The impact of cross-reactive immunity on the emergence of SARS-CoV-2 variants (preprint)

A key feature of the COVID-19 pandemic has been the emergence of SARS-CoV-2 variants with different transmission characteristics. However, when a novel variant arrives in a host population, it will not necessarily lead to many cases. Instead, it may fade out, due to stochastic effects and the level of immunity in the population. Immunity against novel SARS-CoV-2 variants may be influenced by prior exposures to related viruses, such as other SARS-CoV-2 variants and seasonal coronaviruses, and the level of cross-reactive immunity conferred by those exposures. Here, we investigate the impact of cross-reactive immunity on the emergence of SARS-CoV-2 variants in a simplified scenario in which a novel SARS-CoV-2 variant is introduced after an antigenically related virus has spread in the population. We use mathematical modelling to explore the risk that the novel variant invades the population and causes a large number of cases, as opposed to fading out. If cross-reactive immunity is complete (i.e. someone infected by the previously circulating virus is no longer susceptible to the novel variant), the novel variant must be more transmissible than the previous virus to invade the population. However, in a more realistic scenario in which cross-reactive immunity is partial, we show that it is possible for novel variants to invade, even if they are less transmissible than previously circulating viruses. This is because partial cross-reactive immunity effectively increases the pool of susceptible hosts that are available to the novel variant compared to complete cross-reactive immunity. Furthermore, if previous infection with the antigenically related virus assists the establishment of infection with the novel variant, as has been proposed following some experimental studies, then even variants with very limited transmissibility are able to invade the host population. Our results highlight that fast assessment of the level of cross-reactive immunity conferred by related viruses on novel SARS-CoV-2 variants is an essential component of novel variant risk assessments.

Risk averse reproduction numbers improve resurgence detection (preprint)

The effective reproduction number R is a prominent statistic for inferring the transmissibility of infectious diseases and the effectiveness of interventions. R purportedly provides an easy-to-interpret threshold for deducing if an epidemic will grow (R >1) or decline (R < 1). We posit that this interpretation can be misleading and overconfident when applied to infections aggregated from groups with heterogeneous dynamics. In these settings, R implicitly weights the dynamics of groups by their number of circulating infections. This induces losses in sensitivity to the dynamics of high-risk groups that may spur resurgences, promotes premature indications of epidemic control, and renders the R = 1 threshold uninformative. Applying E-optimal experimental design theory, we derive the risk averse reproduction number E to ameliorate these issues. Using analytic approaches, simulations, and a real-world case study, we find that E achieves more meaningful consensus of the dynamics across groups without being overconfident in its estimates. An E >1 generates timely resurgence signals (upweighting the impact of high-risk groups), while E < 1 ensures constituent sub-outbreaks are under control. We propose E as an alternative to R for informing policy and assessing transmissibility over large scales (e.g., state or nationwide), where well-mixed assumptions break down.

Assessing COVID-19 vaccination strategies in varied demographics using an individual-based model (preprint)

Background: New variants of SARS-CoV-2 are constantly discovered. Administration of COVID-19 vaccines and booster doses, combined with applications of non-pharmaceutical interventions (NPIs), is often used to prevent outbreaks of emerging variants. Such outbreak dynamics are further complicated by the population's behavior and demographic composition. Hence, realistic simulations are needed to estimate the efficiency of proposed vaccination strategies in conjunction with NPIs. Methods: We developed an individual-based model of COVID-19 dynamics that considers age-dependent parameters such as contact matrices, probabilities of symptomatic and severe disease, and households' age distribution. As a case study, we simulate outbreak dynamics under the demographic compositions of two Israeli cities with different household sizes and age distributions. We compare two vaccination strategies: vaccinate individuals in a currently prioritized age group, or dynamically prioritize neighborhoods with a high estimated reproductive number. Total infections and hospitalizations are used to compare the efficiency of the vaccination strategies under the two demographic structures, in conjunction with different NPIs. Results: We demonstrate the effectiveness of vaccination strategies targeting highly infected localities and of NPIs actively detecting asymptomatic infections. We further show that there are different optimal vaccination strategies for each demographic composition of sub-populations, and that their application is superior to a uniformly applied strategy. Conclusion: Our study emphasizes the importance of tailoring vaccination strategies to subpopulations' infection rates and to the unique characteristics of their demographics (e.g., household size and age distributions). The presented simulation framework and our findings can help better design future responses against the following emerging variants.

