Immune Heterogeneity and Epistasis Explain Punctuated Evolution of SARS-CoV-2

Identifying drivers of viral diversity is key to understanding the evolutionary as well as epidemiological dynamics of the COVID-19 pandemic. Using rich viral genomic data sets, we show that periods of steadily rising diversity have been punctuated by sudden, enormous increases followed by similarly abrupt collapses of diversity. We introduce a mechanistic model of saltational evolution with epistasis and demonstrate that these features parsimoniously account for the observed temporal dynamics of inter-genomic diversity. Our results provide support for recent proposals that saltational evolution may be a signature feature of SARS-CoV-2, allowing the pathogen to more readily evolve highly transmissible variants. These findings lend theoretical support to a heightened awareness of biological contexts where increased diversification may occur. They also underline the power of pathogen genomics and other surveillance streams in clarifying the phylodynamics of emerging and endemic infections. In public health terms, our results further underline the importance of equitable distribution of up-to-date vaccines.

Pancreatic α and ß cells are globally phase-locked.

The Ca2+ modulated pulsatile glucagon and insulin secretions by pancreatic α and ß cells play a crucial role in glucose homeostasis. However, how α and ß cells coordinate to produce various Ca2+ oscillation patterns is still elusive. Using a microfluidic device and transgenic mice, we recorded Ca2+ signals from islet α and ß cells, and observed heterogeneous Ca2+ oscillation patterns intrinsic to each islet. After a brief period of glucose stimulation, α and ß cells' oscillations were globally phase-locked. While the activation of α cells displayed a fixed time delay of ~20 s to that of ß cells, ß cells activated with a tunable period. Moreover, islet α cell number correlated with oscillation frequency. We built a mathematical model of islet Ca2+ oscillation incorporating paracrine interactions, which quantitatively agreed with the experimental data. Our study highlights the importance of cell-cell interaction in generating stable but tunable islet oscillation patterns.

Tradeoff between speed and infectivity in pathogen evolution

Given the present pandemic and the constantly arising new variants of SARS-CoV-2, there is an urgent need to understand the factors driving disease evolution. Here, we investigate the tradeoff between the speed at which a disease progresses and its reproductive number. Using SEIR and agent-based models, we show that in the exponential growth phase of an epidemic, there will be an optimal duration of new disease variants, balancing the advantage of developing fast with the advantage of infecting many new people. In the endemic state this optimum disappears, and lasting longer is always advantageous for the disease. However, if we take into account the possibility of quarantining the infected, this leads to a new optimum disease duration emerging. This work thereby comments on the observation of ever shorter generation times in the evolution of variants of SARS-CoV-2 from the original strain to the Alpha, Delta, and finally Omicron variants.

The timing of natural killer cell response in coronavirus infection: a concise model perspective

Coronaviruses, including SARS-CoV, MERS-CoV, and SARS-CoV-2 cause respiratory diseases with remarkably heterogeneous progression. This in part reflects the viral ability to influence the cytokine secretion and thereby the innate immune system. Especially the viral interference of IFN-I signaling and the subsequent deficiency of innate immune response in the early phase have been associated with rapid virus replication and later excessive immune responses. We propose a mathematical framework to analyze IFN-I signaling and its impact on the interaction motif between virus, NK cells and macrophages. The model recapture divergent dynamics of coronavirus infections including the possibility for elevated secretion of IL-6 and IFN-{gamma} as a consequence of exacerbated macrophage activation. Dysfunction of NK cells recruitment increase disease severity by leading to a higher viral load peak, the possibility for excessive macrophage activation, and an elevated risk of the cytokine storm. Thus the model predicts that delayed IFN-I signaling could lead to pathogenicity in the latter stage of an infection. Reversely, in case of strong NK recruitment from infected cells we predict a possible chronic disease state with moderate and potentially oscillating virus/cytokine levels.

