Evidence map›Paper›PMID 38166666›Full record

ArticleBMC infectious diseases2024

Reconstructing the COVID-19 incidence in India using airport screening data in Japan.

Shiqi Liu, Asami Anzai, Hiroshi Nishiura

Open access · goldAbstract read
In one paragraph

Article in BMC infectious diseases, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
3.2field-weighted citation impact, top 9% 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

2 citing papers in PubMed, 6 citations in OpenAlex.

  1. Article
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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

3 authors at 1 institution in 1 country.

Shiqi LiuKyoto University School of Public Health, Yoshidakonoe cho, Sakyo ku, Kyoto City, 6068501, Japan.
Asami AnzaiKyoto University School of Public Health, Yoshidakonoe cho, Sakyo ku, Kyoto City, 6068501, Japan.
Hiroshi NishiuraKyoto University School of Public Health, Yoshidakonoe cho, Sakyo ku, Kyoto City, 6068501, Japan. nishiura.hiroshi.5r@kyoto-u.ac.jp.
Kyoto University · JP

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundA major epidemic of COVID-19 caused by the Delta variant (B.1.617.2) occurred in India from March to July 2021, resulting in 19 million documented cases. Given the limited healthcare and testing capacities, the actual number of infections is likely to have been greater than reported, and several modelling studies and excess mortality research indicate that this epidemic involved substantial morbidity and mortality.

methodsTo estimate the incidence during this epidemic, we used border entry screening data in Japan to estimate the daily incidence and cumulative incidence of COVID-19 infection in India. Analysing the results of mandatory testing among non-Japanese passengers entering Japan from India, we calculated the prevalence and then backcalculated the incidence in India from February 28 to July 3, 2021.

resultsThe estimated number of infections ranged from 448 to 576 million people, indicating that 31.8% (95% confidence interval (CI): 26.1, 37.7) - 40.9% (95% CI: 33.5, 48.4) of the population in India had experienced COVID-19 infection from February 28 to July 3, 2021. In addition to obtaining cumulative incidence that was consistent with published estimates, we showed that the actual incidence of COVID-19 infection during the 2021 epidemic in India was approximately 30 times greater than that based on documented cases, giving a crude infection fatality risk of 0.47%. Adjusting for test-negative certificate before departure, the quality control of which was partly questionable, the cumulative incidence can potentially be up to 2.3-2.6 times greater than abovementioned estimates.

conclusionsOur estimate of approximately 32-41% cumulative infection risk from February 28 to July 3, 2021 is roughly consistent with other published estimates, and they can potentially be greater, given an exit screening before departure. The present study results suggest the potential utility of border entry screening data to backcalculate the incidence in countries with limited surveillance capacity owing to a major surge in infections.

Indexed as

COVID-19AirportsHumansIncidenceIndiaJapanSARS-CoV-2Border entry screening, severe acute respiratory syndrome coronavirus 2EpidemiologyPolymerase chain reactionStatistical estimation

Identifiers

PMID38166666
PMCPMC10763058
OpenAlexW4390509327

What OpenQuestion holds

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LicenceCC BY
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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.