Immunogenicity and safety of fractional doses of 17D-213 yellow fever vaccine in children (YEFE): a randomised, double-blind, non-inferiority substudy of a phase 4 trial.

BACKGROUND: Current supply shortages constrain yellow fever vaccination activities, particularly outbreak response. Although fractional doses of all WHO-prequalified yellow fever vaccines have been shown to be safe and immunogenic in a randomised controlled trial in adults, they have not been evaluated in a randomised controlled trial in young children (9-59 months old). We aimed to assess the immunogenicity and safety of fractional doses compared with standard doses of the WHO-prequalified 17D-213 vaccine in young children. METHODS: This substudy of the YEFE phase 4 study was conducted at the Epicentre Mbarara Research Centre (Mbarara, Uganda). Eligible children were aged 9-59 months without contraindications for vaccination, without history of previous yellow fever vaccination or infection and not requiring yellow fever vaccination for travelling. Participants were randomly assigned, using block randomisation, 1:1 to standard or fractional (one-fifth) dose of yellow fever vaccine. Investigators, participants, and laboratory personnel were blinded to group allocation. Participants were followed for immunogenicity and safety at 10 days, 28 days, and 1 year after vaccination. The primary outcome was non-inferiority in seroconversion (-10 percentage point margin) 28 days after vaccination measured by 50% plaque reduction neutralisation test (PRNT50) in the per-protocol population. Safety and seroconversion at 10 days and 12-16 months after vaccination (given COVID-19 resctrictions) were secondary outcomes. This study is registered with ClinicalTrials.gov, NCT02991495. FINDINGS: Between Feb 20, 2019, and Sept 9, 2019, 433 children were assessed, and 420 were randomly assigned to fractional dose (n=210) and to standard dose (n=210) 17D-213 vaccination. 28 days after vaccination, 202 (97%, 95% CI 95-99) of 207 participants in the fractional dose group and 191 (100%, 98-100) of 191 in the standard dose group seroconverted. The absolute difference in seroconversion between the study groups in the per-protocol population was -2 percentage points (95% CI -5 to 1). 154 (73%) of 210 participants in the fractional dose group and 168 (80%) of 210 in the standard dose group reported at least one adverse event 28 days after vaccination. At 10 days follow-up, seroconversion was lower in the fractional dose group than in the standard dose group. The most common adverse events were upper respiratory tract infections (n=221 [53%]), diarrhoea (n=68 [16%]), rhinorrhoea (n=49 [12%]), and conjunctivitis (n=28 [7%]). No difference was observed in incidence of adverse events and serious adverse events between study groups. CONCLUSIONS: Fractional doses of the 17D-213 vaccine were non-inferior to standard doses in inducing seroconversion 28 days after vaccination in children aged 9-59 months when assessed with PRNT50, but we found fewer children seroconverted at 10 days. The results support consideration of the use of fractional dose of yellow fever vaccines in WHO recommendations for outbreak response in the event of a yellow fever vaccine shortage to include children. FUNDING: Médecins Sans Frontières Foundation.

Quantifying Uncertainty in Mechanistic Models of Infectious Disease.

This primer describes the statistical uncertainty in mechanistic models and provides R code to quantify it. We begin with an overview of mechanistic models for infectious disease, and then describe the sources of statistical uncertainty in the context of a case study on severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). We describe the statistical uncertainty as belonging to 3 categories: data uncertainty, stochastic uncertainty, and structural uncertainty. We demonstrate how to account for each of these via statistical uncertainty measures and sensitivity analyses broadly, as well as in a specific case study on estimating the basic reproductive number, ${R}_0$, for SARS-CoV-2.

In-person schooling and associated COVID-19 risk in the United States over spring semester 2021.

Because of the importance of schools to childhood development, the relationship between in-person schooling and COVID-19 risk has been one of the most important questions of this pandemic. Previous work in the United States during winter 2020-2021 showed that in-person schooling carried some risk for household members and that mitigation measures reduced this risk. Schooling and the COVID-19 landscape changed radically over spring semester 2021. Here, we use data from a massive online survey to characterize changes in in-person schooling behavior and associated risks over that period. We find increases in in-person schooling and reductions in mitigations over time. In-person schooling is associated with increased reporting of COVID-19 outcomes even among vaccinated individuals (although the absolute risk among the vaccinated is greatly reduced). Vaccinated teachers working outside the home were less likely to report COVID-19-related outcomes than unvaccinated teachers working exclusively from home. Adequate mitigation measures appear to eliminate the excess risk associated with in-person schooling.

