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Viral load and contact heterogeneity predict SARS-CoV-2 transmission and super-spreading events.

ABSTRACT: SARS-CoV-2 is difficult to contain because many transmissions occur during pre-symptomatic infection. Unlike influenza, most SARS-CoV-2-infected people do not transmit while a small percentage infect large numbers of people. We designed mathematical models which link observed viral loads with epidemiologic features of each virus, including distribution of transmissions attributed to each infected person and duration between symptom onset in the transmitter and secondarily infected person. We identify that people infected with SARS-CoV-2 or influenza can be highly contagious for less than 1 day, congruent with peak viral load. SARS-CoV-2 super-spreader events occur when an infected person is shedding at a very high viral load and has a high number of exposed contacts. The higher predisposition of SARS-CoV-2 toward super-spreading events cannot be attributed to additional weeks of shedding relative to influenza. Rather, a person infected with SARS-CoV-2 exposes more people within equivalent physical contact networks, likely due to aerosolization.

SUBMITTER: Goyal A

PROVIDER: S-EPMC7929560 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature

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Viral load and contact heterogeneity predict SARS-CoV-2 transmission and super-spreading events.

Goyal Ashish A Reeves Daniel B DB Cardozo-Ojeda E Fabian EF Schiffer Joshua T JT Mayer Bryan T BT

eLife 20210223

SARS-CoV-2 is difficult to contain because many transmissions occur during pre-symptomatic infection. Unlike influenza, most SARS-CoV-2-infected people do not transmit while a small percentage infect large numbers of people. We designed mathematical models which link observed viral loads with epidemiologic features of each virus, including distribution of transmissions attributed to each infected person and duration between symptom onset in the transmitter and secondarily infected person. We ide ...[more]

PMID: 33620317

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Project description:BackgroundIn current epidemiology of tuberculosis (TB), heterogeneity in infectiousness among TB patients is a challenge, which is not well studied. We aimed to quantify this heterogeneity and the presence of "super-spreading" events that can assist in designing optimal public health interventions.MethodsTB epidemiologic investigation data notified between 1 January 2005 and 31 December 2015 from Victoria, Australia were used to quantify TB patients' heterogeneity in infectiousness and super-spreading events. We fitted a negative binomial offspring distribution (NBD) for the number of secondary infections and secondary active TB disease each TB patient produced. The dispersion parameter, k, of the NBD measures the level of heterogeneity, where low values of k (e.g. k?<?1) indicate over-dispersion. Super-spreading was defined as patients causing as many or more secondary infections as the 99th centile of an equivalent homogeneous distribution. Contact infection was determined based on a tuberculin skin test (TST) result of ?10?mm. A NBD model was fitted to identify index characteristics that were associated with the number of contacts infected and risk ratios (RRs) were used to quantify the strength of this association.ResultsThere were 4190 (2312 pulmonary and 1878 extrapulmonary) index TB patients and 18,030 contacts. A total of 15,522 contacts were tested with TST, of whom 3213 had a result of ?10?mm. The dispersion parameter, k for secondary infections was estimated at 0.16 (95%CI 0.14-0.17) and there were 414 (9.9%) super-spreading events. From the 3213 secondary infections, 2415 (75.2%) were due to super-spreading events. There were 226 contacts who developed active TB disease and a higher level of heterogeneity was found for this outcome than for secondary infection, with k estimated at 0.036 (95%CI 0.025-0.046). In regression analyses, we found that infectiousness was greater among index patients found by clinical presentation and those with bacteriological confirmation.ConclusionTB transmission is highly over dispersed and super-spreading events are responsible for a substantial majority of secondary infections. Heterogeneity of transmission and super-spreading are critical issues to consider in the design of interventions and models of TB transmission dynamics.

Project description:BackgroundFactors that lead to successful SARS-CoV-2 transmission are still not well described. We investigated the association between a case's viral load and the risk of transmission to contacts in the context of other exposure-related factors.MethodsData were generated through routine testing and contact tracing at a large university. Case viral loads were obtained from cycle threshold values associated with a positive polymerase chain reaction test result from October 1, 2020 to April 15, 2021. Cases were included if they had at least one contact who tested 3-14 days after the exposure. Case-contact pairs were formed by linking index cases with contacts. Chi-square tests were used to evaluate differences in proportions of contacts testing positive. Generalized estimating equation models with a log link were used to evaluate whether viral load and other exposure-related factors were associated with a contact testing positive.ResultsMedian viral load among the 212 cases included in the study was 5.6 (1.8-10.4) log10 RNA copies per mL of saliva. Among 365 contacts, 70 (19%) tested positive following their exposure; 36 (51%) were exposed to a case that was asymptomatic or pre-symptomatic on the day of exposure. The proportion of contacts that tested positive increased monotonically with index case viral load (12%, 23% and 25% corresponding to < 5, 5-8 and > 8 log10 copies per mL, respectively; X2 = 7.18, df = 2, p = 0.03). Adjusting for cough, time between test and exposure, and physical contact, the risk of transmission to a close contact was significantly associated with viral load (RR = 1.27, 95% CI 1.22-1.32).ConclusionsFurther research is needed to understand whether these relationships persist for newer variants. For those variants whose transmission advantage is mediated through a high viral load, public health measures could be scaled accordingly. Index cases with higher viral loads could be prioritized for contact tracing and recommendations to quarantine contacts could be made according to the likelihood of transmission based on risk factors such as viral load.

