Estimating transmission dynamics and serial interval of the first wave of COVID-19 infections under different control measures: A statistical analysis in Tunisia from February 29 to May 5, 2020

Abstract Background Describing transmission dynamics of the outbreak and impact of intervention measures are critical to planning responses to future outbreaks and providing timely information to guide policy makers decision. We estimate serial interval (SI) and temporal reproduction number (Rt) of SARS-CoV-2 in Tunisia. Methods We collected data of investigations and contact tracing between March 1, 2020 and May 5, 2020 as well as illness onset data during the period February 29-May 5, 2020 from National Observatory of New and Emerging Diseases of Tunisia. Maximum likelihood (ML) approach is used to estimate dynamics of Rt. Results 491 of infector-infectee pairs were involved, with 14.46% reported pre-symptomatic transmission. SI follows Gamma distribution with mean 5.30 days [95% CI 4.66–5.95] and standard deviation 0.26 [95% CI 0.23–0.30]. Also, we estimated large changes in Rt in response to the combined lockdown interventions. The Rt moves from 3.18 [95% CI 2.73–3.69] to 1.77 [95% CI 1.49–2.08] with curfew prevention measure, and under the epidemic threshold (0.89 [95% CI 0.84–0.94]) by national lockdown measure. Conclusions Overall, our findings highlight contribution of interventions to interrupt transmission of SARS-CoV-2 in Tunisia.

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Estimating transmission dynamics and serial interval of the first wave of COVID-19 infections under different control measures: A statistical analysis in Tunisia from February 29 to May 5, 2020

10.21203/rs.3.rs-31349/v2 ◽

2020 ◽

Author(s):

Khouloud Talmoudi ◽

Mouna Safer ◽

Hejer Letaief ◽

Aicha Hchaichi ◽

Chahida Harizi ◽

...

Keyword(s):

Emerging Diseases ◽

Control Measures ◽

Transmission Dynamics ◽

Contact Tracing ◽

Epidemic Threshold ◽

Illness Onset ◽

Intervention Measures ◽

Temporal Reproduction ◽

Prevention Measure ◽

Serial Interval

Abstract Background: Describing transmission dynamics of the outbreak and impact of intervention measures are critical to planning responses to future outbreaks and providing timely information to guide policy makers decision. We estimate serial interval (SI) and temporal reproduction number (Rt) of SARS-CoV-2 in Tunisia. Methods: We collected data of investigations and contact tracing between March 1, 2020 and May 5, 2020 as well as illness onset data during the period February 29-May 5, 2020 from National Observatory of New and Emerging Diseases of Tunisia. Maximum likelihood (ML) approach is used to estimate dynamics of Rt. Results: 491 of infector-infectee pairs were involved, with 14.46% reported pre-symptomatic transmission. SI follows Gamma distribution with mean 5.30 days [95% CI 4.66-5.95] and standard deviation 0.26 [95% CI 0.23-0.30]. Also, we estimated large changes in Rt in response to the combined lockdown interventions. The Rt moves from 3.18 [95% CI 2.73-3.69] to 1.77 [95% CI 1.49-2.08] with curfew prevention measure, and under the epidemic threshold (0.89 [95% CI 0.84-0.94]) by national lockdown measure.Conclusions: Overall, our findings highlight contribution of interventions to interrupt transmission of SARS-CoV-2 in Tunisia.

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Estimating transmission dynamics and serial interval of the first wave of COVID-19 infections under different control measures: a statistical analysis in Tunisia from February 29 to May 5, 2020

BMC Infectious Diseases ◽

10.1186/s12879-020-05577-4 ◽

2020 ◽

Vol 20 (1) ◽

Author(s):

Khouloud Talmoudi ◽

Mouna Safer ◽

Hejer Letaief ◽

Aicha Hchaichi ◽

Chahida Harizi ◽

...

