Bayesian time-varying autoregressive models of COVID-19 epidemics

The COVID-19 pandemic has highlighted the importance of reliable statistical models which, based on the available data, can provide accurate forecasts and impact analysis of alternative policy measures. Here we propose Bayesian time-dependent Poisson autoregressive models that include time-varying coefficients to estimate the effect of policy covariates on disease counts. The model is applied to the observed series of new positive cases in Italy and in the United States. The results suggest that our proposed models are capable of capturing nonlinear growth of disease counts. We also find that policy measures and, in particular, closure policies and the distribution of vaccines, lead to a significant reduction in disease counts in both countries.     

Networks, epidemics and vaccination through contact tracing

We consider a (social) network whose structure can be represented by a simple random graph having a pre-specified degree distribution. A Markovian susceptible-infectious-removed (SIR) epidemic model is defined on such a social graph. We then consider two real-time vaccination models for contact tracing during the early stages of an epidemic outbreak. The first model considers vaccination of each friend of an infectious individual (once identified) independently with probability ρ. The second model is related to the first model but also sets a bound on the maximum number an infectious individual can infect before being identified. Expressions are derived for the influence on the reproduction number of these vaccination models. We give some numerical examples and simulation results based on the Poisson and heavy-tail degree distributions where it is shown that the second vaccination model has a bigger advantage compared to the first model for the heavy-tail degree distribution.     

Genetic tracing of HCoV-19 for the re-emerging outbreak of COVID-19 in Beijing,China

The ongoing pandemic of coronavirus disease 2019(COVID-19)caused by a novel severe acute respiratory syndrome coronavirus 2(SARS-CoV-2,also named as 2019-nCoV or HCoV-19)poses an unprecedented threat to public health(Zhu et al.,2020;Wang et al.,2020;Jiang et al.,2020).The novel HCoV-19 virus has rapidly spread into multiple countries across the world since it was first reported in December 2019.The World Health Organization(WHO)declared COVID-19 as a pandemic on 11th March 2020.As of 4th July,over 10 million confirmed COVID-19 cases have been reported in over 200 countries/regions with more than 0.5 million deaths,including 85,287 documented cases and 4,648 deaths in China(WHO,2020a).     

COVID-19 infection and transmission includes complex sequence diversity

SARS-CoV-2 whole genome sequencing has played an important role in documenting the emergence of polymorphisms in the viral genome and its continuing evolution during the COVID-19 pandemic. Here we present data from over 360 patients to characterize the complex sequence diversity of individual infections identified during multiple variant surges (e.g., Alpha and Delta). Across our survey, we observed significantly increasing SARS-CoV-2 sequence diversity during the pandemic and frequent occurrence of multiple biallelic sequence polymorphisms in all infections. This sequence polymorphism shows that SARS-CoV-2 infections are heterogeneous mixtures. Convention for reporting microbial pathogens guides investigators to report a majority consensus sequence. In our study, we found that this approach would under-report sequence variation in all samples tested. As we find that this sequence heterogeneity is efficiently transmitted from donors to recipients, our findings illustrate that infection complexity must be monitored and reported more completely to understand SARS-CoV-2 infection and transmission dynamics. Many of the nucleotide changes that would not be reported in a majority consensus sequence have now been observed as lineage defining SNPs in Omicron BA.1 and/or BA.2 variants. This suggests that minority alleles in earlier SARS-CoV-2 infections may play an important role in the continuing evolution of new variants of concern.     

Contact tracing and disease control

Contact tracing, followed by treatment or isolation, is a key control measure in the battle against infectious diseases. It is an extreme form of locally targeted control, and as such has the potential to be highly efficient when dealing with low numbers of cases. For this reason it is frequently used to combat sexually transmitted diseases and new invading pathogens. Accurate modelling of contact tracing requires explicit information about the disease-transmission pathways from each individual, and hence the network of contacts. Here, pairwise-approximation methods and full stochastic simulations are used to investigate the utility of contact tracing. A simple relationship is found between the efficiency of contact tracing necessary for eradication and the basic reproductive ratio of the disease. This holds for a wide variety of realistic situations including heterogeneous networks containing core-groups or super-spreaders, and asymptomatic individuals. Clustering (transitivity) within the transmission network is found to destroy the relationship, requiring lower efficiency than predicted.     

Post COVID-19: a solution scan of options for preventing future zoonotic epidemics

