Daily Probability Research Articles

Infection prediction in swine populations with machine learning

The pork industry is an essential part of the global food system, providing a significant source of protein for people around the world. A major factor restraining productivity and compromising animal wellbeing in the pork industry is disease outbreaks in pigs throughout the production process: widespread outbreaks can lead to losses as high as 10% of the U.S. pig population in extreme years. In this study, we present a machine learning model to predict the emergence of infection in swine production systems throughout the production process on a daily basis, a potential precursor to outbreaks whose detection is vital for disease prevention and mitigation. We determine features that provide the most value in predicting infection, which include nearby farm density, historical test rates, piglet inventory, feed consumption during the gestation period, and wind speed and direction. We utilize these features to produce a generalizable machine learning model, evaluate the model’s ability to predict outbreaks both seven and 30 days in advance, allowing for early warning of disease infection, and evaluate our model on two swine production systems and analyze the effects of data availability and data granularity in the context of our two swine systems with different volumes of data. Our results demonstrate good ability to predict infection in both systems with a balanced accuracy of 85.3% on any disease in the first system and balanced accuracies (average prediction accuracy on positive and negative samples) of 58.5%, 58.7%, 72.8% and 74.8% on porcine reproductive and respiratory syndrome, porcine epidemic diarrhea virus, influenza A virus, and Mycoplasma hyopneumoniae in the second system, respectively, using the six most important predictors in all cases. These models provide daily infection probabilities that can be used by veterinarians and other stakeholders as a benchmark to more timely support preventive and control strategies on farms.

Multiscale Influences on Rainfall in Northeast Australia

Abstract This study examines the multiscale modulation of mean and extreme rainfall in Northeast (NE) Australia under different background modes of variability, which is a new aspect given the high-resolution and long-term observational datasets. Daily rainfall probability is significantly modified by the Madden–Julian oscillation (MJO), and its influence varies with the seasons and is associated with atmospheric circulation anomalies. Rainfall generally decreases during El Niño and increases during La Niña years; however, there is a notable spatial nuance to El Niño–Southern Oscillation (ENSO)-associated extreme rainfall, with some locations showing the opposite precipitation response to the typical ENSO–rainfall relationship. Despite more precipitation overall in La Niña years, the mean and extreme precipitation responses to the MJO appear to be stronger and more often statistically significant during El Niño compared to La Niña periods. The impact of ENSO on the MJO–rainfall relationship is stronger than the variation of the MJO itself with ENSO, and likely reflects a change in the MJO modulation of rain-bearing atmospheric processes. During El Niño periods, diurnal rainfall amplitude is generally stronger in the central and southern subtropical parts of the study area than during La Niña periods, while the opposite tendency occurs in the northern tropical part. The diurnal cycle of both mean and extreme precipitation is amplified during suppressed convection phases compared to enhanced convection phases of the MJO. In general, the peak time of diurnal cycle does not change with MJO regimes, but there are some notable differences in rainfall propagation between enhanced and suppressed MJO phases. Significance Statement This study presents a new perspective on the relationship between precipitation in Northeast (NE) Australia and two important climate modes, the Madden–Julian oscillation (MJO) and El Niño–Southern Oscillation (ENSO). Rainfall in NE Australia is strongly influenced by the MJO, ENSO, and their interaction, suggesting that climate models need to capture both individual climate modes and their interactions for reliable rainfall projections. These findings have societal climate risk implications, as NE Australia is an area prone to catastrophic flooding due to extreme rainfall.

Camera traps link population-level activity patterns with wildfire smoke events for mammals in Eastern Washington State

