Genetic evidence for the association between COVID-19 epidemic severity and timing of non-pharmaceutical interventions

Evolutionary and epidemic dynamics of COVID-19 in Germany exemplified by three Bayesian phylodynamic case studies

The importance of genomic surveillance strategies for pathogens has been particularly evident during the coronavirus disease 2019 (COVID-19) pandemic, as genomic data from the causative agent, severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2), have guided public health decisions worldwide. Bayesian phylodynamic inference, integrating epidemiology and evolutionary biology, has become an essential tool in genomic epidemiological surveillance. It enables the estimation of epidemiological parameters, such as the reproductive number, from pathogen sequence data alone. Despite the phylodynamic approach being widely adopted, the abundance of phylodynamic models often makes it challenging to select the appropriate model for specific research questions. This article illustrates the application of phylodynamic birth-death-sampling models in public health using genomic data, with a focus on SARS-CoV-2. Targeting researchers less familiar with phylodynamics, it introduces a comprehensive workflow, including the conceptualisation of a research study and detailed steps for data preprocessing and postprocessing. In addition, we demonstrate the versatility of birth-death-sampling models through three case studies from Germany, utilising the BEAST2 software and its model implementations. Each case study addresses a distinct research question relevant not only to SARS-CoV-2 but also to other pathogens: Case study 1 finds traces of a superspreading event at the start of an early outbreak, exemplifying how simple models for genomic data can provide information that would otherwise only be accessible through extensive contact tracing. Case study 2 compares transmission dynamics in a nosocomial outbreak to community transmission, highlighting distinct dynamics through integrative analysis. Case study 3 investigates whether local transmission patterns align with national trends, demonstrating how phylodynamic models can disentangle complex population substructure with little additional information. For each case study, we emphasise critical points where model assumptions and data properties may misalign and outline appropriate validation assessments. Overall, we aim to provide researchers with examples on using birth-death-sampling models in genomic epidemiology, balancing theoretical and practical aspects.

Patterns and drivers of excess mortality during the COVID-19 pandemic in 13 Western European countries

BACKGROUND

Important differences in excess mortality between European countries during the COVID-19 pandemic have been reported. Understanding the drivers of these differences is essential to pandemic preparedness.

METHODS

We examined patterns in age- and sex-standardized cumulative excess mortality in 13 Western European countries during the first 30 months of the COVID-19 pandemic and the correlation of country-level characteristics of interest with excess mortality.

RESULTS

In a timeline analysis, we identified notable differences in seeding events, particularly in early 2020 and when the Alpha variant emerged, likely contributing to notable differences in excess mortality between countries (lowest in Denmark during that period). These differences were more limited from July 2021 onwards. Lower excess mortality was associated with implementing stringent non-pharmaceutical interventions (NPIs) when hospital admissions were still low in 2020 (correlation coefficient rho = 0.65, p = 0.03) and rapid rollout of vaccines in the elderly in early 2021 (rho =  - 0.76, p = 0.002). Countries which implemented NPIs while hospital admissions were low tended to experience lower gross domestic product (GDP) losses in 2020 (rho =  - 0.55, p = 0.08). Structural factors, such as high trust in the national government (rho =  - 0.77, p = 0.002) and low ratio of population at risk of poverty (rho = 0.55, p = 0.05), were also associated with lower excess mortality.

CONCLUSIONS

These results suggest the benefit of early implementation of NPIs and swift rollout of vaccines to the most vulnerable. Further analyses are required at a more granular level to better understand how these factors impacted excess mortality and help guide pandemic preparedness plans.

