Pretražite po imenu i prezimenu autora, mentora, urednika, prevoditelja

Napredna pretraga

Pregled bibliografske jedinice broj: 1136528

Understanding COVID-19 Pandemic in Croatia Using Veridical Data Science


Štajduhar, Andrija; Lipić, Tomislav; Kujundžić Tiljak, Mirjana
Understanding COVID-19 Pandemic in Croatia Using Veridical Data Science // BIOSTAT 2021
Poreč, Hrvatska, 2021. (predavanje, domaća recenzija, neobjavljeni rad, znanstveni)


CROSBI ID: 1136528 Za ispravke kontaktirajte CROSBI podršku putem web obrasca

Naslov
Understanding COVID-19 Pandemic in Croatia Using Veridical Data Science

Autori
Štajduhar, Andrija ; Lipić, Tomislav ; Kujundžić Tiljak, Mirjana

Vrsta, podvrsta i kategorija rada
Sažeci sa skupova, neobjavljeni rad, znanstveni

Skup
BIOSTAT 2021

Mjesto i datum
Poreč, Hrvatska, 8-10.09.2021

Vrsta sudjelovanja
Predavanje

Vrsta recenzije
Domaća recenzija

Ključne riječi
covid-19 data ; veridical data science ; machine learning ; causal inference ; stringency index ; mobility data

Sažetak
The recent pandemic continues to plague our society, while nations are making tremendous efforts to respond effectively and mitigate risks through targeted actions, from the individual to the collective level. Because these actions have long-term impacts on society, they must be planned and executed with care. To support the global response to novel strains of coronavirus, various relevant data are being collected and shared, providing unique resources to scientific and data driven research communities and policy makers. Understanding and properly interpreting these data is the most important tool in the fight against the pandemic and its all-pervasive impact on our lives. Here, we use openly available governmental, socio-economic, political and public health data and investigate the temporal dynamics of the COVID-19 pandemic in Croatia with the aim of understanding, explaining, visualizing and predicting its nationwide and EU-wide impact under the Croatian policy regime. Motivated by the recently proposed Predictability, Computability and Stability (PCS) framework for Veridical Data Science, we present a new data analysis methodology that answers specific questions about the course of the pandemic in Croatia at the national and subnational levels. We build on and extend the principles of statistics, machine learning, and scientific inquiry, and embed the scientific principles of prediction and reproducibility into data-driven decision making. We estimate the causal impact of the application of health policies, social distancing measures, geographic mobility, and public events on outbreak spread metrics. We provide a comparison across EU countries, an overview of efforts by other approaches, and an assessment of the effectiveness of the methods used. Our work supports well the methodological foundations of Veridical Data Science and trustworthy AI needed to solve problems of great societal importance.

Izvorni jezik
Engleski

Znanstvena područja
Matematika, Računarstvo, Javno zdravstvo i zdravstvena zaštita, Informacijske i komunikacijske znanosti



POVEZANOST RADA


Ustanove:
Institut "Ruđer Bošković", Zagreb,
Medicinski fakultet, Zagreb


Citiraj ovu publikaciju

Štajduhar, Andrija; Lipić, Tomislav; Kujundžić Tiljak, Mirjana
Understanding COVID-19 Pandemic in Croatia Using Veridical Data Science // BIOSTAT 2021
Poreč, Hrvatska, 2021. (predavanje, domaća recenzija, neobjavljeni rad, znanstveni)
Štajduhar, A., Lipić, T. & Kujundžić Tiljak, M. (2021) Understanding COVID-19 Pandemic in Croatia Using Veridical Data Science. U: BIOSTAT 2021.
@article{article, year = {2021}, keywords = {covid-19 data, veridical data science, machine learning, causal inference, stringency index, mobility data}, title = {Understanding COVID-19 Pandemic in Croatia Using Veridical Data Science}, keyword = {covid-19 data, veridical data science, machine learning, causal inference, stringency index, mobility data}, publisherplace = {Pore\v{c}, Hrvatska} }
@article{article, year = {2021}, keywords = {covid-19 data, veridical data science, machine learning, causal inference, stringency index, mobility data}, title = {Understanding COVID-19 Pandemic in Croatia Using Veridical Data Science}, keyword = {covid-19 data, veridical data science, machine learning, causal inference, stringency index, mobility data}, publisherplace = {Pore\v{c}, Hrvatska} }




Contrast
Increase Font
Decrease Font
Dyslexic Font