BackgroundDue to anthropogenic climate change and historic fire suppression, wildfire frequency and severity are increasing across the western United States. Whereas the indirect effects of fire on wildlife via habitat change are well studied, less is known about the impacts of wildfire smoke on animal health and behavior. In this study, we explore the effects of wildfire smoke on the behavior of eight medium- to large-bodied mammalian species in a heterogenous study area in Washington, USA. We linked population-level activity metrics derived from camera trap data to concentrations of fire-specific fine particulate matter (PM2.5). We hypothesized that mammalian activity would decline during smoke events, as animals attempt to reduce potential health impacts of smoke inhalation. We used occupancy models and Poisson regression models to test the effect of fire-specific PM2.5 levels on daily detection probability and the number of detections per day, respectively, for each study species.ResultsWhile we did not observe any significant responses to daily mean concentrations of PM2.5 in the occupancy models, we found three species with significant responses in their rates of detections per day in the Poisson regression. Specifically, for each standard deviation increase in the daily mean concentration of PM2.5, there was a 12.9% decrease in the number of bobcat detections per day, an 11.2% decrease in the number of moose detections per day, and a 5.8% increase in the number of mule deer detections per day. In general, the effects of PM2.5 were small compared to other relevant covariates.ConclusionsWe generally found little evidence to support our hypothesis that animals would reduce their activity in response to wildfire smoke. However, our study demonstrated that mammals exhibited species-specific behavioral responses to smoke, which are possibly adaptive responses to reduce health impacts from smoke inhalation. Though we found only a few immediate behavioral responses to smoke exposure, we note that longer-term health consequences of smoke exposure for wildlife are also likely and generally unknown. Our study shows how camera traps, which are already widely used to study wildlife, can also be used to investigate the impacts of wildfire smoke on animal behavior and provides a step towards developing methods to better understand this increasing source of environmental stress on wildlife.

Temporal prediction of shallow landslides exploiting soil saturation degree derived by ERA5-Land products

ERA5-Land service has been released recently as an integral and operational component of Copernicus Climate Change Service. Within its set of climatological and atmospheric parameters, it provides soil moisture estimates at different soil depths, represeting an important tool for retrieving saturation degree for predicting natural hazards as shallow landslides. This paper represents an innovative attempt aiming to exploit the use of saturation degree derived from ERA5-Land soil moisture products in a data-driven model to predict the daily probability of occurence of shallow landslides. The study was carried out by investigating a multi-temporal inventory of shallow landslides occurred in Oltrepò Pavese (northern Italy). The achieved results follow: (i) ERA5-Land-derived saturation degree reconstructs well field trends measured in the study area until 1 m from ground; (ii) in agreement with the typical sliding surfaces depth, saturation degree values obtained since ERA5-Land 28–100 cm layer represent a significant predictor for the estimation of temporal probability of occurrence of shallow landslides, able especially to reduce overestimation of triggering events; (iii) saturation degree estimated by ERA5-Land 28–100 cm layer allows to detect soil hydrological conditions leading to triggering in the study area, represented by saturation degree in this layer close to complete saturation. Even if other works of research are required in different geological and geomorphological settings, this study demonstrates that ERA5-Land-derived saturation degree could be implemented to identify triggering conditions and to develop prediction methods of shallow landslides, thanks also to its free availability and constantly updating with a delay of 5 days.

Long-term forecast of thermal mortality with climate warming in riverine amphipods.

Forecasting long-term consequences of global warming requires knowledge on thermal mortality and how heat stress interacts with other environmental stressors on different timescales. Here, we describe a flexible analytical framework to forecast mortality risks by combining laboratory measurements on tolerance and field temperature records. Our framework incorporates physiological acclimation effects, temporal scale differences and the ecological reality of fluctuations in temperature, and other factors such as oxygen. As a proof of concept, we investigated the heat tolerance of amphipods Dikerogammarus villosus and Echinogammarus trichiatus in the river Waal, the Netherlands. These organisms were acclimated to different temperatures and oxygen levels. By integrating experimental data with high-resolution field data, we derived the daily heat mortality probabilities for each species under different oxygen levels, considering current temperatures as well as 1 and 2°C warming scenarios. By expressing heat stress as a mortality probability rather than a upper critical temperature, these can be used to calculate cumulative annual mortality, allowing the scaling up from individuals to populations. Our findings indicate a substantial increase in annual mortality over the coming decades, driven by projected increases in summer temperatures. Thermal acclimation and adequate oxygenation improved heat tolerance and their effects were magnified on longer timescales. Consequently, acclimation effects appear to be more effective than previously recognized and crucial for persistence under current temperatures. However, even in the best-case scenario, mortality of D. villosus is expected to approach 100% by 2100, while E. trichiatus appears to be less vulnerable with mortality increasing to 60%. Similarly, mortality risks vary spatially: In southern, warmer rivers, riverine animals will need to shift from the main channel toward the cooler head waters to avoid thermal mortality. Overall, this framework generates high-resolution forecasts on how rising temperatures, in combination with other environmental stressors such as hypoxia, impact ecological communities.

Hidden Markov movement models reveal diverse seasonal movement patterns in two North American ungulates.

