Open AccessCOVID-19 Vigilance: Towards Better Risk Assessment and Communication During the Next Wave
Author(s) -
Petra Fic Žagar,
Tina Bregant,
Matjaž Perc,
Anja Goričan,
Aleks Jakulin,
Janez Žibert,
Žiga Zaplotnik,
Milan Batista,
Matjaž Leskovar,
Andraž Stožer,
Brane Leskošek,
Drago Bokal
Publication year - 2021
Language(s) - English
Resource type - Conference proceedings
DOI - 10.18690/978-961-286-442-2.15
Subject(s) - risk analysis (engineering) , computer science , pessimism , covid-19 , management science , vigilance (psychology) , data science , psychology , business , medicine , engineering , infectious disease (medical specialty) , cognitive psychology , philosophy , disease , epistemology , pathology
Since December 2019, SARS-CoV-2 infections have altered many aspects of our societies. Citizens were faced with circumstances to which even experts and scientists did not yet know the answers and were applying the scientific method to make daily steps of progress towards better understanding the threat and how to contain it. Within a year, several vaccines were produced to protect individuals from the virus, thereby resolving the most important medical problem. However, not just medical issues call for the application of the scientific method. The management of epidemics also can, and in fact should, benefit significantly from a science-based approach. The novel complexity of the situation left us torn between permissive and authoritarian approaches of containment, and it is still subject to debate what works best and why. In our contribution, we model the emerging complexity of the epidemics and propose a scientific-based data driven approach that aims to aid the decision makers in their focus on the most relevant issues and thus helping them to make informed and consistent decisions. The resulting monitoring and control system, termed COVID-19 vigilance, helps with risk assessment and communication during regional COVID-19 outbreaks. The system is based on the Cynefin decision complexity framework and the universal process model, and it uses several mathematical models that describe epidemic spreading. Different future scenarios are used to predict the impact of realistic, optimistic, and pessimistic outcomes, in turn allowing for a more efficient communication of involved risk.