Evolutionary and spatiotemporal analyses reveal multiple introductions and cryptic transmission of SARS-CoV-2 VOC/VOI in Malta

Abstract

Genomic surveillance and epidemiology have shed light on the viral diversity driving coronavirus disease 2019 (COVID-19) outbreaks and are important during waves of highly transmissible and immune-escaping variants of interest or of concern (VOCs). We analyzed the epidemiological data of the understudied country of Malta and related the patterns observed with viral genetic sequences obtained through the surveillance system headed by the Mater Dei Hospital and the University of Malta. We reconstructed the evolutionary history and spatiotemporal dynamics of Maltese severe acute respiratory syndrome coronavirus 2 viruses using a phylodynamics framework. Our findings suggest that the number of cases associated with B.1.1.7/Alpha, B.1.617.2.X/Delta, and B.1.1.529.X/Omicron VOCs was nine times higher than those associated with wild-type variants. The positivity rates in Malta remained low to moderate (<10%). A combination of public health interventions appeared to have allowed Malta to mitigate the impact of COVID-19. Our phylodynamic reconstruction traced most of the 173 viral introductions inferred to countries in Northern Europe, which is consistent with flight connectivity patterns. We also observed prolonged periods of cryptic transmission (median = 102 days) until expansion into larger outbreaks. These larger outbreaks were more easily detected by the intermittent genomic surveillance in Malta, characterized by periods of sequencing hiatus. Our study demonstrates that integrating epidemiological and genomic data are crucial for uncovering the COVID-19 dynamics of understudied locations, particularly when genomic surveillance is suboptimal. Accordingly, strengthening the genomic surveillance system in Malta should help in the earlier detection of introductions and minimize viral expansion in the country while informing public health interventions. IMPORTANCE Our study provides insights into the evolution of the coronavirus disease 2019 (COVID-19) pandemic in Malta, a highly connected and understudied country. We combined epidemiological and phylodynamic analyses to analyze trends in the number of new cases, deaths, tests, positivity rates, and evolutionary and dispersal patterns from August 2020 to January 2022. Our reconstructions inferred 173 independent severe acute respiratory syndrome coronavirus 2 introductions into Malta from various global regions. Our study demonstrates that characterizing epidemiological trends coupled with phylodynamic modeling can inform the implementation of public health interventions to help control COVID-19 transmission in the community.