scholarly journals Contact Tracing of COVID-19 in Karnataka, India: Superspreading and Determinants of Infectiousness and Symptomaticity

2020 ◽  
Author(s):  
Mohak Gupta ◽  
Giridara G Parameswaran ◽  
Manraj S Sra ◽  
Rishika Mohanta ◽  
Devarsh Patel ◽  
...  
Keyword(s):  
Symptom Onset ◽  
Attack Rate ◽  
Clinical Severity ◽  
Contact Tracing ◽  
Significant Heterogeneity ◽  
Symptomatic Infection ◽  
Individual Level ◽  
Serial Interval ◽  
Index Cases ◽  
Degree Of Heterogeneity

Brief AbstractWe analysed SARS-CoV-2 surveillance and contact tracing data from Karnataka, India up to 21 July 2020. We estimated metrics of infectiousness and the tendency for superspreading (overdispersion), and evaluated potential determinants of infectiousness and symptomaticity in COVID-19 cases. Among 956 cases confirmed to be forward-traced, 8.7% of index cases had 14.4% of contacts but caused 80% of all secondary cases, suggesting significant heterogeneity in individual-level transmissibility of SARS-CoV-2 which could not be explained by the degree of heterogeneity in underlying number of contacts. Secondary attack rate was 3.6% among 16715 close contacts. Transmission was higher when index case was aged >18 years, or was symptomatic (adjusted risk ratio, aRR 3.63), or was lab-confirmed ≥4 days after symptom onset (aRR 3.01). Probability of symptomatic infection increased with age, and symptomatic infectors were 8.16 times more likely to generate symptomatic secondaries. This could potentially cause a snowballing effect on infectiousness and clinical severity across transmission generations; further studies are suggested to confirm this. Mean serial interval was 5.4 days. Adding backward contact tracing and targeting control measures to curb super-spreading may be prudent. Due to low symptomaticity and infectivity, interventions aimed at children might have a relatively small impact on reducing transmission.Structured AbstractBackgroundIndia has experienced the second largest outbreak of COVID-19 globally, yet there is a paucity of studies analysing contact tracing data in the region. Such studies can elucidate essential transmission metrics which can help optimize disease control policies.MethodsWe analysed contact tracing data collected under the Integrated Disease Surveillance Programme from Karnataka, India between 9 March and 21 July 2020. We estimated metrics of disease transmission including the reproduction number (R), overdispersion (k), secondary attack rate (SAR), and serial interval. R and k were jointly estimated using a Bayesian Markov Chain Monte Carlo approach. We evaluated the effect of age and other factors on the risk of transmitting the infection, probability of asymptomatic infection, and mortality due to COVID-19.FindingsUp to 21 July, we found 111 index cases that crossed the super-spreading threshold of ≥8 secondary cases. R and k were most reliably estimated at R 0.75 (95% CI, 0.62-0.91) and k 0.12 (0.11-0.15) for confirmed traced cases (n=956); and R 0.91 (0.72-1.15) and k 0.22 (0.17-0.27) from the three largest clusters (n=394). Among 956 confirmed traced cases, 8.7% of index cases had 14.4% of contacts but caused 80% of all secondary cases. Among 16715 contacts, overall SAR was 3.6% (3.4-3.9) and symptomatic cases were more infectious than asymptomatic cases (SAR 7.7% vs 2.0%; aRR 3.63 [3.04-4.34]). As compared to infectors aged 19-44 years, children were less infectious (aRR 0.21 [0.07-0.66] for 0-5 years and 0.47 [0.32-0.68] for 6-18 years). Infectors who were confirmed ≥4 days after symptom onset were associated with higher infectiousness (aRR 3.01 [2.11-4.31]). Probability of symptomatic infection increased with age, and symptomatic infectors were 8.16 (3.29-20.24) times more likely to generate symptomatic secondaries. Serial interval had a mean of 5.4 (4.4-6.4) days with a Weibull distribution. Overall case fatality rate was 2.5% (2.4-2.7) which increased with age.ConclusionWe found significant heterogeneity in the individual-level transmissibility of SARS-CoV-2 which could not be explained by the degree of heterogeneity in the underlying number of contacts. To strengthen contact tracing in over-dispersed outbreaks, testing and tracing delays should be minimised, retrospective contact tracing should be considered, and contact tracing performance metrics should be utilised. Targeted measures to reduce potential superspreading events should be implemented. Interventions aimed at children might have a relatively small impact on reducing SARS-CoV-2 transmission owing to their low symptomaticity and infectivity. There is some evidence that symptomatic cases produce secondary cases that are more likely to be symptomatic themselves which may potentially cause a snowballing effect on infectiousness and clinical severity across transmission generations; further studies are needed to confirm this finding.FundingGiridhara R Babu is funded by an Intermediate Fellowship by the Wellcome Trust DBT India Alliance (Clinical and Public Health Research Fellowship); grant number: IA/CPHI/14/1/501499.