Animal movement is the mechanism connecting landscapes to fitness, and understanding variation in seasonal animal movements has benefited from the analysis and categorization of animal displacement. However, seasonal movement patterns can defy classification when movements are highly variable. Hidden Markov movement models (HMMs) are a class of latent-state models well-suited to modeling movement data. Here, we used HMMs to assess seasonal patterns of variation in the movement of pronghorn (Antilocapra americana), a species known for variable seasonal movements that challenge analytical approaches, while using a population of mule deer (Odocoileus hemionus), for whom seasonal movements are well-documented, as a comparison. We used population-level HMMs in a Bayesian framework to estimate a seasonal trend in the daily probability of transitioning between a short-distance local movement state and a long-distance movement state. The estimated seasonal patterns of movements in mule deer closely aligned with prior work based on indices of animal displacement: a short period of long-distance movements in the fall season and again in the spring, consistent with migrations to and from seasonal ranges. We found seasonal movement patterns for pronghorn were more variable, as a period of long-distance movements in the fall was followed by a winter period in which pronghorn were much more likely to further initiate and remain in a long-distance movement pattern compared with the movement patterns of mule deer. Overall, pronghorn were simply more likely to be in a long-distance movement pattern throughout the year. Hidden Markov movement models provide inference on seasonal movements similar to other methods, while providing a robust framework to understand movement patterns on shorter timescales and for more challenging movement patterns. Hidden Markov movement models can allow a rigorous assessment of the drivers of changes in movement patterns such as extreme weather events and land development, important for management and conservation.

0368 Vulnerability to influenza-like illness is associated with insomnia severity

Abstract Introduction Decreased immune function is associated with sleep disorders, including insomnia. Our goal was to evaluate the vulnerability to influenza-like illness (ILI) in insomnia as quantified by the insomnia severity index (ISI). To evaluate ILI vulnerability, we leveraged a published model that estimates the daily probability of ILI symptoms from a smart bed using ballistocardiograph sensors. Methods A longitudinal IRB-approved study was conducted with Sleep Number customers between November 2021 and April 2022 to assess the longitudinal course of insomnia. Four surveys incorporating the ISI were administered at six-week intervals. Individuals responding to at least three surveys were included in the analysis (n=3117). ISI changes were modeled using linear regression to identify respondents with quasi-constant ISI for which ILI vulnerability was quantified. For each respondent, the mean ISI (μI) was calculated and categorized into two levels: no insomnia (μI ≤ 14) and insomnia (μI > 14). Bed data, including daily and restful sleep duration, sleep latency, sleep quality, heart rate, breathing rate, and motion level, were queried for 2019 (pre-COVID) and 2021 to evaluate the daily probability of experiencing ILI symptoms. Results Respondents with quasi-constant ISI (n=1680) included no-insomnia (1431) and insomnia (249). For 2019, the no-insomnia group had 1.23 (SD 1.82) ILI episodes on average which was significantly lower (p< 0.01) than the insomnia group, 1.49 (SD 1.77). The ILI-risk odds ratio for insomnia was 1.15 (CI 0.99-1.32). The mean ILI episode duration for the no-insomnia group was 4.29 (SD 7.19) days, which was significantly lower (p< 0.01) than the insomnia group 6.06 (SD 8.90) days. For 2021, similar results were obtained, with the no-insomnia group having significantly (p< 0.01) lower ILI episodes 1.22 (SD 1.63) on average compared to the insomnia group 1.49 (SD 1.67). The mean ILI episode duration for the no-insomnia group, 4.98 (SD 8.22) days, was significantly lower (p< 0.01) than the insomnia group, 6.14 (SD 7.93) days. Conclusion Insomnia may be significantly associated with more and lengthier ILI episodes per year. This was found for both 2019 and 2021 data. Additional research is needed to evaluate causality. Support (if any) Sleep Number Corporation funded this study.

