Evidence map›Paper›PMID 41851212›Full record

ArticleScientific reports2026

Post-acute sequelae of SARS-CoV-2 infection symptom network centrality analysis of Taiwan population to unveil intricate symptomatology patterns.

Shikha Kukreti, Chun-Yin Yeh, Meng-Ting Lu, Kashif Imteyaz, Carol Strong, Nai-Ying Ko

Abstract read
In one paragraph

Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Shikha KukretiDepartment of Nursing, College of Medicine, National Cheng Kung University, Tainan, Taiwan.
Chun-Yin YehDepartment of Nursing, National Sun Yat-sen University, Kaohsiung, Taiwan.
Meng-Ting LuDepartment of Nursing, College of Medicine, National Cheng Kung University, Tainan, Taiwan.
Kashif ImteyazKhoury College of Computer Sciences, Northeastern University, Boston, USA.
Carol StrongDepartment of Public Health, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan, Taiwan. carol.chiajung@gmail.com.
Nai-Ying KoDepartment of Nursing, College of Medicine, National Cheng Kung University, Tainan, Taiwan. naiyingko@gmail.com.

Funding

National Science and Technology Council 114-2811-B-006-028
6 · The paper itself

Abstract

Post-acute sequelae of SARS-CoV-2 infection (PASC), or long COVID, present a complex array of persistent symptoms that remain poorly understood. Identifying the central symptoms and their interrelationships is crucial for effective management and intervention. This study applied network centrality analysis to 672 individuals diagnosed with COVID-19 in southern Taiwan, who completed an online survey on persistent symptoms. Participants were classified into two groups: post-acute COVID (symptoms lasting 4–12 weeks post-infection) and long COVID (symptoms lasting > 12 weeks). Symptom networks were constructed using polychoric correlations, with centrality measures (betweenness, closeness, strength, expected influence) used to assess symptom influence. A network comparison test evaluated invariance and global strength between the two groups. The mean age of participants was 38.75 years, with significant age differences between the post-acute COVID group (40.31 years) and the long COVID group (37.76 years). Diarrhea showed high betweenness centrality in both groups, acting as a key connector. Palpitations exhibited higher closeness centrality in long COVID, while fever was central in post-acute cases. Strength centrality was notably high for palpitations and chest pain in long COVID. No gender differences were found, and the global strength of the networks did not differ significantly between the groups (p = 0.93). This study reveals that long COVID has a more tightly interconnected symptom network compared to post-acute COVID, with central symptoms like palpitations and chest pain offering potential targets for tailored treatment strategies. The findings emphasize the importance of symptom interrelationships, though longitudinal studies are needed to further explore causal relationships and the progression of symptoms over time.

Indexed as

COVID-19AdultFemaleHumansMaleMiddle AgedPost-Acute COVID-19 SyndromeSARS-CoV-2Symptom BurdenTaiwanLong COVIDNetwork analysisPost-acute sequelae of SARS-CoV-2 infectionSymptomatology

Identifiers

PMID41851212
PMCPMC13133340

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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.