The crisis generated by the emergence and pandemic spread of COVID-19 has thrown into the global spotlight the dangers associated with novel diseases, as well as the key role of animals, especially wild animals, as potential sources of pathogens to humans. There is a widespread demand for a new relationship with wild and domestic animals, including suggested bans on hunting, wildlife trade, wet markets or consumption of wild animals. However, such policies risk ignoring essential elements of the problem as well as alienating and increasing hardship for local communities across the world, and might be unachievable at scale. There is thus a need for a more complex package of policy and practical responses. We undertook a solution scan to identify and collate 161 possible options for reducing the risks of further epidemic disease transmission from animals to humans, including potential further SARS-CoV-2 transmission (original or variants). We include all categories of animals in our responses (i.e. wildlife, captive, unmanaged/feral and domestic livestock and pets) and focus on pathogens (especially viruses) that, once transmitted from animals to humans, could acquire epidemic potential through high rates of human-to-human transmission. This excludes measures to prevent well-known zoonotic diseases, such as rabies, that cannot readily transmit between humans. We focused solutions on societal measures, excluding the development of vaccines and other preventive therapeutic medicine and veterinary medicine options that are discussed elsewhere. We derived our solutions through reading the scientific literature, NGO position papers, and industry guidelines, collating our own experiences, and consulting experts in different fields. Herein, we review the major zoonotic transmission pathways and present an extensive list of options. The potential solutions are organised according to the key stages of the trade chain and encompass solutions that can be applied at the local, regional and international scales. This is a set of options targeted at practitioners and policy makers to encourage careful examination of possible courses of action, validating their impact and documenting outcomes.     

OpenABM-Covid19—An agent-based model for non-pharmaceutical interventions against COVID-19 including contact tracing

SARS-CoV-2 has spread across the world, causing high mortality and unprecedented restrictions on social and economic activity. Policymakers are assessing how best to navigate through the ongoing epidemic, with computational models being used to predict the spread of infection and assess the impact of public health measures. Here, we present OpenABM-Covid19: an agent-based simulation of the epidemic including detailed age-stratification and realistic social networks. By default the model is parameterised to UK demographics and calibrated to the UK epidemic, however, it can easily be re-parameterised for other countries. OpenABM-Covid19 can evaluate non-pharmaceutical interventions, including both manual and digital contact tracing, and vaccination programmes. It can simulate a population of 1 million people in seconds per day, allowing parameter sweeps and formal statistical model-based inference. The code is open-source and has been developed by teams both inside and outside academia, with an emphasis on formal testing, documentation, modularity and transparency. A key feature of OpenABM-Covid19 are its Python and R interfaces, which has allowed scientists and policymakers to simulate dynamic packages of interventions and help compare options to suppress the COVID-19 epidemic.     

COVID-19, leprosy,and neutrophils

Coronavirus Disease 2019 (COVID-19), a disease caused by the betacoronavirus Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), has only recently emerged, while Mycobacterium leprae, the etiological agent of leprosy, has endured for more than 2,000 years. As soon as the initial reports of COVID-19 became public, several entities, including the Brazilian Leprosy Society, warned about the possible impact of COVID-19 on leprosy patients. It has been verified that COVID-19 carriers can be either asymptomatic or present varying degrees of severe respiratory failure in association with cytokine storm and death, among other diseases. Severe COVID-19 patients show increased numbers of neutrophils and serum neutrophil extracellular trap (NET) markers, in addition to alterations in the neutrophil-to-lymphocyte ratio (NLR). The absence of antiviral drugs and the speed of COVID-19 transmission have had a major impact on public health systems worldwide, leading to the almost total collapse of many national and local healthcare services. Leprosy, an infectious neurological and dermatological illness, is widely considered to be the most frequent cause of physical disabilities globally. The chronic clinical course of the disease may be interrupted by acute inflammatory episodes, named leprosy reactions. These serious immunological complications, characterized by cytokine storms, are responsible for amplifying peripheral nerve damage. From 30% to 40% of all multibacillary leprosy (MB) patients experience erythema nodosum leprosum (ENL), a neutrophilic immune-mediated condition. ENL patients often present these same COVID-19-like symptoms, including high levels of serum NET markers, altered NLR, and neutrophilia. Moreover, the consequences of a M. leprae–SARS-CoV-2 coinfection have yet to be fully investigated. The goal of the present viewpoint is to describe some of the similarities that may be found between COVID-19 and leprosy disease in the context of neutrophilic biology.     

Contact tracing in stochastic and deterministic epidemic models

We consider a simple unstructured individual based stochastic epidemic model with contact tracing. Even in the onset of the epidemic, contact tracing implies that infected individuals do not act independent of each other. Nevertheless, it is possible to analyze the embedded non-stationary Galton-Watson process. Based upon this analysis, threshold theorems and also the probability for major outbreaks can be derived. Furthermore, it is possible to obtain a deterministic model that approximates the stochastic process, and in this way, to determine the prevalence of disease in the quasi-stationary state and to investigate the dynamics of the epidemic.     

