Models for COVID-19 Pandemic: A Comparative Analysis.

Aniruddha Adiga, Devdatt Dubhashi, Bryan Lewis, Madhav Marathe, Srinivasan Venkatramanan, Anil Vullikanti

Abstract readPreprint

In one paragraph

Article in ArXiv, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it

0cells of the map it votes in

1citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

Multiscale predictive cellular modeling: integrating hypothesis grammars, digital twins, and multi-omics for In silico oncology and precision theranostics.Functional & integrative genomics · 2026
Review

4 · The record

Corrections and comments

Updated by
Mathematical Models for COVID-19 Pandemic: A Comparative Analysis.2020

5 · Who and what money

Authors and funding

6 authors.

Aniruddha AdigaBIOCOMPLEXITY INSTITUTE AND INITITIATIVE, UNIVERSITY OF VIRGINIA.

Devdatt DubhashiDEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING, CHALMERS UNIVERSITY.

Bryan LewisBIOCOMPLEXITY INSTITUTE AND INITITIATIVE, UNIVERSITY OF VIRGINIA.

Madhav MaratheBIOCOMPLEXITY INSTITUTE AND INITITIATIVE, UNIVERSITY OF VIRGINIA.

Srinivasan VenkatramananBIOCOMPLEXITY INSTITUTE AND INITITIATIVE, UNIVERSITY OF VIRGINIA.

Anil VullikantiBIOCOMPLEXITY INSTITUTE AND INITITIATIVE, UNIVERSITY OF VIRGINIA.

Funding

Systems Analysis of Social Pathways of Epidemics to Reduce Health DisparitiesR01GM109718 · NIGMS · VIRGINIA POLYTECHNIC INST AND ST UNIV · PI MARATHE, ACHLA, VULLIKANTI, ANIL · 2014 to 2022

$3.6M

NIGMS NIH HHS R01 GM109718

6 · The paper itself

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.

Identifiers

PMID32995366

PMCPMC7523122

What OpenQuestion holds

Textmetadata

LicenceCC BY

Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.