Disease momentum: Estimating the reproduction number in the presence of superspreading

Johnson, Kory D.; Beiglböck, Mathias; Eder, Manuel; Grass, Annemarie; Hermisson, Joachim; Pammer, Gudmund; Polechová, Jitka; Toneian, Daniel; Wölfl, Benjamin

doi:10.1016/j.idm.2021.03.006

Cited by 13 publications

(40 citation statements)

References 21 publications

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“…Larremore et al [7] indicate that if half of the population would self-test every 3 days with a rapid antigen test and (immediately) isolate in the case of a positive result, we could achieve approximately a 40% reduction of the effective reproduction number R. For example, an R of 1.3 could be reduced to 0.8, and the epidemic would dissipate. Furthermore, it has been reported that over 80% of new infections are caused by fewer than 20% of cases [21][22][23]. Such so-called ‗superspreading' is caused by ‗superspreaders' who have both a large number of contacts and often a higher viral load (at the time of superspreading).…”

Section: What Is Newmentioning

confidence: 99%

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SARS-CoV-2 rapid antigen tests provide benefits for epidemic control – observations from Austrian schools

Polechová¹,

Johnson²,

Payne³

et al. 2022

Journal of Clinical Epidemiology

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Objective : This paper motivates and justifies the use of antigen tests for epidemic control as distinct from a diagnostic test. Study Design and Setting : We discuss the relative advantages of antigen and PCR tests, summarising evidence from both the literature as well as Austrian schools, which conducted frequent, mass rapid antigen testing during the spring of 2021. While our report on testing predates Delta, we have updated the review with recent data on viral loads in breakthrough infections and more information about testing efficacy, especially in children. Results : Rapid antigen tests detect proteins at the surface of virus particles, identifying the disease during its infectious phase. In contrast, PCR tests detect viral genomes: they can thus diagnose COVID-19 before the infectious phase but also react to remnants of the virus genome, even weeks after live virus ceases to be detectable in the respiratory tract. Furthermore, the logistics for administering the tests are different. Large-scale rapid antigen testing in Austrian schools showed low false-positive rates along with an approximately 10% lower effective reproduction number in the tested cohort. Conclusion : Using antigen tests at least 2-3 times per week could become a powerful tool to suppress the COVID-19 pandemic.

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Section: What Is Newmentioning

confidence: 99%

“…In particular, it has been reported that over 80% of new infections are caused by fewer than 20% of cases [16][17][18]. Such so-called 'superspreading' is caused by 'superspreaders' who have both a large number of contacts and often a higher viral load.…”

Section: Figurementioning

confidence: 99%

SARS-CoV-2 rapid antigen tests provide benefits for epidemic control – observations from Austrian schools

Polechová¹,

Johnson²,

Payne³

et al. 2022

Journal of Clinical Epidemiology

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“…Considering identically and independently gamma distributed individual reproduction factors with shape parameter k and scale parameter R t /k leads to a negative binomial model (gamma-Poisson mixture) for the total number of new cases, which can be written as where the first parameter is understood as the number of allowed failures and the second parameter is the success probability. This approach allows the model to better comply with the observed stochasticity in reported case numbers [2, 4, 24, 30, 31, 34, 35] by considering an additional free parameter k , which can be viewed as a dispersion parameter that reflects the variance in individual secondary cases. Large dispersion, which is associated with a scenario that shows great variability in the individual numbers of secondary cases, can be modelled in (5) by choosing k small.…”

Section: Resultsmentioning

confidence: 99%

“…Methods for extracting the relevant inter-patient time distribution from a number of observed infection pairs were also directly included in the algorithms for calculating reproduction factors [49]. Furthermore, in the inference of reproduction factors, reported cases are widely used as a surrogate for actual contagion events [11, 16, 20, 22, 30, 40, 49, 51] (infectious activity). The corresponding ‘simplified’ configuration of the interval model and the associated forms of infectious load and activity corresponds to the formula where w τ are the probability masses of the distribution of the case interval Δ case , which is usually replaced with either Δ ser or Δ gen .…”

