scholarly journals Estimation of the serial interval and proportion of pre-symptomatic transmission events of COVID− 19 in Ireland using contact tracing data

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Conor G. McAloon ◽  
Patrick Wall ◽  
John Griffin ◽  
Miriam Casey ◽  
Ann Barber ◽  
...  
Keyword(s):  
Reproduction Number ◽  
Contact Tracing ◽  
Secondary Case ◽  
Large Sample Size ◽  
Epidemiological Models ◽  
Period Distribution ◽  
Serial Interval ◽  
Age Cohort ◽  
Degree Of Certainty ◽  
Close Contacts

Abstract Background The serial interval is the period of time between the onset of symptoms in an infector and an infectee and is an important parameter which can impact on the estimation of the reproduction number. Whilst several parameters influencing infection transmission are expected to be consistent across populations, the serial interval can vary across and within populations over time. Therefore, local estimates are preferable for use in epidemiological models developed at a regional level. We used data collected as part of the national contact tracing process in Ireland to estimate the serial interval of SARS-CoV-2 infection in the Irish population, and to estimate the proportion of transmission events that occurred prior to the onset of symptoms. Results After data cleaning, the final dataset consisted of 471 infected close contacts from 471 primary cases. The median serial interval was 4 days, mean serial interval was 4.0 (95% confidence intervals 3.7, 4.3) days, whilst the 25th and 75th percentiles were 2 and 6 days respectively. We found that intervals were lower when the primary or secondary case were in the older age cohort (greater than 64 years). Simulating from an incubation period distribution from international literature, we estimated that 67% of transmission events had greater than 50% probability of occurring prior to the onset of symptoms in the infector. Conclusions Whilst our analysis was based on a large sample size, data were collected for the primary purpose of interrupting transmission chains. Similar to other studies estimating the serial interval, our analysis is restricted to transmission pairs where the infector is known with some degree of certainty. Such pairs may represent more intense contacts with infected individuals than might occur in the overall population. It is therefore possible that our analysis is biased towards shorter serial intervals than the overall population.

scholarly journals Estimation of the serial interval of COVID-19 in Ireland using contact tracing data

2021 ◽  
Author(s):  
Conor G. McAloon ◽  
Patrick Wall ◽  
John Griffin ◽  
Miriam Casey ◽  
Ann Barber ◽  
...  
Keyword(s):  
Reproduction Number ◽  
Contact Tracing ◽  
Secondary Case ◽  
Large Sample Size ◽  
Epidemiological Models ◽  
Serial Interval ◽  
Age Cohort ◽  
Degree Of Certainty ◽  
The Mean ◽  
Close Contacts

Abstract BackgroundThe serial interval is the period of time between the onset of symptoms in an infector and an infectee and is an important parameter which can impact on the estimation of the reproduction number. Whilst several parameters influencing infection transmission are expected to be consistent across populations, the serial interval can vary across and within populations over time. Therefore, local estimates are preferable for use in epidemiological models developed at a regional level. We used data collected as part of the national contact tracing process in Ireland to estimate the serial interval of SARS-CoV-2 infection in the Irish population, and to estimate the proportion of transmission events that occurred prior to the onset of symptoms.ResultsAfter data cleaning, the final dataset consisted of 471 infected close contacts from 471 primary cases. The mean serial interval was 4.0 (95% confidence intervals 3.75, 4.31) days, whilst the 25th, 50th and 75th percentiles were 2, 4 and 6 days respectively. We found that intervals were lower when the primary or secondary case were in the older age cohort (greater than 64 years). Simulating from an incubation period distribution from international literature, we estimated that 67% of transmission events had greater than 50% probability of occurring prior to the onset of symptoms in the infector.ConclusionsWhilst our analysis was based on a large sample size, data were collected for the primary purpose of interrupting transmission chains. Similar to other studies estimating the serial interval, our analysis is restricted to transmission pairs where the infector is known with some degree of certainty. Such pairs may represent more intense contacts with infected individuals than might occur in the overall population. It is therefore possible that our analysis is biased towards shorter serial intervals than the overall population.

scholarly journals Reducing contacts to stop SARS-CoV-2 transmission during the second pandemic wave in Brussels, Belgium

2020 ◽  
Author(s):  
Brecht Ingelbeen ◽  
Laurène Peckeu ◽  
Marie Laga ◽  
Ilona Hendrix ◽  
Inge Neven ◽  
...  
Keyword(s):  
European Commission ◽  
Risk Ratio ◽  
Reproduction Number ◽  
Case Reports ◽  
Contact Tracing ◽  
Social Mixing ◽  
Before School ◽  
Specific Number ◽  
Pandemic Wave ◽  
Close Contacts

