outcomes. In addition, optimization frameworks rarely consider multiple types of uncertainty (we describe five major types) and their interrelationships. Thus, feedbacks from management or other sources that could magnify uncertainty in dispersal are rarely considered. Incorporating uncertainty is crucial for improving transparency in decision risks and identifying optimal management strategies. We discuss gaps and solutions to the challenges of optimization using dynamic spatial models to increase the practical application of these important tools and improve the consistency and robustness of management recommendations for invasions.

Risks of introduction and economic consequences associated with African swine fever, classical swine fever and foot-and-mouth disease: A review of the literature.

African swine fever (ASF), classical swine fever (CSF) and foot-and-mouth disease (FMD) are considered to be three of the most detrimental animal diseases and are currently foreign to the U.S. Emerging and re-emerging pathogens can have tremendous impacts in terms of livestock morbidity and mortality events, production losses, forced trade restrictions, and costs associated with treatment and control. The United States is the world's top producer of beef for domestic and export use and the world's third-largest producer and consumer of pork and pork products; it has also recently been either the world's largest or second largest exporter of pork and pork products. Understanding the routes of introduction into the United States and the potential economic impact of each pathogen are crucial to (a) allocate resources to prevent routes of introduction that are believed to be more probable, (b) evaluate cost and efficacy of control methods and (c) ensure that protections are enacted to minimize impact to the most vulnerable industries. With two scoping literature reviews, pulled from global data, this study assesses the risk posed by each disease in the event of a viral introduction into the United States and illustrates what is known about the economic costs and losses associated with an outbreak.

Estimation of wildlife damage from federal crop insurance data.

BACKGROUND: Wildlife damage to crops is a persistent and costly problem for many farmers in the USA. Most existing estimates of crop damage have relied on direct assessment methods such as field studies conducted by trained biologists or surveys distributed to farmers. In this paper, we describe a new method of estimating wildlife damage that exploits federal crop insurance data. We focused our study on four crops: corn, soybean, wheat, and cotton, chosen because of their economic importance and their vulnerability to wildlife damage. RESULTS: We determined crop-raiding hot spots across the USA over the 2015-2019 period and identified the eastern and southern regions of the USA as being the most susceptible to wildlife damage. We estimated lower bounds for dollar and percent losses attributable to wildlife to these four crops. The combined loss across four crops was estimated at $592.6 million. The highest total estimated losses to wildlife were incurred by soybeans ($323.9 million) and corn ($194.0 million) and the highest percentage losses were estimated for soybeans (0.87%) and cotton (0.72%). CONCLUSION: We believe the proposed method is a reliable way to evaluate geographic and temporal heterogeneity in damages for the coming years. Accurate information on damages benefits various management agencies by allowing them to allocate management resources to crops and regions where the problem is relatively severe. A better understanding of damage heterogeneity can also help guide research and development of new management techniques.

The MUK eight protocol: a randomised phase II trial of cyclophosphamide and dexamethasone in combination with ixazomib, in relapsed or refractory multiple myeloma (RRMM) patients who have relapsed after treatment with thalidomide, lenalidomide and a proteasome inhibitor.

BACKGROUND: Multiple myeloma is a plasma cell tumour with approximately 5500 new cases in the UK each year. Ixazomib is a next generation inhibitor of the 20S proteasome and is thought to be an effective treatment for those who have relapsed from bortezomib. The combination of cyclophosphamide and dexamethasone (CD) is a recognised treatment option for patients with relapsed refractory multiple myeloma (RRMM) who have relapsed after treatment with bortezomib and lenalidomide, whilst also often being combined with newer proteasome inhibitors. The most apparent combination for ixazomib is therefore with CD. METHODS: MUK eight is a randomised, controlled, open, parallel group, multi-centre phase II trial that will recruit patients with RRMM who have relapsed after treatment with thalidomide, lenalidomide, and a proteasome inhibitor. The primary objective of the trial is to evaluate whether ixazomib in combination with cyclophosphamide and dexamethasone (ICD) has improved clinical activity compared to CD in terms of progression-free survival (PFS). Secondary objectives include comparing toxicity profiles and the activity and cost-effectiveness of both treatments. Since opening, the trial has been amended to allow all participants who experience disease progression (as per the IMWG criteria) on the CD arm to subsequently switch to receive ICD treatment, once progression has been confirmed with two clinical members of the Trial Management Group (TMG). This 'switch' phase of the study is exploratory and will assess second progression-free survival measured from randomisation to second disease progression (PFS2) and progression-free survival from the point of switching to second disease progression (PFS Switch) in participants who switch from CD to ICD treatment. DISCUSSION: Development of ixazomib offers the opportunity to further investigate the value of proteasome inhibition through oral administration in the treatment of RRMM. Previous studies investigating the safety and efficacy of ICD in patients with RRMM demonstrate a toxicity profile consistent with ixazomib in combination with lenalidomide and dexamethasone, whilst the combination showed possible activity in RRMM patients. Further investigation of the anti-tumour effect of this drug in RRMM patients is therefore warranted, especially since no trials comparing CD with ICD have been completed at present. TRIAL REGISTRATION: ISRCTN number: ISRCTN58227268 . Registered on 26 August 2015.

