Evidence map›Paper›PMID 33457497›Full record

ArticleApplied network science2021

Assessing the interplay between travel patterns and SARS-CoV-2 outbreak in realistic urban setting.

Rohan Patil, Raviraj Dave, Harsh Patel, Viraj M Shah, Deep Chakrabarti, Udit Bhatia

Open access · goldAbstract read
In one paragraph

Article in Applied network science, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
0.2field-weighted citation impact, top 57% of its field
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

8 citing papers in PubMed, 2 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Spatial networks and the spread of COVID-19: results and policy implications from Germany.Jahrbuch fur Regionalwissenschaftt = Review of regional research · 2023
    Article
  8. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors at 2 institutions in 1 country.

Rohan Patil *Discipline of Computer Science and Engineering, Indian Institute of Technology, Gandhinagar, India.
Raviraj Dave *Discipline of Civil Engineering, Indian Institute of Technology, Gandhinagar, India.
Harsh PatelDiscipline of Computer Science and Engineering, Indian Institute of Technology, Gandhinagar, India.
Viraj M ShahDiscipline of Mechanical Engineering, Indian Institute of Technology, Gandhinagar, India.
Deep ChakrabartiKing George Medical University, Lucknow, India.
Udit BhatiaDiscipline of Civil Engineering, Indian Institute of Technology, Gandhinagar, India.ORCID 0000-0002-0017-9085
Indian Institute of Technology Gandhinagar · INKing George's Medical University · IN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe dense social contact networks and high mobility in congested urban areas facilitate the rapid transmission of infectious diseases. Typical mechanistic epidemiological models are either based on uniform mixing with ad-hoc contact processes or need real-time or archived population mobility data to simulate the social networks. However, the rapid and global transmission of the novel coronavirus (SARS-CoV-2) has led to unprecedented lockdowns at global and regional scales, leaving the archived datasets to limited use.

findingsWhile it is often hypothesized that population density is a significant driver in disease propagation, the disparate disease trajectories and infection rates exhibited by the different cities with comparable densities require a high-resolution description of the disease and its drivers. In this study, we explore the impact of creation of containment zones on travel patterns within the city. Further, we use a dynamical network-based infectious disease model to understand the key drivers of disease spread at sub-kilometer scales demonstrated in the city of Ahmedabad, India, which has been classified as a SARS-CoV-2 hotspot. We find that in addition to the contact network and population density, road connectivity patterns and ease of transit are strongly correlated with the rate of transmission of the disease. Given the limited access to real-time traffic data during lockdowns, we generate road connectivity networks using open-source imageries and travel patterns from open-source surveys and government reports. Within the proposed framework, we then analyze the relative merits of social distancing, enforced lockdowns, and enhanced testing and quarantining mitigating the disease spread. SCOPE: Our results suggest that the declaration of micro-containment zones within the city with high road network density combined with enhanced testing can help in containing the outbreaks until clinical interventions become available.

Indexed as

SARS CoV2Social networksTransportation gravity modelsTransportation network

Identifiers

PMID33457497
PMCPMC7803387
OpenAlexW3088921142

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.