Inferring time-varying generation time, serial interval, and incubation period distributions for COVID-19

Abstract

The generation time distribution, reflecting the time between successive infections in transmission chains, is a key epidemiological parameter for describing COVID-19 transmission dynamics. However, because exact infection times are rarely known, it is often approximated by the serial interval distribution. This approximation holds under the assumption that infectors and infectees share the same incubation period distribution, which may not always be true. We estimated incubation period and serial interval distributions using 629 transmission pairs reconstructed by investigating 2989 confirmed cases in China in January-February 2020, and developed an inferential framework to estimate the generation time distribution that accounts for variation over time due to changes in epidemiology, sampling biases and public health and social measures. We identified substantial reductions over time in the serial interval and generation time distributions. Our proposed method provides more reliable estimation of the temporal variation in the generation time distribution, improving assessment of transmission dynamics.

Inhaled Lipid Nanoparticles Alleviate Established Pulmonary Fibrosis

“…Then, the whole apparatus was equilibrated at 37 °C for 1 h. Next, 40 µL of ICG-loaded liposomes was added into the upper layer of mucus. Then, 50 µL of the medium in the receptor chamber was withdrawn at different time durations (1,6,12, and 24 h), and the concentration of ICG was measured through a microplate reader (endptor chamberem = 821 nm).…”

“…Pulmonary fibrosis, a condition of the lung with excessive scarring, is a kind of chronic, progressive, and ultimately fatal interstitial lung disease that affects >5 million people worldwide. [1,2] Severe infection, [3] chemotherapy, [4] radiotherapy, [5] environmental exposure, [6] or unknown etiologies [7] are able to cause pulmonary fibrosis. According to clinical reports, pulmonary fibrosis is an important clinical manifestation and a common sequela of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection.…”

Key Challenges for Respiratory Virus Surveillance while Transitioning out of Acute Phase of COVID-19 Pandemic

“…For example, the generation interval can most directly be estimated by linking dates of infection onset for infector–infectee pairs, data not collected through traditional case-based surveillance. Contact tracing activities and outbreak investigations do typically collect relevant data ( 8 ); however, those activities are not routine for patients with viral respiratory infections (outside of a pandemic), leaving a potential knowledge gap during interpandemic periods. Knowledge of the generation interval is required for accurate estimation of the effective reproduction number (the expected number of secondary infections caused by a primary infection) ( 44 ), which is critical in evaluating the likely effect of alternative control strategies.…”

“…The generation interval of SARS-CoV-2 changed as the pathogen evolved ( 45 , 46 ) and as viewed under different policy ( 8 ) and transmission ( 45 ) settings. If surveillance were augmented by genomic data (Appendix, section C) from infector–infectee pairs during periods of variant emergence, changes in the generation interval between variants could be measured in real time and used to assess potential mechanisms of transmission advantage.…”

“…For example, in the United Kingdom, national infection prevalence surveys provided near–real-time insight into the infection (as opposed to case) dynamics of SARS-CoV-2 ( 4 , 5 ), including infection rates over time by age group ( 6 ). Household studies and the systematic collection of contact tracing data, particularly in Europe ( 7 ) and Asia ( 8 ), enabled estimation of key biological quantities affecting transmission (e.g., the generation interval). Systematic collection of behavioral data from population surveys—with prominent examples in Australia ( 9 ), Hong Kong ( 10 ), and the United Kingdom ( 11 )—provided information on the influence of public health measures, including the functioning of various surveillance components.…”