Keyword(s):

Credible Interval ◽

Emerging Diseases ◽

Control Measures ◽

Transmission Dynamics ◽

Contact Tracing ◽

Epidemic Threshold ◽

Illness Onset ◽

Intervention Measures ◽

Temporal Reproduction ◽

Serial Interval

Abstract Background Describing transmission dynamics of the outbreak and impact of intervention measures are critical to planning responses to future outbreaks and providing timely information to guide policy makers decision. We estimate serial interval (SI) and temporal reproduction number (Rt) of SARS-CoV-2 in Tunisia. Methods We collected data of investigations and contact tracing between March 1, 2020 and May 5, 2020 as well as illness onset data during the period February 29–May 5, 2020 from National Observatory of New and Emerging Diseases of Tunisia. Maximum likelihood (ML) approach is used to estimate dynamics of Rt. Results Four hundred ninety-one of infector-infectee pairs were involved, with 14.46% reported pre-symptomatic transmission. SI follows Gamma distribution with mean 5.30 days [95% Confidence Interval (CI) 4.66–5.95] and standard deviation 0.26 [95% CI 0.23–0.30]. Also, we estimated large changes in Rt in response to the combined lockdown interventions. The Rt moves from 3.18 [95% Credible Interval (CrI) 2.73–3.69] to 1.77 [95% CrI 1.49–2.08] with curfew prevention measure, and under the epidemic threshold (0.89 [95% CrI 0.84–0.94]) by national lockdown measure. Conclusions Overall, our findings highlight contribution of interventions to interrupt transmission of SARS-CoV-2 in Tunisia.

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Estimating transmission dynamics and serial interval of the first wave of COVID-19 infections under different control measures: A statistical analysis in Tunisia from February 29 to May 5, 2020

10.21203/rs.3.rs-31349/v3 ◽

2020 ◽

Author(s):

Khouloud Talmoudi ◽

Mouna Safer ◽

Hejer Letaief ◽

Aicha Hchaichi ◽

Chahida Harizi ◽

...

Keyword(s):

Credible Interval ◽

Emerging Diseases ◽

Control Measures ◽

Transmission Dynamics ◽

Contact Tracing ◽

Epidemic Threshold ◽

Illness Onset ◽

Intervention Measures ◽

Temporal Reproduction ◽

Serial Interval

Abstract Background: Describing transmission dynamics of the outbreak and impact of intervention measures are critical to planning responses to future outbreaks and providing timely information to guide policy makers decision. We estimate serial interval (SI) and temporal reproduction number (Rt) of SARS-CoV-2 in Tunisia. Methods: We collected data of investigations and contact tracing between March 1, 2020 and May 5, 2020 as well as illness onset data during the period February 29-May 5, 2020 from National Observatory of New and Emerging Diseases of Tunisia. Maximum likelihood (ML) approach is used to estimate dynamics of Rt. Results: 491 of infector-infectee pairs were involved, with 14.46% reported pre-symptomatic transmission. SI follows Gamma distribution with mean 5.30 days [95% Confidence Interval (CI) 4.66-5.95] and standard deviation 0.26 [95% CI 0.23-0.30]. Also, we estimated large changes in Rt in response to the combined lockdown interventions. The Rt moves from 3.18 [95% Credible Interval (CrI) 2.73-3.69] to 1.77 [95% CrI 1.49-2.08] with curfew prevention measure, and under the epidemic threshold (0.89 [95% CrI 0.84-0.94]) by national lockdown measure.Conclusions: Overall, our findings highlight contribution of interventions to interrupt transmission of SARS-CoV-2 in Tunisia.

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Transmission Dynamics of the COVID-19 Epidemic at the District Level in India: Prospective Observational Study (Preprint)

10.2196/preprints.22678 ◽

2020 ◽

Author(s):

Suman Saurabh ◽

Mahendra Kumar Verma ◽

Vaishali Gautam ◽

Nitesh Kumar ◽

Akhil Dhanesh Goel ◽

...

