Evidence map›Paper›PMID 34568118›Full record

ReviewJournal of family medicine and primary care2021

Flashback and lessons learnt from history of pandemics before COVID-19.

Shivay Parihar, Rimple Jeet Kaur, Surjit Singh

Abstract readReview
In one paragraph

Review in Journal of family medicine and primary care, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
  4. Article
  5. Article
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.

Shivay PariharDr. S.N. Medical College, Jodhpur, Rajasthan, India.
Rimple Jeet KaurDepartment of Pharmacology, AIIMS, Jodhpur, Rajasthan, India.
Surjit SinghDepartment of Pharmacology, AIIMS, Jodhpur, Rajasthan, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

With an increasing frequency of infectious disease outbreaks, the COVID-19 pandemic causing mortality around the world and the threat of similar future events looming large, mankind is faced with the herculean task of counteracting such threats with the best possible strategies and public health decisions. It is key that such decisions should be guided by previous examples of similar health emergencies. Here we review some of the significant infectious disease outbreaks, including epidemics and pandemics occurring worldwide in the past including their impact at population and global levels, unique challenges presented by each and the measures taken by authorities worldwide as well as the crucial lessons each epidemic or pandemic provided. This review highlights that throughout history measures such as contact tracing, quarantine and isolation have been incredibly effective in limiting an outbreak in its severity, thus ensuring accurate information flow to the public is as essential as limiting the spread of misinformation. With global populations rising, surveillance for emerging and re-emerging pathogens will play an immense role in preventing future epidemics or pandemics. And finally that even though for novel strains or pathogens, although vaccines are thought to be an irreplaceable defense, but their development and distribution in time to curb an epidemic has seldom been witnessed and remains an important challenge for the future. Hence, we conclude that looking at these past examples not only highlights the important knowledge gained for the strategies to devise, but also the mistakes that can be avoided in the way forward.

Indexed as

COVID-19H1N1MERSpandemicssevere acute respiratory syndromeSpanish flu

Identifiers

PMID34568118
PMCPMC8415662

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-SA
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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.