Integrating Contact Tracing Data to Enhance Outbreak Phylodynamic Inference: A Deep Learning Approach

Phylodynamics is central to understanding infectious disease dynamics through the integration of genomic and epidemiological data. Despite advancements, including the application of deep learning to overcome computational limitations, significant challenges persist due to data inadequacies and statistical unidentifiability of key parameters. These issues are particularly pronounced in poorly resolved phylogenies, commonly observed in outbreaks such as SARS-CoV-2. In this study, we conducted a thorough evaluation of PhyloDeep, a deep learning inference tool for phylodynamics, assessing its performance on poorly resolved phylogenies. Our findings reveal the limited predictive accuracy of PhyloDeep (and other state-of-the-art approaches) in these scenarios. However, models trained on poorly resolved, realistically simulated trees demonstrate improved predictive power, despite not being infallible, especially in scenarios with superspreading dynamics, whose parameters are challenging to capture accurately. Notably, we observe markedly improved performance through the integration of minimal contact tracing data, which refines poorly resolved trees. Applying this approach to a sample of SARS-CoV-2 sequences partially matched to contact tracing from Hong Kong yields informative estimates of superspreading potential, extending beyond the scope of contact tracing data alone. Our findings demonstrate the potential for enhancing phylodynamic analysis through complementary data integration, ultimately increasing the precision of epidemiological predictions crucial for public health decision-making and outbreak control.

Lineage frequency time series reveal elevated levels of genetic drift in SARS-CoV-2 transmission in England

Genetic drift in infectious disease transmission results from randomness of transmission and host recovery or death. The strength of genetic drift for SARS-CoV-2 transmission is expected to be high due to high levels of superspreading, and this is expected to substantially impact disease epidemiology and evolution. However, we don't yet have an understanding of how genetic drift changes over time or across locations. Furthermore, noise that results from data collection can potentially confound estimates of genetic drift. To address this challenge, we develop and validate a method to jointly infer genetic drift and measurement noise from time-series lineage frequency data. Our method is highly scalable to increasingly large genomic datasets, which overcomes a limitation in commonly used phylogenetic methods. We apply this method to over 490,000 SARS-CoV-2 genomic sequences from England collected between March 2020 and December 2021 by the COVID-19 Genomics UK (COG-UK) consortium and separately infer the strength of genetic drift for pre-B.1.177, B.1.177, Alpha, and Delta. We find that even after correcting for measurement noise, the strength of genetic drift is consistently, throughout time, higher than that expected from the observed number of COVID-19 positive individuals in England by 1 to 3 orders of magnitude, which cannot be explained by literature values of superspreading. Our estimates of genetic drift suggest low and time-varying establishment probabilities for new mutations, inform the parametrization of SARS-CoV-2 evolutionary models, and motivate future studies of the potential mechanisms for increased stochasticity in this system.

Phase-wise evaluation and optimization of non-pharmaceutical interventions to contain the COVID-19 pandemic in the U.S

Given that the effectiveness of COVID-19 vaccines and other therapies is greatly limited by the continuously emerging variants, non-pharmaceutical interventions have been adopted as primary control strategies in the global fight against the COVID-19 pandemic. However, implementing strict interventions over extended periods of time is inevitably hurting the economy. Many countries are faced with the dilemma of how to take appropriate policy actions for socio-economic recovery while curbing the further spread of COVID-19. With an aim to solve this multi-objective decision-making problem, we investigate the underlying temporal dynamics and associations between policies, mobility patterns, and virus transmission through vector autoregressive models and the Toda-Yamamoto Granger causality test. Our findings reveal the presence of temporal lagged effects and Granger causality relationships among various transmission and human mobility variables. We further assess the effectiveness of existing COVID-19 control measures and explore potential optimal strategies that strike a balance between public health and socio-economic recovery for individual states in the U.S. by employing the Pareto optimality and genetic algorithms. The results highlight the joint power of the state of emergency declaration, wearing face masks, and the closure of bars, and emphasize the necessity of pursuing tailor-made strategies for different states and phases of epidemiological transmission. Our framework enables policymakers to create more refined designs of COVID-19 strategies and can be extended to other countries regarding best practices in pandemic response.