Section: Resultsmentioning

confidence: 99%

Assessing the heterogeneity in the transmission of infectious diseases from time series of epidemiological data

Schneckenreither

Herrmann

Reisenhofer

et al. 2022

Preprint

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Structural features and the heterogeneity of disease transmissions play an essential role in the dynamics of epidemic spread. But these aspects can not completely be assessed from aggregate data or macroscopic indicators such as the effective reproduction number. We propose an index of effective aggregate dispersion (EffDI) that indicates the significance of infection clusters and superspreading events in the progression of outbreaks by carefully measuring the level of stochasticity in time series of reported case numbers. This allows to detect the transition from predominantly clustered spreading to a diffusive regime with diminishing significance of singular clusters, which can be decisive for the future progression of outbreaks and relevant in the planning of containment measures. We evaluate EffDI for SARS-CoV-2 case data in different countries and compare the results with a quantifier for the socio-demographic heterogeneity in disease transmissions in a case study to substantiate that EffDI qualifies as a measure for the heterogeneity in transmission dynamics.

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“…The adequate lag was determined from the lag order range of 1–8, based on the Akaike’s information criterion which is one of the most frequently used methods [ 20 ]. We used the lag range of 1–8 weeks because more than 2 months of lag to predict COVID-19 positivity by the keyword trend might be virtually too long, considering the time span of increase / decrease in the effective reproduction number of COVID-19 as a reference of disease momentum [ 21 , 22 ]. The following equations (A-B) describe an example of VAR model (of which lag order = 1) used in this study: …”

Section: Methodsmentioning

confidence: 99%

Need of care in interpreting Google Trends-based COVID-19 infodemiological study results: potential risk of false-positivity

Sato

Mano

Iwata

et al. 2021

BMC Med Res Methodol

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Background Google Trends (GT) is being used as an epidemiological tool to study coronavirus disease (COVID-19) by identifying keywords in search trends that are predictive for the COVID-19 epidemiological burden. However, many of the earlier GT-based studies include potential statistical fallacies by measuring the correlation between non-stationary time sequences without adjusting for multiple comparisons or the confounding of media coverage, leading to concerns about the increased risk of obtaining false-positive results. In this study, we aimed to apply statistically more favorable methods to validate the earlier GT-based COVID-19 study results. Methods We extracted the relative GT search volume for keywords associated with COVID-19 symptoms, and evaluated their Granger-causality to weekly COVID-19 positivity in eight English-speaking countries and Japan. In addition, the impact of media coverage on keywords with significant Granger-causality was further evaluated using Japanese regional data. Results Our Granger causality-based approach largely decreased (by up to approximately one-third) the number of keywords identified as having a significant temporal relationship with the COVID-19 trend when compared to those identified by Pearson or Spearman’s rank correlation-based approach. “Sense of smell” and “loss of smell” were the most reliable GT keywords across all the evaluated countries; however, when adjusted with their media coverage, these keyword trends did not Granger-cause the COVID-19 positivity trends (in Japan). Conclusions Our results suggest that some of the search keywords reported as candidate predictive measures in earlier GT-based COVID-19 studies may potentially be unreliable; therefore, caution is necessary when interpreting published GT-based study results.

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Disease momentum: Estimating the reproduction number in the presence of superspreading

Cited by 13 publications

References 21 publications

SARS-CoV-2 rapid antigen tests provide benefits for epidemic control – observations from Austrian schools

SARS-CoV-2 rapid antigen tests provide benefits for epidemic control – observations from Austrian schools

Assessing the heterogeneity in the transmission of infectious diseases from time series of epidemiological data

Need of care in interpreting Google Trends-based COVID-19 infodemiological study results: potential risk of false-positivity

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