COVID-19 cases and deaths in the United States follow Taylor's law for heavy-tailed distributions with infinite variance.

The spatial and temporal patterns of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) cases and COVID-19 deaths in the United States are poorly understood. We show that variations in the cumulative reported cases and deaths by county, state, and date exemplify Taylor's law of fluctuation scaling. Specifically, on day 1 of each month from April 2020 through June 2021, each state's variance (across its counties) of cases is nearly proportional to its squared mean of cases. COVID-19 deaths behave similarly. The lower 99% of counts of cases and deaths across all counties are approximately lognormally distributed. Unexpectedly, the largest 1% of counts are approximately Pareto distributed, with a tail index that implies a finite mean and an infinite variance. We explain why the counts across the entire distribution conform to Taylor's law with exponent two using models and mathematics. The finding of infinite variance has practical consequences. Local jurisdictions (counties, states, and countries) that are planning for prevention and care of largely unvaccinated populations should anticipate the rare but extremely high counts of cases and deaths that occur in distributions with infinite variance. Jurisdictions should prepare collaborative responses across boundaries, because extremely high local counts of cases and deaths may vary beyond the resources of any local jurisdiction.

Global patterns of antigen receptor repertoire disruption across adaptive immune compartments in COVID-19.

Whereas pathogen-specific T and B cells are a primary focus of interest during infectious disease, we have used COVID-19 to ask whether their emergence comes at a cost of broader B cell and T cell repertoire disruption. We applied a genomic DNA-based approach to concurrently study the immunoglobulin-heavy (IGH) and T cell receptor (TCR) ß and δ chain loci of 95 individuals. Our approach detected anticipated repertoire focusing for the IGH repertoire, including expansions of clusters of related sequences temporally aligned with SARS-CoV-2-specific seroconversion, and enrichment of some shared SARS-CoV-2-associated sequences. No significant age-related or disease severity-related deficiencies were noted for the IGH repertoire. By contrast, whereas focusing occurred at the TCRß and TCRδ loci, including some TCRß sequence-sharing, disruptive repertoire narrowing was almost entirely limited to many patients aged older than 50 y. By temporarily reducing T cell diversity and by risking expansions of nonbeneficial T cells, these traits may constitute an age-related risk factor for COVID-19, including a vulnerability to new variants for which T cells may provide key protection.

Time series estimation of the dynamic effects of disaster-type shocks

This paper provides three results for SVARs under the assumption that the primitive shocks are mutually independent. First, a framework is proposed to accommodate a disaster-type variable with infinite variance into a SVAR. We show that the least squares estimates of the SVAR are consistent but have non-standard asymptotics. Second, the disaster shock is identified as the component with the largest kurtosis. An estimator that is robust to infinite variance is used to recover the mutually independent components. Third, an independence test on the residuals pre-whitened by the Choleski decomposition is proposed to test the restrictions imposed on a SVAR. The test can be applied whether the data have fat or thin tails, and to over as well as exactly identified models. Three applications are considered. In the first, the independence test is used to shed light on the conflicting evidence regarding the role of uncertainty in economic fluctuations. In the second, disaster shocks are shown to have short term economic impact arising mostly from feedback dynamics. The third uses the framework to study the dynamic effects of economic shocks post-covid.

On the radar: Predicting near-future surges in skills’ hiring demand to provide early warning to educators

The AI-driven Fourth Industrial Revolution and the COVID-19 pandemic have one important thing in common: they both have caused significant and rapid changes to the skill set landscape of various industries. These disruptive forces mean that the early identification of the newly rising skills in a labour market — which we call its “emerging skills” — is crucial to its workforce. It is also crucial to the educators who, in order to provide lifelong training to the workforce, need to quickly adapt their curricula to the new skills. We propose a classification methodology that uses the past job ad trends of skills to predict the emerging skills of a future period, defined as the skills that have experienced a surge in hiring demand in said period. This general definition allows for freedom in specifying the criteria for a skill being emerging (through thresholds on hiring demand and its growth), which could be important to educators. Applying our methodology to the Information and Communication Technologies (ICT) labour market in Singapore, we show that we are able to predict future emerging skills with good precision and recall and beat two baseline classifiers for multiple threshold sets. Our methodology also allows us to see where job ads fail to provide sufficient predictive signals, pointing to auxiliary data sources (such as Stack Overflow for ICT) and skill ontologies as potential remedies. The success of our method shows how AI can be used to empower learners and educators in the ICT domain (and potentially other domains) with useful and well-curated insights at a moment’s notice, thus helping speed up the process of curricular change.