scholarly journals Investigation of Four Clusters of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) in Rwanda, 2020

2021 ◽  
Vol 18 (13) ◽  
pp. 7018
Author(s):  
Olivier Nsekuye ◽  
Edson Rwagasore ◽  
Marie Aime Muhimpundu ◽  
Ziad El-Khatib ◽  
Daniel Ntabanganyimana ◽  
...  
Keyword(s):  
High Risk ◽  
Severe Acute Respiratory Syndrome ◽  
Symptom Onset ◽  
Attack Rate ◽  
Positive Sample ◽  
Contact Tracing ◽  
Face To Face ◽  
Laboratory Confirmation ◽  
Closed Environment ◽  
Index Cases

We reported the findings of the first Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) four clusters identified in Rwanda. Case-investigations included contact elicitation, testing, and isolation/quarantine of confirmed cases. Socio-demographic and clinical data on cases and contacts were collected. A confirmed case was a person with laboratory confirmation of SARS-CoV-2 infection (PCR) while a contact was any person who had contact with a SARS-CoV-2 confirmed case within 72 h prior, to 14 days after symptom onset; or 14 days before collection of the laboratory-positive sample for asymptomatic cases. High risk contacts were those who had come into unprotected face-to-face contact or had been in a closed environment with a SARS-CoV-2 case for >15 min. Forty cases were reported from four clusters by 22 April 2020, accounting for 61% of locally transmitted cases within six weeks. Clusters A, B, C and D were associated with two nightclubs, one house party, and different families or households living in the same compound (multi-family dwelling). Thirty-six of the 1035 contacts tested were positive (secondary attack rate: 3.5%). Positivity rates were highest among the high-risk contacts compared to low-risk contacts (10% vs. 2.2%). Index cases in three of the clusters were imported through international travelling. Fifteen of the 40 cases (38%) were asymptomatic while 13/25 (52%) and 8/25 (32%) of symptomatic cases had a cough and fever respectively. Gatherings in closed spaces were the main early drivers of transmission. Systematic case-investigations contact tracing and testing likely contributed to the early containment of SARS-CoV-2 in Rwanda.

scholarly journals Surveillance of SARS-CoV-2 transmission in educational institutions, August to December 2020, Germany

2021 ◽  
Vol 149 ◽  
Author(s):  
Anja Schoeps ◽  
Dietmar Hoffmann ◽  
Claudia Tamm ◽  
Bianca Vollmer ◽  
Sabine Haag ◽  
...  
Keyword(s):  
High Risk ◽  
Day Care ◽  
Index Case ◽  
Transmission Risk ◽  
Contact Tracing ◽  
Educational Institutions ◽  
Individual Level ◽  
Mandatory Notification ◽  
Day Care Centres ◽  
Index Cases

