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 interval. 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.
Highlights
On December 31 2019, the city of Wuhan (Hubei province, China) reported an outbreak of atypical pneumonia caused by a novel coronavirus, later on named SARS-CoV-2 [1]
The extent of the effect thereof depends on the efficacy of the control measure in place, on the relationship between symptom onset and infectiousness and on the proportion of infectious individuals that can be detected
We show that their variances and their mean values can differ, suggesting that the use of the mean serial interval as a proxy for the mean generation time can lead to biased estimates of epidemiological quantities
Summary
On December 31 2019, the city of Wuhan (Hubei province, China) reported an outbreak of atypical pneumonia caused by a novel coronavirus, later on named SARS-CoV-2 [1]. Viral and epidemiological characteristics of COVID-19 outbreaks such as asymptomatic transmissions [2, 3] and undiagnosed individuals [4] hastened the spread of the disease, resulting in a global pandemic In response to this threat, several countries adopted drastic and unprecedented control measures such as national and regional lockdown in which diagnosed or traced individuals were confined in isolation or quarantine [5]. Control measures such as quarantine or isolation of infectives intervene in reducing the social contacts of such individuals, and in decreasing their probability of transmission after diagnosis These interventions lead to a delay of the epidemic peak [12] and to a decrease of the effective reproduction number [13]. It is reasonable to think that other epidemic characteristics are affected by public health interventions
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