Model-based assessment of sampling protocols for infectious disease genomic surveillance
Olivera-Nappa, Alvaro
- 1Max Planck Society
- 2Universidad de Talca
- 3Universidad de Chile
- 4
- 5
Journal
Chaos, Solitons and Fractals
ISSN
0960-0779
1873-2887
Open Access
green
Volume
167
Genomic surveillance of infectious diseases allows monitoring circulating and emerging variants and quantify-ing their epidemic potential. However, due to the high costs associated with genomic sequencing, only a limited number of samples can be analysed. Thus, it is critical to understand how sampling impacts the information generated. Here, we combine a compartmental model for the spread of COVID-19 (distinguishing several SARS-CoV-2 variants) with different sampling strategies to assess their impact on genomic surveillance. In particular, we compare adaptive sampling, i.e., dynamically reallocating resources between screening at points of entry and inside communities, and constant sampling, i.e., assigning fixed resources to the two locations. We show that adaptive sampling uncovers new variants up to five weeks earlier than constant sampling, significantly reducing detection delays and estimation errors. This advantage is most prominent at low sequencing rates. Although increasing the sequencing rate has a similar effect, the marginal benefits of doing so may not always justify the associated costs. Consequently, it is convenient for countries with comparatively few resources to operate at lower sequencing rates, thereby profiting the most from adaptive sampling. Finally, our methodology can be readily adapted to study undersampling in other dynamical systems.
Name
document.pdf
Type
Main Article
Size
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Format
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