Keyword(s):

Capacity Planning ◽

Local Level ◽

Time Windows ◽

District Level ◽

Control Measures ◽

Transmission Dynamics ◽

Contact Tracing ◽

Northwestern Part ◽

Epidemic Size ◽

Serial Interval

BACKGROUND On March 9, 2020, the first COVID-19 case was reported in Jodhpur, Rajasthan, in the northwestern part of India. Understanding the epidemiology of COVID-19 at a local level is becoming increasingly important to guide measures to control the pandemic. OBJECTIVE The aim of this study was to estimate the serial interval and basic reproduction number (R0) to understand the transmission dynamics of the COVID-19 outbreak at a district level. We used standard mathematical modeling approaches to assess the utility of these factors in determining the effectiveness of COVID-19 responses and projecting the size of the epidemic. METHODS Contact tracing of individuals infected with SARS-CoV-2 was performed to obtain the serial intervals. The median and 95th percentile values of the SARS-CoV-2 serial interval were obtained from the best fits with the weibull, log-normal, log-logistic, gamma, and generalized gamma distributions. Aggregate and instantaneous R0 values were derived with different methods using the EarlyR and EpiEstim packages in R software. RESULTS The median and 95th percentile values of the serial interval were 5.23 days (95% CI 4.72-5.79) and 13.20 days (95% CI 10.90-18.18), respectively. R0 during the first 30 days of the outbreak was 1.62 (95% CI 1.07-2.17), which subsequently decreased to 1.15 (95% CI 1.09-1.21). The peak instantaneous R0 values obtained using a Poisson process developed by Jombert et al were 6.53 (95% CI 2.12-13.38) and 3.43 (95% CI 1.71-5.74) for sliding time windows of 7 and 14 days, respectively. The peak R0 values obtained using the method by Wallinga and Teunis were 2.96 (95% CI 2.52-3.36) and 2.92 (95% CI 2.65-3.22) for sliding time windows of 7 and 14 days, respectively. R0 values of 1.21 (95% CI 1.09-1.34) and 1.12 (95% CI 1.03-1.21) for the 7- and 14-day sliding time windows, respectively, were obtained on July 6, 2020, using method by Jombert et al. Using the method by Wallinga and Teunis, values of 0.32 (95% CI 0.27-0.36) and 0.61 (95% CI 0.58-0.63) were obtained for the 7- and 14-day sliding time windows, respectively. The projection of cases over the next month was 2131 (95% CI 1799-2462). Reductions of transmission by 25% and 50% corresponding to reasonable and aggressive control measures could lead to 58.7% and 84.0% reductions in epidemic size, respectively. CONCLUSIONS The projected transmission reductions indicate that strengthening control measures could lead to proportionate reductions of the size of the COVID-19 epidemic. Time-dependent instantaneous R0 estimation based on the process by Jombart et al was found to be better suited for guiding COVID-19 response at the district level than overall R0 or instantaneous R0 estimation by the Wallinga and Teunis method. A data-driven approach at the local level is proposed to be useful in guiding public health strategy and surge capacity planning.

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Transmission Dynamics of the COVID-19 Epidemic at the District Level in India: Prospective Observational Study

JMIR Public Health and Surveillance ◽

10.2196/22678 ◽

2020 ◽

Vol 6 (4) ◽

pp. e22678 ◽

Cited By ~ 1

Author(s):

Suman Saurabh ◽

Mahendra Kumar Verma ◽

Vaishali Gautam ◽

Nitesh Kumar ◽

Akhil Dhanesh Goel ◽

...

Keyword(s):