Safety and immunogenicity of one versus two doses of the COVID-19 vaccine BNT162b2 for patients with cancer: interim analysis of a prospective observational study.

BACKGROUND: The efficacy and safety profiles of vaccines against SARS-CoV-2 in patients with cancer is unknown. We aimed to assess the safety and immunogenicity of the BNT162b2 (Pfizer-BioNTech) vaccine in patients with cancer. METHODS: For this prospective observational study, we recruited patients with cancer and healthy controls (mostly health-care workers) from three London hospitals between Dec 8, 2020, and Feb 18, 2021. Participants who were vaccinated between Dec 8 and Dec 29, 2020, received two 30 µg doses of BNT162b2 administered intramuscularly 21 days apart; patients vaccinated after this date received only one 30 µg dose with a planned follow-up boost at 12 weeks. Blood samples were taken before vaccination and at 3 weeks and 5 weeks after the first vaccination. Where possible, serial nasopharyngeal real-time RT-PCR (rRT-PCR) swab tests were done every 10 days or in cases of symptomatic COVID-19. The coprimary endpoints were seroconversion to SARS-CoV-2 spike (S) protein in patients with cancer following the first vaccination with the BNT162b2 vaccine and the effect of vaccine boosting after 21 days on seroconversion. All participants with available data were included in the safety and immunogenicity analyses. Ongoing follow-up is underway for further blood sampling after the delayed (12-week) vaccine boost. This study is registered with the NHS Health Research Authority and Health and Care Research Wales (REC ID 20/HRA/2031). FINDINGS: 151 patients with cancer (95 patients with solid cancer and 56 patients with haematological cancer) and 54 healthy controls were enrolled. For this interim data analysis of the safety and immunogenicity of vaccinated patients with cancer, samples and data obtained up to March 19, 2021, were analysed. After exclusion of 17 patients who had been exposed to SARS-CoV-2 (detected by either antibody seroconversion or a positive rRT-PCR COVID-19 swab test) from the immunogenicity analysis, the proportion of positive anti-S IgG titres at approximately 21 days following a single vaccine inoculum across the three cohorts were 32 (94%; 95% CI 81-98) of 34 healthy controls; 21 (38%; 26-51) of 56 patients with solid cancer, and eight (18%; 10-32) of 44 patients with haematological cancer. 16 healthy controls, 25 patients with solid cancer, and six patients with haematological cancer received a second dose on day 21. Of the patients with available blood samples 2 weeks following a 21-day vaccine boost, and excluding 17 participants with evidence of previous natural SARS-CoV-2 exposure, 18 (95%; 95% CI 75-99) of 19 patients with solid cancer, 12 (100%; 76-100) of 12 healthy controls, and three (60%; 23-88) of five patients with haematological cancers were seropositive, compared with ten (30%; 17-47) of 33, 18 (86%; 65-95) of 21, and four (11%; 4-25) of 36, respectively, who did not receive a boost. The vaccine was well tolerated; no toxicities were reported in 75 (54%) of 140 patients with cancer following the first dose of BNT162b2, and in 22 (71%) of 31 patients with cancer following the second dose. Similarly, no toxicities were reported in 15 (38%) of 40 healthy controls after the first dose and in five (31%) of 16 after the second dose. Injection-site pain within 7 days following the first dose was the most commonly reported local reaction (23 [35%] of 65 patients with cancer; 12 [48%] of 25 healthy controls). No vaccine-related deaths were reported. INTERPRETATION: In patients with cancer, one dose of the BNT162b2 vaccine yields poor efficacy. Immunogenicity increased significantly in patients with solid cancer within 2 weeks of a vaccine boost at day 21 after the first dose. These data support prioritisation of patients with cancer for an early (day 21) second dose of the BNT162b2 vaccine. FUNDING: King's College London, Cancer Research UK, Wellcome Trust, Rosetrees Trust, and Francis Crick Institute.

