How to avoid future "Covid-19 origins" questions?

Z Su, D McDonnell, A Cheshmehzangi, J Ahmad, S Šegalo, C P da Veiga, Y-T Xiang

Abstract read

In one paragraph

Article in Ethics, medicine, and public health, 2022. 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

–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

0 citing papers in PubMed.

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

7 authors.

Z SuSchool of Public Health; Institute for Human Rights, Southeast University, 210009 Nanjing, China.

D McDonnellDepartment of Humanities, South East Technological University, R93 V960, Ireland.

A CheshmehzangiFaculty of Science and Engineering, University of Nottingham Ningbo China, 315100 Ningbo, Zhejiang, China.

J AhmadRufaidah Nursing College, Peshawar, Pakistan.

S ŠegaloFaculty of Health Studies, University of Sarajevo, 71000 Sarajevo, Bosnia and Herzegovina.

C P da VeigaFundação Dom Cabral - FDC, Av. Princesa Diana, 760, Alphaville, Lagoa dos Ingleses, 34018-006 Nova Lima, MG, Brazil.

Y-T XiangUnit of Psychiatry, Department of Public Health and Medicinal Administration, University of Macau, Macao SAR, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Origins debates regarding Covid-19 are gaining momentum again. In light of the continued infections and deaths of Covid-19 seen in countries rich and poor, rather than focusing the approach with "whodunit", developing solutions that can help societies become better prepared for future pandemics might be a more meaningful way to move forward. In this paper, we propose a solution that could help society better predict and prevent future pandemics. A system could allow humans to anonymously report potential infectious disease outbreaks without fearing backlash or prejudice and could automatically surveil for potential disease transfers or virus leaks. The proposed autonomous and anonymous pandemic reporting and surveillance system has the potential to help health officials locate infectious disease outbreaks before they form into pandemics. And in turn, it better prevents future pandemics and avoids Covid-19 origins debates.

Indexed as

AI, artificial intelligenceCovid-19Disease surveillanceInfectious diseasePublic health

Identifiers

PMID36406280

DOI10.1016/j.jemep.2022.100856

PMCPMC9666368

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.