scholarly journals More studies showing longer COVID‐19 incubation period in older adults and questioning the appropriate times for quarantine and contact tracing

2020 ◽  
Vol 3 (4) ◽  
pp. 278-279
Author(s):  
Tak‐kwan Kong
Keyword(s):  
Older Adults ◽  
Incubation Period ◽  
Contact Tracing

scholarly journals Subcritical Transmission in the Early Stage of COVID-19 in Korea

2021 ◽  
Vol 18 (3) ◽  
pp. 1265
Author(s):  
Yong Sul Won ◽  
Jong-Hoon Kim ◽  
Chi Young Ahn ◽  
Hyojung Lee
Keyword(s):  
Incubation Period ◽  
Early Stage ◽  
Probability Distributions ◽  
Likelihood Estimation ◽  
Contact Tracing ◽  
Statistical Characterization ◽  
Serial Interval ◽  
Imported Cases ◽  
The Mean ◽  
Reporting Delays

While the coronavirus disease 2019 (COVID-19) outbreak has been ongoing in Korea since January 2020, there were limited transmissions during the early stages of the outbreak. In the present study, we aimed to provide a statistical characterization of COVID-19 transmissions that led to this small outbreak. We collated the individual data of the first 28 confirmed cases reported from 20 January to 10 February 2020. We estimated key epidemiological parameters such as reporting delay (i.e., time from symptom onset to confirmation), incubation period, and serial interval by fitting probability distributions to the data based on the maximum likelihood estimation. We also estimated the basic reproduction number (R0) using the renewal equation, which allows for the transmissibility to differ between imported and locally transmitted cases. There were 16 imported and 12 locally transmitted cases, and secondary transmissions per case were higher for the imported cases than the locally transmitted cases (nine vs. three cases). The mean reporting delays were estimated to be 6.76 days (95% CI: 4.53, 9.28) and 2.57 days (95% CI: 1.57, 4.23) for imported and locally transmitted cases, respectively. The mean incubation period was estimated to be 5.53 days (95% CI: 3.98, 8.09) and was shorter than the mean serial interval of 6.45 days (95% CI: 4.32, 9.65). The R0 was estimated to be 0.40 (95% CI: 0.16, 0.99), accounting for the local and imported cases. The fewer secondary cases and shorter reporting delays for the locally transmitted cases suggest that contact tracing of imported cases was effective at reducing further transmissions, which helped to keep R0 below one and the overall transmissions small.

Tracing COVID-19 - Older Adults’ Attitudes Toward Digital Contact Tracing and How to Increase Their Participation

2021 ◽  
Author(s):  
Philipp Wagner ◽  
Anna Winkler ◽  
Irina Paraschivoiu ◽  
Alexander Meschtscherjakov ◽  
Magdalena Gärtner ◽  
...  
Keyword(s):  
Older Adults ◽  
Contact Tracing

scholarly journals Estimation of incubation period and serial interval of COVID-19: analysis of 178 cases and 131 transmission chains in Hubei province, China

2020 ◽  
Vol 148 ◽  
Author(s):  
Lin Yang ◽  
Jingyi Dai ◽  
Jun Zhao ◽  
Yunfu Wang ◽  
Pingji Deng ◽  
...  
Keyword(s):  
Incubation Period ◽  
Failure Time ◽  
Age Groups ◽  
Hubei Province ◽  
Interval Censoring ◽  
Contact Tracing ◽  
Considerable Proportion ◽  
Outbreak Period ◽  
Serial Interval ◽  
Incubation Periods

Abstract A novel coronavirus disease, designated as COVID-19, has become a pandemic worldwide. This study aims to estimate the incubation period and serial interval of COVID-19. We collected contact tracing data in a municipality in Hubei province during a full outbreak period. The date of infection and infector–infectee pairs were inferred from the history of travel in Wuhan or exposed to confirmed cases. The incubation periods and serial intervals were estimated using parametric accelerated failure time models, accounting for interval censoring of the exposures. Our estimated median incubation period of COVID-19 is 5.4 days (bootstrapped 95% confidence interval (CI) 4.8–6.0), and the 2.5th and 97.5th percentiles are 1 and 15 days, respectively; while the estimated serial interval of COVID-19 falls within the range of −4 to 13 days with 95% confidence and has a median of 4.6 days (95% CI 3.7–5.5). Ninety-five per cent of symptomatic cases showed symptoms by 13.7 days (95% CI 12.5–14.9). The incubation periods and serial intervals were not significantly different between male and female, and among age groups. Our results suggest a considerable proportion of secondary transmission occurred prior to symptom onset. And the current practice of 14-day quarantine period in many regions is reasonable.

