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IMPORTANCEThe dynamics of coronavirus disease 2019 transmissibility are yet to be fully understood. Better understanding of the transmission dynamics is important for the development and evaluation of effective control policies.OBJECTIVE To delineate the transmission dynamics of COVID-19 and evaluate the transmission risk at different exposure window periods before and after symptom onset. DESIGN, SETTING, AND PARTICIPANTSThis prospective case-ascertained study in Taiwan included laboratory-confirmed cases of COVID-19 and their contacts. The study period was from January 15 to March 18, 2020. All close contacts were quarantined at home for 14 days after their last exposure to the index case. During the quarantine period, any relevant symptoms (fever, cough, or other respiratory symptoms) of contacts triggered a COVID-19 test. The final follow-up date was April 2, 2020. MAIN OUTCOMES AND MEASURESSecondary clinical attack rate (considering symptomatic cases only) for different exposure time windows of the index cases and for different exposure settings (such as household, family, and health care). RESULTSWe enrolled 100 confirmed patients, with a median age of 44 years (range, 11-88 years), including 44 men and 56 women. Among their 2761 close contacts, there were 22 paired index-secondary cases. The overall secondary clinical attack rate was 0.7% (95% CI, 0.4%-1.0%). The attack rate was higher among the 1818 contacts whose exposure to index cases started within 5 days of symptom onset (1.0% [95% CI, 0.6%-1.6%]) compared with those who were exposed later (0 cases from 852 contacts; 95% CI, 0%-0.4%). The 299 contacts with exclusive presymptomatic exposures were also at risk (attack rate, 0.7% [95% CI, 0.2%-2.4%]). The attack rate was higher among household (4.6% [95% CI, 2.3%-9.3%]) and nonhousehold (5.3% [95% CI, 2.1%-12.8%]) family contacts than that in health care or other settings. The attack rates were higher among those aged 40 to 59 years (1.1% [95% CI, 0.6%-2.1%]) and those aged 60 years and older (0.9% [95% CI, 0.3%-2.6%]). CONCLUSIONS AND RELEVANCEIn this study, high transmissibility of COVID-19 before and immediately after symptom onset suggests that finding and isolating symptomatic patients alone may not suffice to contain the epidemic, and more generalized measures may be required, such as social distancing.

Preterm birth is commonly defined as any birth before 37 weeks completed weeks of gestation. An estimated 15 million infants are born preterm globally, disproportionately affecting low and middle income countries (LMIC). It contributes directly to estimated one million neonatal deaths annually and is a significant contributor to childhood morbidity. However, in many clinical settings, the information available to calculate completed weeks of gestation varies widely. Accurate dating of the last menstrual period (LMP), as well as access to clinical and ultrasonographic evaluation are important components of gestational age assessment antenatally. This case definition assign levels of confidence to categorisation of births as preterm, utilising assessment modalities which may be available across different settings. These are designed to enable systematic safety evaluation of vaccine clinical trials and post-implementation programmes of immunisations in pregnancy.

Taiwan Centres for Disease Control.

BackgroundThe dynamics of coronavirus disease 2019 (COVID-19) transmissibility after symptom onset remains unknown. MethodsWe conducted a prospective case-ascertained study on laboratory-confirmed COVID-19 cases and their contacts. Secondary clinical attack rate (considering symptomatic cases only) was analyzed for different exposure windows after symptom onset of index cases and for different exposure settings. ResultsThirty-two confirmed patients were enrolled and 12 paired data (index-secondary cases) were identified among the 1,043 contacts. The secondary clinical attack rate was 0.9% (95% CI 0.5-1.7%). The attack rate was higher among those whose exposure to index cases started within five days of symptom onset (2.4%, 95% CI 1.1-4.5%) than those who were exposed later (zero case from 605 close contacts, 95% CI 0-0.61%). The attack rate was also higher among household contacts (13.6%, 95% CI 4.7-29.5%) and nonhousehold family contacts (8.5%, 95% CI 2.4-20.3%) than that in healthcare or other settings. The higher secondary clinical attack rate for contacts near symptom onset remained when the analysis was restricted to household and family contacts. There was a trend of increasing attack rate with the age of contacts (p for trend < 0.001). ConclusionsHigh transmissibility of COVID-19 near symptom onset suggests that finding and isolating symptomatic patients alone may not suffice to contain the epidemic, and more generalized social distancing measures are required. Rapid reduction of transmissibility over time implies that prolonged hospitalization of mild cases might not be necessary in large epidemics.

Beginning in December of 2019, a novel coronavirus, SARS-CoV-2, emerged in China and is now a global pandemic with extensive morbidity and mortality. With the emergence of this threat, an unprecedented effort to develop vaccines against this virus began. As vaccines are now being introduced globally, we face the prospect of millions of people being vaccinated with multiple types of vaccines many of which use new vaccine platforms. Since medical events happen without vaccines, it will be important to know at what rate events occur in the background so that when adverse events are identified one has a frame of reference with which to compare the rates of these events so as to make an initial assessment as to whether there is a potential safety concern or not. Background rates vary over time, by geography, by sex, socioeconomic status and by age group. Here we describe two key steps for post-introduction safety evaluation of COVID-19 vaccines: Defining a dynamic list of Adverse Events of Special Interest (AESI) and establishing background rates for these AESI. We use multiple examples to illustrate use of rates and caveats for their use. In addition we discuss tools available from the Brighton Collaboration that facilitate case evaluation and understanding of AESI.

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BackgroundDuring the 2009 H1N1 pandemic, pregnant women were prioritized to receive the unadjuvanted or MF59®-adjuvanted pandemic A (H1N1) 2009 monovalent vaccines (“2009 H1N1 vaccines”) in Taiwan regardless of stage of pregnancy. Monitoring adverse events following 2009 H1N1 vaccination in pregnant women was a priority for the mass immunization campaign beginning November 2009.Methods/FindingsWe characterized reports to the national passive surveillance from November 2009 through August 2010 involving adverse events following 2009 H1N1 vaccines among pregnant women. Reports from the passive surveillance were matched to a large-linked database on a unique identifier, date of vaccination, and date of diagnosis in a capture-recapture analysis to estimate the true number of spontaneous abortion after 2009 H1N1 vaccination. We verified 16 spontaneous abortions, 11 stillbirths, 4 neonatal deaths, 4 nonpregnancy-specific adverse events, and 2 inadvertent immunizations in recipients who were unaware of pregnancy at time of vaccination. The Chapman capture-recapture estimator of true number of spontaneous abortion after 2009 H1N1 vaccination was 329 (95% confidence interval [CI] 196–553). Of the 14,474 pregnant women who received the 2009 H1N1 vaccines, the estimated risk of spontaneous abortion was 2.3 (95% CI, 1.4–3.8) per 100 pregnancies, compared with a local background rate of 12.8 (95% CI, 12.8–12.9) per 100 pregnancies.ConclusionsThe passive surveillance provided rapid initial assessment of adverse events after 2009 H1N1 vaccination among pregnant women. Its findings were reassuring for the safety of 2009 H1N1 vaccines in pregnancy.