Data-driven multi-scale mathematical modeling of SARS-CoV-2 infection reveals heterogeneity among COVID-19 patients

Patients with coronavirus disease 2019 (COVID-19) often exhibit diverse disease progressions associated with various infectious ability, symptoms, and clinical treatments. To systematically and thoroughly understand the heterogeneous progression of COVID-19, we developed a multi-scale computational model to quantitatively understand the heterogeneous progression of COVID-19 patients infected with severe acute respiratory syndrome (SARS)-like coronavirus (SARS-CoV-2). The model consists of intracellular viral dynamics, multicellular infection process, and immune responses, and was formulated using a combination of differential equations and stochastic modeling. By integrating multi-source clinical data with model analysis, we quantified individual heterogeneity using two indexes, i.e., the ratio of infected cells and incubation period. Specifically, our simulations revealed that increasing the host antiviral state or virus induced type I interferon (IFN) production rate can prolong the incubation period and postpone the transition from asymptomatic to symptomatic outcomes. We further identified the threshold dynamics of T cell exhaustion in the transition between mild-moderate and severe symptoms, and that patients with severe symptoms exhibited a lack of naïve T cells at a late stage. In addition, we quantified the efficacy of treating COVID-19 patients and investigated the effects of various therapeutic strategies. Simulations results suggested that single antiviral therapy is sufficient for moderate patients, while combination therapies and prevention of T cell exhaustion are needed for severe patients. These results highlight the critical roles of IFN and T cell responses in regulating the stage transition during COVID-19 progression. Our study reveals a quantitative relationship underpinning the heterogeneity of transition stage during COVID-19 progression and can provide a potential guidance for personalized therapy in COVID-19 patients.     

Neuroinflammation and COVID-19

Coronavirus disease 2019 (COVID-19) has caused a historic pandemic of respiratory disease. COVID-19 also causes acute and post-acute neurological symptoms, which range from mild, such as headaches, to severe, including hemorrhages. Current evidence suggests that there is no widespread infection of the central nervous system (CNS) by SARS-CoV-2, thus what is causing COVID-19 neurological disease? Here, we review potential immunological mechanisms driving neurological disease in COVID-19 patients. We begin by discussing the implications of imbalanced peripheral immunity on CNS function. Next, we examine the evidence for dysregulation of the blood-brain barrier during SARS-CoV-2 infection. Last, we discuss the role myeloid cells may play in promoting COVID-19 neurological disease. Combined, we highlight the role of innate immunity in COVID-19 neuroinflammation and suggest areas for future research.     

Digging the rabbit hole,COVID-19 edition: anti-vaccine themes and the discourse around COVID-19

This article draws on a broadcast popular among the anti-vaccine community to map out six themes used by the broadcast to mislead viewers about COVID-19. The themes are the claim that “they” – government and pharma – are lying to you, claims that COVID-19 is an excuse to remove civil liberties, viewing everyone as an expert, claiming that science cannot save us, skewing the science, and a claim that “they” are out to harm the viewers. The article points out that similar themes are used to mislead followers with anti-vaccine information. It highlights the concern that these themes will not only mislead people who are already anti-vaccine about the pandemic, but may draw in people who are not anti-vaccine but are seeking information about COVID-19, and suggests some options for dealing with the misinformation. Scientists benefit from understanding these claims, as we are often tasked with providing rebuttals to this misinformation.     

Uncertainty quantification and sensitivity analysis of COVID-19 exit strategies in an individual-based transmission model

Many countries are currently dealing with the COVID-19 epidemic and are searching for an exit strategy such that life in society can return to normal. To support this search, computational models are used to predict the spread of the virus and to assess the efficacy of policy measures before actual implementation. The model output has to be interpreted carefully though, as computational models are subject to uncertainties. These can stem from, e.g., limited knowledge about input parameters values or from the intrinsic stochastic nature of some computational models. They lead to uncertainties in the model predictions, raising the question what distribution of values the model produces for key indicators of the severity of the epidemic. Here we show how to tackle this question using techniques for uncertainty quantification and sensitivity analysis. We assess the uncertainties and sensitivities of four exit strategies implemented in an agent-based transmission model with geographical stratification. The exit strategies are termed Flattening the Curve, Contact Tracing, Intermittent Lockdown and Phased Opening. We consider two key indicators of the ability of exit strategies to avoid catastrophic health care overload: the maximum number of prevalent cases in intensive care (IC), and the total number of IC patient-days in excess of IC bed capacity. Our results show that uncertainties not directly related to the exit strategies are secondary, although they should still be considered in comprehensive analysis intended to inform policy makers. The sensitivity analysis discloses the crucial role of the intervention uptake by the population and of the capability to trace infected individuals. Finally, we explore the existence of a safe operating space. For Intermittent Lockdown we find only a small region in the model parameter space where the key indicators of the model stay within safe bounds, whereas this region is larger for the other exit strategies.     

Digital contact tracing and network theory to stop the spread of COVID-19 using big-data on human mobility geolocalization

The spread of COVID-19 caused by the SARS-CoV-2 virus has become a worldwide problem with devastating consequences. Here, we implement a comprehensive contact tracing and network analysis to find an optimized quarantine protocol to dismantle the chain of transmission of coronavirus with minimal disruptions to society. We track billions of anonymized GPS human mobility datapoints to monitor the evolution of the contact network of disease transmission before and after mass quarantines. As a consequence of the lockdowns, people’s mobility decreases by 53%, which results in a drastic disintegration of the transmission network by 90%. However, this disintegration did not halt the spreading of the disease. Our analysis indicates that superspreading k-core structures persist in the transmission network to prolong the pandemic. Once the k-cores are identified, an optimized strategy to break the chain of transmission is to quarantine a minimal number of ‘weak links’ with high betweenness centrality connecting the large k-cores.