Capacity Planning ◽

Local Level ◽

Time Windows ◽

District Level ◽

Control Measures ◽

Transmission Dynamics ◽

Contact Tracing ◽

Northwestern Part ◽

Epidemic Size ◽

Serial Interval

Background On March 9, 2020, the first COVID-19 case was reported in Jodhpur, Rajasthan, in the northwestern part of India. Understanding the epidemiology of COVID-19 at a local level is becoming increasingly important to guide measures to control the pandemic. Objective The aim of this study was to estimate the serial interval and basic reproduction number (R0) to understand the transmission dynamics of the COVID-19 outbreak at a district level. We used standard mathematical modeling approaches to assess the utility of these factors in determining the effectiveness of COVID-19 responses and projecting the size of the epidemic. Methods Contact tracing of individuals infected with SARS-CoV-2 was performed to obtain the serial intervals. The median and 95th percentile values of the SARS-CoV-2 serial interval were obtained from the best fits with the weibull, log-normal, log-logistic, gamma, and generalized gamma distributions. Aggregate and instantaneous R0 values were derived with different methods using the EarlyR and EpiEstim packages in R software. Results The median and 95th percentile values of the serial interval were 5.23 days (95% CI 4.72-5.79) and 13.20 days (95% CI 10.90-18.18), respectively. R0 during the first 30 days of the outbreak was 1.62 (95% CI 1.07-2.17), which subsequently decreased to 1.15 (95% CI 1.09-1.21). The peak instantaneous R0 values obtained using a Poisson process developed by Jombert et al were 6.53 (95% CI 2.12-13.38) and 3.43 (95% CI 1.71-5.74) for sliding time windows of 7 and 14 days, respectively. The peak R0 values obtained using the method by Wallinga and Teunis were 2.96 (95% CI 2.52-3.36) and 2.92 (95% CI 2.65-3.22) for sliding time windows of 7 and 14 days, respectively. R0 values of 1.21 (95% CI 1.09-1.34) and 1.12 (95% CI 1.03-1.21) for the 7- and 14-day sliding time windows, respectively, were obtained on July 6, 2020, using method by Jombert et al. Using the method by Wallinga and Teunis, values of 0.32 (95% CI 0.27-0.36) and 0.61 (95% CI 0.58-0.63) were obtained for the 7- and 14-day sliding time windows, respectively. The projection of cases over the next month was 2131 (95% CI 1799-2462). Reductions of transmission by 25% and 50% corresponding to reasonable and aggressive control measures could lead to 58.7% and 84.0% reductions in epidemic size, respectively. Conclusions The projected transmission reductions indicate that strengthening control measures could lead to proportionate reductions of the size of the COVID-19 epidemic. Time-dependent instantaneous R0 estimation based on the process by Jombart et al was found to be better suited for guiding COVID-19 response at the district level than overall R0 or instantaneous R0 estimation by the Wallinga and Teunis method. A data-driven approach at the local level is proposed to be useful in guiding public health strategy and surge capacity planning.

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A prospect on the use of antiviral drugs to control local outbreaks of COVID-19

10.1101/2020.03.19.20038182 ◽

2020 ◽

Author(s):

Andrea Torneri ◽

Pieter Libin ◽

Joris Vanderlocht ◽

Anne-Mieke Vandamme ◽

Johan Neyts ◽

...

Keyword(s):

Control Measure ◽

Health Care Systems ◽

Significant Proportion ◽

Antiviral Drug ◽

Control Measures ◽

Contact Tracing ◽

Final Size ◽

Antiviral Compound ◽

Intervention Measures ◽

Care Systems

AbstractBackgroundCurrent outbreaks of COVID-19 are threatening the health care systems of several countries around the world. Control measures, based on isolation and quarantine, have been shown to decrease and delay the burden of the ongoing epidemic. With respect to the ongoing COVID-19 epidemic, recent modelling work shows that this intervention technique may be inadequate to control local outbreaks, even when perfect isolation is assumed. Furthermore, the effect of infectiousness prior to symptom onset combined with a significant proportion of asymptomatic infectees further complicates the use of contact tracing. Antivirals, which decrease the viral load and reduce the infectiousness, could be integrated in the control measures in order to augment the feasibility of controlling the epidemic.MethodsUsing a simulation-based model of viral transmission we tested the efficacy of different intervention measures for the control of COVID-19. For individuals that were identified through contact tracing, we evaluate two procedures: monitoring individuals for symptoms onset and testing of individuals. Moreover, we investigate the effect of a potent antiviral compound on the contact tracing process.FindingsThe use of an antiviral drug, in combination with contact tracing, quarantine and isolation, results in a significant decrease of the final size, the peak incidence, and increases the probability that the outbreak will fade out.InterpretationFor an infectious disease in which presymptomatic infections are plausible, an intervention measure based on contact tracing performs better when realized together with testing instead of monitoring, provided that the test is able to detect infections during the incubation period. In addition, in all tested scenarios, the model highlights the benefits of the administration of an antiviral drug in addition to quarantine, isolation and contact tracing. The resulting control measure, could be an effective strategy to control local and re-emerging out-breaks of COVID-19.