Genome Structure, Life Cycle, and Taxonomy of Coronaviruses and the Evolution of SARS-CoV-2

Coronaviruses have a broad host range and exhibit high zoonotic potential. In this chapter, we describe their genomic organization in terms of encoded proteins and provide an introduction to the peculiar discontinuous transcription mechanism. Further, we present evolutionary conserved genomic RNA secondary structure features, which are involved in the complex replication mechanism. With a focus on computational methods, we review the emergence of SARS-CoV-2 starting with the 2019 strains. In that context, we also discuss the debated hypothesis of whether SARS-CoV-2 was created in a laboratory. We focus on the molecular evolution and the epidemiological dynamics of this recently emerged pathogen and we explain how variants of concern are detected and characterised. COVID-19, the disease caused by SARS-CoV-2, can spread through different transmission routes and also depends on a number of risk factors. We describe how current computational models of viral epidemiology, or more specifically, phylodynamics, have facilitated and will continue to enable a better understanding of the epidemic dynamics of SARS-CoV-2.

Estimating SARS-CoV-2 variant fitness and the impact of interventions in England using statistical and geo-spatial agent-based models

The SARS-CoV-2 epidemic has been extended by the evolution of more transmissible viral variants. In autumn 2020, the B.1.177 lineage became the dominant variant in England, before being replaced by the B.1.1.7 (Alpha) lineage in late 2020, with the sweep occurring at different times in each region. This period coincided with a large number of non-pharmaceutical interventions (e.g. lockdowns) to control the epidemic, making it difficult to estimate the relative transmissibility of variants. In this paper, we model the spatial spread of these variants in England using a meta-population agent-based model which correctly characterizes the regional variation in cases and distribution of variants. As a test of robustness, we additionally estimated the relative transmissibility of multiple variants using a statistical model based on the renewal equation, which simultaneously estimates the effective reproduction number R. Relative to earlier variants, the transmissibility of B.1.177 is estimated to have increased by 1.14 (1.12-1.16) and that of Alpha by 1.71 (1.65-1.77). The vaccination programme starting in December 2020 is also modelled. Counterfactual simulations demonstrate that the vaccination programme was essential for reopening in March 2021, and that if the January lockdown had started one month earlier, up to 30 k (24 k-38 k) deaths could have been prevented. This article is part of the theme issue 'Technical challenges of modelling real-life epidemics and examples of overcoming these'.

Estimating SARS-CoV-2 variant fitness and the impact of interventions in England using statistical and geo-spatial agent-based models

The SARS-CoV-2 epidemic has been extended by the evolution of more transmissible viral variants. In autumn 2020, the B.1.177 lineage became the dominant variant in England, before being replaced by the B.1.1.7 (Alpha) lineage in late 2020, with the sweep occurring at different times in each region. This period coincided with a large number of non-pharmaceutical interventions (e.g. lockdowns) to control the epidemic, making it difficult to estimate the relative transmissibility of variants. In this paper, we model the spatial spread of these variants in England using a meta-population agent-based model which correctly characterizes the regional variation in cases and distribution of variants. As a test of robustness, we additionally estimated the relative transmissibility of multiple variants using a statistical model based on the renewal equation, which simultaneously estimates the effective reproduction number R. Relative to earlier variants, the transmissibility of B.1.177 is estimated to have increased by 1.14 (1.12-1.16) and that of Alpha by 1.71 (1.65-1.77). The vaccination programme starting in December 2020 is also modelled. Counterfactual simulations demonstrate that the vaccination programme was essential for reopening in March 2021, and that if the January lockdown had started one month earlier, up to 30 k (24 k-38 k) deaths could have been prevented. This article is part of the theme issue 'Technical challenges of modelling real-life epidemics and examples of overcoming these'.

Phylogenetic and phylodynamic approaches to understanding and combating the early SARS-CoV-2 pandemic

Determining the transmissibility, prevalence and patterns of movement of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections is central to our understanding of the impact of the pandemic and to the design of effective control strategies. Phylogenies (evolutionary trees) have provided key insights into the international spread of SARS-CoV-2 and enabled investigation of individual outbreaks and transmission chains in specific settings. Phylodynamic approaches combine evolutionary, demographic and epidemiological concepts and have helped track virus genetic changes, identify emerging variants and inform public health strategy. Here, we review and synthesize studies that illustrate how phylogenetic and phylodynamic techniques were applied during the first year of the pandemic, and summarize their contributions to our understanding of SARS-CoV-2 transmission and control.

