Multiple cohort study of hospitalized SARS-CoV-2 in-host infection dynamics: parameter estimates, sensitivity and the eclipse phase profile

Abstract

Within-host SARS-CoV-2 modelling studies have been published throughout the COVID-19 pandemic. These studies contain highly variable numbers of individuals and capture varying timescales of pathogen dynamics; some studies capture the time of disease onset, the peak viral load and subsequent heterogeneity in clearance dynamics across individuals, while others capture late-time post-peak dynamics. In this study, we curate multiple previously published SARS-CoV-2 viral load data sets, fit these data with a consistent modelling approach, and estimate the variability of in-host parameters including the basic reproduction number, R0. We find that fitted dynamics can be highly variable across data sets, and highly variable within data sets, particularly when key components of the dynamic trajectories (e.g. peak viral load) are not represented in the data. Further, we investigated the role of the eclipse phase time distribution in fitting SARS-CoV-2 viral load data. By varying the shape parameter of an Erlang distribution, we demonstrate that models with either no eclipse phase, or with an exponentially-distributed eclipse phase, offer significantly worse fits to these data, whereas models with less dispersion around the mean eclipse time (shape parameter two or more) offered the best fits to the available data.

Long-term durability of immune responses to the BNT162b2 and mRNA-1273 vaccines based on dosage, age and sex

“…For future work, with appropriately high-resolved data we can expand the model to robustly predict cellular responses, or we can extend the model to include additional booster doses separated by variable timescales. Vaccine-free within-host pathogen dynamics have been studied for SARS-CoV-2 [66][67][68][69][70][71][72] , coupling a vaccine model, such as that presented in Eq. ( 9) in this work, with an infection model would be an interesting course of future work to better understand the mechanisms driving immunity vaccination together with challenge by infection.…”

Monkeypox: A review of epidemiological modelling studies and how modelling has led to mechanistic insight

“…Modelling of in-host pathogen dynamics has proven critical towards furthering our understanding of HIV, HCV, HBV, HSV, influenza, and SARS-CoV-2 as well as aiding the development of vaccine therapies [8][9][10][11][12][14][15][16][118][119][120].…”

“…Models based on the TEIV model have been used extensively to estimate within-host properties of disease dynamics thus contributing to our understanding of the disease progression at the within-host scale [12,119,[122][123][124][125][126][127][128]. Models can inform and predict various aspects of disease dynamics.…”