Data-Driven Modeling for Different Stages of Pandemic Response.
J Indian Inst Sci
; 100(4): 901-915, 2020.
Article
in English
| MEDLINE | ID: covidwho-1235810
Preprint
This scientific journal article is probably based on a previously available preprint. It has been identified through a machine matching algorithm, human confirmation is still pending.
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This scientific journal article is probably based on a previously available preprint. It has been identified through a machine matching algorithm, human confirmation is still pending.
See preprint
ABSTRACT
Some of the key questions of interest during the COVID-19 pandemic (and all outbreaks) include where did the disease start, how is it spreading, who are at risk, and how to control the spread. There are a large number of complex factors driving the spread of pandemics, and, as a result, multiple modeling techniques play an increasingly important role in shaping public policy and decision-making. As different countries and regions go through phases of the pandemic, the questions and data availability also change. Especially of interest is aligning model development and data collection to support response efforts at each stage of the pandemic. The COVID-19 pandemic has been unprecedented in terms of real-time collection and dissemination of a number of diverse datasets, ranging from disease outcomes, to mobility, behaviors, and socio-economic factors. The data sets have been critical from the perspective of disease modeling and analytics to support policymakers in real time. In this overview article, we survey the data landscape around COVID-19, with a focus on how such datasets have aided modeling and response through different stages so far in the pandemic. We also discuss some of the current challenges and the needs that will arise as we plan our way out of the pandemic.
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Type of study:
Observational study
/
Prognostic study
Language:
English
Journal:
J Indian Inst Sci
Year:
2020
Document Type:
Article
Affiliation country:
S41745-020-00206-0
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