Why hibernate? Tests of four hypotheses to explain intraspecific variation in hibernation phenology

Abstract Hibernation is a remarkable behaviour deployed by a diverse array of endotherms within many clades that greatly reduces metabolic need, but also has somatic costs. Hibernation in modern endotherms is often assumed to be an adaptation allowing animals to avoid extreme thermal conditions or food shortages in seasonal environments. However, many animals hibernate when foraging conditions are energetically profitable, suggesting other causal factors influence hibernation behaviour. Understanding the selection pressures responsible for intraspecific variation in the timing and duration of hibernation can help elucidate the relative evolutionary influences of the ultimate ecological causes of hibernation. We tested four previously proposed mechanistic hypotheses to explain intraspecific variation in hibernation phenology in the federally threatened northern Idaho ground squirrel (Urocitellus brunneus): (1) thermal tolerance, (2) food limitation, (3) predation avoidance and (4) sexual selection. The predation avoidance and sexual selection hypotheses received the most support, although we also found some support for the thermal tolerance and food limitation hypotheses. Heavy squirrels increased hibernation duration regardless of environmental conditions, as predicted solely by the predation avoidance hypothesis. Reproductive males emerged from hibernation earlier in spring than other sex–age classes, a pattern predicted by the sexual selection hypothesis. Temperature and food availability explained a much smaller amount of the variation in hibernation behaviour, only partially supporting predictions of the thermal tolerance and food limitation hypotheses. Our results indicate that animals navigate life‐history trade‐offs between energetic allocation to survival and reproduction via state‐dependent optimization of hibernation phenology. Consequently, any future environmental changes that influence body condition will have implications for population ecology and life‐history evolution of hibernating animals due to stark differences in daily survival probability between hibernation and the active season. Read the free Plain Language Summary for this article on the Journal blog.

Variation in mesopredator abundance and nest predation rate of the endangered Strange‐tailed Tyrant (Alectrurus risora)

Nest predation is the main cause of nest failure in passerine birds and thus is a key determinant of breeding traits. Accordingly, nest predator community composition and abundance modulate nest predation rates. When top predators are absent, mesopredator populations may become unchecked, which in turn increases the predation pressure on their prey species. We evaluated whether nest predator abundance and breeding traits of an endangered grassland bird, the Strange‐tailed Tyrant Alectrurus risora, differed between two protected areas: Iberá National Park (INP), where top predators have been extinct for 50 years, and Reserva El Bagual (REB), where Pumas Puma concolor still have a stable population. We measured abundances of potential nest predator species and during the 2015–2018 breeding seasons we monitored Strange‐tailed Tyrant nests and set camera traps to identify nest predators. Mesopredator abundance was higher in INP, and foxes were identified as the main nest predators. Nest predation rate was also higher in INP, where the daily nest predation rate and cumulative probability of nest predation were 0.05 ± 0.006 and 0.81, whereas in REB they were 0.04 ± 0.004 and 0.70, respectively. The 10‐fold higher frequency of fox records in INP could explain the higher nest predation rate observed, suggesting that the extinction of top predators may increase nest predation rates of endangered grassland birds in protected areas through release of native mesopredator species. The lower nest predation observed in REB was compensated for by a higher nest abandonment rate, which resulted in a similar fledgling production in the two study sites. Clutch size, hatching success and number of nestlings did not differ between study sites. The low breeding success recorded for this endangered species despite its populations being in protected areas of high conservation value is alarming given the global decline that grassland bird populations are facing.

Forecasting extreme negative returns in gold and silver: A discrete‐duration approach to POT models

AbstractSudden and massive drops in precious metal prices severely impact the investment risk on these commodity markets. In this study, we examine the dynamics of extreme negative returns on gold and silver, as well as propose the discrete‐duration version of the autoregressive conditional duration peaks‐over‐threshold (ACD‐POT) model for measuring market risk. The model is tailored for the dynamics of extreme events in precious metal markets; this is because it can exhibit both the strong clustering of extreme‐event days, and the serial correlation in the magnitudes of extreme losses. From an econometric perspective, our approach complements the existing dynamic versions of POT models; this is so that the time interval (i.e., duration) between the extreme negative returns is treated not as a continuous variable, but as a discrete one. The discrete hazard function in our model represents the daily extreme loss event probability and it can be used to derive one‐day‐ahead forecasts of both the value at risk and expected shortfall for investments in gold and silver. Formal backtesting methods show that the discretized version of the POT model has a superior in‐sample fit and forecasting performance than the continuous‐duration POT models.