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 a median 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.

Modeling of Future COVID-19 Cases, Hospitalizations, and Deaths, by Vaccination Rates and Nonpharmaceutical Intervention Scenarios - United States, April-September 2021.

After a period of rapidly declining U.S. COVID-19 incidence during January-March 2021, increases occurred in several jurisdictions (1,2) despite the rapid rollout of a large-scale vaccination program. This increase coincided with the spread of more transmissible variants of SARS-CoV-2, the virus that causes COVID-19, including B.1.1.7 (1,3) and relaxation of COVID-19 prevention strategies such as those for businesses, large-scale gatherings, and educational activities. To provide long-term projections of potential trends in COVID-19 cases, hospitalizations, and deaths, COVID-19 Scenario Modeling Hub teams used a multiple-model approach comprising six models to assess the potential course of COVID-19 in the United States across four scenarios with different vaccination coverage rates and effectiveness estimates and strength and implementation of nonpharmaceutical interventions (NPIs) (public health policies, such as physical distancing and masking) over a 6-month period (April-September 2021) using data available through March 27, 2021 (4). Among the four scenarios, an accelerated decline in NPI adherence (which encapsulates NPI mandates and population behavior) was shown to undermine vaccination-related gains over the subsequent 2-3 months and, in combination with increased transmissibility of new variants, could lead to surges in cases, hospitalizations, and deaths. A sharp decline in cases was projected by July 2021, with a faster decline in the high-vaccination scenarios. High vaccination rates and compliance with public health prevention measures are essential to control the COVID-19 pandemic and to prevent surges in hospitalizations and deaths in the coming months.

Maximizing and evaluating the impact of test-trace-isolate programs: A modeling study.

BACKGROUND: Test-trace-isolate programs are an essential part of coronavirus disease 2019 (COVID-19) control that offer a more targeted approach than many other nonpharmaceutical interventions. Effective use of such programs requires methods to estimate their current and anticipated impact. METHODS AND FINDINGS: We present a mathematical modeling framework to evaluate the expected reductions in the reproductive number, R, from test-trace-isolate programs. This framework is implemented in a publicly available R package and an online application. We evaluated the effects of completeness in case detection and contact tracing and speed of isolation and quarantine using parameters consistent with COVID-19 transmission (R0: 2.5, generation time: 6.5 days). We show that R is most sensitive to changes in the proportion of cases detected in almost all scenarios, and other metrics have a reduced impact when case detection levels are low (<30%). Although test-trace-isolate programs can contribute substantially to reducing R, exceptional performance across all metrics is needed to bring R below one through test-trace-isolate alone, highlighting the need for comprehensive control strategies. Results from this model also indicate that metrics used to evaluate performance of test-trace-isolate, such as the proportion of identified infections among traced contacts, may be misleading. While estimates of the impact of test-trace-isolate are sensitive to assumptions about COVID-19 natural history and adherence to isolation and quarantine, our qualitative findings are robust across numerous sensitivity analyses. CONCLUSIONS: Effective test-trace-isolate programs first need to be strong in the "test" component, as case detection underlies all other program activities. Even moderately effective test-trace-isolate programs are an important tool for controlling the COVID-19 pandemic and can alleviate the need for more restrictive social distancing measures.

Coordinated Strategy for a Model-Based Decision Support Tool for Coronavirus Disease, Utah, USA.

The coronavirus disease pandemic has highlighted the key role epidemiologic models play in supporting public health decision-making. In particular, these models provide estimates of outbreak potential when data are scarce and decision-making is critical and urgent. We document the integrated modeling response used in the US state of Utah early in the coronavirus disease pandemic, which brought together a diverse set of technical experts and public health and healthcare officials and led to an evidence-based response to the pandemic. We describe how we adapted a standard epidemiologic model; harmonized the outputs across modeling groups; and maintained a constant dialogue with policymakers at multiple levels of government to produce timely, evidence-based, and coordinated public health recommendations and interventions during the first wave of the pandemic. This framework continues to support the state's response to ongoing outbreaks and can be applied in other settings to address unique public health challenges.

A scenario modeling pipeline for COVID-19 emergency planning.