Modelling COVID-19 Vaccine Breakthrough Infections in Highly Vaccinated Israel - the effects of waning immunity and third vaccination dose (preprint)

In August 2021, a major wave of the SARS-CoV-2 Delta variant erupted in the highly vaccinated population of Israel. The Delta variant has a transmission advantage over the Alpha variant, and thus replaced it in approximately two months. The outbreak led to an unexpectedly large proportion of breakthrough infections (BTI)-- a phenomenon that received worldwide attention. The BTI proportion amongst cases in the age group of 60+ years reached levels as high as [~]85% in August 2021. Most of the Israeli population, especially those 60+ age, received their second dose of the vaccination, four months before the invasion of the Delta variant. Hence, either the vaccine induced immunity dropped significantly or the Delta variant possesses immunity escaping abilities. In this work, we analyzed and model age-structured cases, vaccination coverage, and vaccine BTI data obtained from the Israeli Ministry of Health, to help understand the epidemiological factors involved in the outbreak. We propose a mathematical model which captures a multitude of factors, including age structure, the time varying vaccine efficacy, time varying transmission rate, BTIs, reduced susceptibility and infectivity of vaccinated individuals, protection duration of the vaccine induced immunity, and the vaccine distribution. We fitted our model to the cases among vaccinated and unvaccinated, for <60 and 60+ age groups, to address the aforementioned factors. We found that the transmission rate was driven by multiple factors including the invasion of Delta variant and the mitigation measures. Through a model reconstruction of the reproductive number R0(t), it was found that the peak transmission rate of the Delta variant was 1.96 times larger than the previous Alpha variant. The model estimated that the vaccine efficacy dropped significantly from >90% to [~]40% over 6 months, and that the immunity protection duration has a peaked Gamma distribution (rather than exponential). We further performed model simulations quantifying the important role of the third vaccination booster dose in reducing the levels of breakthrough infections. This allowed us to explore "what if" scenarios should the booster not have been rolled out. Application of this framework upon invasion of new pathogens, or variants of concern, can help elucidate important factors in the outbreak dynamics and highlight potential routes of action to mitigate their spread.

Combination therapy of infliximab and thiopurines, but not monotherapy with infliximab or vedolizumab, is associated with attenuated IgA and neutralisation responses to SARS-CoV-2 in inflammatory bowel disease (preprint)

There is substantial interest regarding the perceived risk that immunomodulator and biologic therapy could have on COVID-19 disease severity among patients with inflammatory bowel disease (IBD) and clinicians. In this study, we show that infliximab/thiopurine combination therapy is associated with significantly lower IgA, a range of lower IgG responses as well as impaired neutralising antibody responses, compared to responses observed in healthy individuals. We also demonstrate that whilst IgG responses were significantly reduced in individuals with IBD treated with infliximab or vedolizumab monotherapy compared to healthy controls, there was no significant reduction in IgA and neutralising antibody responses. As neutralising antibody responses correlate with protection, this observation may provide the mechanistic explanation for the observation reported by the SECURE-IBD study that individuals on infliximab/thiopurine combination therapy were at greater risk of severe COVID-19 outcomes than patients on monotherapy.

Fatal COVID-19 outcomes are associated with an antibody response targeting epitopes shared with endemic coronaviruses (preprint)

It is unclear whether prior endemic coronavirus infections affect COVID-19 severity. Here, we show that in cases of fatal COVID-19, antibody responses to the SARS-COV-2 spike are directed against epitopes shared with endemic beta-coronaviruses in the S2 subunit of the SARS-CoV-2 spike protein. This immune response is associated with the compromised production of a de novo SARS-CoV-2 spike response among individuals with fatal COVID-19 outcomes.

DOPE: D-Optimal Pooling Experimental design with application for SARS-CoV-2 screening (preprint)

Testing individuals for the presence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the pathogen causing the coronavirus disease 2019 (COVID-19), is crucial for curtailing transmission chains. Moreover, rapidly testing many potentially infected individuals is often a limiting factor in controlling COVID-19 outbreaks. Hence, pooling strategies, wherein individuals are grouped and tested simultaneously, are employed. We present a novel pooling strategy that implements D-Optimal Pooling Experimental design (DOPE). DOPE defines optimal pooled tests as those maximizing the mutual information between data and infection states. We estimate said mutual information via Monte-Carlo sampling and employ a discrete optimization heuristic for maximizing it. DOPE outperforms common pooling strategies both in terms of lower error rates and fewer tests utilized. DOPE holds several additional advantages: it provides posterior distributions of the probability of infection, rather than only binary classification outcomes; it naturally incorporates prior information of infection probabilities and test error rates; and finally, it can be easily extended to include other, newly discovered information regarding COVID-19. Hence, we believe that implementation of Bayesian D-optimal experimental design holds a great promise for the efforts of combating COVID-19 and other future pandemics.

An Accurate Model for SARS-CoV-2 Pooled RT-PCR Test Errors (preprint)

PCR testing is an important tool to mitigate outbreaks of infectious diseases. One way of increasing testing throughput is by simultaneously testing multiple samples for the presence of a pathogen, a technique known as pooling . During the current COVID-19 pandemic, rapidly testing individuals for the presence of SARS-CoV-2 is conducted in large amounts. Since testing is often a bottleneck in mitigating the spread of SARS-CoV-2, pooling is increasing in popularity. Most analyses of the error rates of pooling schemes assume that including more than a single infected sample in a pooled test does not increase the probability of a positive outcome. We challenge this assumption with experimental data and suggest a novel probabilistic model for the outcomes of pooled tests. As an application, we analyze the false-negative rates of one common pooling scheme known as Dorfman pooling. We show that the false-negative rates of Dorfman pooling increase when the prevalence of infection decreases. However, low infection prevalence is exactly the condition under which Dorfman pooling achieves highest throughput. We therefore implore the cautious use of pooling and development of pooling schemes that consider correctly accounting for tests’ error rates.

