Models for COVID-19 Pandemic: A Comparative Analysis
- URL: http://arxiv.org/abs/2009.10014v1
- Date: Mon, 21 Sep 2020 16:42:00 GMT
- Title: Models for COVID-19 Pandemic: A Comparative Analysis
- Authors: Aniruddha Adiga, Devdatt Dubhashi, Bryan Lewis, Madhav Marathe,
Srinivasan Venkatramanan, Anil Vullikanti
- Abstract summary: COVID-19 pandemic represents an unprecedented global health crisis in the last 100 years.
Economic, social and health impact continues to grow and is likely to end up as one of the worst global disasters since the 1918 pandemic and the World Wars.
Mathematical models have played an important role in the ongoing crisis.
- Score: 15.22097752961091
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: COVID-19 pandemic represents an unprecedented global health crisis in the
last 100 years. Its economic, social and health impact continues to grow and is
likely to end up as one of the worst global disasters since the 1918 pandemic
and the World Wars. Mathematical models have played an important role in the
ongoing crisis; they have been used to inform public policies and have been
instrumental in many of the social distancing measures that were instituted
worldwide.
In this article we review some of the important mathematical models used to
support the ongoing planning and response efforts. These models differ in their
use, their mathematical form and their scope.
Related papers
- Human Behavior in the Time of COVID-19: Learning from Big Data [71.26355067309193]
Since March 2020, there have been over 600 million confirmed cases of COVID-19 and more than six million deaths.
The pandemic has impacted and even changed human behavior in almost every aspect.
Researchers have been employing big data techniques such as natural language processing, computer vision, audio signal processing, frequent pattern mining, and machine learning.
arXiv Detail & Related papers (2023-03-23T17:19:26Z) - When a crisis strikes: Emotion analysis and detection during COVID-19 [96.03869351276478]
We present CovidEmo, 1K tweets labeled with emotions.
We examine how well large pre-trained language models generalize across domains and crises.
arXiv Detail & Related papers (2021-07-23T04:07:14Z) - Pandemic Informatics: Preparation, Robustness, and Resilience; Vaccine
Distribution, Logistics, and Prioritization; and Variants of Concern [6.6946518757677635]
Infectious diseases cause more than 13 million deaths a year, worldwide.
The ongoing COVID-19 pandemic-the first since the H1N1 outbreak more than a decade ago-illustrates these matters vividly.
Pandemic will continue to have significant disruptive impacts upon the United States and the world for years.
arXiv Detail & Related papers (2020-12-16T22:33:29Z) - Steering a Historical Disease Forecasting Model Under a Pandemic: Case
of Flu and COVID-19 [75.99038202534628]
We propose CALI-Net, a neural transfer learning architecture which allows us to'steer' a historical disease forecasting model to new scenarios where flu and COVID co-exist.
Our experiments demonstrate that our approach is successful in adapting a historical forecasting model to the current pandemic.
arXiv Detail & Related papers (2020-09-23T22:35:43Z) - The Past, Present, and Future of COVID-19: A Data-Driven Perspective [4.373183416616983]
We report results on our development and deployment of a web-based integrated real-time operational dashboard as an important decision support system for COVID-19.
We conducted data-driven analysis based on available data from diverse authenticated sources to predict upcoming consequences of the pandemic.
We also explored correlations between pandemic spread and important socio-economic and environmental factors.
arXiv Detail & Related papers (2020-08-12T19:03:57Z) - Data-driven Simulation and Optimization for Covid-19 Exit Strategies [16.31545249131776]
The rapid spread of the Coronavirus SARS-2 is a major challenge that led almost all governments worldwide to take drastic measures to respond to the tragedy.
We have built a pandemic simulation and forecasting toolkit that combines a deep learning estimation of the epidemiological parameters of the disease.
arXiv Detail & Related papers (2020-06-12T11:18:25Z) - Pandemic Pulse: Unraveling and Modeling Social Signals during the
COVID-19 Pandemic [12.050597862123313]
We present and begin to explore a collection of social data that represents part of the COVID-19 pandemic's effects on the United States.
This data is collected from a range of sources and includes longitudinal trends of news topics, social distancing behaviors, community mobility changes, web searches, and more.
arXiv Detail & Related papers (2020-06-10T17:55:44Z) - Cross-lingual Transfer Learning for COVID-19 Outbreak Alignment [90.12602012910465]
We train on Italy's early COVID-19 outbreak through Twitter and transfer to several other countries.
Our experiments show strong results with up to 0.85 Spearman correlation in cross-country predictions.
arXiv Detail & Related papers (2020-06-05T02:04:25Z) - When and How to Lift the Lockdown? Global COVID-19 Scenario Analysis and
Policy Assessment using Compartmental Gaussian Processes [111.69190108272133]
coronavirus disease 2019 (COVID-19) global pandemic has led many countries to impose unprecedented lockdown measures.
Data-driven models that predict COVID-19 fatalities under different lockdown policy scenarios are essential.
This paper develops a Bayesian model for predicting the effects of COVID-19 lockdown policies in a global context.
arXiv Detail & Related papers (2020-05-13T18:21:50Z) - When Wireless Communication Faces COVID-19: Combating the Pandemic and
Saving the Economy [93.08344893433639]
The year 2020 is experiencing a global health and economic crisis due to the COVID-19 pandemic.
Countries across the world are using digital technologies to fight this global crisis.
We show how these technologies are helping to combat this pandemic, including monitoring of the virus spread.
We discuss the challenges faced by wireless technologies, including privacy, security, and misinformation.
arXiv Detail & Related papers (2020-05-12T12:27:29Z) - Analysis of the COVID-19 pandemic by SIR model and machine learning
technics for forecasting [0.0]
This work is a trial in which we propose SIR model and machine learning tools to analyze the coronavirus pandemic in the real world.
Based on the public data from citedatahub, we estimate main key pandemic parameters and make predictions on the inflection point and possible ending time for the real world and specifically for Senegal.
arXiv Detail & Related papers (2020-04-03T13:56:54Z)
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.