Does the internet help governments contain the COVID-19 pandemic? Multi-country evidence from online human behaviour

The effectiveness of social distancing and other public health interventions for containing the COVID-19 pandemic has been demonstrated. However, whether and how Internet use behaviours can lead to enhanced self-protection and reduced transmission when considered in conjunction with behavioural interventions remains unclear. This study investigated the strength of effective Internet behaviours and its interaction with global public health interventions for controlling the COVID-19 pandemic. We conducted an econometric analysis of multisource infection and policy information, Internet behaviour, and meteorological information from worldwide in a 3-month period. People's Internet behaviours may contribute crucially to pandemic containment. Furthermore, they may help enhance the effects of public health interventions, particularly behavioural interventions. We discussed plausible mechanisms through which Internet behaviours reduce epidemic spread independently or in tandem with behavioural interventions. Further investigation into the heterogeneity of the interventions demonstrates Internet behaviour's significance in heightening the effects of difficult-to-implement, primitive crisis orientation, and specific objectives of interventions. Governments should recognise the importance of the Internet and leverage it in managing social crises. Our findings serve as a reference for the formulation of global public health policy. Specifically, the insights provided herein can facilitate the implementation of strategies for containing ongoing secondary outbreaks of COVID-19 or outbreaks of other emergent infectious diseases.

A Single Early Introduction Governed Viral Diversity in the Second Wave of SARS-CoV-2 Epidemic in Hungary

Retrospective evaluation of past waves of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) epidemic is key for designing optimal interventions against future waves and novel pandemics. Here, we report on analysing genome sequences of SARS-CoV-2 from the first two waves of the epidemic in 2020 in Hungary, mirroring a suppression and a mitigation strategy, respectively. Our analysis reveals that the two waves markedly differed in viral diversity and transmission patterns. Specifically, unlike in several European areas or in the USA, we have found no evidence for early introduction and cryptic transmission of the virus in the first wave of the pandemic in Hungary. Despite the introduction of multiple viral lineages, extensive community spread was prevented by a timely national lockdown in March 2020. In sharp contrast, the majority of the cases in the much larger second wave can be linked to a single transmission lineage of the pan-European B.1.160 variant. This lineage was introduced unexpectedly early, followed by a 2-month-long cryptic transmission before a soar of detected cases in September 2020. Epidemic analysis has revealed that the dominance of this lineage in the second wave was not associated with an intrinsic transmission advantage. This finding is further supported by the rapid replacement of B.1.160 by the alpha variant (B.1.1.7) that launched the third wave of the epidemic in February 2021. Overall, these results illustrate how the founder effect in combination with the cryptic transmission, instead of repeated international introductions or higher transmissibility, can govern viral diversity.

Proteomic and Metabolomic Characterization of SARS-CoV-2-Infected Cynomolgus Macaque at Early Stage

Although tremendous effort has been exerted to elucidate the pathogenesis of severe COVID-19 cases, the detailed mechanism of moderate cases, which accounts for 90% of all patients, remains unclear yet, partly limited by lacking the biopsy tissues. Here, we established the COVID-19 infection model in cynomolgus macaques (CMs), monitored the clinical and pathological features, and analyzed underlying pathogenic mechanisms at early infection stage by performing proteomic and metabolomic profiling of lung tissues and sera samples from COVID-19 CMs models. Our data demonstrated that innate immune response, neutrophile and platelet activation were mainly dysregulated in COVID-19 CMs. The symptom of neutrophilia, lymphopenia and massive "cytokines storm", main features of severe COVID-19 patients, were greatly weakened in most of the challenged CMs, which are more semblable as moderate patients. Thus, COVID-19 model in CMs is rational to understand the pathogenesis of moderate COVID-19 and may be a candidate model to assess the safety and efficacy of therapeutics and vaccines against SARS-CoV-2 infection.

