Time-varying transmission heterogeneity of SARS and COVID-19 in Hong Kong

Adam, Dillon C.; Gostic, Katelyn M.; Tsang, Tim K.; Wu, Peng; Lim, Wey Wen; Yeung, Amy; Wong, Jessica Y.; Lau, Eric H. Y.; Du, Zhanwei; Ho, Lok Sang; Martín-Sánchez, Mario; Cauchemez, Simon; Cobey, Sarah; Leung, Gabriel M.; Cowling, Benjamin J.

doi:10.21203/rs.3.rs-1407962/v1

Cited by 10 publications

(17 citation statements)

References 22 publications

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“…When secondary attack rate is higher, it is expected that more contacts are infected and therefore the observed number of secondary cases would be less heterogeneous. The strong association between p 0 and infectiousness variation suggests that the observed variations may also be attributed to some cases that are less infectious than average, which is consistent with Hong Kong data ( 53 ). In contrast, the SD of the distribution of number of secondary cases ( σ sec ) is only weakly correlated with infectiousness variation.…”

Section: Discussionsupporting

confidence: 87%

The effect of variation of individual infectiousness on SARS-CoV-2 transmission in households

Tsang

Huang

Wang

et al. 2022

Preprint

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Quantifying variation of individual infectiousness is critical to inform disease control. Previous studies reported substantial heterogeneity in transmission of many infectious diseases (including SARS-CoV-2). However, those results are difficult to interpret since the number of contacts is rarely considered in such approaches. Here, we analyze data from 17 SARS-CoV-2 household transmission studies conducted in periods dominated by ancestral strains, in which the number of contacts was known. By fitting individual-based household transmission models to these data, accounting for number of contacts and baseline transmission probabilities, the pooled estimate suggests that the 20% most infectious cases have 3.1-fold (95% confidence interval: 2.2-4.2 fold) higher infectiousness than average cases, which is consistent with the observed heterogeneity in viral shedding. Household data can inform the estimation of transmission heterogeneity, which is important for epidemic management.One Sentence SummaryIn this study, variation of individual infectiousness is quantified. Potential sources of such variation, particularly heterogeneity of viral shedding is discussed.

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Section: Discussionsupporting

confidence: 87%

The effect of variation of individual infectiousness on SARS-CoV-2 transmission in households

Tsang

Huang

Wang

et al. 2022

Preprint

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“…Increasing evidence shows superspreading plays a substantial role in SARS-CoV-2 transmission, with a small proportion of infected individuals causing a large proportion of secondary cases. Previous studies estimated superspreading using the dispersion parameter k in transmission clusters in the range of 0.06 to 2.97 22,23 , while estimates using two clusters in Hong Kong between 2 and 21 January 2022 were around 0.2 and 0.33 for BA.1 and BA.2, respectively 24,25 . In more recent work, temporal changes in the dispersion parameter in Hong Kong was estimated to be closer to 0.1 when stringent PHSMs were in place 25 .…”

Section: An Estimation Of Incidence and Prevalence Based On Levels Of...mentioning

confidence: 99%

“…Previous studies estimated superspreading using the dispersion parameter k in transmission clusters in the range of 0.06 to 2.97 22,23 , while estimates using two clusters in Hong Kong between 2 and 21 January 2022 were around 0.2 and 0.33 for BA.1 and BA.2, respectively 24,25 . In more recent work, temporal changes in the dispersion parameter in Hong Kong was estimated to be closer to 0.1 when stringent PHSMs were in place 25 . Since most cases during January to April 2022 resulted from a single introduction of BA.2, we used phylodynamic models to compare the reported and estimated case numbers at varying degrees of overdispersion.…”