AbstractBackgroundReducing contacts is a cornerstone of containing SARS-CoV-2. We evaluated the effect of physical distancing measures and of school reopening on contacts and consequently on SARS-CoV-2 transmission in Brussels, a hotspot during the second European wave.MethodsUsing SARS-CoV-2 case reports and contact tracing data during August-November 2020, we estimated changes in the age-specific number of reported contacts. We associated these trends with changes in the instantaneous reproduction number Rt and in age-specific transmission-events during distinct intervention periods in the Brussels region. Furthermore, we analysed trends in age-specific case numbers, pre- and post-school opening.FindingsWhen schools reopened and physical distancing measures relaxed, the weekly mean number of reported contacts surged from 2.01 (95%CI 1.73-2.29) to 3.04 (95%CI 2.93-3.15), increasing across all ages. The fraction of cases aged 10-19 years started increasing before school reopening, with no further increase following school reopening (risk ratio 1.23, 95%CI 0.79-1.94). During the subsequent month, 8.9% (67/755) of infections identified were from teenagers to other ages, while 17.0% (131/755) from other ages to teenagers. Rt peaked mid-September at 1.48 (95%CI 1.35-1.63). Reintroduction of physical distancing measures reduced reported contacts to 1.85 (95%CI 1.78-1.91), resulting in Rt dropping below 1 within 3 weeks.InterpretationThe second pandemic wave in Brussels was the result of increased contacts across all ages following school reopening. Stringent physical distancing measures, including closure of bars and limiting close contacts while schools remain open, reduced social mixing, in turn controlling SARS-CoV-2 transmission.FundingEuropean Commission H2020. GGC Brussel.

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 Epidemiologic Characteristics of COVID-19 in Guizhou Province, China

2021 ◽  
Vol 15 (03) ◽  
pp. 389-397
Author(s):  
Kaike Ping ◽  
Mingyu Lei ◽  
Yun Gou ◽  
Zhongfa Tao ◽  
Guanghai Yao ◽  
...  
Keyword(s):  
Incubation Period ◽  
Reproduction Number ◽  
Asymptomatic Carrier ◽  
Control Measures ◽  
Guizhou Province ◽  
Serial Interval ◽  
Two Peaks ◽  
Epidemiologic Characteristics ◽  
Close Contacts

Introduction: At the end of 2019, the COVID-19 broke out, and spread to Guizhou province in January of 2020. Methodology: To acquire the epidemiologic characteristics of COVID-19 in Guizhou province, we collected data from 169 laboratory-confirmed COVID-19 related cases. We described the demographic characteristics of the cases and estimated the incubation period, serial interval and the effective reproduction number. We also presented two representative case studies in Guizhou province: Case Study 1 was an example of the asymptomatic carrier; while Case Study 2 was an example of a large and complex infection chain that involved four different regions, spanning three provinces and eight families. Results: Two peaks in the incidence distribution associated with COVID-19 in Guizhou province were related to the 6.04 days (95% CI: 5.00 – 7.10) of incubation period and 6.14±2.21 days of serial interval. We also discussed the effectiveness of the control measures based on the instantaneous effective reproduction number that was a constantly declining curve. Conclusions: As of February 2, 2020, the estimated effective reproduction number was below 1, and no new cases were reported since February 26. These showed that Guizhou Province had achieved significant progress in preventing the spread of the epidemic. The medical isolation of close contacts was consequential. Meanwhile, the asymptomatic carriers and the super-spreaders must be isolated in time, who would cause a widespread infection.

scholarly journals Serial interval, basic reproduction number and prediction of COVID-19 epidemic size in Jodhpur, India

2020 ◽  
Author(s):  
Suman Saurabh ◽  
Mahendra Kumar Verma ◽  
Vaishali Gautam ◽  
Akhil Goel ◽  
Manoj Kumar Gupta ◽  
...  
Keyword(s):  
Basic Reproduction Number ◽  
Capacity Planning ◽  
Reproduction Number ◽  
Local Level ◽  
Substantial Reduction ◽  
Control Measures ◽  
Contact Tracing ◽  
Effective Control ◽  
Epidemic Size ◽  
Serial Interval