Lockdowns exert selection pressure on overdispersion of SARS-CoV-2 variants

The SARS-CoV-2 ancestral strain has caused pronounced super-spreading events, reflecting a disease characterized by overdispersion, where about 10% of infected people causes 80% of infections. New variants of the disease have different person-to-person variations in viral load, suggesting for example that the Alpha (B.1.1.7) variant is more infectious but relatively less prone to superspreading. Meanwhile, mitigation of the pandemic has focused on limiting social contacts (lockdowns, regulations on gatherings) and decreasing transmission risk through mask wearing and social distancing. Using a mathematical model, we show that the competitive advantage of disease variants may heavily depend on the restrictions imposed. In particular, we find that lockdowns exert an evolutionary pressure which favours variants with lower levels of overdispersion. We find that overdispersion is an evolutionarily unstable trait, with a tendency for more homogeneously spreading variants to eventually dominate. SignificanceOne of the most important and complex properties of viral pathogens is their ability to mutate. The SARS-CoV-2 pandemic has been characterized by overdispersion - a propensity for superspreading, which means that around 10% of those who become infected cause 80% of infections. However, evidence is mounting that this is not a stable property of the virus and that the Alpha variant spreads more homogeneously. We use a mathematical model to show that lockdowns exert a selection pressure, driving the pathogen towards more homogeneous transmission. In general, we highlight the importance of understanding how non-pharmaceutical interventions exert evolutionary pressure on pathogens. Our results imply that overdispersion should be taken into account when assessing the transmissibility of emerging variants.

Variability of Individual Infectiousness Derived from Aggregate Statistics of COVID-19

The quantification of spreading heterogeneity in the COVID-19 epidemic is crucial as it affects the choice of efficient mitigating strategies irrespective of whether its origin is biological or social. We present a method to deduce temporal and individual variations in the basic reproduction number R directly from epidemic trajectories at a community level. Using epidemic data from the 98 districts in Denmark we estimate an overdispersion factor k for COVID-19 to be about 0.11 (95% confidence interval 0.08 - 0.18), implying that 10 % of the infected cause between 70 % to 87 % of all infections.

Airborne Pathogens in a Heterogeneous World: Superspreading & Mitigation

Epidemics are regularly associated with reports of superspreading: single individuals infecting many others. How do we determine if such events are due to people inherently being biological superspreaders or simply due to random chance? We present an analytically solvable model for airborne diseases which reveal the spreading statistics of epidemics in socio-spatial heterogeneous spaces and provide a baseline to which data may be compared. In contrast to classical SIR models, we explicitly model social events where airborne pathogen transmission allows a single individual to infect many simultaneously, a key feature that generates distinctive output statistics. We find that diseases that have a short duration of high infectiousness can give extreme statistics such as 20 % infecting more than 80 %, depending on the socio-spatial heterogeneity. Quantifying this by a distribution over sizes of social gatherings, tracking data of social proximity for university students suggest that this can be a approximated by a power law. Finally, we study mitigation efforts applied to our model. We find that the effect of banning large gatherings works equally well for diseases with any duration of infectiousness, but depends strongly on socio-spatial heterogeneity.

Superspreaders provide essential clues for mitigation of COVID-19

Although COVID-19 has caused severe suffering globally, the efficacy of non-pharmaceutical interventions has been greater than typical models have predicted. Meanwhile, evidence is mounting that the pandemic is characterized by superspreading. Capturing this phenomenon theoretically requires modeling at the scale of individuals. Using a mathematical model, we show that superspreading drastically enhances mitigations which reduce the overall personal contact number, and that social clustering ("social bubbles") increases this effect.

COVID-19 superspreading in cities versus the countryside

So far, the COVID-19 pandemic has been characterised by an initial rapid rise in new cases followed by a peak and a more erratic behaviour that varies between regions. This is not easy to reproduce with traditional SIR models, which predict a more symmetric epidemic. Here, we argue that superspreaders and population heterogeneity are the core factors explaining this discrepancy. We do so through an agent-based lattice model of a disease spreading in a heterogeneous population. We predict that an epidemic driven by superspreaders will spread rapidly in cities, but not in the countryside where the sparse population limits the maximal number of secondary infections. This suggests that mitigation strategies should include restrictions on venues where people meet a large number of strangers. Furthermore, mitigating the epidemic in cities and in the countryside may require different levels of restrictions.