Decoupling between carbon source and sink induced by responses of daily stem growth to water availability in subtropical urban forests

Urban forests are anticipated to offer sustainable ecosystem services, necessitating a comprehensive understanding of the ways in which trees respond to environmental changes. This study monitored stem radius fluctuations in Cinnamomum camphora and Taxodium distichum var. imbricatum trees using high-resolution dendrometers at two sites, respectively. Gross primary production (GPP) was measured using eddy-covariance techniques and aggregated to daily sums. Hourly and daily stem radius fluctuations were estimated across both species, and the responses of stems to radiation (Rg), air temperature (Tair), vapor pressure deficit (VPD), and soil humidity (SoilH) were quantified using Bayesian linear models. The diel growth patterns of the monitored trees showed similar characteristics at the species level. Results revealed that trees growth occurred primarily at night, with the lowest hourly contribution to total growth and probability for growth occurring in the afternoon. Furthermore, the Bayesian models indicated that VPD was the most important driver of daily growth and growth probability. After considering the potential constraints imposed by VPD, a modified Gompertz equation showed good performance, with R2 ranging from 0.94 to 0.99 for the relationship between accumulative growth and time. Bayes-based model-independent data assimilation using advanced Markov chain Monte Carlo (MCMC) algorithms provided deeper insights into nonlinear model parameterization. Finally, the quantified relationship between GPP and stem daily growth revealed that the decoupling between carbon source and sink increased with VPD. These findings provided direct empirical evidence for VPD as a key driver of daily growth patterns and raise questions about carbon neutrality accounting under future climate change given the uncertainties induced by increased water stress limitations on carbon utilization.

Factors influencing survival of female eastern wild turkeys in northeastern South Dakota

AbstractPopulation growth can be sensitive to changes in survival rates for many avian species. Understanding sources of mortality, and how to mitigate negative effects on survival, can give managers insight into factors contributing to population change. Harvest trends of eastern wild turkeys (Meleagris gallopavo silvestris) in northeastern South Dakota suggest a decline in abundance. We investigated factors influencing survival of wild turkeys to identify potential factors contributing to the decline. We monitored 122 female wild turkeys using VHF radio transmitters from February 2017 to April 2019. Annual survival was 0.52 (95% CI = 0.33–0.64) for juvenile and 0.49 (95% CI = 0.23–0.63) for adult females, respectively. Daily survival probability was significantly lower during the spring (log‐odds ratio [LOR] = −0.9; 95% CI = −1.5–−0.2) and while a juvenile female was incubating (LOR = −0.67; 95% CI = −1.23–−0.09) but not while an adult female was incubating (LOR = −0.35; 95% CI = −0.92–0.24). Mammalian predation was the leading cause of mortality, and female wild turkeys were most vulnerable to predation during the spring while engaging in nesting and rearing of young broods. Wild turkeys were at risk for additional sources of mortality while incubating nests that were not contributors to mortality during other periods of the year, as deaths caused by haying equipment—the second greatest cause of mortality—only occurred while a female was incubating. Reducing female mortality due to haying, by delaying cutting, installing flushing bars on haying equipment, or increasing availability of suitable nesting cover types to reduce the probability of nesting in hayfields, could improve female survival. Annual survival during our study was about 16–29% lower than survival estimates from northeastern South Dakota during the 1990s, suggesting that reduced female survival could be contributing to the apparent decline in wild turkey abundance.

Logistic Regression and Machine Learning Models for Predicting Whether Intensive Care Patients Who Are Alert and Without Delirium Remain As Such for at Least Two More Days.

Background Some intensive care unit patients are alert and without delirium for atleast two consecutive days. These patients, like other critically ill individuals, are at risk for dignity-related distress. An interval ofat least two days would provide for apalliative care multidisciplinary team to be consulted in the late morning or afternoon of day one and visit the next day. An assessment would include the administration of the validated Patient Dignity Inventory in a reflective manner. To determine whether dignity-related distress can be identified and treated during patients' intensive care unit stay, we evaluated whether asubstantive fraction of such patients (≥5%) have asubstantial (>90%) probability ofremaining alert and without delirium in the intensive care unit for at least four consecutive days. Methods The retrospective cohort study used data from one large teaching hospital in the United States of America, from 2012 to June 2022. The inclusion criteria were: a)adults, b)present in an intensive care unit at 12 PM one day and continually sofor the next 48 hours, c)during those two days had every Riker sedation-agitation scale score "4, calm and cooperative," and d) during those two days had all Confusion Assessment Method for the Intensive Care Unit (CAM-ICU) scores negative (i.e., no delirium) and all Delirium Observation Screening Scale (DOS) scores less than three (i.e., no delirium). Results Among the 10,314 patients alert and without delirium in an intensive care unit over two-day periods that included threesuccessive 12PMs, 3,826 (37%) maintained this status for at least two successive 12PMs. Six patient characteristics (e.g., hemodynamic infusion or ventilatory support) had value in predicting those 37% ofpatients. However, logistic regression and classification models each predicted a few (≈0.2%) patients with >90% probability of maintaining these criteria. Forecasts were inaccurate for nearly all patients remaining alert and without delirium in the intensive care unit (≈37%) because the models predicted no patient alert, without delirium, and in the intensive care unit for twodays would remain so for at least four days. That ≈63% accuracy was improved upon by random forest machine learning, but only with ≈3% improvement. Conclusion Although many intensive care unit patients remain alert and without delirium for several consecutive days, each patient has a high daily probability of intensive care unit discharge or deterioration in medical condition. Therefore, the results of our prediction modeling show that care models for the assessment and treatment of patients with intensive care unit-associated dignity-related distress should not rely solely on the intensive care unit team but instead should be taken from the perspective ofthe entire hospitalization.