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Transmission dynamics, serial interval and epidemiology of COVID-19 diseases in Hong Kong under different control measures

Wellcome Open Research ◽

10.12688/wellcomeopenres.15896.2 ◽

2020 ◽

Vol 5 ◽

pp. 91

Author(s):

Yung-Wai Desmond Chan ◽

Stefan Flasche ◽

Tin-Long Terence Lam ◽

Mei-Hung Joanna Leung ◽

Miu-Ling Wong ◽

...

Keyword(s):

Hong Kong ◽

Population Level ◽

Credible Interval ◽

Control Measures ◽

Transmission Dynamics ◽

Local Data ◽

First Case ◽

Serial Interval ◽

Imported Cases ◽

Epidemiological Characteristics

Background: The outbreak of coronavirus disease 2019 (COVID-19) started in Wuhan, China in late December 2019, and subsequently became a pandemic. Hong Kong had implemented a series of control measures since January 2020, including enhanced surveillance, isolation and quarantine, border control and social distancing. Hong Kong recorded its first case on 23 January 2020, who was a visitor from Wuhan. We analysed the surveillance data of COVID-19 to understand the transmission dynamics and epidemiology in Hong Kong. Methods: We constructed the epidemic curve of daily COVID-19 incidence from 23 January to 6 April 2020 and estimated the time-varying reproduction number (Rt) with the R package EpiEstim, with serial interval computed from local data. We described the demographic and epidemiological characteristics of reported cases. We computed weekly incidence by age and residential district to understand the spatial and temporal transmission of the disease. Results: COVID-19 disease in Hong Kong was characterised with local cases and clusters detected after two waves of importations, first in late January (week 4 to 6) and the second one in early March (week 9 to 10). The Rt increased to approximately 2 95% credible interval (CI): 0.3-3.3) and approximately 1 (95%CI: 0.2-1.7), respectively, following these importations; it decreased to below 1 afterwards from weeks 11 to 13, which coincided with the implementation, modification and intensification of different control measures. Compared to local cases, imported cases were younger (mean age: 52 years among local cases vs 35 years among imported cases), had a lower proportion of underlying disease (9% vs 5%) and severe outcome (13% vs 5%). Cases were recorded in all districts but the incidence was highest in those in the Hong Kong Island region. Conclusions: Stringent and sustained public health measures at population level could contain the COVID-19 disease at a relatively low level.

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On realized serial and generation intervals given control measures: The COVID-19 pandemic case

PLoS Computational Biology ◽

10.1371/journal.pcbi.1008892 ◽

2021 ◽

Vol 17 (3) ◽

pp. e1008892

Author(s):

Andrea Torneri ◽

Pieter Libin ◽

Gianpaolo Scalia Tomba ◽

Christel Faes ◽

James G. Wood ◽

...

Keyword(s):

