Evidence map›Paper›PMID 38528471›Full record

ArticleBMC infectious diseases2024

Evolution of COVID-19 infection in Punjab; trends during five waves of infection in the province of Punjab.

Hasnain Javed, Aasia Khaliq, Shaper Mirza, Rimsha Khan, Warda Fatima

Abstract 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
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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

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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

5 authors.

Hasnain JavedProvincial Public Health Reference Laboratory, Punjab AIDS Control Program, Lahore, Pakistan.
Aasia KhaliqDepartment of Life Sciences, Lahore University of Management Sciences (LUMS), Lahore, Pakistan.
Shaper MirzaDepartment of Life Sciences, Lahore University of Management Sciences (LUMS), Lahore, Pakistan. shaper.mirza@lums.edu.pk.
Rimsha KhanProvincial Public Health Reference Laboratory, Punjab AIDS Control Program, Lahore, Pakistan.
Warda FatimaInstitute of Microbiology and Molecular Genetics, University of the Punjab, Lahore, Pakistan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundPakistan witnessed five waves of COVID-19 infections during the pandemic. Punjab, the largest province of Pakistan, remained the epicentre due to a high infection rate. Administrative data for five waves of the pandemic was analyzed to determine the rate of infections and the significance of pharmacological and non-pharmacological interventions on the severity and duration of infection. METHODOLOGY: COVID-19 data from March 2020 to May 2023 was obtained from the Provincial Public Health Reference Laboratory (PPHRL), Punjab AIDS Control Program, Lahore. The data included samples from index cases, contacts, and recovered patients. A total of 36,252,48 cases were screened for COVID-19, and 90,923 (2.50%) were detected positive by RT-PCR, accounting for 5.69% of the cases reported positive throughout the country.

resultsAmong the positive cases, 50.86% (n = 46,244) cases were new cases (registered for the first time), 40.41% (n = 36751) were the contact cases traced from the newly identified cases and 8.62% (n = 7842) repeated cases. The positivity rates among index cases were reported to be 2.37%, 2.34%, 4.61%, 2.09%, and 1.19%, respectively, for the five respective COVID-19 pandemic waves. Distribution by gender indicated that 64% of males and 35% of females were infected during the pandemic. The age factor demonstrated the most susceptibility to infection in women aged 19-29 years, whereas most males between the ages of 29-39 had an infection. Susceptibility to COVID-19 infection was observed to be equally likely between males and females; however, clinical outcomes indicated that infections in males were more severe and often resulted in fatalities as compared to those in females. This trend was also reflected in the viral titer as measured by the Ct values, where 40% of males had Ct values < 25 (an indicator of high viral titers) compared to 30% of females with Ct values < 25.

conclusionOverall, our data indicated that infection rates remained stable throughout the pandemic except for 3rd wave, which showed a higher incidence of infection rate of 4%. Additionally, data showed a positive impact of masking, social distancing, and immunization, as indicated by the shorter window of high infection rates.

Indexed as

COVID-19AdultAge FactorsFemaleHumansImmunizationMalePakistanPandemicsCOVID-19EpidemiologyIncident rateRT-PCR

Identifiers

PMID38528471
PMCPMC10962076

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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.