Detection of SARS-CoV-2 infection clusters: The useful combination of spatiotemporal clustering and genomic analyses

Abstract

BackgroundThe need for effective public health surveillance systems to track virus spread for targeted interventions was highlighted during the COVID-19 pandemic. It spurred an interest in the use of spatiotemporal clustering and genomic analyses to identify high-risk areas and track the spread of the SARS-CoV-2 virus. However, these two approaches are rarely combined in surveillance systems to complement each one's limitations; spatiotemporal clustering approaches usually consider only one source of virus transmission (i.e., the residential setting) to detect case clusters, while genomic studies require significant resources and processing time that can delay decision-making. Here, we clarify the differences and possible synergies of these two approaches in the context of infectious disease surveillance systems by investigating to what extent geographically-defined clusters are confirmed as transmission clusters based on genome sequences, and how genomic-based analyses can improve the epidemiological investigations associated with spatiotemporal cluster detection.MethodsFor this purpose, we sequenced the SARS-CoV-2 genomes of 172 cases that were part of a collection of spatiotemporal clusters found in a Swiss state (Vaud) during the first epidemic wave. We subsequently examined intra-cluster genetic similarities and spatiotemporal distributions across virus genotypes.ResultsOur results suggest that the congruence between the two approaches might depend on geographic features of the area (rural/urban) and epidemic context (e.g., lockdown). We also identified two potential superspreading events that started from cases in the main urban area of the state, leading to smaller spreading events in neighboring regions, as well as a large spreading in a geographically-isolated area. These superspreading events were characterized by specific mutations assumed to originate from Mulhouse and Milan, respectively. Our analyses propose synergistic benefits of using two complementary approaches in public health surveillance, saving resources and improving surveillance efficiency.

Environmental and geographical factors influencing the spread of SARS-CoV-2 over 2 years: a fine-scale spatiotemporal analysis

“…Our analyses included 41,672 positive SARS-CoV-2 RT-PCR tests from a total of 283,135 tests administered to 138,774 residents of the canton of Vaud (population 800,000), Switzerland, between March 2, 2020, and April 15, 2022. The testing procedure relied only on quantitative real-time PCRs and has been described in detail in previous studies ( 8 , 10 ). The study received approval from the Cantonal Research Ethics Commission of Vaud (CER-VD), Switzerland (n°2020-01302).…”

“…The SARS-CoV-2 pandemic has had a significant impact on the world's population and understanding the spatial and temporal patterns of its spread and its evolution is crucial for epidemic surveillance and control (1)(2)(3)(4)(5). Techniques such as hot-spot analysis, spatiotemporal clustering, and space-time scan statistics have been widely employed to analyze georeferenced data from SARS-CoV-2 RT-PCR testing (6)(7)(8)(9)(10)(11). These analyses have revealed that the incidence and the mortality of the disease are not evenly distributed but rather cluster in certain areas and peak at certain times, indicating a high degree of heterogeneity in the diffusion dynamics of the virus (12-14).…”

Data_Sheet_1.docx

“…Our analyses included 41,672 positive SARS-CoV-2 RT-PCR tests from a total of 283,135 tests administered to 138,774 residents of the canton of Vaud (population 800,000), Switzerland, between March 2, 2020, and April 15, 2022. The testing procedure relied only on quantitative real-time PCRs and has been described in detail in previous studies (8,10). The study received approval from the Cantonal Research Ethics Commission of Vaud (CER-VD), Switzerland (n°2020-01302).…”

“…The SARS-CoV-2 pandemic has had a significant impact on the world's population and understanding the spatial and temporal patterns of its spread and its evolution is crucial for epidemic surveillance and control (1)(2)(3)(4)(5). Techniques such as hot-spot analysis, spatiotemporal clustering, and space-time scan statistics have been widely employed to analyze georeferenced data from SARS-CoV-2 RT-PCR testing (6)(7)(8)(9)(10)(11). These analyses have revealed that the incidence and the mortality of the disease are not evenly distributed but rather cluster in certain areas and peak at certain times, indicating a high degree of heterogeneity in the diffusion dynamics of the virus (12-14).…”