Optimal spatial prioritization of control resources for elimination of invasive species under demographic uncertainty.

Populations of invasive species often spread heterogeneously across a landscape, consisting of local populations that cluster in space but are connected by dispersal. A fundamental dilemma for invasive species control is how to optimally allocate limited fiscal resources across local populations. Theoretical work based on perfect knowledge of demographic connectivity suggests that targeting local populations from which migrants originate (sources) can be optimal. However, demographic processes such as abundance and dispersal can be highly uncertain, and the relationship between local population density and damage costs (damage function) is rarely known. We used a metapopulation model to understand how budget and uncertainty in abundance, connectivity, and the damage function, together impact return on investment (ROI) for optimal control strategies. Budget, observational uncertainty, and the damage function had strong effects on the optimal resource allocation strategy. Uncertainty in dispersal probability was the least important determinant of ROI. The damage function determined which resource prioritization strategy was optimal when connectivity was symmetric but not when it was asymmetric. When connectivity was asymmetric, prioritizing source populations had a higher ROI than allocating effort equally across local populations, regardless of the damage function, but uncertainty in connectivity structure and abundance reduced ROI of the optimal prioritization strategy by 57% on average depending on the control budget. With low budgets (monthly removal rate of 6.7% of population), there was little advantage to prioritizing resources, especially when connectivity was high or symmetric, and observational uncertainty had only minor effects on ROI. Allotting funding for improved monitoring appeared to be most important when budgets were moderate (monthly removal of 13-20% of the population). Our result showed that multiple sources of observational uncertainty should be considered concurrently for optimizing ROI. Accurate estimates of connectivity direction and abundance were more important than accurate estimates of dispersal rates. Developing cost-effective surveillance methods to reduce observational uncertainties, and quantitative frameworks for determining how resources should be spatially apportioned to multiple monitoring and control activities are important and challenging future directions for optimizing ROI for invasive species control programs.

Clinical Capital and the Risk of Maternal Labor and Delivery Complications: Hospital Scheduling, Timing, and Cohort Turnover Effects.

The establishment of interventions to maximize maternal health requires the identification of modifiable risk factors. Toward the identification of modifiable hospital-based factors, we analyze over 2 million births from 2005 to 2010 in Texas, employing a series of quasi-experimental tests involving hourly, daily, and monthly circumstances where medical service quality (or clinical capital) is known to vary exogenously. Motivated by a clinician's choice model, we investigate whether maternal delivery complications (1) vary by work shift, (2) increase by the hours worked within shifts, (3) increase on weekends and holidays when hospitals are typically understaffed, and (4) are higher in July when a new cohort of residents enter teaching hospitals. We find consistent evidence of a sizable statistical relationship between deliveries during nonstandard schedules and negative patient outcomes. Delivery complications are higher during night shifts (OR = 1.21, 95% CI: 1.18-1.25), and on weekends (OR = 1.09, 95% CI: 1.04-1.14) and holidays (OR = 1.29, 95% CI: 1.04-1.60), when hospitals are understaffed and less experienced doctors are more likely to work. Within shifts, we show deterioration of occupational performance per additional hour worked (OR = 1.02, 95% CI: 1.01-1.02). We observe substantial additional risk at teaching hospitals in July (OR = 1.28, 95% CI: 1.14-1.43), reflecting a cohort-turnover effect. All results are robust to the exclusion of noninduced births and intuitively falsified with analyses of chromosomal disorders. Results from our multiple-test strategy indicate that hospitals can meaningfully attenuate harm to maternal health through strategic scheduling of staff.