Vaccines and variants: modelling insights into emerging issues in COVID-19 epidemiology

Mathematical modelling has played a pivotal role in understanding the epidemiology of and guiding public health responses to the ongoing coronavirus disease of 2019 (COVID-19) pandemic. Here, we review the role of epidemiological models in understanding evolving epidemic characteristics, including the effects of vaccination and Variants of Concern (VoC). We highlight ways in which models continue to provide important insights, including (1) calculating the herd immunity threshold and evaluating its limitations; (2) verifying that nascent vaccines can prevent severe disease, infection, and transmission but may be less efficacious against VoC; (3) determining optimal vaccine allocation stratgies under efficacy and supply constraints; and (4) determining that VoC are more transmissible and lethal than previously circulating strains, and that immune escape may jeopardize vaccine-induced herd immunity. Finally, we explore how models can help us anticipate and prepare for future stages of COVID-19 epidemiology (and that of other diseases) through forecasts and scenario projections, given current uncertainties and data limitations

The REMAP-CAP (Randomized Embedded Multifactorial Adaptive Platform for Community-acquired Pneumonia) Study. Rationale and Design.

There is broad interest in improved methods to generate robust evidence regarding best practice, especially in settings where patient conditions are heterogenous and require multiple concomitant therapies. Here, we present the rationale and design of a large, international trial that combines features of adaptive platform trials with pragmatic point-of-care trials to determine best treatment strategies for patients admitted to an intensive care unit with severe community-acquired pneumonia. The trial uses a novel design, entitled "a randomized embedded multifactorial adaptive platform." The design has five key features: 1) randomization, allowing robust causal inference; 2) embedding of study procedures into routine care processes, facilitating enrollment, trial efficiency, and generalizability; 3) a multifactorial statistical model comparing multiple interventions across multiple patient subgroups; 4) response-adaptive randomization with preferential assignment to those interventions that appear most favorable; and 5) a platform structured to permit continuous, potentially perpetual enrollment beyond the evaluation of the initial treatments. The trial randomizes patients to multiple interventions within four treatment domains: antibiotics, antiviral therapy for influenza, host immunomodulation with extended macrolide therapy, and alternative corticosteroid regimens, representing 240 treatment regimens. The trial generates estimates of superiority, inferiority, and equivalence between regimens on the primary outcome of 90-day mortality, stratified by presence or absence of concomitant shock and proven or suspected influenza infection. The trial will also compare ventilatory and oxygenation strategies, and has capacity to address additional questions rapidly during pandemic respiratory infections. As of January 2020, REMAP-CAP (Randomized Embedded Multifactorial Adaptive Platform for Community-acquired Pneumonia) was approved and enrolling patients in 52 intensive care units in 13 countries on 3 continents. In February, it transitioned into pandemic mode with several design adaptations for coronavirus disease 2019. Lessons learned from the design and conduct of this trial should aid in dissemination of similar platform initiatives in other disease areas.Clinical trial registered with www.clinicaltrials.gov (NCT02735707)

The REMAP-CAP (Randomized Embedded Multifactorial Adaptive Platform for Community-acquired Pneumonia) Study. Rationale and Design.

There is broad interest in improved methods to generate robust evidence regarding best practice, especially in settings where patient conditions are heterogenous and require multiple concomitant therapies. Here, we present the rationale and design of a large, international trial that combines features of adaptive platform trials with pragmatic point-of-care trials to determine best treatment strategies for patients admitted to an intensive care unit with severe community-acquired pneumonia. The trial uses a novel design, entitled "a randomized embedded multifactorial adaptive platform." The design has five key features: 1) randomization, allowing robust causal inference; 2) embedding of study procedures into routine care processes, facilitating enrollment, trial efficiency, and generalizability; 3) a multifactorial statistical model comparing multiple interventions across multiple patient subgroups; 4) response-adaptive randomization with preferential assignment to those interventions that appear most favorable; and 5) a platform structured to permit continuous, potentially perpetual enrollment beyond the evaluation of the initial treatments. The trial randomizes patients to multiple interventions within four treatment domains: antibiotics, antiviral therapy for influenza, host immunomodulation with extended macrolide therapy, and alternative corticosteroid regimens, representing 240 treatment regimens. The trial generates estimates of superiority, inferiority, and equivalence between regimens on the primary outcome of 90-day mortality, stratified by presence or absence of concomitant shock and proven or suspected influenza infection. The trial will also compare ventilatory and oxygenation strategies, and has capacity to address additional questions rapidly during pandemic respiratory infections. As of January 2020, REMAP-CAP (Randomized Embedded Multifactorial Adaptive Platform for Community-acquired Pneumonia) was approved and enrolling patients in 52 intensive care units in 13 countries on 3 continents. In February, it transitioned into pandemic mode with several design adaptations for coronavirus disease 2019. Lessons learned from the design and conduct of this trial should aid in dissemination of similar platform initiatives in other disease areas.Clinical trial registered with www.clinicaltrials.gov (NCT02735707)

The remap-cap (Randomized embedded multifactorial adaptive platform for community-acquired pneumonia) Study rationale and design