scholarly journals Epidemiological characteristics of the first 100 cases of coronavirus disease 2019 (COVID-19) in Hong Kong Special Administrative Region, China, a city with a stringent containment policy

2020 ◽  
Vol 49 (4) ◽  
pp. 1096-1105 ◽  
Author(s):  
Christopher K C Lai ◽  
Rita W Y Ng ◽  
Martin C S Wong ◽  
Ka Chun Chong ◽  
Yun Kit Yeoh ◽  
...  
Keyword(s):  
Hong Kong ◽  
Incubation Period ◽  
Mainland China ◽  
Control Measures ◽  
Contact Tracing ◽  
Border Control ◽  
Government Information ◽  
Epidemic Curve ◽  
Containment Policy ◽  
Epidemiological Characteristics

Abstract Background Hong Kong (HK) is a densely populated city near the epicentre of the coronavirus disease 2019 (COVID-19) outbreak. Stringent border control together with aggressive case finding, contact tracing, social distancing and quarantine measures were implemented to halt the importation and spread of the virus. Methods We performed an epidemiological study using government information covering the first 100 confirmed cases to examine the epidemic curve, incidence, clusters, reproduction number (Rt), incubation period and time to containment. Results A total of 93 of the 100 cases were HK residents (6 infected in Mainland China, 10 on the Diamond Princess Cruise). Seven were visitors infected in Mainland China before entering HK. The majority (76%) were aged ≥45 years, and the incidence increased with age (P < 0.001). Escalation of border control measures correlated with a decrease in the proportion (62.5% to 0%) of cases imported from Mainland China, and a reduction in Rt (1.07 to 0.75). The median incubation period was 4.2 days [95% confidence interval (CI), 4.0–4.5; 5th and 95th percentiles: 1.3 and 14.0). Most clusters with identifiable epidemiological links were households involving 2–4 people. Three medium-spreading events were identified: two from New Year gatherings (6–11 people), and another from environmental contamination of a worship hall (12 people). Despite intensified contact tracing, containment was delayed in 78.9% of cases (mean = 5.96 days, range = 0–24 days). An unusual transmission in a multi-storey building via faulty toilet plumbing was suspected with >100 residents evacuated overnight. Our analysis indicated that faulty plumbing was unlikely to be the source of this transmission. Conclusion Timely stringent containment policies minimized the importation and transmission of COVID-19 in HK.

scholarly journals Detecting pathogen exposure during the non-symptomatic incubation period using physiological data

2017 ◽  
Author(s):  
Lauren Milechin ◽  
Shakti Davis ◽  
Tejash Patel ◽  
Mark Hernandez ◽  
Greg Ciccarelli ◽  
...  
Keyword(s):  
Early Detection ◽  
Incubation Period ◽  
Clinical Symptoms ◽  
Characteristic Curve ◽  
Contact Tracing ◽  
Physiological Data ◽  
False Alarms ◽  
Waveform Data ◽  
Random Forest Classification ◽  
Pathogen Exposure

AbstractEarly pathogen exposure detection allows better patient care and faster implementation of public health measures (patient isolation, contact tracing). Existing exposure detection most frequently relies on overt clinical symptoms, namely fever, during the infectious prodromal period. We have developed a robust machine learning based method to better detect asymptomatic states during the incubation period using subtle, sub-clinical physiological markers. Starting with high-resolution physiological waveform data from non-human primate studies of viral (Ebola, Marburg, Lassa, and Nipah viruses) and bacterial (Y. pestis) exposure, we processed the data to reduce short-term variability and normalize diurnal variations, then provided these to a supervised random forest classification algorithm and post-classifier declaration logic step to reduce false alarms. In most subjects detection is achieved well before the onset of fever; subject cross-validation across exposure studies (varying viruses, exposure routes, animal species, and target dose) lead to 51h mean early detection (at 0.93 area under the receiver-operating characteristic curve [AUCROC]). Evaluating the algorithm against entirely independent datasets for Lassa, Nipah, andY. pestisexposures un-used in algorithm training and development yields a mean 51h early warning time (at AUCROC=0.95). We discuss which physiological indicators are most informative for early detection and options for extending this capability to limited datasets such as those available from wearable, non-invasive, ECG-based sensors.

