No abstract

The soft factorization theorem for 4D abelian gauge theory states that the Smatrix factorizes into soft and hard parts, with the universal soft part containing all soft and collinear poles. Similarly, correlation functions on the sphere in a 2D CFT with a U(1) KacMoody current algebra factorize into current algebra and non-current algebra factors, with the current algebra factor fully determined by its pole structure. In this paper, we show that these 4D and 2D factorizations are mathematically the same phenomena. The soft 't HooftWilson lines and soft photons are realized as a complexified 2D current algebra on the celestial sphere at null infinity. The current algebra level is determined by the cusp anomalous dimension. The associated complex U(1) boson lives on a torus whose modular parameter is τ = 2πi e 2 + θ 2π . The correlators of this 2D current algebra fully reproduce the known soft part of the 4D S-matrix, as well as a conjectured generalization involving magnetic charges.

In the absence of pharmaceutical interventions, social distancing is being used worldwide to curb the spread of COVID-19. The impact of these measures has been inconsistent, with some regions rapidly nearing disease elimination and others seeing delayed peaks or nearly flat epidemic curves. Here we build a stochastic epidemic model to examine the effects of COVID-19 clinical progression and transmission network structure on the outcomes of social distancing interventions. We find that the strength of within-household transmission is a critical determinant of success, governing the timing and size of the epidemic peak, the rate of decline, individual risks of infection, and the success of partial relaxation measures. The structure of residual external connections, driven by workforce participation and essential businesses, interacts to determine outcomes. These findings can improve future predictions of the timescale and efficacy of interventions needed to control similar outbreaks, and highlight the need for better quantification and control of household transmission.

In the absence of pharmaceutical interventions, social distancing is being used worldwide to curb the spread of COVID-19. The impact of these measures has been inconsistent, with some regions rapidly nearing disease elimination and others seeing delayed peaks or nearly flat epidemic curves. Here we build a stochastic epidemic model to examine the effects of COVID-19 clinical progression and transmission network structure on the outcomes of social distancing interventions. Our simulations show that long delays between the adoption of control measures and observed declines in cases, hospitalizations, and deaths occur in many scenarios. We find that the strength of within-household transmission is a critical determinant of success, governing the timing and size of the epidemic peak, the rate of decline, individual risks of infection, and the success of partial relaxation measures. The structure of residual external connections, driven by workforce participation and essential businesses, interacts to determine outcomes. We suggest limited conditions under which the formation of household “bubbles” can be safe. These findings can improve future predictions of the timescale and efficacy of interventions needed to control second waves of COVID-19 as well as other similar outbreaks, and highlight the need for better quantification and control of household transmission.

Billions of doses of COVID-19 vaccines have been administered globally, dramatically reducing SARS-CoV-2 incidence and severity in some settings. Many studies suggest vaccines provide a high degree of protection against infection and disease, but precise estimates vary and studies differ in design, outcomes measured, dosing regime, location, and circulating virus strains. Here we conduct a systematic review of COVID-19 vaccines through February 2022. We included efficacy data from Phase 3 clinical trials for 15 vaccines undergoing WHO Emergency Use Listing evaluation and real-world effectiveness for 8 vaccines with observational studies meeting inclusion criteria. Vaccine metrics collected include protection against asymptomatic infection, any infection, symptomatic COVID-19, and severe outcomes including hospitalization and death, for partial or complete vaccination, and against variants of concern Alpha, Beta, Gamma, Delta, and Omicron. We additionally review the epidemiological principles behind the design and interpretation of vaccine efficacy and effectiveness studies, including important sources of heterogeneity.

Billions of doses of COVID-19 vaccines have been administered around the world, dramatically reducing SARS-CoV-2 incidence in some settings. Many studies suggest vaccines provide a high degree of protection against infection and disease, but precise estimates vary and studies differ in design, outcomes measured, dosing regime, location, and circulating virus strains. Here we conduct a systematic review of COVID-19 vaccines as of August 2021. We included efficacy data from Phase 3 clinical trials for 13 vaccines within the WHO Emergency Use Listing evaluation process and real-world effectiveness for 5 vaccines with observational studies meeting inclusion criteria. Vaccine metrics collected include effects against asymptomatic infection, any infection, symptomatic COVID-19, and severe outcomes including hospitalization and death, for both partial and complete vaccination, and against SARS-CoV-2 variants of concern. In addition, we review the epidemiological principles behind the design and interpretation of vaccine effects, and explain important sources of heterogeneity between studies.

34The COVID-19 pandemic is straining public health systems worldwide and major non-35 pharmaceutical interventions have been implemented to slow its spread [1][2][3][4] . During the initial phase 36 of the outbreak the spread was primarily determined by human mobility 5,6 . Yet empirical evidence 37 on the effect of key geographic factors on local epidemic spread is lacking 7 . We analyse highly-38 resolved spatial variables for cities in China together with case count data in order to investigate 39 the role of climate, urbanization, and variation in interventions across China. Here we show that 40 the epidemic intensity of COVID-19 is strongly shaped by crowding, such that epidemics in dense 41cities are more spread out through time, and denser cities have larger total incidence. Observed 42 differences in epidemic intensity are well captured by a metapopulation model of COVID-19 that 43 explicitly accounts for spatial hierarchies. Densely-populated cities worldwide may experience more 44 prolonged epidemics. Whilst stringent interventions can shorten the time length of these local 45 epidemics, although these may be difficult to implement in many affected settings. 46 47 Main text identify drivers of local transmission in Chinese cities, with a focus on epidemic intensity among 68 provinces in China. 69 70To explore the impact of urbanization, temperature, and humidity, we used daily incidence data of 71 confirmed COVID-19 cases (date of onset) aggregated at the prefectural level (n = 293) in China. 72Prefectures are administrative units that typically have one urban center (Figure 1). We aggregate 73 individual level data that were collected from official government reports 18 . Epidemiological data in each 74 prefecture were truncated to exclude dates before the first and after the last day of reported cases. The 75 shape of epidemic curves varied between prefectures with some showing rapid rises and declines in cases 76 and others showing more prolonged epidemics (Figure 1A). We estimate epidemic intensity for each 77 prefecture from these data by calculating the inverse Shannon entropy of the distribution of incident 78 cases 9 . We define the incidence distribution "# for a given city to be the proportion of COVID-19 cases 79

Massive unemployment during the COVID-19 pandemic could result in an eviction crisis in US cities. Here we model the effect of evictions on SARS-CoV-2 epidemics, simulating viral transmission within and among households in a theoretical metropolitan area. We recreate a range of urban epidemic trajectories and project the course of the epidemic under two counterfactual scenarios, one in which a strict moratorium on evictions is in place and enforced, and another in which evictions are allowed to resume at baseline or increased rates. We find, across scenarios, that evictions lead to significant increases in infections. Applying our model to Philadelphia using locally-specific parameters shows that the increase is especially profound in models that consider realistically heterogenous cities in which both evictions and contacts occur more frequently in poorer neighborhoods. Our results provide a basis to assess eviction moratoria and show that policies to stem evictions are a warranted and important component of COVID-19 control.