Attempt to Understand Public Health Relevant Social Dimensions of COVID-19 Outbreak in Poland

Jarynowski, Andrzej; Wójta-Kempa, Monika; Płatek, Daniel; Czopek, Karolina

doi:10.2139/ssrn.3570609

Cited by 27 publications

(31 citation statements)

References 3 publications

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“…This is no trivial task. Indeed, previous research on pandemics and epidemics suggests that a policy that is effective in one context may be ineffective in another due to a whole host of potentially conditioning factors, including the pathogenesis of the particular disease 12,13 , the characteristics of the underlying population [14][15][16][17] and the available medical 18,19 and communication [20][21][22][23] technology at the time.…”

mentioning

confidence: 99%

COVID-19 Government Response Event Dataset (CoronaNet v.1.0)

et al. 2020

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Governments worldwide have implemented countless policies in response to the COVID-19 pandemic. We present an initial public release of a large hand-coded dataset of over 13,000 such policy announcements across more than 195 countries. The dataset is updated daily, with a 5-day lag for validity checking. We document policies across numerous dimensions, including the type of policy, national versus subnational enforcement, the specific human group and geographical region targeted by the policy, and the time frame within which each policy is implemented. We further analyse the dataset using a Bayesian measurement model, which shows the quick acceleration of the adoption of costly policies across countries beginning in mid-March 2020 through 24 May 2020. We believe that these data will be instrumental for helping policymakers and researchers assess, among other objectives, how effective different policies are in addressing the spread and health outcomes of COVID-19.

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confidence: 99%

COVID-19 Government Response Event Dataset (CoronaNet v.1.0)

et al. 2020

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“…Social behavior has a fundamental impact on the dynamics of the spread of infectious diseases (Prem et al, 2017). Inhabitants of larger Polish cities are more afraid of overcrowded hospitals and inefficient healthcare than small towns and villages (Jarynowski et al, 2020).…”

Section: Social Distance During Covid-19mentioning

confidence: 99%

Peer Review #1 of "Analysis of pedestrian activity before and during COVID-19 lockdown, using webcam time-lapse from Cracow and machine learning (v0.1)"

2020

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, COVID-19 pandemic reached Europe. Many countries, including Poland imposed lockdown as a method of securing social distance between potentially infected. Stay-at-home orders and movement control within public space not only affected the touristm industry, but also the everyday life of the inhabitants. The hourly time-lapse from four HD webcams in Cracow (Poland) are used in this study to estimate how pedestrian activity changed during COVID-19 lockdown. The collected data covers the period from June 9, 2016 to April 19, 2020 and comes from various urban zones. One zone is tourist, one is residential and two are mixed. In the first stage of the analysis, state-of-the-art machine learning algorithm (YOLOv3) is used to detect people. Additionally, a non-standard application of the YOLO method is proposed, oriented to the images from HD webcams. This approach (YOLOtiled) is less prone to pedestrian detection errors with the only drawback being the longer computation time. Splitting the HD image into smaller tiles increases the number of detected pedestrians by over 50%. In the second stage, the analysis of pedestrian activity before and during the COVID-19 lockdown is conducted for hourly, daily and weekly averages. Depending on the type of urban zone, the number of pedestrians decreased from 33% in residential zones to 85% in tourist zones located in the Old Town. The presented method allows for more efficient detection and counting of pedestrians from HD time-lapse webcam images compared to SSD, YOLOv3 and Faster R-CNN. The result of the research is a published database with the detected number of pedestrians from the four-year observation period for four locations in Cracow.

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“…1,8,9 Throughout it all, the collection of reliable data has helped advance collective understanding of which policies are effective in curbing the effects of a given disease outbreak. 10,11 This is no trivial task given that a policy that is effective in one context may be ineffective in another due to a whole host of potentially conditioning factors, including the pathogenesis of the particular disease 12,13 , the characteristics of the underlying population [14][15][16][17] , and the available medical 18,19 and communication [20][21][22][23] technology at the time.…”

Section: Introductionmentioning

confidence: 99%

CoronaNet: A Dyadic Dataset of Government Responses to the COVID-19 Pandemic

Cheng¹,

Barceló²,

Hartnett³

et al. 2020

Preprint

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As the COVID-19 pandemic spreads around the world, governments have implemented a broad set of policies to limit the spread of the pandemic. In this paper we present an initial release of a large hand-coded dataset of more than 4,500 separate policy announcements from governments around the world. This data is being made publicly available, in combination with other data that we have collected (including COVID-19 tests, cases, and deaths) as well as a number of country-level covariates. Due to the speed of the COVID-19 outbreak, we will be releasing this data on a daily basis with a 5-day lag for record validity checking. In a truly global effort, our team is comprised of more than 190 research assistants across 18 time zones and makes use of cloud-based managerial and data collection technology in addition to machine learning coding of news sources. We analyze the dataset with a Bayesian time-varying ideal point model showing the quick acceleration of more harsh policies across countries beginning in mid-March and continuing to the present. While some relatively low-cost policies like task forces and health monitoring began early, countries generally adopted more harsh measures within a narrow time window, suggesting strong policy diffusion effects.

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Attempt to Understand Public Health Relevant Social Dimensions of COVID-19 Outbreak in Poland

Cited by 27 publications

References 3 publications

COVID-19 Government Response Event Dataset (CoronaNet v.1.0)

COVID-19 Government Response Event Dataset (CoronaNet v.1.0)

Peer Review #1 of "Analysis of pedestrian activity before and during COVID-19 lockdown, using webcam time-lapse from Cracow and machine learning (v0.1)"

CoronaNet: A Dyadic Dataset of Government Responses to the COVID-19 Pandemic

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