Evidence map›Paper›PMID 36747967›Full record

ArticleVirusdisease2023

An algorithm to estimate the real time secondary infections in sub-urban bus travel: COVID-19 epidemic experience at Chennai Metropolitan city India.

Ganesh Ram Arumugam, Bakiya Ambikapathy, Kamalanand Krishnamurthy, Ashwani Kumar, Lourduraj De Britto

Open access · bronzeAbstract read
In one paragraph

Article in Virusdisease, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
0.2field-weighted citation impact, top 56% 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

0 citing papers in PubMed, 1 citations in OpenAlex.

No citing paper in PubMed yet.

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

5 authors at 2 institutions in 1 country.

Ganesh Ram ArumugamDepartment of Instrumentation Engineering, MIT Campus, Anna University, Chennai, 600044 Tamil Nadu India.
Bakiya AmbikapathyDepartment of Instrumentation Engineering, MIT Campus, Anna University, Chennai, 600044 Tamil Nadu India.
Kamalanand KrishnamurthyDepartment of Instrumentation Engineering, MIT Campus, Anna University, Chennai, 600044 Tamil Nadu India.
Ashwani KumarIndian Council of Medical Research, Vector Control Research Centre, Puducherry, 605006 India.
Lourduraj De BrittoIndian Council of Medical Research, Vector Control Research Centre, Puducherry, 605006 India.ORCID 0000-0001-5815-6823
Anna University, Chennai · INIndian Council of Medical Research · IN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Globalization, global climatic changes, and human behavior pose threats to highly pathogenic avian influenza (HPAI) virus spillover from animals to human. Current SARS-CoV2 transmission continues in several countries despite drastic reduction in COVID-19 cases following world-wide containment measures including RNA vaccines. China reimposed lockdown in November 2022 following the surge in commercial hubs. Urban population density and intracity travel in over-crowded public transport play crucial roles in early transition to an exponential phase of the epidemic in metro-cities. Based on the SARS-CoV2 transmission during the lockdown period in Chennai metro-city, we developed an algorithm that mimics a real-time scenario of passengers boarding and deboarding at each bus-stop on a trip of 36.1 km in 21G bus service in Chennai city to understand the pattern of secondary infections on a daily basis. The algorithm was simulated to estimate R0, and the COVID-19 secondary infections was estimated for each bus trip. Results showed that the R0 depended on the boarding and deboarding of the infected individuals at various bus stops. R0 varied from 0 to 1.04, each trip generated 5-9 secondary infections and four bus stops as potential locations for a higher transmission level. More than 80% of the working population in metro-cities depends on unorganized sectors, and separate mitigation strategies must be in place for successful epidemic containment. The developed algorithm has significant public health relevance and can be utilized to draw necessary containment plans in near future in the event of new COVID-19 wave or any other similar epidemic. Supplementary Information: The online version contains supplementary material available at 10.1007/s13337-022-00804-9.

Indexed as

COVID-19Epidemic containmentModellingRNA vaccineSARS-CoV-2Secondary infection

Identifiers

PMID36747967
PMCPMC9893963
OpenAlexW4318973261

What OpenQuestion holds

Textmetadata
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.