Data-Driven Decision Making in COVID-19 Response: A Survey
- URL: http://arxiv.org/abs/2202.11435v1
- Date: Wed, 23 Feb 2022 11:28:26 GMT
- Title: Data-Driven Decision Making in COVID-19 Response: A Survey
- Authors: Shuo Yu, Qing Qing, Chen Zhang, Ahsan Shehzad, Giles Oatley, Feng Xia
- Abstract summary: Data clearly plays a vital role in effective decision making.
Data-driven decision making uses data related evidence and insights to guide the decision making process.
This survey paper sheds light on current policy making driven by data.
- Score: 9.610132964009178
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: COVID-19 has spread all over the world, having an enormous effect on our
daily life and work. In response to the epidemic, a lot of important decisions
need to be taken to save communities and economies worldwide. Data clearly
plays a vital role in effective decision making. Data-driven decision making
uses data related evidence and insights to guide the decision making process
and to verify the plan of action before it is committed. To better handle the
epidemic, governments and policy making institutes have investigated abundant
data originating from COVID-19. These data include those related to medicine,
knowledge, media, etc. Based on these data, many prevention and control
policies are made. In this survey paper, we summarize the progress of
data-driven decision making in the response to COVID-19, including COVID-19
prevention and control, psychological counselling, financial aid, work
resumption, and school re-opening. We also propose some current challenges and
open issues in data-driven decision making, including data collection and
quality, complex data analysis, and fairness in decision making. This survey
paper sheds light on current policy making driven by data, which also provides
a feasible direction for further scientific research.
Related papers
- A Survey on Data Selection for Language Models [148.300726396877]
Data selection methods aim to determine which data points to include in a training dataset.
Deep learning is mostly driven by empirical evidence and experimentation on large-scale data is expensive.
Few organizations have the resources for extensive data selection research.
arXiv Detail & Related papers (2024-02-26T18:54:35Z) - On Responsible Machine Learning Datasets with Fairness, Privacy, and Regulatory Norms [56.119374302685934]
There have been severe concerns over the trustworthiness of AI technologies.
Machine and deep learning algorithms depend heavily on the data used during their development.
We propose a framework to evaluate the datasets through a responsible rubric.
arXiv Detail & Related papers (2023-10-24T14:01:53Z) - Tracking the State and Behavior of People in Response to COVID-1 19
Through the Fusion of Multiple Longitudinal Data Streams [2.477349483168562]
We describe a rich panel dataset of active and passive data from U.S. residents collected between August 2020 and July 2021.
Such a dataset allows important research questions to be answered; for example, to determine the factors underlying the heterogeneous behavioral responses to COVID-19 restrictions imposed by local governments.
arXiv Detail & Related papers (2022-09-23T18:49:23Z) - Data-Centric Epidemic Forecasting: A Survey [56.99209141838794]
This survey delves into various data-driven methodological and practical advancements.
We enumerate the large number of epidemiological datasets and novel data streams that are relevant to epidemic forecasting.
We also discuss experiences and challenges that arise in real-world deployment of these forecasting systems.
arXiv Detail & Related papers (2022-07-19T16:15:11Z) - COVID-19: An exploration of consecutive systemic barriers to
pathogen-related data sharing during a pandemic [3.192308005611312]
In 2020, the COVID-19 pandemic resulted in a rapid response from governments and researchers worldwide.
As of late 2023, over millions have died as a result of COVID-19.
Data professionals working with pandemic-relevant data often face significant systemic barriers to accessing, sharing or re-using this data.
arXiv Detail & Related papers (2022-05-24T14:25:09Z) - A Global Survey of Technological Resources and Datasets on COVID-19 [0.0]
The application and successful utilization of technological resources in developing solutions to health, safety, and economic issues caused by COVID-19 indicate the importance of technology in curbing COVID-19.
arXiv Detail & Related papers (2022-02-06T04:37:14Z) - COVIDx-US -- An open-access benchmark dataset of ultrasound imaging data
for AI-driven COVID-19 analytics [116.6248556979572]
COVIDx-US is an open-access benchmark dataset of COVID-19 related ultrasound imaging data.
It consists of 93 lung ultrasound videos and 10,774 processed images of patients infected with SARS-CoV-2 pneumonia, non-SARS-CoV-2 pneumonia, as well as healthy control cases.
arXiv Detail & Related papers (2021-03-18T03:31:33Z) - FLOP: Federated Learning on Medical Datasets using Partial Networks [84.54663831520853]
COVID-19 Disease due to the novel coronavirus has caused a shortage of medical resources.
Different data-driven deep learning models have been developed to mitigate the diagnosis of COVID-19.
The data itself is still scarce due to patient privacy concerns.
We propose a simple yet effective algorithm, named textbfFederated textbfL textbfon Medical datasets using textbfPartial Networks (FLOP)
arXiv Detail & Related papers (2021-02-10T01:56:58Z) - Data-Driven Methods to Monitor, Model, Forecast and Control Covid-19
Pandemic: Leveraging Data Science, Epidemiology and Control Theory [1.5469452301122177]
This document analyzes the role of data-driven methodologies in Covid-19 pandemic.
A 3M-analysis is presented: Monitoring, Modelling and Making decisions.
The focus is on the potential of well-known datadriven schemes to address different challenges raised by the pandemic.
arXiv Detail & Related papers (2020-06-01T12:56:43Z) - A Study of Knowledge Sharing related to Covid-19 Pandemic in Stack
Overflow [69.5231754305538]
Study of 464 Stack Overflow questions posted mainly in February and March 2020 and leveraging the power of text mining.
Findings reveal that indeed this global crisis sparked off an intense and increasing activity in Stack Overflow with most post topics reflecting a strong interest on the analysis of Covid-19 data.
arXiv Detail & Related papers (2020-04-18T08:19:46Z) - A County-level Dataset for Informing the United States' Response to
COVID-19 [5.682299443164938]
We present a dataset that aggregates relevant data from governmental, journalistic, and academic sources on the U.S. county level.
Our dataset contains more than 300 variables that summarize population estimates, demographics, ethnicity, housing, education, employment and income, climate, transit, scores, and healthcare system-related metrics.
arXiv Detail & Related papers (2020-04-01T05:07:27Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.