Abstract This study aims at providing estimates on the transmission risk of SARS-CoV-2 in schools and day-care centres. We calculated secondary attack rates (SARs) using individual-level data from state-wide mandatory notification of index cases in educational institutions, followed by contact tracing and PCR-testing of high-risk contacts. From August to December 2020, every sixth of overall 784 independent index cases was associated with secondary cases in educational institutions. Monitoring of 14 594 institutional high-risk contacts (89% PCR-tested) of 441 index cases during quarantine revealed 196 secondary cases (SAR 1.34%, 0.99–1.78). SARS-CoV-2 infection among high-risk contacts was more likely around teacher-indexes compared to student-/child-indexes (incidence rate ratio (IRR) 3.17, 1.79–5.59), and in day-care centres compared to secondary schools (IRR 3.23, 1.76–5.91), mainly due to clusters around teacher-indexes in day-care containing a higher mean number of secondary cases per index case (142/113 = 1.26) than clusters around student-indexes in schools (82/474 = 0.17). In 2020, SARS-CoV-2 transmission risk in educational settings was low overall, but varied strongly between setting and role of the index case, indicating the chance for targeted intervention. Surveillance of SARS-CoV-2 transmission in educational institutions can powerfully inform public health policy and improve educational justice during the pandemic.

scholarly journals On the relationship between serial interval, infectiousness profile and generation time

2021 ◽  
Vol 18 (174) ◽  
pp. 20200756
Author(s):  
Sonja Lehtinen ◽  
Peter Ashcroft ◽  
Sebastian Bonhoeffer
Keyword(s):  
Symptom Onset ◽  
Generation Time ◽  
Time Distribution ◽  
Contact Tracing ◽  
Time Interval ◽  
Single Individual ◽  
Generation Times ◽  
Serial Interval ◽  
The Impact ◽  
The Relationship

The timing of transmission plays a key role in the dynamics and controllability of an epidemic. However, observing generation times—the time interval between the infection of an infector and an infectee in a transmission pair—requires data on infection times, which are generally unknown. The timing of symptom onset is more easily observed; generation times are therefore often estimated based on serial intervals—the time interval between symptom onset of an infector and an infectee. This estimation follows one of two approaches: (i) approximating the generation time distribution by the serial interval distribution or (ii) deriving the generation time distribution from the serial interval and incubation period—the time interval between infection and symptom onset in a single individual—distributions. These two approaches make different—and not always explicitly stated—assumptions about the relationship between infectiousness and symptoms, resulting in different generation time distributions with the same mean but unequal variances. Here, we clarify the assumptions that each approach makes and show that neither set of assumptions is plausible for most pathogens. However, the variances of the generation time distribution derived under each assumption can reasonably be considered as upper (approximation with serial interval) and lower (derivation from serial interval) bounds. Thus, we suggest a pragmatic solution is to use both approaches and treat these as edge cases in downstream analysis. We discuss the impact of the variance of the generation time distribution on the controllability of an epidemic through strategies based on contact tracing, and we show that underestimating this variance is likely to overestimate controllability.

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 Role of children in household transmission of COVID-19

2020 ◽  
pp. archdischild-2020-319910 ◽  
Author(s):  
Jieun Kim ◽  
Young June Choe ◽  
Jin Lee ◽  
Young Joon Park ◽  
Ok Park ◽  
...  
Keyword(s):  
Attack Rate ◽  
Index Case ◽  
Clinical Findings ◽  
International Travel ◽  
Contact Tracing ◽  
Secondary Case ◽  
Household Transmission ◽  
Household Members ◽  
Index Cases

ObjectiveTransmissibility of COVID-19 by children in the household is not clear. Herein, we describe children’s role in household transmission of COVID-19.Design and settingAll paediatric COVID-19 index cases and their household members reported from 20 January to 6 April 2020 in South Korea were reviewed. The secondary attack rate (SAR) from child index case to household secondary case was calculated. Epidemiological and clinical findings of child index case-household secondary case pair was assessed.ResultsA total of 107 paediatric COVID-19 index cases and 248 of their household members were identified. One pair of paediatric index-secondary household case was identified, giving a household SAR of 0.5% (95% CI 0.0% to 2.6%). The index case was self-quarantined at home after international travel, stayed in her room, but shared a meal table with the secondary case.ConclusionThe SAR from children to household members was low in the setting of social distancing, underscoring the importance of rigorous contact tracing and early isolation in limiting transmission within households.