Heterogeneity is essential for contact tracing

Contact tracing is suggested as an effective strategy for controlling an epidemic without severely limiting personal mobility. Here, we explore how social structure affects contact tracing of COVID-19. Using smartphone proximity data, we simulate the spread of COVID-19 and find that heterogeneity in the social network and activity levels of individuals decreases the severity of an epidemic and improves the effectiveness of contact tracing. As a mitigation strategy, contact tracing depends strongly on social structure and can be remarkably effective, even if only frequent contacts are traced. In perspective, this highlights the necessity of incorporating social heterogeneity into models of mitigation strategies. O_TEXTBOXSignificance StatementThe COVID-19 epidemic has put severe limitations on individual mobility in the form of lockdowns and closed national borders. Mitigation strategies permitting individual mobility while limiting disease spreading are needed, and contact tracing is a potentially effective example of such a strategy. We use smartphone proximity data to monitor contacts between people, and find that contact tracing is highly dependent on social structure, being very effective on real contact networks. This shows that mitigation of COVID-19 may be possible with contact tracing, and that epidemiological models must incorporate social network structure. C_TEXTBOX

Impact of Superspreaders on dissemination and mitigation of COVID-19

BackgroundThe draconian measures used to control COVID-19 dissemination have been highly effective but only at enormous socioeconomic cost. Evidence suggests that "superspreaders" who transmit the virus to a large number of people, play a substantial role in transmission; recent estimates suggest that about 1-20% of people with the virus are the source for about 80% of infections. We used an agent-based model to explore the interplay between social structure, mitigation and superspreading. MethodsWe developed an agent-based model with a subset of "superspreader" agents that transmit disease far more efficiently. These agents act in a social network that allows transmission during contacts in three sectors: "home," "work/school" and "other". We simulated the effect of various mitigation strategies that limit contacts in each of these sectors, and used the model to fit COVID-19 mortality data from Sweden. FindingsReducing contacts in the "other" sector had a far greater impact on epidemic trajectory than did reducing "home" or "work/school" contacts; this effect was substantially enhanced when the infectivity of children was reduced relative to that of adults. The model fit Swedish hospitalization data with reasonable assumptions about the effect of Swedens mitigation policies on contacts in the different sectors. InterpretationOur results suggest COVID-19 could be controlled by limiting large gatherings and other opportunities for contacts between people in restaurants, sporting events, concerts and worship services) while still allowing regular contacts in the home or at work and school. O_TEXTBOXResearch in contextO_ST_ABSEvidence before this studyC_ST_ABSSuperspreading events have long been known to be important in the epidemiology of many infectious diseases, including tuberculosis, measles, Ebola and SARS. Since the emergence of SARS-CoV-2, epidemiologic analyses have inferred substantial individual-level variation in transmissibility, with an estimated 1% to 20% of infected persons causing about 80% of all COVID-19 cases. Added value of this studyWe developed an agent-based socially structured model to simulate the effect of superspreaders in COVID-19 transmission in the context of country-wide "lockdown" policies. These simulations indicate that COVID-19 can be effectively mitigated by limiting contacts between people who otherwise rarely meet, while allowing home and most work/school contacts to continue. Implications of all available evidenceIt is crucial to include heterogeneity in individual infectiousness when modeling the impact of mitigation strategies on observed COVID-19 epidemic patterns. Reducing opportunities for superspreading by limiting random contacts outside home and work could be the most effective way to control COVID-19. Our findings suggest why the epidemic has continued to decline following re-opening of work and school in European countries. The superspreader phenomenon may also explain the variability in COVID-19 incidence in rural and urban areas within a country. C_TEXTBOX

Estimating cost-benefit of quarantine length for Covid-19 mitigation

The international community has been put in an unprecedented situation by the COVID-19 pandemic. Creating models to describe and quantify alternative mitigation strategies becomes increasingly urgent. In this study, we propose an agent-based model of disease transmission in a society divided into closely connected families, workplaces, and social groups. This allows us to discuss mitigation strategies, including targeted quarantine measures. We find that workplace and more diffuse social contacts are roughly equally important to disease spread, and that an effective lockdown must target both. We examine the cost-benefit of replacing a lockdown with tracing and quarantining contacts of the infected. Quarantine can contribute substantially to mitigation, even if it has short duration and is done within households. When reopening society, testing and quarantining is a strategy that is much cheaper in terms of lost workdays than a long lockdown of workplaces. A targeted quarantine strategy is quite efficient with only 5 days of quarantine, and its effect increases when testing is more widespread.