Fine scale human mobility changes in 26 US cities in 2020 in response to the COVID-19 pandemic were associated with distance and income

Human mobility patterns changed greatly due to the COVID-19 pandemic. Despite many analyses investigating general mobility trends, there has been less work characterising changes in mobility on a fine spatial scale and developing frameworks to model these changes. We analyse zip code-level mobility data from 26 US cities between February 2 - August 31, 2020. We use Bayesian models to characterise the initial decrease in mobility and mobility patterns between June - August at this fine spatial scale. There were similar temporal trends across cities but large variations in the magnitude of mobility reductions. Long-distance routes and higher-income subscribers, but not age, were associated with greater mobility reductions. At the city level, mobility rates around early April, when mobility was lowest, and over summer showed little association with non-pharmaceutical interventions or case rates. Changes in mobility patterns lasted until the end of the study period, despite overall numbers of trips recovering to near baseline levels in many cities.

Accounting for assay performance when estimating the temporal dynamics in SARS-CoV-2 seroprevalence in the U.S.

Estimating the incidence of SARS-CoV-2 infection is central to understanding the state of the pandemic. Seroprevalence studies are often used to assess cumulative infections as they can identify asymptomatic infection. Since July 2020, commercial laboratories have conducted nationwide serosurveys for the U.S. CDC. They employed three assays, with different sensitivities and specificities, potentially introducing biases in seroprevalence estimates. Using mechanistic models, we show that accounting for assays explains some of the observed state-to-state variation in seroprevalence, and when integrating case and death surveillance data, we show that when using the Abbott assay, estimates of proportions infected can differ substantially from seroprevalence estimates. We also found that states with higher proportions infected (before or after vaccination) had lower vaccination coverages, a pattern corroborated using a separate dataset. Finally, to understand vaccination rates relative to the increase in cases, we estimated the proportions of the population that received a vaccine prior to infection.

Effect of specific non-pharmaceutical intervention policies on SARS-CoV-2 transmission in the counties of the United States

Non-pharmaceutical interventions (NPIs) remain the only widely available tool for controlling the ongoing SARS-CoV-2 pandemic. We estimated weekly values of the effective basic reproductive number (Reff) using a mechanistic metapopulation model and associated these with county-level characteristics and NPIs in the United States (US). Interventions that included school and leisure activities closure and nursing home visiting bans were all associated with an Reff below 1 when combined with either stay at home orders (median Reff 0.97, 95% confidence interval (CI) 0.58-1.39)* or face masks (median Reff 0.97, 95% CI 0.58-1.39)*. While direct causal effects of interventions remain unclear, our results suggest that relaxation of some NPIs will need to be counterbalanced by continuation and/or implementation of others.

The usefulness of SARS-CoV-2 test positive proportion as a surveillance tool

Comparison of COVID-19 case numbers over time and between locations is complicated by limits to virologic testing confirm SARS-CoV-2 infection, leading to under-reporting of incidence, and by variations in testing capacity between locations and over time. The proportion of tested individuals who have tested positive (test positive proportion, TPP) can potentially be used to qualitatively assess the testing capacity of a location; a high TPP could provide evidence that too few people are tested, leading to more under-reporting. In this study we propose a simple model for testing in a population experiencing an epidemic of COVID-19, and derive an expression for TPP in terms of well-defined parameters in the model, related to testing and presence of other pathogens causing COVID-19 like symptoms. We use simulations to show situations in which the TPP is higher or lower than we expect based on these parameters, and the effect of testing strategies on the TPP. In our simulations, we find in the absence of dramatic shifts of testing practices in time or between spatial locations, the TPP is positively correlated with the incidence of infection. As a corollary, the TPP can be used to distinguish between a decline in confirmed cases due to decline in incidence (in which case TPP should decline) and a decline in confirmed cases due to testing constraints (in which case TPP should remain constant). We show that the proportion of tested individuals who present COVID-19 like symptoms (test symptomatic proportion, TSP) encodes similar information to the TPP but has different relationships with the testing parameters, and can thus provide additional information regarding dynamic changes in TPP and incidence. Finally, we compare data on confirmed cases and TPP from US states. We conjecture why states may have higher or lower TPP than average. We suggest that collection of symptom status and age/risk category of tested individuals can aid interpretation of changes in TPP and increase the utility of TPP in assessing the state of the pandemic in different locations and times. SummaryO_LIKey question: when can we use the proportion of tests that are positive (test positive proportion, TPP) as an indicator of the burden of infection in a state? C_LIO_LIIf testing strategies are broadly similar between locations and over time, the TPP is positively correlated with incidence rates. C_LIO_LIHowever, changes in testing practices over time and between locations can affect the TPP independently of the number of cases. C_LIO_LIMore testing of asymptomatic individuals, e.g. through population-level testing, lowers the TPP. C_LIO_LIWe can identify locations that have a lower or higher TPP than expected, given how many cases they are reporting. C_LIO_LIEfficient transmission increases detected cases exponentially, resulting in large changes in confirmed cases compared to factors that change linearly. C_LIO_LIData that could aid interpretability of the TPP include: age of individuals who test positive and negative, and other data on testing performed in high-prevalence settings; and symptom status of tested individuals. C_LI