Project description:Simultaneously controlling COVID-19 epidemics and limiting economic and societal impacts presents a difficult challenge, especially with limited public health budgets. Testing, contact tracing, and isolating/quarantining is a key strategy that has been used to reduce transmission of SARS-CoV-2, the virus that causes COVID-19 and other pathogens. However, manual contact tracing is a time-consuming process and as case numbers increase a smaller fraction of cases' contacts can be traced, leading to additional virus spread. Delays between symptom onset and being tested (and receiving results), and a low fraction of symptomatic cases being tested and traced can also reduce the impact of contact tracing on transmission. We examined the relationship between increasing cases and delays and the pathogen reproductive number Rt, and the implications for infection dynamics using deterministic and stochastic compartmental models of SARS-CoV-2. We found that Rt increased sigmoidally with the number of cases due to decreasing contact tracing efficacy. This relationship results in accelerating epidemics because Rt initially increases, rather than declines, as infections increase. Shifting contact tracers from locations with high and low case burdens relative to capacity to locations with intermediate case burdens maximizes their impact in reducing Rt (but minimizing total infections may be more complicated). Contact tracing efficacy decreased sharply with increasing delays between symptom onset and tracing and with lower fraction of symptomatic infections being tested. Finally, testing and tracing reductions in Rt can sometimes greatly delay epidemics due to the highly heterogeneous transmission dynamics of SARS-CoV-2. These results demonstrate the importance of having an expandable or mobile team of contact tracers that can be used to control surges in cases. They also highlight the synergistic value of high capacity, easy access testing and rapid turn-around of testing results, and outreach efforts to encourage symptomatic cases to be tested immediately after symptom onset.

Project description:Background One measure to quantify the degree of dysregulation is allostatic load (AL). Typically, AL incorporates information on diverse biomarkers and is associated with health outcomes such as cardiovascular diseases or the incidence of coronary events (C-E). Aims This study investigates the predictive performance of different AL scoring methods on the incidence of coronary events (C-E). This study also elaborates sex differences in the baseline risks of C-E and the AL associated risks of C-E. Design Longitudinal data analysis of the Heinz Nixdorf Recall Study (Risk Factors, Evaluation of Coronary Calcification, and Lifestyle) of 4327 participants free of C-E at study baseline aged 45–75. The data contains over 13 biomarkers measuring AL. Methods After conducting multiple imputations on missing values on AL for 826 participants, the analysis sample consisted of N = 4327 participants. We applied the two most commonly used methods of AL scoring AL (count-based and Z-score) and a recently developed logistic regression weighting method (LRM) approach. Cox regression was used to predict the incidence of C-E for each AL score. Results were estimated without (M0) and with (M1) covariate adjustment, and in a final model (M2), with an interaction between AL and sex. Results We found no violation of the proportional hazard assumption and significant differences in the survival curves between the sexes for C-E (Log-rank test: prob. > Chi2 = 0.000). In M0, all AL-scoring methods predicted C-E significantly, with the LRM based AL-score having the best performance (hazard ratio = 3.133; CI: [2.630, 3.732]; Somer's D = 0.717). After covariate inclusion, differences between the scoring methods levelled, though the count-based method and LRM performed better than the Z-scoring method. The interaction analysis in M2 showed a significant multiplicative interaction for the count-based method (1.254; [1.066, 1.475]) and for the LRM (1.746; [1.132, 2.692]). The additive relative excess risk due to interaction (RERI) measure was negative for the count-based method (RERI = −1.967; [-3.778; −0.156]) and the LRM (RERI = −1.909 [-3.910; 0.091]), indicating subadditivity. Conclusion AL scores are suitable for predicting C-E. Differences between the AL-scoring algorithms were only present after including interactions. We value the count-based method as suitable for clinical practice since its calculation is relatively simple, and performance was among the best. Interaction analysis revealed that despite strong sex differences in baseline C-E, the effect of AL is more pronounced for females at high levels of AL; thus, females could benefit more from a potential intervention on AL. We suggest further investigation of sex differences concerning the mediation by physiological and psychological intermediates.

Dataset Information

Viral load and contact heterogeneity predict SARS-CoV-2 transmission and super-spreading events.

Publications

Viral load and contact heterogeneity predict SARS-CoV-2 transmission and super-spreading events.

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