scholarly journals The Dutch COVID-19 Contact Tracing App (the CoronaMelder): Usability Study

10.2196/27882 ◽  
2021 ◽  
Vol 5 (3) ◽  
pp. e27882
Author(s):  
Britt Elise Bente ◽  
Jan Willem Jaap Roderick van 't Klooster ◽  
Maud Annemarie Schreijer ◽  
Lea Berkemeier ◽  
Joris Elmar van Gend ◽  
...  
Keyword(s):  
Public Health ◽  
Older Adults ◽  
The Netherlands ◽  
Health Workers ◽  
Contact Tracing ◽  
Usability Tests ◽  
Health Authorities ◽  
Increased Risk ◽  
Public Health Authorities ◽  
Lower Education

Background Adoption and evaluation of contact tracing tools based on information and communications technology may expand the reach and efficacy of traditional contact tracing methods in fighting COVID-19. The Dutch Ministry of Health, Welfare and Sports initiated and developed CoronaMelder, a COVID-19 contact tracing app. This app is based on a Google/Apple Exposure Notification approach and aims to combat the spread of the coronavirus among individuals by notifying those who are at increased risk of infection due to proximity to someone who later tests positive for COVID-19. The app should support traditional contact tracing by faster tracing and greater reach compared to regular contact tracing procedures. Objective The main goal of this study is to investigate whether the CoronaMelder is able to support traditional contact tracing employed by public health authorities. To achieve this, usability tests were conducted to answer the following question: is the CoronaMelder user-friendly, understandable, reliable and credible, and inclusive? Methods Participants (N=44) of different backgrounds were recruited: youth with varying educational levels, youth with an intellectual disability, migrants, adults (aged 40-64 years), and older adults (aged >65 years) via convenience sampling in the region of Twente in the Netherlands. The app was evaluated with scenario-based, think-aloud usability tests and additional interviews. Findings were recorded via voice recordings, observation notes, and the Dutch User Experience Questionnaire, and some participants wore eye trackers to measure gaze behavior. Results Our results showed that the app is easy to use, although problems occurred with understandability and accessibility. Older adults and youth with a lower education level did not understand why or under what circumstances they would receive notifications, why they must share their key (ie, their assigned identifier), and what happens after sharing. In particular, youth in the lower-education category did not trust or understand Bluetooth signals, or comprehend timing and follow-up activities after a risk exposure notification. Older adults had difficulties multitasking (speaking with a public health worker and simultaneously sharing the key in the app). Public health authorities appeared to be unprepared to receive support from the app during traditional contact tracing because their telephone conversation protocol lacks guidance, explanation, and empathy. Conclusions The study indicated that the CoronaMelder app is easy to use, but participants experienced misunderstandings about its functioning. The perceived lack of clarity led to misconceptions about the app, mostly regarding its usefulness and privacy-preserving mechanisms. Tailored and targeted communication through, for example, public campaigns or social media, is necessary to provide correct information about the app to residents in the Netherlands. Additionally, the app should be presented as part of the national coronavirus measures instead of as a stand-alone app offered to the public. Public health workers should be trained to effectively and empathetically instruct users on how to use the CoronaMelder app.

scholarly journals Estimating the generation interval for COVID-19 based on symptom onset data

2020 ◽  
Author(s):  
Ganyani Tapiwa ◽  
Kremer Cécile ◽  
Chen Dongxuan ◽  
Torneri Andrea ◽  
Faes Christel ◽  
...  
Keyword(s):  
Incubation Period ◽  
Symptom Onset ◽  
Contact Tracing ◽  
Transmission Network ◽  
Generation Interval ◽  
Period Distribution ◽  
Reproduction Numbers ◽  
Serial Interval ◽  
Modelling Studies ◽  
Interval Distribution

