Modelling the viral dynamics of the SARS-CoV-2 Delta and Omicron variants in different cell types (preprint)

We use viral kinetic models fitted to viral load data from in vitro studies to explain why the SARS-CoV-2 Omicron variant replicates faster than the Delta variant in nasal cells, but slower than Delta in lung cells, which could explain Omicron's higher transmission potential and lower severity. We find that in both nasal and lung cells, viral infectivity is higher for Omicron but the virus production rate is higher for Delta. However, the differences are unequal between cell types, and ultimately leads to the basic reproduction number and growth rate being higher for Omicron in nasal cells, and higher for Delta in lung cells. In nasal cells, Omicron alone can enter via a TMPRSS2-independent pathway, but it is primarily increased efficiency of TMPRSS2-dependent entry which accounts for Omicron's increased activity. This work paves the way for using within-host mathematical models to understand the transmission potential and severity of future variants.

Impact of antigen test target failure and testing strategies on the transmission of SARS-CoV-2 variants (preprint)

Population testing remains central to COVID-19 control and surveillance, with countries increasingly using antigen tests rather than molecular tests. Here we describe a SARS-CoV-2 variant that escapes N antigen tests due to multiple disruptive amino-acid substitutions in the N protein. By fitting a multistrain compartmental model to genomic and epidemiological data, we show that widespread antigen testing in the Italian region of Veneto favored the undetected spread of the antigen-escape variant compared to the rest of Italy. We highlight novel limitations of widespread antigen testing in the absence of molecular testing for diagnostic or confirmatory purposes. Critically, in the presence of a variant that escapes antigen testing, following up a proportion of negative antigen tests with a molecular test is the optimal testing strategy. Together, these findings highlight the importance of retaining molecular testing for surveillance purposes, also in contexts where the use of antigen tests is widespread.

Rapid longitudinal SARS-CoV-2 intra-host emergence of novel haplotypes regardless of immune deficiencies (preprint)

On February 2020, the municipality of Vo, a small town near Padua (Italy), was quarantined due to the first coronavirus disease 19 (COVID-19)-related death detected in Italy. The entire population was swab tested in two sequential surveys. Here we report the analysis of the viral genomes, which revealed that the unique ancestor haplotype introduced in Vo belongs to lineage B and, more specifically, to the subtype found at the end of January 2020 in two Chinese tourists visiting Rome and other Italian cities, carrying mutations G11083T and G26144T. The sequences, obtained for 87 samples, allowed us to investigate viral evolution while being transmitted within and across households and the effectiveness of the non-pharmaceutical interventions implemented in Vo. We report, for the first time, evidence that novel viral haplotypes can naturally arise intra-host within an interval as short as two weeks, in approximately 30% of the infected individuals, regardless of symptoms severity or immune system deficiencies. Moreover, both phylogenetic and minimum spanning network analyses converge on the hypothesis that the viral sequences evolved from a unique common ancestor haplotype, carried by an index case. The lockdown extinguished both viral spread and the emergence of new variants, confirming the efficiency of this containment strategy. The information gathered from household was used to reconstructs possible transmission events.

Refining reproduction number estimates to account for unobserved generations of infection in emerging epidemics (preprint)

BackgroundEstimating the transmissibility of infectious diseases is key to inform situational awareness and for response planning. Several methods tend to overestimate the basic (R0) and effective (Rt) reproduction numbers during the initial phases of an epidemic. The reasons driving the observed bias are unknown. In this work we explore the impact of incomplete observations and underreporting of the first generations of infections during the initial epidemic phase. MethodsWe propose a debiasing procedure which utilises a linear exponential growth model to infer unobserved initial generations of infections and apply it to EpiEstim. We assess the performance of our adjustment using simulated data, considering different levels of transmissibility and reporting rates. We also apply the proposed correction to SARS-CoV-2 incidence data reported in Italy, Sweden, the United Kingdom and the United States of America. ResultsIn all simulation scenarios, our adjustment outperforms the original EpiEstim method. The proposed correction reduces the systematic bias and the quantification of uncertainty is more precise, as better coverage of the true R0 values is achieved with tighter credible intervals. When applied to real world data, the proposed adjustment produces basic reproduction number estimates which closely match the estimates obtained in other studies while making use of a minimal amount of data. ConclusionsThe proposed adjustment refines the reproduction number estimates obtained with the current EpiEstim implementation by producing improved, more precise estimates earlier than with the original method. This has relevant public health implications. SummaryWe propose a back-imputation procedure tackling the issue of unobserved initial generations of infections to reduce the bias observed in the early R0 and Rt estimates and apply it to EpiEstim using simulated and reported COVID-19 data to evaluate it.