Estimation of Extreme Daily Rainfall Probabilities: A Case Study in Kyushu Region, Japan

Extreme rainfall causes floods and landslides, and so damages humans and socioeconomics; for instance, floods and landslides have been triggered by repeated torrential precipitation and have caused severe damage in the Kyushu region, Japan. Therefore, evaluating extreme rainfall in Kyushu is necessary to provide basic information for measures of rainfall-induced disasters. In this study, we estimated the probability of daily rainfall in Kyushu. The annual maximum values for daily rainfall at 23 long-record stations were normalized using return values at each station, corresponding to 2 and 10 years, and were combined by the station-year method. Additionally, the return period (RP) was calculated by fitting them to the generalized extreme value distribution. Based on the relationship between the normalized values of annual maximum daily rainfall and the RP, we obtained a regression equation to accurately estimate the RP up to 300 years by using data at given stations, considering outliers. In addition, we verified this equation using data from short-record stations where extreme rainfall events triggering floods and landslides were observed, and thereby elucidated that our method was consistent with previous techniques. Thus, this study develops strategies of measures for floods and landslides.

Evaluating western North Pacific tropical cyclone forecast in the subseasonal to seasonal prediction project database

The Daily Tropical Cyclone Probability (DTCP), defined as the probability of tropical cyclone occurrence within 500 km of a location in 1 day, is proposed and used in evaluating subseasonal to seasonal (S2S) predictions from the subseasonal to seasonal Prediction Project Database, from May 1 to October 31, 1999 to 2010. The ensemble forecasts are collected from eleven operational centers, the Bureau of Meteorology (BoM), China Meteorological Administration (CMA), Environment and Climate Change Canada (ECCC), European Centre for Medium-Range Weather Forecasts (ECMWF), Hydrometeorological Centre of Russia (HMCR), the Institute of Atmospheric Sciences and Climate of the National Research Council of Italy (ISAC), the Japan Meteorological Agency (JMA), the Korea Meteorological Administration (KMA), Météo-France/Centre National de Recherche Meteorologiques (METFR), the United States National Centers for Environmental Prediction (NCEP), and the United Kingdom Met Office (UKMO). In both observation and these eleven forecast models, the daily tropical cyclone probability is modulated by the Boreal Summer Intraseasonal Oscillation (BSISO), depicted by the two indices, boreal summer intraseasonal oscillation 1 and boreal summer intraseasonal oscillation 2. During boreal summer intraseasonal oscillation 1 phases 1, 5, 6, 7, and 8, the daily tropical cyclone probability in the western North Pacific region is ∼3.5 times higher. Similarly, during phases 1, 2, 3, 4, and 8 of boreal summer intraseasonal oscillation 2, the daily tropical cyclone probability is ∼2.5 times higher. Among the eleven models, the European Centre for Medium-Range Weather Forecasts model best reproduces the climatological daily tropical cyclone probability and its modulation by the boreal summer intraseasonal oscillation in the western North Pacific region, followed by the United States National Centers for Environmental Prediction, the Korea Meteorological Administration, the Japan Meteorological Agency models. Using the daily tropical cyclone probability metric, the highest debiased Brier Skill Score of the eleven models is from European Centre for Medium-Range Weather Forecasts, which has a slightly less skillful prediction than the reference climatological forecast with lead time 11–30 days. The skill of the eleven models is higher during the non-active phases of tropical cyclone activity than their skill during the active phases.