AbstractBackgroundEstimating key infectious disease parameters from the COVID-19 outbreak is quintessential for modelling studies and guiding intervention strategies. Whereas different estimates for the incubation period distribution and the serial interval distribution have been reported, estimates of the generation interval for COVID-19 have not been provided.MethodsWe used outbreak data from clusters in Singapore and Tianjin, China to estimate the generation interval from symptom onset data while acknowledging uncertainty about the incubation period distribution and the underlying transmission network. From those estimates we obtained the proportions pre-symptomatic transmission and reproduction numbers.ResultsThe mean generation interval was 5.20 (95%CI 3.78-6.78) days for Singapore and 3.95 (95%CI 3.01-4.91) days for Tianjin, China when relying on a previously reported incubation period with mean 5.2 and SD 2.8 days. The proportion of pre-symptomatic transmission was 48% (95%CI 32-67%) for Singapore and 62% (95%CI 50-76%) for Tianjin, China. Estimates of the reproduction number based on the generation interval distribution were slightly higher than those based on the serial interval distribution.ConclusionsEstimating generation and serial interval distributions from outbreak data requires careful investigation of the underlying transmission network. Detailed contact tracing information is essential for correctly estimating these quantities.

scholarly journals Evolving Transmission Network Dynamics of COVID-19 Cluster Infections in South Korea: a descriptive study

2020 ◽  
Author(s):  
Yejin Kim ◽  
Xiaoqian Jiang
Keyword(s):  
Older Adults ◽  
Nursing Home ◽  
South Korea ◽  
Transmission Dynamics ◽  
Contact Tracing ◽  
Transmission Network ◽  
Middle Aged ◽  
Reproduction Numbers ◽  
Path Lengths ◽  
Middle Aged Adults

AbstractBackgroundExtensive contact tracing and testing in South Korea allows us to investigate the transmission dynamics of the COVID-19 into diverse local communities.ObjectiveUnderstand the critical aspects of transmission dynamics in a different age, sex, and clusters with various activities.MethodsWe conducted a retrospective observational study with 3,127 confirmed cases’ contact tracing data from the Center for Disease and Prevention (CDC) of South Korea. We investigated network property concerning infected persons’ demographics and different infection clusters.FindingsOverall, women had higher centrality scores than men after week four, when the confirmed cases rapidly increased. Older adults have higher centrality than young/middle-aged adults after week 9. In the infection clusters, young/middle-aged adults’ infection clusters (such as religious gatherings and gym facilities) have higher average path lengths and diameter than older adult’s nursing home infection clusters.InterpretationSome women had higher reproduction numbers and bridged successive transmission than men when the confirmed cases rapidly increased. Similarly, some older adults (who were not residents of nursing homes) had higher reproduction numbers and bridged successive transmission than young/middle-aged adults after the peak has passed. The young/middle-aged adults’ religious gatherings and group workout have caused long successive transmissions. In contrast, the older adults’ nursing homes were a small world where the transmissions within a few steps can reach out to many persons.FundingUT Startup award, UT STARs award, and Cancer Prevention Research in Texas, and National Institute of General Medical SciencesResearch in contextEvidence before this study:On May 1, 2020, PubMed query (“COVID-19” OR “SARS-nCoV-2” OR “novel coronavirus” OR “nCoV”) AND (“transmission network” OR “transmission dynamics” OR “transmission pattern” OR “centrality”) AND (“cluster” OR “community”) yield eight peer-reviewed papers. These papers have provided an evolving epidemiology and transmission dynamics via estimated reproduction number. However, most of them have focused on the entire system in one location and there was no comparison between transmission dynamics of different clusters.Added value of this study:This study, to the best of our knowledge, is the first to compare the transmission dynamics of different cluster infections. We present the transmission dynamic with varying levels of granularity: entire country vs cluster infections as a longitudinal view. From the whole country-level analysis, we found that females have higher centrality (degree or betweenness) than males. From the cluster infection view, we found that young/middle-aged adults’ infection clusters (such as religious gatherings and gym facilities) have higher average path lengths and diameter than older adult’s nursing home infection clusters.Implications of all the available evidence:This study sheds light on different transmission dynamics concerning demographics (age and sex) and diverse behavior in cluster infections. These findings are essential for planning tailored policies to diverse communities. Our analysis code is publicly available to adapt to newly reported cases.

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