Evidence map›Paper›PMID 39283438›Full record

ArticleAdvances in experimental medicine and biology2024

Estimating the Real Impact of the COVID-19 Pandemic in Kazakhstan: Factors Associated with Detection of the "True Infections".

Antonio Sarria-Santamera, Nurlan Abdukadyrov, Usman Abubakar Haruna, Natalya Glushkova, Yuliya Semenova, Zhandos Salpynov, Paolo Colet, Zaule Zhumambayeva

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Article in Advances in experimental medicine and biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

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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

3 citing papers in PubMed.

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4 · The record

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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

8 authors.

Antonio Sarria-SantameraDepartment of Medicine Nazarbayev University School of Medicine, Nazarbayev University, Astana, 020000, Kazakhstan. antonio.sarria@nu.edu.kz.
Nurlan AbdukadyrovDepartment of Mathematics, Statistics and Computer Sciences, University of Illinois at Chicago, Chicago, IL, 60607, USA.
Usman Abubakar HarunaDepartment of Biomedical Sciences, Nazarbayev University School of Medicine, Astana, 020000, Kazakhstan.
Natalya GlushkovaDepartment of Epidemiology, Biostatistics and Evidence-Based Medicine, Al-Farabi Kazakh National University, Almaty, 050040, Kazakhstan.
Yuliya SemenovaDepartment of Medicine Nazarbayev University School of Medicine, Nazarbayev University, Astana, 020000, Kazakhstan.
Zhandos SalpynovDepartment of Medicine Nazarbayev University School of Medicine, Nazarbayev University, Astana, 020000, Kazakhstan.
Paolo ColetDepartment of Medicine Nazarbayev University School of Medicine, Nazarbayev University, Astana, 020000, Kazakhstan.
Zaule ZhumambayevaDepartment of Children Diseases, Astana Medical University, Beibitshilik St 49/A, Astana, 010000, Kazakhstan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The COVID-19 pandemic is ongoing worldwide, and various case and death numbers are being reported to track its spread. However, the number of actual cases is uncertain due to under-reporting. Using mortality data as a more reliable indicator, this study in Kazakhstan evaluated the extent of under-reporting and under-detection of COVID-19 cases from March 2020 to September 2022 using back-casting and capture-recapture methods. The results indicate that official case reporting in Kazakhstan significantly underestimates the number of infections by at least 50%. The study also suggests that improved testing capabilities may have led to a decrease in the percentage of unreported cases, however, early in the pandemic, Kazakhstan faced significant testing shortages. The study presents a mathematical model based on mortality data that highlights the severe under-reporting of COVID-19 cases in Kazakhstan and argues that understanding the true estimate of actual cases could aid in making informed decisions to end the pandemic.

Indexed as

COVID-19SARS-CoV-2HumansKazakhstanModels, TheoreticalPandemicsCOVID-19EstimateKazakhstanPandemicTrue infectionsUnder-reporting

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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.