Interventions targeting nonsymptomatic cases can be important to prevent local outbreaks: COVID-19 as a case-study (preprint)

During infectious disease epidemics, an important question is whether cases travelling to new locations will trigger local outbreaks. The risk of this occurring depends on the transmissibility of the pathogen, the susceptibility of the host population and, crucially, the effectiveness of surveillance in detecting cases and preventing onward spread. For many pathogens, transmission from presymptomatic and/or asymptomatic (together referred to as nonsymptomatic) infectious hosts can occur, making effective surveillance challenging. Here, using SARS-CoV-2 as a case-study, we show how the risk of local outbreaks can be assessed when nonsymptomatic transmission can occur. We construct a branching process model that includes nonsymptomatic transmission, and explore the effects of interventions targeting nonsymptomatic or symptomatic hosts when surveillance resources are limited. We consider whether the greatest reductions in local outbreak risks are achieved by increasing surveillance and control targeting nonsymptomatic or symptomatic cases, or a combination of both. We find that seeking to increase surveillance of symptomatic hosts alone is typically not the optimal strategy for reducing outbreak risks. Adopting a strategy that combines an enhancement of surveillance of symptomatic cases with efforts to find and isolate nonsymptomatic infected hosts leads to the largest reduction in the probability that imported cases will initiate a local outbreak.

Potential impact of individual exposure histories to endemic human coronaviruses on age-dependence in severity of COVID-19 (preprint)

Cross-reactivity to SARS-CoV-2 from previous exposure to endemic coronaviruses (eHCoV) is gaining increasing attention as a possible driver of both protection against infection and severity of COVID-19 disease. Here, we use a stochastic individual-based model to show that heterogeneities in individual exposure histories to endemic coronaviruses are able to explain observed age patterns of hospitalisation due to COVID-19 in EU/EEA countries and the UK, provided there is (i) a decrease in cross-protection to SARS-CoV-2 with the number of eHCoV exposures and (ii) an increase in potential disease severity with number of eHCoV exposures or as a result of immune senescence. We also show that variation in health care capacity and testing efforts is compatible with country-specific differences in hospitalisation rates. Our findings call for further research on the role of cross-reactivity to endemic coronaviruses and highlight potential challenges arising from heterogeneous health care capacity and testing.

Inferring the effective start dates of non-pharmaceutical interventions during COVID-19 outbreaks (preprint)

During Feb-Apr 2020, many countries implemented non-pharmaceutical interventions, such as school closures and lockdowns, with variable schedules, to control the COVID-19 pandemic caused by the SARS-CoV-2 virus. Overall, these interventions seem to have successfully reduced the spread of the pandemic. We hypothesise that the official and effective start date of such interventions can significantly differ, for example due to slow adoption by the population, or because the authorities and the public are unprepared. We fit an SEIR model to case data from 12 countries to infer the effective start dates of interventions and contrast them with the official dates. We find mostly late, but also early effects of interventions. For example, Italy implemented a nationwide lockdown on Mar 11, but we infer the effective date on Mar 17 ({+/-}2.99 days 95% CI). In contrast, Germany announced a lockdown on Mar 22, but we infer an effective start date on Mar 19 ({+/-}1.05 days 95% CI). We discuss potential causes and consequences of our results.

Estimating COVID-19 outbreak risk through air travel (preprint)

BackgroundSARS-CoV-2 has spread rapidly across the globe during the first several months of 2020, resulting in a pandemic. Substantial, non-discriminatory limitations have been imposed on air travel to inhibit further spread of the virus. However, as disease prevalence and incidence decreases, more specific control measures will be sought so that commercial air travel can operate yet not impose a high threat of COVID-19 resurgence. We considered the risk posed by different locations to initiate a resurgence of COVID-19 at such times. MethodsWe use modelled global air travel data for October (just before a second wave of COVID-19 might be expected) and population density to analyse the risk posed by 1364 airports to initiate a COVID-19 outbreak. We use a probabilistic, branching-process based approach that considers the volume of air travelers between airports and the R0 of each location, scaled by population density. This exercise is performed globally as well as specifically for two potentially vulnerable locations: Africa and India. ResultsWe show that globally, many of the airports posing the highest risk are in China and India. An outbreak of COVID-19 in Africa is most likely to originate in a passenger travelling from Europe. On the other hand, the highest risk to India is from domestic travellers. Our results are robust to changes in the underlying epidemiological assumptions. ConclusionsVariation in flight volumes and destinations creates a non-uniform distribution of the risk different airports pose to resurgence of a COVID-19 outbreak. We suggest the method presented here as a tool for the estimation of this risk. Our method can be used to inform efficient allocation of resources, such as tests identifying infected passengers, so that they could be differentially deployed in various locations.