There is broad interest in improved methods to generate robust evidence regarding best practice, especially in settings where patient conditions are heterogenous and require multiple concomitant therapies. Here, we present the rationale and design of a large, international trial that combines features of adaptive platform trials with pragmatic point-of-care trials to determine best treatment strategies for patients admitted to an intensive care unit with severe community-acquired pneumonia. The trial uses a novel design, entitled “a randomized embedded multifactorial adaptive platform.” The design has five key features: 1) randomization, allowing robust causal inference; 2) embedding of study procedures into routine care processes, facilitating enrollment, trial efficiency, and generalizability; 3) a multifactorial statistical model comparing multiple interventions across multiple patient subgroups; 4) response-adaptive randomization with preferential assignment to those interventions that appear most favorable; and 5) a platform structured to permit continuous, potentially perpetual enrollment beyond the evaluation of the initial treatments. The trial randomizes patients to multiple interventions within four treatment domains: antibiotics, antiviral therapy for influenza, host immunomodulation with extended macrolide therapy, and alternative corticosteroid regimens, representing 240 treatment regimens. The trial generates estimates of superiority, inferiority, and equivalence between regimens on the primary outcome of 90-day mortality, stratified by presence or absence of concomitant shock and proven or suspected influenza infection. The trial will also compare ventilatory and oxygenation strategies, and has capacity to address additional questions rapidly during pandemic respiratory infections. As of January 2020, REMAP-CAP (Randomized Embedded Multifactorial Adaptive Platform for Community-acquired Pneumonia) was approved and enrolling patients in 52 intensive care units in 13 countries on 3 continents. In February, it transitioned into pandemic mode with several design adaptations for coronavirus disease 2019. Lessons learned from the design and conduct of this trial should aid in dissemination of similar platform initiatives in other disease areas

Using research to prepare for outbreaks of severe acute respiratory infection

Abstract Severe acute respiratory infections (SARI) remain one of the leading causes of mortality around the world in all age groups. There is large global variation in epidemiology, clinical management and outcomes, including mortality. We performed a short period observational data collection in critical care units distributed globally during regional peak SARI seasons from 1 January 2016 until 31 August 2017, using standardised data collection tools. Data were collected for 1 week on all admitted patients who met the inclusion criteria for SARI, with follow-up to hospital discharge. Proportions of patients across regions were compared for microbiology, management strategies and outcomes. Regions were divided geographically and economically according to World Bank definitions. Data were collected for 682 patients from 95 hospitals and 23 countries. The overall mortality was 9.5%. Of the patients, 21.7% were children, with case fatality proportions of 1% for those less than 5 years. The highest mortality was in those above 60 years, at 18.6%. Case fatality varied by region: East Asia and Pacific 10.2% (21 of 206), Sub-Saharan Africa 4.3% (8 of 188), South Asia 0% (0 of 35), North America 13.6% (25 of 184), and Europe and Central Asia 14.3% (9 of 63). Mortality in low-income and low-middle-income countries combined was 4% as compared with 14% in high-income countries. Organ dysfunction scores calculated on presentation in 560 patients where full data were available revealed Sequential Organ Failure Assessment (SOFA) scores on presentation were significantly associated with mortality and hospital length of stay. Patients in East Asia and Pacific (48%) and North America (24%) had the highest SOFA scores of >12. Multivariable analysis demonstrated that initial SOFA score and age were independent predictors of hospital survival. There was variability across regions and income groupings for the critical care management and outcomes of SARI. Intensive care unit-specific factors, geography and management features were less reliable than baseline severity for predicting ultimate outcome. These findings may help in planning future outbreak severity assessments, but more globally representative data are required

Coronal Heating as Determined by the Solar Flare Frequency Distribution Obtained by Aggregating Case Studies

Flare frequency distributions represent a key approach to addressing one of the largest problems in solar and stellar physics: determining the mechanism that counter-intuitively heats coronae to temperatures that are orders of magnitude hotter than the corresponding photospheres. It is widely accepted that the magnetic field is responsible for the heating, but there are two competing mechanisms that could explain it: nanoflares or Alfv\'en waves. To date, neither can be directly observed. Nanoflares are, by definition, extremely small, but their aggregate energy release could represent a substantial heating mechanism, presuming they are sufficiently abundant. One way to test this presumption is via the flare frequency distribution, which describes how often flares of various energies occur. If the slope of the power law fitting the flare frequency distribution is above a critical threshold, $\alpha=2$ as established in prior literature, then there should be a sufficient abundance of nanoflares to explain coronal heating. We performed $>$600 case studies of solar flares, made possible by an unprecedented number of data analysts via three semesters of an undergraduate physics laboratory course. This allowed us to include two crucial, but nontrivial, analysis methods: pre-flare baseline subtraction and computation of the flare energy, which requires determining flare start and stop times. We aggregated the results of these analyses into a statistical study to determine that $\alpha = 1.63 \pm 0.03$. This is below the critical threshold, suggesting that Alfv\'en waves are an important driver of coronal heating.Comment: 1,002 authors, 14 pages, 4 figures, 3 tables, published by The Astrophysical Journal on 2023-05-09, volume 948, page 7