Comparing library preparation methods for SARS-CoV-2 multiplex amplicon sequencing on the Illumina MiSeq platform

Genomic surveillance has a key role in tracking the ongoing COVID-19 pandemic, but information on how different sequencing library preparation approaches affect the data produced are lacking. We compared three library preparation methods using both tagmentation (Nextera XT and Nextera Flex) and ligation-based (KAPA HyperPrep) approaches on both positive and negative samples to provide insights into any methodological differences between the methods, and validate their use in SARS-CoV-2 amplicon sequencing. We show that all three library preparation methods allow us to recover near-complete SARS-CoV-2 genomes with identical SNP calls. The Nextera Flex and KAPA library preparation methods gave better coverage than libraries prepared with Nextera XT, which required more reads to call the same number of genomic positions. The KAPA ligation-based approach shows the lowest levels of human contamination, but contaminating reads had no effect on the downstream analysis. We found some examples of library preparation-specific differences in minority variant calling. Overall our data shows that the choice of Illumina library preparation method has minimal effects on consensus base calling and downstream phylogenetic analysis, and suggests that all methods would be suitable for use if specific reagents are difficult to obtain.

A systematic review of antibody mediated immunity to coronaviruses: antibody kinetics, correlates of protection, and association of antibody responses with severity of disease

The duration and nature of immunity generated in response to SARS-CoV-2 infection is unknown. Many public health responses and modeled scenarios for COVID-19 outbreaks caused by SARS-CoV-2 assume that infection results in an immune response that protects individuals from future infections or illness for some amount of time. The timescale of protection is a critical determinant of the future impact of the pathogen. The presence or absence of protective immunity due to infection or vaccination (when available) will affect future transmission and illness severity. The dynamics of immunity and nature of protection are relevant to discussions surrounding therapeutic use of convalescent sera as well as efforts to identify individuals with protective immunity. Here, we review the scientific literature on antibody immunity to coronaviruses, including SARS-CoV-2 as well as the related SARS-CoV-1, MERS-CoV and human endemic coronaviruses (HCoVs). We reviewed 1281 abstracts and identified 322 manuscripts relevant to 5 areas of focus: 1) antibody kinetics, 2) correlates of protection, 3) immunopathogenesis, 4) antigenic diversity and cross-reactivity, and 5) population seroprevalence. While studies of SARS-CoV-2 are necessary to determine immune responses to it, evidence from other coronaviruses can provide clues and guide future research. Key QuestionsO_TEXTBOXKey Questions for SARS-CoV-2 O_LIWhat are the kinetics of immune responses to infection? C_LIO_LIDo people who have more severe disease mount stronger antibody responses after infection? C_LIO_LIHow do antibody responses vary between different types of antibodies or as measured by different assays? C_LIO_LIHow does the presence of antibodies impact the clinical course and severity of the disease? C_LIO_LIIs there cross-reactivity with different coronaviruses? C_LIO_LIDoes cross-reactivity lead to cross-protection? C_LIO_LIWill infection protect you from future infection? C_LIO_LIHow long will immunity last? C_LIO_LIWhat are correlates of protection? C_LI C_TEXTBOX