Identifying SARS-CoV-2 regional introductions and transmission clusters in real time

The unprecedented severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) global sequencing effort has suffered from an analytical bottleneck. Many existing methods for phylogenetic analysis are designed for sparse, static datasets and are too computationally expensive to apply to densely sampled, rapidly expanding datasets when results are needed immediately to inform public health action. For example, public health is often concerned with identifying clusters of closely related samples, but the sheer scale of the data prevents manual inspection and the current computational models are often too expensive in time and resources. Even when results are available, intuitive data exploration tools are of critical importance to effective public health interpretation and action. To help address this need, we present a phylogenetic heuristic that quickly and efficiently identifies newly introduced strains in a region, resulting in clusters of infected individuals, and their putative geographic origins. We show that this approach performs well on simulated data and yields results largely congruent with more sophisticated Bayesian phylogeographic modeling approaches. We also introduce Cluster-Tracker (https://clustertracker.gi.ucsc.edu/), a novel interactive web-based tool to facilitate effective and intuitive SARS-CoV-2 geographic data exploration and visualization across the USA. Cluster-Tracker is updated daily and automatically identifies and highlights groups of closely related SARS-CoV-2 infections resulting from the transmission of the virus between two geographic areas by travelers, streamlining public health tracking of local viral diversity and emerging infection clusters. The site is open-source and designed to be easily configured to analyze any chosen region, making it a useful resource globally. The combination of these open-source tools will empower detailed investigations of the geographic origins and spread of SARS-CoV-2 and other densely sampled pathogens.

Statistical Challenges in Tracking the Evolution of SARS-CoV-2

Genomic surveillance of SARS-CoV-2 has been instrumental in tracking the spread and evolution of the virus during the pandemic. The availability of SARS-CoV-2 molecular sequences isolated from infected individuals, coupled with phylodynamic methods, have provided insights into the origin of the virus, its evolutionary rate, the timing of introductions, the patterns of transmission, and the rise of novel variants that have spread through populations. Despite enormous global efforts of governments, laboratories, and researchers to collect and sequence molecular data, many challenges remain in analyzing and interpreting the data collected. Here, we describe the models and methods currently used to monitor the spread of SARS-CoV-2, discuss long-standing and new statistical challenges, and propose a method for tracking the rise of novel variants during the epidemic.

Evolution of resistance to COVID-19 vaccination with dynamic social distancing

The greatest hope for a return to normalcy following the COVID-19 pandemic is worldwide vaccination. Yet, a relaxation of social distancing that allows increased transmissibility, coupled with selection pressure due to vaccination, will probably lead to the emergence of vaccine resistance. We analyse the evolutionary dynamics of COVID-19 in the presence of dynamic contact reduction and in response to vaccination. We use infection and vaccination data from six different countries. We show that under slow vaccination, resistance is very likely to appear even if social distancing is maintained. Under fast vaccination, the emergence of mutants can be prevented if social distancing is maintained during vaccination. We analyse multiple human factors that affect the evolutionary potential of the virus, including the extent of dynamic social distancing, vaccination campaigns, vaccine design, boosters and vaccine hesitancy. We provide guidelines for policies that aim to minimize the probability of emergence of vaccine-resistant variants.

Markov genealogy processes

We construct a family of genealogy-valued Markov processes that are induced by a continuous-time Markov population process. We derive exact expressions for the likelihood of a given genealogy conditional on the history of the underlying population process. These lead to a nonlinear filtering equation which can be used to design efficient Monte Carlo inference algorithms. We demonstrate these calculations with several examples. Existing full-information approaches for phylodynamic inference are special cases of the theory.

Optimal non-pharmaceutical intervention policy for Covid-19 epidemic via neuroevolution algorithm

Background

National responses to the Covid-19 pandemic varied markedly across countries, from business-as-usual to complete shutdowns. Policies aimed at disrupting the viral transmission cycle and preventing the overwhelming of healthcare systems inevitably exact an economic toll.

