Is a vaccination-based public health policy «history-proof»?

14.04.2022 / Séveric Yersin, University of Basel, This email address is being protected from spambots. You need JavaScript enabled to view it.

Any fact-based policy must consider long-term effects as well as possible alternatives. This is what we learn from the authorities’ handling of smallpox in Switzerland.
Vaccination against Covid-19 has been the most central instrument of public health policy since the very beginning of the Pandemic. Alain Berset, Head of the Federal Department of Home Affairs, which oversees the Federal Office of Public Health, declared in March 2020 that «we need a vaccine.»  
Accordingly, the Federal Council preordered millions of vaccine doses, and criticism of a too vaccine-centered approach was soon dismissed as «antivax». However, a historical analysis of the public health policies implemented to control smallpox in the late 19th and the early 20th century suggests that a narrow focus on vaccination is not necessarily the best and only path to control a large-scale epidemic.
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SUPPORTING AVALANCHE FORECASTING IN THE SWISS ALPS USING MACHINE LEARNING

The creation of avalanche bulletins is still a largely expert driven and manual task. Forecasters manually inspect vast amounts of spatio-temporal data, which describe the condition of the snowpack and of the local weather in the Alps. Based on their intuition and knowledge, they will then assign danger levels on an ordinal scale from 1 to 5 (low to high avalanche danger) to create the avalanche bulletin for the Swiss Alps. This labour intensive task is carried out once or twice a day during snow season, and becomes vulnerable to errors and biases. Forecasters can hardly explore all of the relevant data. In this SDSC collaborative project, we aim at exploring the feasibility of using data-driven statistical models to support the process of avalanche danger forecast, explore relevant data, and ultimately get one step closer to obtain an automated decision tool supporting human experts.
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