Modelling the daily probability of lightning-caused ignition in the Iberian Peninsula

Background Lightning is the most common origin of natural fires, being strongly linked to specific synoptic conditions associated with atmospheric instability, such as dry thunderstorms; dry fuels are required for ignition to take place and for subsequent propagation. Aims The aim was to predict the daily probability of ignition by exploiting a large dataset of lightning and fire data to anticipate ignition over the entire Iberian Peninsula. Methods We trained and tested a machine learning model using lightning strikes (>17 million) in the period 2009–2015. For each lightning strike, we extracted information relating to fuel condition, structural features of vegetation, topography, and the specific characteristics of the strikes (polarity, intensity and flash density). Key results Naturally triggered ignitions are typically initiated at higher elevations (above 1000 m above sea level) under conditions of low dead fuel moisture (<10–13%) and moderate live moisture content (Drought Code > 300). Negative-polarity lightning strikes (−10 kA) appear to trigger fires more frequently. Conclusions and implications Our approach was able to provide ignition forecasts at multiple temporal and spatial scales, thus enhancing forest fire risk assessment systems.

Estimation of the impact of hospital-onset SARS-CoV-2 infections on length of stay in English hospitals using causal inference.

From March 2020 through August 2021, 97,762 hospital-onset SARS-CoV-2 infections were detected in English hospitals. Resulting excess length of stay (LoS) created a potentially substantial health and economic burden for patients and the NHS, but we are currently unaware of any published studies estimating this excess. We implemented appropriate causal inference methods to determine the extent to which observed additional hospital stay is attributable to the infection rather than the characteristics of the patients. Hospital admissions records were linked to SARS-CoV-2 test data to establish the study population (7.5 million) of all non-COVID-19 admissions to English hospitals from 1st March 2020 to 31st August 2021 with a stay of at least two days. The excess LoS due to hospital-onset SARS-CoV-2 infection was estimated as the difference between the mean LoS observed and in the counterfactual where infections do not occur. We used inverse probability weighted Kaplan-Meier curves to estimate the mean survival time if all hospital-onset SARS-CoV-2 infections were to be prevented, the weights being based on the daily probability of acquiring an infection. The analysis was carried out for four time periods, reflecting phases of the pandemic differing with respect to overall case numbers, testing policies, vaccine rollout and prevalence of variants. The observed mean LoS of hospital-onset cases was higher than for non-COVID-19 hospital patients by 16, 20, 13 and 19days over the four phases, respectively. However, when the causal inference approach was used to appropriately adjust for time to infection and confounding, the estimated mean excess LoS caused by hospital-onset SARS-CoV-2 was: 2.0 [95% confidence interval 1.8-2.2] days (Mar-Jun 2020), 1.4 [1.2-1.6] days (Sep-Dec 2020); 0.9 [0.7-1.1] days (Jan-Apr 2021); 1.5 [1.1-1.9] days (May-Aug 2021). Hospital-onset SARS-CoV-2 is associated with a small but notable excess LoS, equivalent to 130,000 bed days. The comparatively high LoS observed for hospital-onset COVID-19 patients is mostly explained by the timing of their infections relative to admission. Failing to account for confounding and time to infection leads to overestimates of additional length of stay and therefore overestimates costs of infections, leading to inaccurate evaluations of control strategies.

Evaluating risks associated with capture and handling of mule deer for individual‐based, long‐term research

AbstractCapture and handling techniques for individual‐based, long‐term research that tracks the life history of animals by recapturing the same individuals for several years has vastly improved study inferences and our understanding of animal ecology. Yet there are corresponding risks to study animals associated with physical trauma or capture myopathy that can occur during or following capture events. Rarely has empirical evidence existed to guide decisions associated with understanding the magnitude of capture‐related risks, how to reduce these risks when possible, and implications for mortality censoring and survival estimates. We used data collected from 2,399 capture events of mule deer (Odocoileus hemionus) via helicopter net‐gunning to compare daily survival probabilities within a 10‐week period centered on a capture event and evaluated how animal age, nutritional condition (body fat), and various handling methods influenced survival before, during, and following a capture event. Direct mortality resulting from capture efforts was 1.59%. Mean daily survival was 0.9993 ± 0.0001 (SE) during the 5‐week pre‐capture window, was depressed the day of capture at 0.9841 ± 0.0004, and rebounded to 0.9990 ± 0.0008 during the 5‐week post‐capture window. Neither capture nor handling had a detectable effect on post‐capture survival, including handling time ( = 13.30 ± 1.87 min), capture time of year (i.e., Dec or Mar), tooth extraction, and the number of times an animal had been recaptured (2–17 times). Although mortality rate was slightly elevated during capture (resulting from physical trauma associated with capture), age and nutritional condition did not influence the probability of mortality during a capture event. Following a capture event, nutritional condition influenced survival; however, that relationship was consistent with expected effects of nutritional condition on winter survival and independent of capture and handling. Overall survival rates 5 weeks before capture and 5 weeks after capture were not different. A specified window of time with depressed survival following capture and handling was not evident, which contradicts the implementation of a predetermined window often used by researchers and managers for censoring mortalities that occur after capture. Previous notions that censorship of all mortality data in the 2 weeks following capture is unwarranted and risks removal of meaningful data. With previous evidence guiding our protocols for capture (e.g., reduced chase time) and handling (e.g., temperature mitigation), low direct mortality and almost undetectable indirect mortality post capture reinforces the efficacy of helicopter net‐gunning for capture and recapture of mule deer in long‐term, individual‐based studies.