scholarly journals A comparison of COVID-19 secondary attack rate in household and close contacts compared to current risk stratification guidelines of the Kerala government

2021 ◽  
pp. 004947552110020
Author(s):  
Balram Rathish ◽  
Arun Wilson ◽  
Sonya Joy
Keyword(s):  
High Risk ◽  
Risk Stratification ◽  
Attack Rate ◽  
Low Risk ◽  
Contact Tracing ◽  
Index Cases ◽  
Current Risk ◽  
Close Contacts

COVID-19 has been found to be highly infectious with a high secondary attack rate with a R0 of 3.3. However, the secondary attack rate based on risk stratification is sparsely reported, if ever. We studied the contact tracing data for two index cases of COVID-19 with some overlap of contacts. We found that 60% of high-risk contacts and 0% of low-risk contacts of symptomatic COVID-19 patients contracted the infection, in keeping with the Kerala government contact risk stratification guidelines.

scholarly journals Using secondary cases to characterize the severity of an emerging or re-emerging infection

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Tim K. Tsang ◽  
Can Wang ◽  
Bingyi Yang ◽  
Simon Cauchemez ◽  
Benjamin J. Cowling
Keyword(s):  
Infectious Disease ◽  
Systematic Review ◽  
Statistical Model ◽  
Disease Severity ◽  
Severe Disease ◽  
Clinical Severity ◽  
Contact Tracing ◽  
Emerging Infection ◽  
Index Cases ◽  
Extract Information

AbstractThe methods to ascertain cases of an emerging infectious disease are typically biased toward cases with more severe disease, which can bias the average infection-severity profile. Here, we conducted a systematic review to extract information on disease severity among index cases and secondary cases identified by contact tracing of index cases for COVID-19. We identified 38 studies to extract information on measures of clinical severity. The proportion of index cases with fever was 43% higher than for secondary cases. The proportion of symptomatic, hospitalized, and fatal illnesses among index cases were 12%, 126%, and 179% higher than for secondary cases, respectively. We developed a statistical model to utilize the severity difference, and estimate 55% of index cases were missed in Wuhan, China. Information on disease severity in secondary cases should be less susceptible to ascertainment bias and could inform estimates of disease severity and the proportion of missed index cases.

scholarly journals Comparative Household Secondary Attack Rates associated with B.1.1.7, B.1.351, and P.1 SARS-CoV-2 Variants

2021 ◽  
Author(s):  
Kevin A Brown ◽  
Semra Tibebu ◽  
Nick Daneman ◽  
Kevin L Schwartz ◽  
Michael Whelan ◽  
...  
Keyword(s):  
Symptom Onset ◽  
Attack Rate ◽  
Surveillance System ◽  
Disease Surveillance ◽  
Index Case ◽  
Wild Type ◽  
Vaccination Rates ◽  
Household Contacts ◽  
Disease Surveillance System ◽  
Index Cases