Reproduction Number ◽

Transmission Probability ◽

Theoretical Explanation ◽

Control Measures ◽

Strong Impact ◽

Generation Interval ◽

Incidence Data ◽

Intervention Measures ◽

Serial Interval ◽

The Mean

The SARS-CoV-2 pathogen is currently spreading worldwide and its propensity for presymptomatic and asymptomatic transmission makes it difficult to control. The control measures adopted in several countries aim at isolating individuals once diagnosed, limiting their social interactions and consequently their transmission probability. These interventions, which have a strong impact on the disease dynamics, can affect the inference of the epidemiological quantities. We first present a theoretical explanation of the effect caused by non-pharmaceutical intervention measures on the mean serial and generation intervals. Then, in a simulation study, we vary the assumed efficacy of control measures and quantify the effect on the mean and variance of realized generation and serial intervals. The simulation results show that the realized serial and generation intervals both depend on control measures and their values contract according to the efficacy of the intervention strategies. Interestingly, the mean serial interval differs from the mean generation interval. The deviation between these two values depends on two factors. First, the number of undiagnosed infectious individuals. Second, the relationship between infectiousness, symptom onset and timing of isolation. Similarly, the standard deviations of realized serial and generation intervals do not coincide, with the former shorter than the latter on average. The findings of this study are directly relevant to estimates performed for the current COVID-19 pandemic. In particular, the effective reproduction number is often inferred using both daily incidence data and the generation interval. Failing to account for either contraction or mis-specification by using the serial interval could lead to biased estimates of the effective reproduction number. Consequently, this might affect the choices made by decision makers when deciding which control measures to apply based on the value of the quantity thereof.

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

10.1101/2020.07.03.20146167 ◽

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.

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What do we know about SARS-CoV-2 transmission? A systematic review and meta-analysis of the secondary attack rate, serial interval, and asymptomatic infection

10.1101/2020.05.21.20108746 ◽

2020 ◽

Cited By ~ 7

Author(s):

Wee Chian Koh ◽

Lin Naing ◽

Muhammad Ali Rosledzana ◽

Mohammad Fathi Alikhan ◽

Liling Chaw ◽

...

Keyword(s):

Systematic Review ◽

Attack Rate ◽

Meta Analysis ◽

Original Data ◽

Asymptomatic Infection ◽

Control Measures ◽

Transmission Risk ◽

Contact Tracing ◽

Random Effects Models ◽

Serial Interval

Background Current SARS-CoV-2 containment measures rely on the capacity to control person-to-person viral transmission. Effective prioritization of these measures can be determined by understanding SARS-CoV-2 transmission dynamics. We conducted a systematic review and meta-analyses of three parameters: (i) secondary attack rate (SAR) in various settings, (ii) clinical onset serial interval (SI), and (iii) the proportion of asymptomatic infection. Methods and Findings We searched PubMed, medRxiv, and bioRxiv databases between January 1, 2020, and May 15, 2020, for articles describing SARS-CoV-2 attack rate, SI, and asymptomatic infection. Studies were included if they presented original data for estimating point estimates and 95% confidence intervals of the three parameters. Random effects models were constructed to pool SAR, mean SI, and asymptomatic proportion. Risk ratios were used to examine differences in transmission risk by setting, type of contact, and symptom status of the index case. Publication and related bias were assessed by funnel plots and Egger's meta-regression test for small-study effects. Our search strategy for SAR, SI, and asymptomatic infection identified 459, 572, and 1624 studies respectively. Of these, 20 studies met the inclusion criteria for SAR, 18 studies for SI, and 66 studies for asymptomatic infection. We estimated the pooled household SAR at 15.4% (95% CI: 12.2%, 18.7%) compared to 4.0% (95% CI: 2.8%, 5.2%) in non-household settings. We observed variation across settings; however, the small number of studies limited power to detect associations and sources of heterogeneity. SAR of symptomatic index cases is significantly higher than cases that were symptom-free at diagnosis (RR 2.55, 95% CI: 1.47, 4.45). Adults appear to be more susceptible to transmission than children (RR 1.40, 95% CI: 1.00, 1.96). The pooled mean SI is estimated at 4.87 days (95% CI: 3.98, 5.77). The pooled proportion of cases who had no symptoms at diagnosis is 25.9% (95% CI: 18.8%, 33.1%). Conclusions Based our pooled estimates, 10 infected symptomatic persons living with 100 contacts would result in 15 additional cases in <5 days. To be effective, quarantine of contacts should occur within 3 days of symptom onset. If testing and tracing relies on symptoms, one-quarter of cases would be missed. As such, while aggressive contact tracing strategies may be appropriate early in an outbreak, as it progresses, control measures should transition to account for SAR variability across settings. Targeted strategies focusing on high-density enclosed settings may be effective without overly restricting social movement.

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