The geospatial phylodynamics of SARS-CoV-2 in Nepal and clinical phenotype of long COVID during the Delta-Omicron waves

The COVID-19 pandemic exposed gaps in global emerging infectious disease preparedness, particularly for countries with high levels of economic migration, such as Nepal. The Epidemic Intelligence project analysed SARS-CoV-2 genomic and epidemiological data from 2,046 COVID-19 patients across three Nepali regions to study viral dynamics, phylogeography, and long COVID. Participants were interviewed at diagnosis, and at 3-, 6-, and 12-months post-infection. Long COVID was explored using three standardised definitions from the literature, and logistic regression was used to identify risk factors, stratified by SARS-CoV-2 variant. Over 40% of participants reported long COVID at 3 months defined by symptoms [≥]12 weeks and the presence of at least one symptom from the Wellcome Trust Longitudinal Population Study questionnaire. 10.6% (n=211/1992) of participants reported at least two of the symptom clusters from the WHO Delphi consensus definition at 3 months, and 0.7% (n=14/1992) reported all three symptom clusters. The prevalence of long COVID was higher among people infected with the Delta variant than the Omicron variant across all definitions and time points. Among Delta cases, female sex, chronic comorbidity, and full vaccination were associated with long COVID; whereas for people infected with Omicron, only age between 40-54 was a risk factor. Phylogeographic analyses revealed the epidemiological importance of Nepals porous border with India and the changing role of migration in viral spread as the pandemic evolved. Our findings also highlight the burden of post-COVID syndrome in low-resource settings and the need for further research to mitigate the financial and clinical impact. Integration of pathogen genomic analysis with clinical and epidemiological data can guide public health responses during the emergence of novel infectious diseases.