Background: The emergence of SARS-CoV-2 variants associated with increased transmissibility are driving a 3rd global surge in COVID-19 incidence. There are currently few reliable estimates for the P.1 and B.1.351 lineages. We sought to compare the secondary attack rates of SARS-COV-2 mutations and variants in Canada's largest province of Ontario, using a previously validated household-based approach. Methods: We identified individuals with confirmed SARS-CoV-2 infection in Ontario's provincial reportable disease surveillance system. Cases were grouped into households based on reported residential address. Index cases had the earliest of symptom onset in the household. Household secondary attack rate was defined as the percentage of household contacts identified as secondary cases within 1-14 days after the index case. Results: We identified 26,888 index household cases during the study period. Among these, 7,555 (28%) were wild-type, 17,058 (63%) were B.1.1.7, 1674 (6%) were B.1.351 or P.1, and 601 (2%) were non-VOC mutants (Table 1). The secondary attack rates, according to index case variant were as follows: 20.2% (wild-type), 25.1% (B.1.1.7), 27.2% (B.1.351 or P.1), and 23.3% (non-VOC mutants). In adjusted analyses, we found that B.1.1.7, B.1.351, and P.1 index cases had the highest transmissibility (presumptive B.1.1.7 OR adjusted=1.49, 95%CI 1.36, 1.64; presumptive B.1.351 or P.1 OR adjusted=1.60, 95%CI 1.37, 1.87). Discussion: Substantially higher transmissibility associated with variants will make control of SARS-CoV-2 more difficult, reinforcing the urgent need to increase vaccination rates globally.

scholarly journals Transmission of SARS-CoV-2 before and after symptom onset: impact of nonpharmaceutical interventions in China

2020 ◽  
Author(s):  
Mary Bushman ◽  
Colin Worby ◽  
Hsiao-Han Chang ◽  
Moritz Kraemer ◽  
William P. Hanage
Keyword(s):  
Symptom Onset ◽  
Relative Frequency ◽  
Population Level ◽  
Interval Data ◽  
Contact Tracing ◽  
Serial Interval ◽  
Before And After ◽  
Primary Means ◽  
Nonpharmaceutical Interventions

AbstractNonpharmaceutical interventions, such as contact tracing and quarantine, are currently the primary means of controlling the spread of SARS-CoV-2; however, it remains uncertain which interventions are most effective at reducing transmission at the population level. Using serial interval data from before and after the rollout of nonpharmaceutical interventions in China, we estimate that the relative frequency of presymptomatic transmission increased from 34% before the rollout to 71% afterward. The shift touward earlier transmission indicates a disproportionate reduction in transmission post-symptom onset. We estimate that, following the rollout of nonpharmaceutical interventions, transmission post-symptom onset was reduced by 82% whereas presymptomatic transmission decreased by only 16%. These findings suggest that interventions which limit opportunities for transmission in the later stages of infection, such as contact tracing and isolation, may have been particularly effective at reducing transmission of SARS-CoV-2.

scholarly journals On the relationship between serial interval, infectiousness profile and generation time

2020 ◽  
Author(s):  
Sonja Lehtinen ◽  
Peter Ashcroft ◽  
Sebastian Bonhoeffer
Keyword(s):  
Symptom Onset ◽  
Generation Time ◽  
Time Distribution ◽  
Contact Tracing ◽  
Time Interval ◽  
Single Individual ◽  
Serial Interval ◽  
The Impact ◽  
The Relationship ◽  
Interval Distribution

The timing of transmission plays a key role in the dynamics and controllability of an epidemic. However, observing the distribution of generation times (time interval between the points of infection of an infector and infectee in a transmission pair) requires data on infection times, which are generally unknown. The timing of symptom onset is more easily observed; the generation time distribution is therefore often estimated based on the serial interval distribution (distribution of time intervals between symptom onset of an infector and an infectee). This estimation follows one of two approaches: i) approximating the generation time distribution by the serial interval distribution; or ii) deriving the generation time distribution from the serial interval and incubation period (time interval between infection and symptom onset in a single individual) distributions. These two approaches make different -- and not always explicitly stated -- assumptions about the relationship between infectiousness and symptoms, resulting in different generation time distributions with the same mean but unequal variances. Here, we clarify the assumptions that each approach makes and show that neither set of assumptions is plausible for most pathogens. However, the variances of the generation time distribution derived under each assumption can reasonably be considered as upper (approximation with serial interval) and lower (derivation from serial interval) bounds. Thus, we suggest a pragmatic solution is to use both approaches and treat these as edge cases in downstream analysis. We discuss the impact of the variance of the generation time distribution on the controllability of an epidemic through strategies based on contact tracing, and we show that underestimating this variance is likely to overestimate controllability.

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