Insights into COVID-19 epidemiology and control from temporal changes in serial interval distributions in Hong Kong (preprint)

The serial interval distribution is used to approximate the generation time distribution, an essential parameter to predict the effective reproductive number "Rt", a measure of transmissibility. However, serial interval distributions may change as an epidemic progresses rather than remaining constant. Here we show that serial intervals in Hong Kong varied over time, closely associated with the temporal variation in COVID-19 case profiles and public health and social measures that were implemented in response to surges in community transmission. Quantification of the variation over time in serial intervals led to improved estimation of Rt, and provided additional insights into the impact of public health measures on transmission of infections. One-Sentence SummaryReal-time estimates of serial interval distributions can improve assessment of COVID-19 transmission dynamics and control.

Time-varying transmission heterogeneity of SARS and COVID-19 in Hong Kong (preprint)

Transmission heterogeneity is a notable feature of the severe acute respiratory syndrome (SARS) and coronavirus disease 2019 (COVID-19) epidemics, though previous efforts to estimate how heterogeneity changes over time are limited. Using contact tracing data, we compared the epidemiology of SARS and COVID-19 infection in Hong Kong in 2003 and 2020-21 and estimated time-varying transmission heterogeneity (kt) by fitting negative binomial models to offspring distributions generated across variable observation windows. kt fluctuated over time for both COVID-19 and SARS on a continuous scale though SARS exhibited significantly greater (p < 0.001) heterogeneity compared to COVID-19 overall and in-time. For COVID-19, kt declined over time and was significantly associated with increasingly stringent non-pharmaceutical interventions though similar evidence for SARS was inconclusive. Underdetection of sporadic COVID-19 cases led to a moderate overestimation of kt, indicating COVID-19 heterogeneity of could be greater than observed. Time-varying or real-time estimates of transmission heterogeneity could become a critical indicator for epidemic intelligence in the future.

Medication Changes and Diagnosis of New Chronic Illnesses in COVID-19 Intensive Care Unit Survivors.

BACKGROUND: Currently, there is limited literature on the impact of the COVID-19 infection on medications and medical conditions in COVID-19 intensive care unit (ICU) survivors. Our study is, to our knowledge, the first multicenter study to describe the prevalence of new medical conditions and medication changes at hospital discharge in COVID-19 ICU survivors. OBJECTIVE: To determine the number of medical conditions and medications at hospital admission compared to at hospital discharge in COVID-19 ICU survivors. METHODS: Retrospective multicenter observational study (7 ICUs) evaluated new medical conditions and medication changes at hospital discharge in patients with COVID-19 infection admitted to an ICU between March 1, 2020, to March 1, 2021. Patient and hospital characteristics, baseline and hospital discharge medication and medical conditions, ICU and hospital length of stay, and Charlson comorbidity index were collected. Descriptive statistics were used to describe patient characteristics and number and type of medical conditions and medications. Paired t-test was used to compare number of medical conditions and medications from hospital discharge to admission. RESULTS: Of the 973 COVID-19 ICU survivors, 67.4% had at least one new medical condition and 88.2% had at least one medication change. Median number of medical conditions (increased from 3 to 4, P < .0001) and medications (increased from 5 to 8, P < .0001) increased from admission to discharge. Most common new medical conditions at discharge were pulmonary disorders, venous thromboembolism, psychiatric disorders, infection, and diabetes. Most common therapeutic categories associated with medication change were cardiology, gastroenterology, pain, hematology, and endocrinology. CONCLUSION AND RELEVANCE: Our study found that the number of medical conditions and medications increased from hospital admission to discharge. Our results provide additional data to help guide providers on using targeted approaches to manage medications and diseases in COVID-19 ICU survivors after hospital discharge.