One health systems strengthening in countries: Tripartite tools and approaches at the human-animal-environment interface.

Unexpected pathogen transmission between animals, humans and their shared environments can impact all aspects of society. The Tripartite organisations-the Food and Agriculture Organization of the United Nations (FAO), the World Health Organization (WHO), and the World Organisation for Animal Health (WOAH)-have been collaborating for over two decades. The inclusion of the United Nations Environment Program (UNEP) with the Tripartite, forming the 'Quadripartite' in 2021, creates a new and important avenue to engage environment sectors in the development of additional tools and resources for One Health coordination and improved health security globally. Beginning formally in 2010, the Tripartite set out strategic directions for the coordination of global activities to address health risks at the human-animal-environment interface. This paper highlights the historical background of this collaboration in the specific area of health security, using country examples to demonstrate lessons learnt and the evolution and pairing of Tripartite programmes and processes to jointly develop and deliver capacity strengthening tools to countries and strengthen performance for iterative evaluations. Evaluation frameworks, such as the International Health Regulations (IHR) Monitoring and Evaluation Framework, the WOAH Performance of Veterinary Services (PVS) Pathway and the FAO multisectoral evaluation tools for epidemiology and surveillance, support a shared global vision for health security, ultimately serving to inform decision making and provide a systematic approach for improved One Health capacity strengthening in countries. Supported by the IHR-PVS National Bridging Workshops and the development of the Tripartite Zoonoses Guide and related operational tools, the Tripartite and now Quadripartite, are working alongside countries to address critical gaps at the human-animal-environment interface.

Triage and monitoring of COVID-19 patients in intensive care using unsupervised machine learning.

BACKGROUND: We designed an algorithm to assess COVID-19 patients severity and dynamic intubation needs and predict their length of stay using the breathing frequency (BF) and oxygen saturation (SpO2) signals. METHODS: We recorded the BF and SpO2 signals for confirmed COVID-19 patients admitted to the ICU of a teaching hospital during both the first and subsequent outbreaks of the pandemic in France. An unsupervised machine-learning algorithm (the Gaussian mixture model) was applied to the patients' data for clustering. The algorithm's robustness was ensured by comparing its results against actual intubation rates. We predicted intubation rates using the algorithm every hour, thus conducting a severity evaluation. We designed a S24 severity score that represented the patient's severity over the previous 24 h; the validity of MS24, the maximum S24 score, was checked against rates of intubation risk and prolonged ICU stay. RESULTS: Our sample included 279 patients. . The unsupervised clustering had an accuracy rate of 87.8% for intubation recognition (AUC = 0.94, True Positive Rate 86.5%, true Negative Rate 90.9%). The S24 score of intubated patients was significantly higher than that of non-intubated patients at 48 h before intubation. The MS24 score allowed for the distinguishing between three severity levels with an increased risk of intubation: green (3.4%), orange (37%), and red (77%). A MS24 score over 40 was highly predictive of an ICU stay greater than 5 days at an accuracy rate of 81.0% (AUC = 0.87). CONCLUSIONS: Our algorithm uses simple signals and seems to efficiently visualize the patients' respiratory situations, meaning that it has the potential to assist staffs' in decision-making. Additionally, real-time computation is easy to implement.