On realized serial and generation intervals given control measures: The COVID-19 pandemic case

Abstract

The SARS-CoV-2 pathogen is currently spreading worldwide and its propensity for presymptomatic and asymptomatic transmission makes it difficult to control. The control measures adopted in several countries aim at isolating individuals once diagnosed, limiting their social interactions and consequently their transmission probability. These interventions, which have a strong impact on the disease dynamics, can affect the inference of the epidemiological quantities. We first present a theoretical explanation of the effect caused by non-pharmaceutical intervention measures on the mean serial and generation intervals. Then, in a simulation study, we vary the assumed efficacy of control measures and quantify the effect on the mean and variance of realized generation and serial intervals. The simulation results show that the realized serial and generation intervals both depend on control measures and their values contract according to the efficacy of the intervention strategies. Interestingly, the mean serial interval differs from the mean generation time. The deviation between these two values depends on two factors. First, the number of undiagnosed infectious individuals. Second, the relationship between infectiousness, symptom onset and timing of isolation. Similarly, the standard deviations of realized serial and generation intervals do not coincide, with the former shorter than the latter on average. The findings of this study are directly relevant to estimates performed for the current COVID-19 pandemic. In particular, the effective reproduction number is often inferred using both daily incidence data and the generation interval. Failing to account for either contraction or mis-specification by using the serial interval could lead to biased estimates of the effective reproduction number. Consequently, this might affect the choices made by decision makers when deciding which control measures to apply based on the value of the quantity thereof.

Exploring the Interaction between E484K and N501Y Substitutions of SARS-CoV-2 in Shaping the Transmission Advantage of COVID-19 in Brazil: A Modeling Study

“…However, the long-term trends of R t estimates are unlikely to change due to slight variation in GT. 75 , 86 Thus, we consider the impact of this limitation on the inference of transmission advantage may be negligible, and our model can be extended to a more complex context with the time-varying GT data available. Fourth, due to the lack of data from different Brazilian regions, we aggregated the national COVID-19 cases in Brazil to reconstruct the reproduction number series ( Figure 1B ).…”

Controlling SARS-CoV-2 in schools using repetitive testing strategies

“…We assume full compliance to testing, that could potentially be reached since saliva sampling is less invasive compared to other specimen collection procedures. Infectious individuals become PCR detectable 2 days after infection, as previously assumed [20,21]. For the reactive screening testing policy, we assume that there is a one day screening delay.…”

“…Individual-based school model Transmission model We extend the SARS-CoV-2 transmission model presented by Torneri et al [21] to investigate school settings. In this model, the infection dynamic is described as follows.…”

Correlation between times to SARS-CoV-2 symptom onset and secondary transmission undermines epidemic control efforts

“…11,12 However, use of the serial interval as a proxy for generation time can lead to biased estimates of the effective reproduction number due to factors such as differences in their variances, the presence of asymptomatic infections, and the ability for the serial interval to have negative values (infectee onset preceding infector onset). 8,13–16…”

“…11,12 However, use of the serial interval as a proxy for generation time can lead to biased estimates of the effective reproduction number due to factors such as differences in their variances, the presence of asymptomatic infections, and the ability for the serial interval to have negative values (infectee onset preceding infector onset). 8,[13][14][15][16] Generation intervals depend on many factors, such as the ratio of infected to susceptible persons among contacts, as well as the behavior of infected persons. 17 Actions such as self-isolation after symptom onset can shorten generation intervals by limiting the opportunity for infected individuals to infect others during their early symptomatic period.…”