ABSTRACTBackgroundUnderstanding the epidemiology of COVID-19 is important for design of effective control measures at local level. We aimed to estimate the serial interval and basic reproduction number for Jodhpur, India and to use it for prediction of epidemic size for next one month.MethodsContact tracing of SARS-CoV-2 infected individuals was done to obtain the serial intervals. Aggregate and instantaneous R0 values were derived and epidemic projection was done using R software v4.0.0.ResultsFrom among 79 infector-infectee pairs, the estimated median and 95 percentile values of serial interval were 5.98 days (95% CI 5.39 – 6.65) and 13.17 days (95% CI 11.27 – 15.57), respectively. The overall R0 value in the first 30 days of outbreak was 1.64 (95% CI 1.12 – 2.25) which subsequently decreased to 1.07 (95% CI 1.06 – 1.09). The instantaneous R0 value over 14 days window ranged from a peak of 3.71 (95% CI 1.85 -2.08) to 0.88 (95% CI 0.81 – 0.96) as on 24 June 2020. The projected COVID-19 case-load over next one month was 1881 individuals. Reduction of R0 from 1.17 to 1.085 could result in 23% reduction in projected epidemic size over the next one month.ConclusionAggressive testing, contact-tracing and isolation of infected individuals in Jodhpur district resulted in reduction of R0. Further strengthening of control measures could lead to substantial reduction of COVID-19 epidemic size. A data-driven strategy was found useful in surge capacity planning and guiding the public health strategy at local level.

scholarly journals Analysis of mobility data to build contact networks for COVID-19

PLoS ONE ◽  
2021 ◽  
Vol 16 (4) ◽  
pp. e0249726
Author(s):  
Katherine Klise ◽  
Walt Beyeler ◽  
Patrick Finley ◽  
Monear Makvandi
Keyword(s):  
Contact Tracing ◽  
Epidemiological Models ◽  
Policy Choices ◽  
Mobility Data ◽  
Single Person ◽  
Rapid Changes ◽  
Network Metrics ◽  
The Impact ◽  
Number Of Customers ◽  
Close Contacts

As social distancing policies and recommendations went into effect in response to COVID-19, people made rapid changes to the places they visit. These changes are clearly seen in mobility data, which records foot traffic using location trackers in cell phones. While mobility data is often used to extract the number of customers that visit a particular business or business type, it is the frequency and duration of concurrent occupancy at those sites that governs transmission. Understanding the way people interact at different locations can help target policies and inform contact tracing and prevention strategies. This paper outlines methods to extract interactions from mobility data and build networks that can be used in epidemiological models. Several measures of interaction are extracted: interactions between people, the cumulative interactions for a single person, and cumulative interactions that occur at particular businesses. Network metrics are computed to identify structural trends which show clear changes based on the timing of stay-at-home orders. Measures of interaction and structural trends in the resulting networks can be used to better understand potential spreading events, the percent of interactions that can be classified as close contacts, and the impact of policy choices to control transmission.

scholarly journals Epidemiological characteristics of COVID-19 cases among Indians residing in Kuwait

2021 ◽  
Author(s):  
Jagan K. Baskaradoss ◽  
Aishah Alsumait ◽  
Shaheer Malik ◽  
Jitendra Ariga ◽  
Amrita Geevarghese ◽  
...  
Keyword(s):  
Healthcare Workers ◽  
Contact Tracing ◽  
Indian Community ◽  
Asymptomatic Patients ◽  
Serial Interval ◽  
The World ◽  
Early Increase ◽  
The Mean ◽  
Epidemiological Characteristics ◽  
Close Contacts

Background: The coronavirus disease 2019 (COVID-19) pandemic has rapidly spread to most countries around the world. Disproportionate spread of COVID-19 among the Indian community in Kuwait prompted heightened surveillance in this community. Aims: To study the epidemiological characteristics of COVID-19 patients and their contacts among the Indian community in Kuwait. Methods: Data collection was done as a part of contact tracing efforts undertaken by the Kuwaiti Ministry of Health. Results: We analysed contact-tracing data for the initial 1348 laboratory-confirmed Indian patients and 6357 contacts (5681 close and 676 casual). The mean (standard deviation) age of the patients was 39.43 (10.5) years and 76.5% of the cases were asymptomatic or had only mild symptoms. Asymptomatic patients were significantly older [40.05 (10.42) years] than patients with severe symptoms [37.54 (10.54) years] (P = 0.024). About 70% of the patients were living in shared accommodation. Most of the close contacts were living in the same household, as compared with casual contacts, who were primarily workplace contacts (P < 0.001). Among the different occupations, healthcare workers had the highest proportion of cases (18.4%). Among the 216 pairs of cases with a clear relationship between the index and secondary cases, the mean serial interval was estimated to be 3.89 (3.69) days, with a median of 3 and interquartile range of 1–5 days. Conclusion: An early increase in the number of COVID-19 cases among the Indian community could be primarily attributed to crowded living conditions and the high proportion of healthcare workers in this community.

scholarly journals Forward-looking serial intervals correctly link epidemic growth to reproduction numbers

2020 ◽  
Vol 118 (2) ◽  
pp. e2011548118
Author(s):  
Sang Woo Park ◽  
Kaiyuan Sun ◽  
David Champredon ◽  
Michael Li ◽  
Benjamin M. Bolker ◽  
...  
Keyword(s):  
Symptom Onset ◽  
Reproduction Number ◽  
Heuristic Method ◽  
Population Level ◽  
Contact Tracing ◽  
Epidemic Dynamics ◽  
Transmission Chain ◽  
Reproduction Numbers ◽  
Serial Interval ◽  
Forward Looking