The probability analysis of ejecta particles damaging a spacecraft operating around asteroids after an artificial impact experiment: Hayabusa 2 ’s SCI operation safety study

On 5 April 2019, the Hayabusa 2 spacecraft performed the first successful artificial impact experiment on an asteroid. The Small Carry-on Impactor (SCI) device was deployed at an altitude of 500 m above Ryugu’s surface. The 2 kg copper projectile hit Ryugu’s surface in 40 min and caused the formation of an artificial crater 14.5 m in diameter. Once the SCI was deployed, the Hayabusa 2 spacecraft performed a two-week escape trajectory reaching altitudes as far as 120 km from Ryugu. The spacecraft then returned to its nominal position at 20 km altitude (Home-Position) from Ryugu for hovering control. This was done to prevent ejecta particles from seriously damaging the spacecraft and compromising its functionality. In this article, we present a method to forecast the daily probability of spacecraft damage along the selected nominal escape trajectory due to the debris cloud formed by an artificial impact. The result of the damage analysis confirmed that the selected escape trajectory experienced a small number of particle collisions under the design threshold, which would not have resulted in damage. Indeed, no damage was reported on the Hayabusa 2 spacecraft and it kept operating normally after the SCI operation. The method here presented serves as a guideline for post-impact mission operations to forecast and estimate the probability of damage to spacecraft or CubeSats operating near a small celestial body after an artificial impact experiment has occurred.

Continued potassium supplementation use following loop diuretic discontinuation in older adults: An evaluation of a prescribing cascade relic.

The use of a new medication (e.g., potassium supplementation) for managing a drug-induced adverse event (e.g., loop diuretic-induced hypokalemia) constitutes a prescribing cascade. However, loop diuretics are often stopped while potassium may be unnecessarily continued (i.e., relic). We aimed to quantify the occurrence of relics using older adults previously experiencing a loop diuretic-potassium prescribing cascade as an example. We conducted a prescription sequence symmetry analysis using the population-based Medicare Fee-For-Service data (2011-2018) and partitioned the 150 days following potassium initiation by day to assess the daily treatment scenarios (i.e., loop diuretics alone, potassium alone, combination of loop diuretics and potassium, or neither). We calculated the proportion of patients developing the relic, proportion of person-days under potassium alone, the daily probability of the relic, and the proportion of patients filling potassium after loop diuretic discontinuation. We also identified the risk factors of the relic. We identified 284,369 loop diuretic initiators who were 8 times more likely to receive potassium supplementation simultaneously or after (i.e., the prescribing cascade), rather than before, loop diuretic initiation (aSR 8.0, 95% CI 7.9-8.2). Among the 66,451 loop diuretic initiators who subsequently (≤30 days) initiated potassium, 20,445 (30.8%) patients remained on potassium after loop diuretic discontinuation, and 9365 (14.1%) patients subsequently filled another potassium supplementation. Following loop diuretic initiation, 4.0% of person-days were for potassium alone, and daily probability of the relic was the highest after day 90 of loop diuretic initiation (5.6%). Older age, female sex, higher diuretic daily dose, and greater baseline comorbidities were risk factors for the relic, while patients having the same prescriber or pharmacy involved in the use of both medications were less likely to experience the relic. Our findings suggest the need for clinicians to be aware of the potential of relic to avoid unnecessary drug use.