Count-valued time series models for COVID-19 daily death dynamics.

We propose a generalized non-linear state-space model for count-valued time series of COVID-19 fatalities. To capture the dynamic changes in daily COVID-19 death counts, we specify a latent state process that involves second-order differencing and an AR(1)-ARCH(1) model. These modelling choices are motivated by the application and validated by model assessment. We consider and fit a progression of Bayesian hierarchical models under this general framework. Using COVID-19 daily death counts from New York City's five boroughs, we evaluate and compare the considered models through predictive model assessment. Our findings justify the elements included in the proposed model. The proposed model is further applied to time series of COVID-19 deaths from the four most populous counties in Texas. These model fits illuminate dynamics associated with multiple dynamic phases and show the applicability of the framework to localities beyond New York City.

Author Correction: A dynamic COVID-19 immune signature includes associations with poor prognosis.

A Correction to this paper has been published: https://doi.org/10.1038/s41591-020-01186-5.

Acute Immune Signatures and Their Legacies in Severe Acute Respiratory Syndrome Coronavirus-2 Infected Cancer Patients.

Given the immune system's importance for cancer surveillance and treatment, we have investigated how it may be affected by SARS-CoV-2 infection of cancer patients. Across some heterogeneity in tumor type, stage, and treatment, virus-exposed solid cancer patients display a dominant impact of SARS-CoV-2, apparent from the resemblance of their immune signatures to those for COVID-19+ non-cancer patients. This is not the case for hematological malignancies, with virus-exposed patients collectively displaying heterogeneous humoral responses, an exhausted T cell phenotype and a high prevalence of prolonged virus shedding. Furthermore, while recovered solid cancer patients' immunophenotypes resemble those of non-virus-exposed cancer patients, recovered hematological cancer patients display distinct, lingering immunological legacies. Thus, while solid cancer patients, including those with advanced disease, seem no more at risk of SARS-CoV-2-associated immune dysregulation than the general population, hematological cancer patients show complex immunological consequences of SARS-CoV-2 exposure that might usefully inform their care.

Using Cardiovascular Cells from Human Pluripotent Stem Cells for COVID-19 Research: Why the Heart Fails.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) led to the coronavirus disease (COVID-19) outbreak that became a pandemic in 2020, causing more than 30 million infections and 1 million deaths to date. As the scientific community has looked for vaccines and drugs to treat or eliminate the virus, unexpected features of the disease have emerged. Apart from respiratory complications, cardiovascular disease has emerged as a major indicator of poor prognosis in COVID-19. It has therefore become of utmost importance to understand how SARS-CoV-2 damages the heart. Human pluripotent stem cell (hPSC) cardiovascular derivatives were rapidly recognized as an invaluable tool to address this, not least because one of the major receptors for the virus is not recognized by SARS-CoV-2 in mice. Here, we outline how hPSC-derived cardiovascular cells have been utilized to study COVID-19, and their potential for further understanding the cardiac pathology and in therapeutic development.

A dynamic COVID-19 immune signature includes associations with poor prognosis.

Improved understanding and management of COVID-19, a potentially life-threatening disease, could greatly reduce the threat posed by its etiologic agent, SARS-CoV-2. Toward this end, we have identified a core peripheral blood immune signature across 63 hospital-treated patients with COVID-19 who were otherwise highly heterogeneous. The signature includes discrete changes in B and myelomonocytic cell composition, profoundly altered T cell phenotypes, selective cytokine/chemokine upregulation and SARS-CoV-2-specific antibodies. Some signature traits identify links with other settings of immunoprotection and immunopathology; others, including basophil and plasmacytoid dendritic cell depletion, correlate strongly with disease severity; while a third set of traits, including a triad of IP-10, interleukin-10 and interleukin-6, anticipate subsequent clinical progression. Hence, contingent upon independent validation in other COVID-19 cohorts, individual traits within this signature may collectively and individually guide treatment options; offer insights into COVID-19 pathogenesis; and aid early, risk-based patient stratification that is particularly beneficial in phasic diseases such as COVID-19.