Methodology

We developed an intervention policy model that comprised the relative human, implementation and healthcare costs of non-pharmaceutical epidemic interventions and identified the optimal strategy using a neuroevolution algorithm. The proposed model finds the minimum required reduction in transmission rates to maintain the burden on the healthcare system below the maximum capacity.

Results

We find that such a policy renders a sharp increase in the control strength during the early stages of the epidemic, followed by a steady increase in the subsequent ten weeks as the epidemic approaches its peak, and finally the control strength is gradually decreased as the population moves towards herd immunity. We have also shown how such a model can provide an efficient adaptive intervention policy at different stages of the epidemic without having access to the entire history of its progression in the population.

Conclusions and implications

This work emphasizes the importance of imposing intervention measures early and provides insights into adaptive intervention policies to minimize the economic impacts of the epidemic without putting an extra burden on the healthcare system.

Lay Summary

We developed an intervention policy model that comprised the relative human, implementation and healthcare costs of non-pharmaceutical epidemic interventions and identified the optimal strategy using a neuroevolution algorithm. Our work emphasizes the importance of imposing intervention measures early and provides insights into adaptive intervention policies to minimize the economic impacts of the epidemic without putting an extra burden on the healthcare system.

COVID-19 Propagation Model Based on Economic Development and Interventions

In order to understand the influencing factors affecting the COVID-19 propagation, and analyze the development trend of the epidemic situation in the world, COVID-19 propagation model to simulate the COVID-19 propagation in the population is proposed in this paper. First of all, this paper analyzes the economic factors and interventions affecting the COVID-19 propagation in various different countries. Then, the touch number for COVID-19 High-risk Population Dynamic Network in this paper was redefined, and it predicts and analyzes the development trend of the epidemic situation in different countries. The simulation data and the published confirmed data by the world health organization could fit well, which also verified the reliability of the model. Finally, this paper also analyzes the impact of public awareness of prevention on the control of the epidemic. The analysis shows that increasing the awareness of prevention, timely and early adoption of protective measures such as wearing masks, and reducing travel can greatly reduce the risk of infection and the outbreak scale.

Early warning signal reliability varies with COVID-19 waves

Early warning signals (EWSs) aim to predict changes in complex systems from phenomenological signals in time series data. These signals have recently been shown to precede the emergence of disease outbreaks, offering hope that policymakers can make predictive rather than reactive management decisions. Here, using a novel, sequential analysis in combination with daily COVID-19 case data across 24 countries, we suggest that composite EWSs consisting of variance, autocorrelation and skewness can predict nonlinear case increases, but that the predictive ability of these tools varies between waves based upon the degree of critical slowing down present. Our work suggests that in highly monitored disease time series such as COVID-19, EWSs offer the opportunity for policymakers to improve the accuracy of urgent intervention decisions but best characterize hypothesized critical transitions.

COVID-19 case doubling time associated with non-pharmaceutical interventions and vaccination: A global experience

Background

Evidence has revealed that nonpharmaceutical interventions (NPIs) were effective in attenuating the spread of COVID-19. However, policymakers have encountered difficulty in identifying the most effective policies under different circumstances. This study investigated the relative effectiveness of different NPIs and vaccination in prolonging COVID-19 case doubling time (DT).

Methods

The study sample consisted of observations from 137 countries during 1 January 2020 to 13 June 2021. DT was calculated on a daily basis per country. Data were retrieved from the Oxford COVID-19 Government Response Tracker, World Development Indicators, and Worldwide Governance Indicators. To capture policy intervention dynamics, we combined a random-effect growth-curve model with nonstandard interrupted time series analysis. We also evaluated the association of policy measures with DT for different outbreak stages and levels of government effectiveness.

