Fundamental principles of epidemic spread highlight the immediate need for large-scale serological surveys to assess the stage of the SARS-CoV-2 epidemic

Abstract

The spread of a novel pathogenic infectious agent eliciting protective immunity is typically characterised by three distinct phases: (I) an initial phase of slow accumulation of new infections (often undetectable), (II) a second phase of rapid growth in cases of infection, disease and death, and (III) an eventual slow down of transmission due to the depletion of susceptible individuals, typically leading to the termination of the (first) epidemic wave. Before the implementation of control measures (e.g. social distancing, travel bans, etc) and under the assumption that infection elicits protective immunity, epidemiological theory indicates that the ongoing epidemic of SARS-CoV-2 will conform to this pattern. Here, we calibrate a susceptible-infected-recovered (SIR) model to data on cumulative reported SARS-CoV-2 associated deaths from the United Kingdom (UK) and Italy under the assumption that such deaths are well reported events that occur only in a vulnerable fraction of the population. We focus on model solutions which take into consideration previous estimates of critical epidemiological parameters such as the basic reproduction number (R0), probability of death in the vulnerable fraction of the population, infectious period and time from infection to death, with the intention of exploring the sensitivity of the system to the actual fraction of the population vulnerable to severe disease and death. Our simulations are in agreement with other studies that the current epidemic wave in the UK and Italy in the absence of interventions should have an approximate duration of 2-3 months, with numbers of deaths lagging behind in time relative to overall infections. Importantly, the results we present here suggest the ongoing epidemics in the UK and Italy started at least a month before the first reported death and have already led to the accumulation of significant levels of herd immunity in both countries. There is an inverse relationship between the proportion currently immune and the fraction of the population vulnerable to severe disease. This relationship can be used to determine how many people will require hospitalisation (and possibly die) in the coming weeks if we are able to accurately determine current levels of herd immunity. There is thus an urgent need for investment in technologies such as virus (or viral pseudotype) neutralization assays and other robust assays which provide reliable read-outs of protective immunity, and for the provision of open access to valuable data sources such as blood banks and paired samples of acute and convalescent sera from confirmed cases of SARS-CoV-2 to validate these. Urgent development and assessment of such tests should be followed by rapid implementation at scale to provide real-time data. These data will be critical to the proper assessment of the effects of social distancing and other measures currently being adopted to slow down the case incidence and for informing future policy direction.

Double trouble? When a pandemic and seasonal virus collide

“…An alternative hypothesis is that of ecological interference between influenza and SARS-Cov2 [27]. Recent modeling studies [28] and genomic analyses [29] suggest that SARS-Cov-2 may have been circulating outside China since mid-January. If this is further substantiated with data, it may suggest that SARS-Cov-2 circulation created pressure for infected individuals to self-isolate, reducing the e↵ective susceptible pool available to the influenza virus.…”

Is the impact of social distancing on coronavirus growth rates effective across different settings? A non-parametric and local regression approach to test and compare the growth rate

“…In terms of prediction, the author supports those epidemiologists who are calling for better data (Lourenço et al, 2020) through testing, and who are presenting a range of scenarios ; until such time that a consensus emerges from a multi-modelling perspective. In particular, the author cautions against giving much weight to models that extrapolate from growth rates, or that assume other countries will necessarily follow the same path as China, in order to avoid a type 1 error and rejection of a true null hypothesis.…”

On Data-Driven Management of the COVID-19 Outbreak in South Africa

“…Thus we scale up the confirmed cases reported on 05/04/2020 using correction factors from Hubei, China and Lombardy Italy [10]. We further assume an infectious period of 14 days as suggested by [1] and explore basic reproductive numbers of 2,3,4 inline with related literature [1,4,9]. Figure 3: Simulation of COVID-19 infected population progression in South Africa using various initialisation from April 5 2020…”