Bounding the levels of transmissibility & immune evasion of the Omicron variant in South Africa (preprint)

A new SARS-CoV-2 variant of concern, Omicron (B.1.1.529), has been identified based on genomic sequencing and epidemiological data in South Africa. Presumptive Omicron cases in South Africa have grown extremely rapidly, despite high prior exposure and moderate vaccination coverage. The available evidence suggests that Omicron spread is at least in part due to evasion of this immune protection, though Omicron may also exhibit higher intrinsic transmissibility. Using detailed laboratory and epidemiological data from South Africa, we estimate the constraints on these two characteristics of the new variant and their relationship. Our estimates and associated uncertainties provide essential information to inform projection and scenario modeling analyses, which are crucial planning tools for governments around the world. One Sentence Summary We report a region of plausibility for the relative transmissibility and immune escape characteristics of the SARS-CoV-2 Omicron variant estimated by integrating laboratory and epidemiological data from South Africa.

Age-dependence of healthcare interventions for COVID-19 in Ontario, Canada (preprint)

Background: Patient age is the most salient clinical indicator of risk from COVID-19. Age-specific distributions of known SARS-CoV-2 infections and COVID-19-related deaths are available for many regions. Less attention has been given to the age distributions of serious medical interventions administered to COVID-19 patients, which could reveal sources of potential pressure on the healthcare system should SARS-CoV-2 prevalence increase. Methods: We analysed 97,957 known SARS-CoV-2 infection records for Ontario, Canada, from 23 January 2020 to 26 November 2020 and estimated the age distributions of hospitalizations, Intensive Care Unit admissions, intubations, and ventilations. We quantified the probability of hospitalization given known SARS-CoV-2 infection, and of survival given COVID-19-related hospitalization. Results: The distribution of hospitalizations peaks with a wide plateau covering ages 54–90, whereas deaths are concentrated in very old ages. The estimated probability of hospitalization given known infection reaches a maximum of 30.9% at age 80 (95% CI 28.0%–33.9%). The probability of survival given hospitalization is near 100% for adults younger than 40, but declines substantially after this age; for example, a hospitalized 54-year-old patient has a 91.5% chance of surviving COVID-19 (95% CI 87.0%–94.9%). Conclusions: Ontario’s healthcare system has not been overstretched by COVID-19 thanks to wide-spread infection control efforts, yet the probability of survival given hospitalization for COVID-19 is lower than is generally perceived for patients over 40. As pervalence continues to increase during this most recent wave of infection, healthcare capacities are at risk of being exceeded. Survival of individuals in the broad age range requiring acute care could decrease, potentially expanding the distribution of COVID-19-related deaths toward younger ages.

Cohort-based approach to understanding the roles of generation and serial intervals in shaping epidemiological dynamics (preprint)

G eneration intervals and serial intervals are critical quantities for characterizing outbreak dynamics. Generation intervals characterize the time between infection and transmission, while serial intervals characterize the time between the onset of symptoms in a chain of transmission. They are often used interchangeably, leading to misunderstanding of how these intervals link the epidemic growth rate r and the reproduction number ℛ . Generation intervals provide a mechanistic link between r and ℛ but are harder to measure via contact tracing. While serial intervals are easier to measure from contact tracing, recent studies suggest that the two intervals give different estimates of ℛ from r . We present a general framework for characterizing epidemiological delays based on cohorts (i.e., a group of individuals that share the same event time, such as symptom onset) and show that forward-looking serial intervals, which correctly link ℛ with r , are not the same as “intrinsic” serial intervals, but instead change with r . We provide a heuristic method for addressing potential biases that can arise from not accounting for changes in serial intervals across cohorts and apply the method to estimating ℛ for the COVID-19 outbreak in China using serial-interval data — our analysis shows that using incorrectly defined serial intervals can severely bias estimates. This study demonstrates the importance of early epidemiological investigation through contact tracing and provides a rationale for reassessing generation intervals, serial intervals, and ℛ estimates, for COVID-19. Significance Statement The generation- and serial-interval distributions are key, but different, quantities in outbreak analyses. Recent theoretical studies suggest that two distributions give different estimates of the reproduction number ℛ from the exponential growth rate r ; however, both intervals, by definition, describe disease transmission at the individual level. Here, we show that the serial-interval distribution, defined from the correct reference time and cohort, gives the same estimate of ℛ as the generation-interval distribution. We then apply our framework to serial-interval data from the COVID-19 outbreak in China. While our study supports the use of serial-interval distributions in estimating ℛ , it also reveals necessary changes to the current understanding and applications of serial-interval distribution.