Investigation of optimized observation periods for estimating a representative home range of free-roaming domestic dogs.

Free-roaming domestic dogs (FRDD), as vectors of zoonotic diseases, are of high relevance for public health. Understanding roaming patterns of dogs can help to design disease control programs and disease transmission simulation models. Studies on GPS tracking of dogs report stark differences in recording periods. So far, there is no accepted number of days required to capture a representative home range (HR) of FRDD. The objective of this study was to evaluate changes in HR size and shape over time of FRDD living in Chad, Guatemala, Indonesia and Uganda and identify the period required to capture stable HR values. Dogs were collared with GPS units, leading to a total of 46 datasets with, at least, 19 recorded days. For each animal and recorded day, HR sizes were estimated using the Biased Random Bridge method and percentages of daily change in size and shape calculated and taken as metrics. The analysis revealed that the required number of days differed substantially between individuals, isopleths, and countries, with the extended HR (95% isopleth value) requiring a longer recording period. To reach a stable HR size and shape values for 75% of the dogs, 26 and 21 days, respectively, were sufficient. However, certain dogs required more extended observational periods.

THE IMPLEMENTATION GAP IN EMERGING DISEASE RISK MANAGEMENT IN THE WILDLIFE TRADE.

The wildlife trade has been characterized as one of the biggest risk factors in the emergence of new infectious diseases. In the shadow of COVID-19, there is growing political and scientific urgency to manage this risk. Existing studies and experiences make it clear that something must be done but are less clear on how to get it done. It is a quite different task to accumulate evidence on the presence of pathogens, their locations in the supply chain, and their spillover to new hosts than to identify effective ways to prevent and mitigate emerging disease under real-world conditions. This study sought peer-reviewed evidence on the effectiveness, acceptability, feasibility, and sustainability of risk reduction interventions for zoonotic and nonzoonotic disease emergence in the wildlife trade. An environmental scan triangulated information from a scoping review following a Preferred Reporting Items for Systematic Reviews and Meta-analysis extension for scoping review protocol, two narrative literature reviews, and key informant interviews of 26 international wildlife health experts. Existing literature has been inattentive to program implementation or evaluation studies. There was insufficient evidence to identify effective and sustainable risk management actions. Studies on the effects of social, epidemiologic, and ecologic context on intervention success was lacking, as was research using a complex systems perspective. The lack of systematic program evaluations or implementation studies leaves decision makers with insufficient evidence to select interventions likely to be acceptable, effective, and sustainable within and across the disparate context of the wildlife trade. This necessitates adaptive risk management and innovations in program implementation and evaluation to ensure evidence-based risk management.

Antimicrobial consumption: Comparison of three different data collection methods.

The increasing incidence of antimicrobial resistance represents a global threat. As a result, surveillance programmes monitoring antimicrobial consumption and resistance in animals have been implemented in several countries throughout the world. However, such programmes depend on the accurate and detailed collection of data on antimicrobial consumption. For this reason, the aim of this longitudinal study was to compare the consistency of data on antimicrobial consumption between three different data collection methods. Antimicrobial consumption data associated to udder health were collected from 20 veterinary practices and 92 dairy farms for 18 months. The compared data sources were: 1) data extracted from veterinary practice software 2) farm treatment journals and 3) on-farm discarded drug packages (garbage). Two different procedures were chosen to analyse the data issued from treatment journals: 1) only complete entries were analysed 2) entries with missing dosage were supplemented with the information provided by the Swiss Compendium of Veterinary Medicinal Products. The antimicrobial data were divided into intramammary preparations used during lactation (IMM), intramammary preparations used for dry off (DRY) and systemic treatments (SYS). We compared the quantities of injectors (IMM and DRY), the quantities of active substances (SYS) and the treatment incidences (TI) for the defined daily dose (DDD) per 1000 cow-days (IMM and SYS) and the defined course dose (DCD) per 1000 cow-days (DRY). Additionally, the variety of antimicrobial products among the different data sources was compared. The highest quantity of antimicrobials for IMM, DRY and SYS could be collected with the software data. The lowest quantity was collected by using the data of the treatment journal with only complete entries. For IMM and DRY, software and garbage performed similar, with agreement on the number of injectors used in 56.1% of the analysed cases. The widest variety of intramammary antimicrobial preparations was found in the garbage whilst most systemic preparations were collected using software data. The results of the study show a lack of data consistency between the three different data sources. None of the methods was able to collect the integral antimicrobial consumption in the participating farms. Finally, the results emphasise the need to implement a standardised system to quantify and assess the antimicrobial consumption at veterinary practice and farm level.

Evolution of COVID-19 mortality over time: results from the Swiss hospital surveillance system (CH-SUR).

BACKGROUND: When the periods of time during and after the first wave of the ongoing SARS-CoV-2/COVID-19 pandemic in Europe are compared, the associated COVID-19 mortality seems to have decreased substantially. Various factors could explain this trend, including changes in demographic characteristics of infected persons and the improvement of case management. To date, no study has been performed to investigate the evolution of COVID-19 in-hospital mortality in Switzerland, while also accounting for risk factors. METHODS: We investigated the trends in COVID-19-related mortality (in-hospital and in-intermediate/intensive-care) over time in Switzerland, from February 2020 to June 2021, comparing in particular the first and the second wave. We used data from the COVID-19 Hospital-based Surveillance (CH-SUR) database. We performed survival analyses adjusting for well-known risk factors of COVID-19 mortality (age, sex and comorbidities) and accounting for competing risk. RESULTS: Our analysis included 16,984 patients recorded in CH-SUR, with 2201 reported deaths due to COVID-19 (13.0%). We found that overall in-hospital mortality was lower during the second wave of COVID-19 than in the first wave (hazard ratio [HR] 0.70, 95% confidence interval [CI] 0.63- 0.78; p <0.001), a decrease apparently not explained by changes in demographic characteristics of patients. In contrast, mortality in intermediate and intensive care significantly increased in the second wave compared with the first wave (HR 1.25, 95% CI 1.05-1.49; p = 0.029), with significant changes in the course of hospitalisation between the first and the second wave. CONCLUSION: We found that, in Switzerland, COVID-19 mortality decreased among hospitalised persons, whereas it increased among patients admitted to intermediate or intensive care, when comparing the second wave to the first wave. We put our findings in perspective with changes over time in case management, treatment strategy, hospital burden and non-pharmaceutical interventions. Further analyses of the potential effect of virus variants and of vaccination on mortality would be crucial to have a complete overview of COVID-19 mortality trends throughout the different phases of the pandemic.