Estimating the Protectiveness of Face Mask on Seasonal Influenza Transmissibility During COVID-19 Early Spread (preprint)

Background: After COVID-19 early expansion occurred in mainland China, seasonal influenza transmission in Hong Kong, which was growing initially, immediately plateaued, and then abruptly declined a few weeks later. These patterns correspond to the three phases of early COVID-19 spread from Wuhan to Hong Kong, i.e. the ordinary: before the occurrence in Wuhan; the awareness: after the evidence of human-to-human transmission was revealed; and the spreading: after the first local case was confirmed in Hong Kong. The available surveyed data on changes in precautionary behavior during these phases, i.e. face mask wearing and avoiding the crowd, provide an opportunity to estimate the protectiveness of face mask on influenza transmissibility. Methods: We developed a time-series susceptible-infected-recovered (TS-SIR) regression model to estimate the time-varying effective reproduction number Rt based on the weekly reported influenza cases. The reporting rate of influenza was adjusted under the assumption that patients with severe influenza were seeking medical care. After separating the effect from herd immunity, the percent reduction in Rt from each behavior was calculated as an indication of the protectiveness. Findings: The average Rt of winter influenza season in 2019/20 was estimated in the three phases: 1.29 (95%CI, 1.27 to 1.32) in the ordinary, 1.00 (95%CI, 0.99 to 1.00) in the awareness, and 0.73 (95%CI, 0.73 to 0.74) in the spreading. Our results showed that face mask wearing protected 22% from being transmitted, which was nearly half of the effect of avoiding the crowd (42%). If more than 79% of the people adopted both precautionary behaviors, the initial Rt reduced to less than one. Interpretation: The results suggested that mandatory face mask wearing along with social distancing practices could be effective in suppressing the transmission of influenza, which may also give hints on preventing COVID-19 infection.Funding Information: We declare no competing interests.Declaration of Interests: We acknowledge the support from grants funded by Health and Medical Research Fund [COVID190329415], City University of Hong Kong [7200573], andWellcome Trust and The Royal Society [213494/Z/18/3Z95].

Environmental drivers of SARS-CoV-2 lineage B.1.1.7 transmission in England, October to December 2020 (preprint)

Previous work has shown that environment affects SARS-CoV-2 transmission, but it is unclear whether emerging strains show similar responses. Here we show that lineage B.1.1.7 spread with greater transmission in colder and more densely populated parts of England. We also find evidence of B.1.1.7's transmission advantage at warmer temperatures versus other strains, implying that spring conditions may facilitate B.1.1.7's invasion in Europe and across the Northern hemisphere, undermining the effectiveness of public health interventions.

Environment influences SARS-CoV-2 transmission in the absence of non-pharmaceutical interventions (preprint)

As COVID-19 continues to spread across the world, it is increasingly important to understand the factors that influence its transmission. Seasonal variation driven by responses to changing environment has been shown to affect the transmission intensity of several coronaviruses. However, the impact of the environment on SARS-CoV-2 remains largely unknown, and thus seasonal variation remains a source of uncertainty in forecasts of SARS-CoV-2 transmission. Here we address this issue by assessing the association of temperature, humidity, UV radiation, and population density with estimates of transmission rate (R). Using data from the United States of America, we explore correlates of transmission across USA states using comparative regression and integrative epidemiological modelling. We find that policy intervention (`lockdown') and reductions in individuals' mobility are the major predictors of SARS-CoV-2 transmission rates, but in their absence lower temperatures and higher population densities are correlated with increased SARS-CoV-2 transmission. Our results show that summer weather cannot be considered a substitute for mitigation policies, but that lower autumn and winter temperatures may lead to an increase in transmission intensity in the absence of policy interventions or behavioural changes. We outline how this information may improve the forecasting of SARS-CoV-2, its future seasonal dynamics, and inform intervention policies.

State-level tracking of COVID-19 in the United States (preprint)

As of 1st June 2020, the US Centers for Disease Control and Prevention reported 104,232 confirmed or probable COVID-19-related deaths in the US. This was more than twice the number of deaths reported in the next most severely impacted country. We jointly modelled the US epidemic at the state-level, using publicly available death data within a Bayesian hierarchical semi-mechanistic framework. For each state, we estimate the number of individuals that have been infected, the number of individuals that are currently infectious and the time-varying reproduction number (the average number of secondary infections caused by an infected person). We used changes in mobility to capture the impact that non-pharmaceutical interventions and other behaviour changes have on the rate of transmission of SARS-CoV-2. Nationally, we estimated 3.7% [3.4%-4.0%] of the population had been infected by 1st June 2020, with wide variation between states, and approximately 0.01% of the population was infectious. We also demonstrated that good model forecasts of deaths for the next 3 weeks with low error and good coverage of our credible intervals.