Results

Vaccine rollouts, workplace closures, and school closures were relatively effective. For each day that these measures were implemented, the DT increased by 1.96% (95% confidence interval (CI) = 0.63 to 3.29; P = 0.004), 1.41% (95% CI = 0.88 to 1.95%; P < 0.001) and 1.38% (95% CI = 0.95 to 1.81%; P < 0.001), respectively. Workplace and school closures were positively associated with DT at all stages; however, the associations weakened in later stages, where vaccine rollouts appeared to be most effective in prolonging DT (95% CI = 1.51% to 3.04%; P < 0.05). For countries with a high level of government effectiveness, most of the containment measures evaluated were effective; vaccine rollouts had the greatest effect size. For countries with medium or low levels of government effectiveness, only the closure of workplaces was consistently associated with prolonged DT.

Conclusions

The effectiveness of vaccine rollouts outweighed that of NPIs, especially in the later outbreak stages. However, vaccination was not associated with prolonged case DT in countries with lower levels of government effectiveness, probably due to low vaccine coverage. Among the NPIs examined, workplace closures were highly effective across all outbreak stages and levels of government effectiveness. Our findings suggest that mass vaccination is critical to reducing SARS-CoV-2 transmission, especially in countries where NPIs are less effective.

Extreme immunotherapy: emergency immunology to defeat pandemics

The ongoing global COVID-19 pandemic has thrown into sharp relief the gap between modern biology's ability to investigate and respond to a novel pathogen and modern medicine's ability to marshal effective front-line interventions to limit its immediate health impact. While we have witnessed the rapid development of innovative vaccines against SARS-CoV-2 using novel molecular platforms, these have yet to alter the pandemic's long-term trajectory in all but a handful of high-income countries. Health workers at the clinical front lines have little more in their clinical armamentarium than was available a century ago-chiefly oxygen and steroids-and yet advances in modern immunology and immunotherapeutics suggest an underuse of extant and effective, if unorthodox, therapies, which we now call "Extreme Immunotherapies for Pandemics (EIPs)."

Decomposing the sources of SARS-CoV-2 fitness variation in the United States

The fitness of a pathogen is a composite phenotype determined by many different factors influencing growth rates both within and between hosts. Determining what factors shape fitness at the host population-level is especially challenging because both intrinsic factors like pathogen genetics and extrinsic factors such as host behavior influence between-host transmission potential. This challenge has been highlighted by controversy surrounding the population-level fitness effects of mutations in the SARS-CoV-2 genome and their relative importance when compared against non-genetic factors shaping transmission dynamics. Building upon phylodynamic birth-death models, we develop a new framework to learn how hundreds of genetic and non-genetic factors have shaped the fitness of SARS-CoV-2. We estimate the fitness effects of all amino acid variants and several structural variants that have circulated in the United States between February 2020 and March 2021 from viral phylogenies. We also estimate how much fitness variation among pathogen lineages is attributable to genetic versus non-genetic factors such as spatial heterogeneity in transmission rates. Before September 2020, most fitness variation between lineages can be explained by background spatial heterogeneity in transmission rates across geographic regions. Starting in late 2020, genetic variation in fitness increased dramatically with the emergence of several new lineages including B.1.1.7, B.1.427, B.1.429 and B.1.526. Our analysis also indicates that genetic variants in less well-explored genomic regions outside of Spike may be contributing significantly to overall fitness variation in the viral population.

Rapid evolution of the COVID-19 pandemic calls for a unified public health response

The globe has witnessed the rapid evolution of SARS-CoV-2 mutations and emerging variants of concern (VOCs) and variants of interest (VOIs) that have broadly impacted the transmissibility, antigenicity, morbidity, and mortality of the virus. Although around 2.5 billion vaccine doses have been administered worldwide, vaccine coverage remains far behind the minimum threshold needed to achieve herd immunity overall and it varies substantially by country. Many countries, and especially low- and middle-income countries (LMICs), are struggling with access to COVID-19 vaccines and a lack of personnel to perform mass vaccination. Effective nonpharmaceutical interventions (NPIs) are also not unanimously accepted and strictly complied with by the public and local communities. Moreover, the global fight against COVID-19 is and continues to face geopolitical, social, economic, and human rights concerns. Taken together, these circumstances call for a unified public health response with well-organized individual, local, national, and global efforts and actions to achieve success in controlling the COVID-19 pandemic and achieving sustainable health and development goals.