The reproduction number R and the growth rate r are critical epidemiological quantities. They are linked by generation intervals, the time between infection and onward transmission. Because generation intervals are difficult to observe, epidemiologists often substitute serial intervals, the time between symptom onset in successive links in a transmission chain. Recent studies suggest that such substitution biases estimates of R based on r. Here we explore how these intervals vary over the course of an epidemic, and the implications for R estimation. Forward-looking serial intervals, measuring time forward from symptom onset of an infector, correctly describe the renewal process of symptomatic cases and therefore reliably link R with r. In contrast, backward-looking intervals, which measure time backward, and intrinsic intervals, which neglect population-level dynamics, give incorrect R estimates. Forward-looking intervals are affected both by epidemic dynamics and by censoring, changing in complex ways over the course of an epidemic. We present a heuristic method for addressing biases that arise from neglecting changes in serial intervals. We apply the method to early (21 January to February 8, 2020) serial interval-based estimates of R for the COVID-19 outbreak in China outside Hubei province; using improperly defined serial intervals in this context biases estimates of initial R by up to a factor of 2.6. This study demonstrates the importance of early contact tracing efforts and provides a framework for reassessing generation intervals, serial intervals, and R estimates for COVID-19.

scholarly journals Estimating the generation interval for coronavirus disease (COVID-19) based on symptom onset data, March 2020

2020 ◽  
Vol 25 (17) ◽  
Author(s):  
Tapiwa Ganyani ◽  
Cécile Kremer ◽  
Dongxuan Chen ◽  
Andrea Torneri ◽  
Christel Faes ◽  
...  
Keyword(s):  
Symptom Onset ◽  
Reproduction Number ◽  
Credible Interval ◽  
Sensitivity Analyses ◽  
Contact Tracing ◽  
Generation Interval ◽  
Reproduction Numbers ◽  
Serial Interval ◽  
Distribution Sensitivity ◽  
Interval Distribution

Background Estimating key infectious disease parameters from the coronavirus disease (COVID-19) outbreak is essential for modelling studies and guiding intervention strategies. Aim We estimate the generation interval, serial interval, proportion of pre-symptomatic transmission and effective reproduction number of COVID-19. We illustrate that reproduction numbers calculated based on serial interval estimates can be biased. Methods We 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 serial interval, proportions of pre-symptomatic transmission and reproduction numbers. Results The mean generation interval was 5.20 days (95% credible interval (CrI): 3.78–6.78) for Singapore and 3.95 days (95% CrI: 3.01–4.91) for Tianjin. The proportion of pre-symptomatic transmission was 48% (95% CrI: 32–67) for Singapore and 62% (95% CrI: 50–76) for Tianjin. Reproduction number estimates based on the generation interval distribution were slightly higher than those based on the serial interval distribution. Sensitivity analyses showed that estimating these quantities from outbreak data requires detailed contact tracing information. Conclusion High estimates of the proportion of pre-symptomatic transmission imply that case finding and contact tracing need to be supplemented by physical distancing measures in order to control the COVID-19 outbreak. Notably, quarantine and other containment measures were already in place at the time of data collection, which may inflate the proportion of infections from pre-symptomatic individuals.

scholarly journals Estimating the protective effect of case isolation with transmission tree reconstruction during the Ebola outbreak in Nigeria, 2014

2020 ◽  
Vol 17 (169) ◽  
pp. 20200498
Author(s):  
Yat Hin Chan ◽  
Hiroshi Nishiura
Keyword(s):  
Protective Effect ◽  
Reproduction Number ◽  
Credible Interval ◽  
Hospitalization Rate ◽  
Contact Tracing ◽  
Transmission Network ◽  
Tree Reconstruction ◽  
Serial Interval ◽  
Formed Part ◽  
The Individual

The mainstream interventions used during the 2014–2016 Ebola epidemic were contact tracing and case isolation. The Ebola outbreak in Nigeria that formed part of the 2014–2016 epidemic demonstrated the effectiveness of control interventions with a 100% hospitalization rate. Here, we aim to explicitly estimate the protective effect of case isolation, reconstructing the time events of onset of illness and hospitalization as well as the transmission network. We show that case isolation reduced the reproduction number and shortened the serial interval. Employing Bayesian inference with the Markov chain Monte Carlo method for parameter estimation and assuming that the reproduction number exponentially declines over time, the protective effect of case isolation was estimated to be 39.7% (95% credible interval: 2.4%–82.1%). The individual protective effect of case isolation was also estimated, showing that the effectiveness was dependent on the speed, i.e. the time from onset of illness to hospitalization.

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