Section: An Estimation Of Incidence and Prevalence Based On Levels Of...mentioning

confidence: 99%

Resurgence of Omicron BA.2 in SARS-CoV-2 infection-naive Hong Kong

Xie

Edwards

Adam

et al. 2022

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After keeping infections at bay for two years, Hong Kong experienced a surge of Omicron BA.2 infections in early 2022 that overwhelmed the health care system, isolation facilities, and contact tracing capacity, leading to one of the highest per-capita death rates of COVID-19 in early 2022. The outbreak occurred against a backdrop of a dense population with low immunity towards natural SARS-CoV-2 infection, high vaccine hesitancy in vulnerable populations, comprehensive disease surveillance and the capacity for stringent public health and social measures. Using genome sequences and epidemiological data from this time, we reconstruct the epidemic trajectory of the BA.2 wave, estimate transmission and incidence rates, and evaluate the effectiveness of policy changes. We identify an increase in the effective reproductive rate (Re) to 9.5 in mid-January 2022, which preceded real-time estimates of transmission (Rt), revealing that BA.2 community transmission was under-ascertained weeks before the epidemic appeared to surge in mid-February 2022. Due to this, public health measures were relaxed in early February (Spring Festival) while Re increased and remained > 1 throughout February. An independent estimation of point prevalence and incidence using phylodynamics also indicates extensive superspreading at this time, which likely contributed to the rapid expansion of the epidemic. This study demonstrates that relying on Rt estimation methods dependent on case reporting can misinform epidemic response planning, sometimes with substantial consequences. There is a need for future research and implementation of improved estimates of epidemic growth in near real-time that combine multiple disparate data sources to better inform outbreak response policy.

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“…The literature estimates whose time window overlapped with our time windows found substantially lower levels of superspreading than what we observe (Figure 3c and corresponding overdispersion parameter shown in Figure S16). It is possible that contact tracing and modeling over- or under-estimates overdispersion due to missed contacts [20]. However, on the other hand, it may be the case that superspreading is not the only mechanism at play.…”

Section: Resultsmentioning

confidence: 99%

“…Contact tracing can directly measure superspreading by following the close contacts of infected individuals to measure the distribution of the number of secondary cases (the offspring number distribution) [2]. However, some secondary cases may be missed which can lead to measurement bias [20].…”

Section: Deme Simulationsmentioning

confidence: 99%

Lineage frequency time series reveal elevated levels of genetic drift in SARS-CoV-2 transmission in England

Ascensao

Okada

et al. 2022

Preprint

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Random genetic drift in the population-level dynamics of an infectious disease outbreak results from the randomness of inter-host transmission and the randomness of host recovery or death. The strength of genetic drift has been found to be high for SARS-CoV-2 due to superspreading, and this is expected to substantially impact the disease epidemiology and evolution. Noise that results from the measurement process, such as biases in data collection across time, geographical areas, etc., can potentially confound estimates of genetic drift as both processes contribute "noise" to the data. To address this challenge, we develop and validate a method to jointly infer genetic drift and measurement noise from time-series lineage frequency data. We apply this method to over 490,000 SARS-CoV-2 genomic sequences from England collected between March 2020 and December 2021 by the COVID-19 Genomics UK (COG-UK) consortium. We find that even after correcting for measurement noise, the strength of genetic drift is consistently, throughout time, higher than that expected from the observed number of COVID-19 positive individuals in England by 1 to 3 orders of magnitude. Corrections taking into account epidemiological dynamics (susceptible-infected-recovered or susceptible-exposed-infected-recovered models) do not explain the discrepancy. Moreover, the levels of genetic drift that we observe are higher than the estimated levels of superspreading found by modeling studies that incorporate data on actual contact statistics in England. We discuss how even in the absence of superspreading, high levels of genetic drift can be generated via community structure in the host contact network. Our results suggest that further investigations of heterogeneous host contact structure may be important for understanding the high levels of genetic drift observed for SARS-CoV-2 in England.

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Time-varying transmission heterogeneity of SARS and COVID-19 in Hong Kong

Cited by 10 publications

References 22 publications

The effect of variation of individual infectiousness on SARS-CoV-2 transmission in households

The effect of variation of individual infectiousness on SARS-CoV-2 transmission in households

Resurgence of Omicron BA.2 in SARS-CoV-2 infection-naive Hong Kong

Lineage frequency time series reveal elevated levels of genetic drift in SARS-CoV-2 transmission in England

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