Remote Sensing of Pasture Degradation in the Highlands of the Kyrgyz Republic: Finer-Scale Analysis Reveals Complicating Factors

Degradation in the highland pastures of the Kyrgyz Republic, a small country in Central Asia, has been reported in several studies relying on coarse spatial resolution imagery, primarily MODIS. We used the results of land surface phenology modeling at higher spatial resolution to characterize spatial and temporal patterns of phenometrics indicative of the seasonal peak in herbaceous vegetation. In particular, we explored whether proximity to villages was associated with substantial decreases in the seasonal peak values. We found that terrain features—elevation and aspect—modulated the strength of the influence of village proximity on the phenometrics. Moreover, using contrasting hotter/drier and cooler/wetter years, we discovered that the growing season weather can interact with aspect to attenuate the negative influences of dry conditions on seasonal peak values. As these multiple contingent and interactive factors that shape the land surface phenology of the highland pastures may be blurred and obscured in coarser spatial resolution imagery, we discuss some limitations with prior and recent studies of pasture degradation.

Abrupt termination of the 2019/20 influenza season following preventive measures against COVID-19 in Denmark, Norway and Sweden

Background In mid-March 2020, a range of public health and social measures (PHSM) against the then new coronavirus disease (COVID-19) were implemented in Denmark, Norway and Sweden. Aim We analysed the development of influenza cases during the implementation of PHSM against SARS-CoV-2 in the Scandinavian countries. Method Based on the established national laboratory surveillance of influenza, we compared the number of human influenza cases in the weeks immediately before and after the implementation of SARS-CoV-2 PHSM by country. The 2019/20 influenza season was compared with the five previous seasons. Results A dramatic reduction in influenza cases was seen in all three countries, with only a 3- to 6-week duration from the peak of weekly influenza cases until the percentage dropped below 1%. In contrast, in the previous nine influenza seasons, the decline from the seasonal peak to below 1% of influenza-positive samples took more than 10 weeks. Conclusions The PHSM against SARS-CoV-2 were followed by a dramatic reduction in influenza cases, indicating a wider public health effect of the implemented measures.

Breeding seasonality of Chital (Axis axis) in the Hetauda Valley of Nepal

Breeding seasonality of Axis axis differs from place to place mainly due to the difference in vegetation and climate. In Nepal, although Chital is found all over the lower Terai region, studies on breeding seasonality are limited. In this study, we observed the annual cycle of antler renewal and casting, monthly changes in frequency of bellowing call and ratio of new born fawn to adult and sub-adult females from October 2016 to March 2019. Seasonal peak of proportion of hard-antlered males was found in June and seasonal peak of bellowing call frequency was found in May. Seasonal peak of fawning occurred in December, January and February, at the beginning of dry season before monsoon, probably to coincide with the energy demanding late lactation season with excessive food available in rainy season. From antler cycle, bellowing call frequency and fawning frequency data, peak breeding season was identified as May-June. This information can be crucial in nutritional management, herd composition, and translocation period determination for in-situ and ex-situ species management programs.

Forecasting Flu Activity in the United States: Benchmarking an Endemic-Epidemic Beta Model

Accurate prediction of flu activity enables health officials to plan disease prevention and allocate treatment resources. A promising forecasting approach is to adapt the well-established endemic-epidemic modeling framework to time series of infectious disease proportions. Using U.S. influenza-like illness surveillance data over 18 seasons, we assessed probabilistic forecasts of this new beta autoregressive model with proper scoring rules. Other readily available forecasting tools were used for comparison, including Prophet, (S)ARIMA and kernel conditional density estimation (KCDE). Short-term flu activity was equally well predicted up to four weeks ahead by the beta model with four autoregressive lags and by KCDE; however, the beta model runs much faster. Non-dynamic Prophet scored worst. Relative performance differed for seasonal peak prediction. Prophet produced the best peak intensity forecasts in seasons with standard epidemic curves; otherwise, KCDE outperformed all other methods. Peak timing was best predicted by SARIMA, KCDE or the beta model, depending on the season. The best overall performance when predicting peak timing and intensity was achieved by KCDE. Only KCDE and naive historical forecasts consistently outperformed the equal-bin reference approach for all test seasons. We conclude that the endemic-epidemic beta model is a performant and easy-to-implement tool to forecast flu activity a few weeks ahead. Real-time forecasting of the seasonal peak, however, should consider outputs of multiple models simultaneously, weighing their usefulness as the season progresses.

Preventing back-to-school asthma attacks

A seasonal peak in asthma attacks has been consistently demonstrated. Hetal Dhruve discusses some of the reasons for this peak and how practice nurses can help to prevent asthma attacks in children Asthma is one of the most common long term conditions in the UK, affecting approximately 1.1 million children. Asthma prevalence, related mortality and health-care utilisation in the UK are estimated to be among the highest in the world. A seasonal peak in asthma attacks has been consistently demonstrated following the summer school holiday and in line with the start of the autumn term. This article discusses some of the reasons for this peak and how asthma attacks can be prevented by practice nurses.

Predicting influenza-like illness-related emergency department visits by modelling spatio-temporal syndromic surveillance data

Predicting the magnitude of the annual seasonal peak in influenza-like illness (ILI)-related emergency department (ED) visit volumes can inform the decision to open influenza care clinics (ICCs), which can mitigate pressure at the ED. Using ILI-related ED visit data from the Alberta Real Time Syndromic Surveillance Net for Edmonton, Alberta, Canada, we developed (training data, 1 August 2004–31 July 2008) and tested (testing data, 1 August 2008–19 February 2014) spatio-temporal statistical prediction models of daily ILI-related ED visits to estimate high visit volumes 3 days in advance. Our Main Model, based on a generalised linear mixed model with random intercept, incorporated prediction residuals over 14 days and captured increases in observed volume ahead of peaks. During seasonal influenza periods, our Main Model predicted volumes within ±30% of observed volumes for 67%–82% of high-volume days and within 0.3%–21% of observed seasonal peak volumes. Model predictions were not as successful during the 2009 H1N1 pandemic. Our model can provide early warning of increases in ILI-related ED visit volumes during seasonal influenza periods of differing intensities. These predictions may be used to support public health decisions, such as if and when to open ICCs, during seasonal influenza epidemics.