Coronavirus disease 2019 (COVID-19) has caused strain on health systems worldwide due to its high mortality rate and the large portion of cases requiring critical care and mechanical ventilation. During these uncertain times, public health decision makers, from city health departments to federal agencies, sought the use of epidemiological models for decision support in allocating resources, developing non-pharmaceutical interventions, and characterizing the dynamics of COVID-19 in their jurisdictions. In response, we developed a flexible scenario modeling pipeline that could quickly tailor models for decision makers seeking to compare projections of epidemic trajectories and healthcare impacts from multiple intervention scenarios in different locations. Here, we present the components and configurable features of the COVID Scenario Pipeline, with a vignette detailing its current use. We also present model limitations and active areas of development to meet ever-changing decision maker needs.

Development and dissemination of infectious disease dynamic transmission models during the COVID-19 pandemic: what can we learn from other pathogens and how can we move forward?

The current COVID-19 pandemic has resulted in the unprecedented development and integration of infectious disease dynamic transmission models into policy making and public health practice. Models offer a systematic way to investigate transmission dynamics and produce short-term and long-term predictions that explicitly integrate assumptions about biological, behavioural, and epidemiological processes that affect disease transmission, burden, and surveillance. Models have been valuable tools during the COVID-19 pandemic and other infectious disease outbreaks, able to generate possible trajectories of disease burden, evaluate the effectiveness of intervention strategies, and estimate key transmission variables. Particularly given the rapid pace of model development, evaluation, and integration with decision making in emergency situations, it is necessary to understand the benefits and pitfalls of transmission models. We review and highlight key aspects of the history of infectious disease dynamic models, the role of rigorous testing and evaluation, the integration with data, and the successful application of models to guide public health. Rather than being an expansive history of infectious disease models, this Review focuses on how the integration of modelling can continue to be advanced through policy and practice in appropriate and conscientious ways to support the current pandemic response.

The use of mobile phone data to inform analysis of COVID-19 pandemic epidemiology.

The ongoing coronavirus disease 2019 (COVID-19) pandemic has heightened discussion of the use of mobile phone data in outbreak response. Mobile phone data have been proposed to monitor effectiveness of non-pharmaceutical interventions, to assess potential drivers of spatiotemporal spread, and to support contact tracing efforts. While these data may be an important part of COVID-19 response, their use must be considered alongside a careful understanding of the behaviors and populations they capture. Here, we review the different applications for mobile phone data in guiding and evaluating COVID-19 response, the relevance of these applications for infectious disease transmission and control, and potential sources and implications of selection bias in mobile phone data. We also discuss best practices and potential pitfalls for directly integrating the collection, analysis, and interpretation of these data into public health decision making.

The Incubation Period of Coronavirus Disease 2019 (COVID-19) From Publicly Reported Confirmed Cases: Estimation and Application.

BACKGROUND: A novel human coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), was identified in China in December 2019. There is limited support for many of its key epidemiologic features, including the incubation period for clinical disease (coronavirus disease 2019 [COVID-19]), which has important implications for surveillance and control activities. OBJECTIVE: To estimate the length of the incubation period of COVID-19 and describe its public health implications. DESIGN: Pooled analysis of confirmed COVID-19 cases reported between 4 January 2020 and 24 February 2020. SETTING: News reports and press releases from 50 provinces, regions, and countries outside Wuhan, Hubei province, China. PARTICIPANTS: Persons with confirmed SARS-CoV-2 infection outside Hubei province, China. MEASUREMENTS: Patient demographic characteristics and dates and times of possible exposure, symptom onset, fever onset, and hospitalization. RESULTS: There were 181 confirmed cases with identifiable exposure and symptom onset windows to estimate the incubation period of COVID-19. The median incubation period was estimated to be 5.1 days (95% CI, 4.5 to 5.8 days), and 97.5% of those who develop symptoms will do so within 11.5 days (CI, 8.2 to 15.6 days) of infection. These estimates imply that, under conservative assumptions, 101 out of every 10 000 cases (99th percentile, 482) will develop symptoms after 14 days of active monitoring or quarantine. LIMITATION: Publicly reported cases may overrepresent severe cases, the incubation period for which may differ from that of mild cases. CONCLUSION: This work provides additional evidence for a median incubation period for COVID-19 of approximately 5 days, similar to SARS. Our results support current proposals for the length of quarantine or active monitoring of persons potentially exposed to SARS-CoV-2, although longer monitoring periods might be justified in extreme cases. PRIMARY FUNDING SOURCE: U.S. Centers for Disease Control and Prevention, National Institute of Allergy and Infectious Diseases, National Institute of General Medical Sciences, and Alexander von Humboldt Foundation.