Inference of person-to-person transmission of COVID-19 reveals hidden super-spreading events during the early outbreak phase

Wang, Liang; Didelot, Xavier; Yang, Jing; Wong, Gary; Shi, Yi; Liu, Wenjun; Gao, George F.; Bi, Yang

doi:10.1038/s41467-020-18836-4

Cited by 92 publications

(88 citation statements)

References 33 publications

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“…2 . Another study using SARS-CoV-2 sequencing data [ 37 ], which appears less affected by the sporadic-case issue, estimated k at 0.32 (95%CI: 0.13, 0.38), and this estimate is highly consistent with our main results (highlighted in Table 1 ).…”

Section: Resultssupporting

confidence: 88%

Inferencing superspreading potential using zero-truncated negative binomial model: exemplification with COVID-19

Zhao

Shen

Musa

et al. 2021

BMC Med Res Methodol

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Background In infectious disease transmission dynamics, the high heterogeneity in individual infectiousness indicates that few index cases generate large numbers of secondary cases, which is commonly known as superspreading events. The heterogeneity in transmission can be measured by describing the distribution of the number of secondary cases as a negative binomial (NB) distribution with dispersion parameter, k. However, such inference framework usually neglects the under-ascertainment of sporadic cases, which are those without known epidemiological link and considered as independent clusters of size one, and this may potentially bias the estimates. Methods In this study, we adopt a zero-truncated likelihood-based framework to estimate k. We evaluate the estimation performance by using stochastic simulations, and compare it with the baseline non-truncated version. We exemplify the analytical framework with three contact tracing datasets of COVID-19. Results We demonstrate that the estimation bias exists when the under-ascertainment of index cases with 0 secondary case occurs, and the zero-truncated inference overcomes this problem and yields a less biased estimator of k. We find that the k of COVID-19 is inferred at 0.32 (95%CI: 0.15, 0.64), which appears slightly smaller than many previous estimates. We provide the simulation codes applying the inference framework in this study. Conclusions The zero-truncated framework is recommended for less biased transmission heterogeneity estimates. These findings highlight the importance of individual-specific case management strategies to mitigate COVID-19 pandemic by lowering the transmission risks of potential super-spreaders with priority.

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Section: Resultssupporting

confidence: 88%

Inferencing superspreading potential using zero-truncated negative binomial model: exemplification with COVID-19

Zhao

Shen

Musa

et al. 2021

BMC Med Res Methodol

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“…• As mentioned above, the strength of transmission fluctuates as it varies significantly from one individual to another. The modeling is complicated by the fact, recently discovered through phylogenetic analysis of the COVID-19 samples collected over many infected individuals [117], that the disease spread is extremely intermittent -a small percentage (10% or less) of individuals cause significant percentage (90% or more) of the secondary cases. Moreover, COVID-19, like most dangerous infections, may transmit asymptomatically, therefore rendering tracing extremely important but also quite challenging.…”

Section: A Uncertainties In Modeling Pandemics: Public Health Introdmentioning

confidence: 99%

Graphical Models of Pandemic

Chertkov

Abrams

Sikaroudi

et al. 2021

Preprint

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Both COVID-19 and novel pandemics challenge those of us within the modeling community, specifically in establishing suitable relations between lifecycles, scales, and existing methods. Herein we demonstrate transitions between models in space/time, individual-to-community, county-to-city, along with models for the trace beginning with exposure, then to symptom manifest, then to community transmission. To that end, we leverage publicly available data to compose a chain of Graphical Models (GMs) for predicting infection rates across communities, space, and time. We'll anchor our GMs against the more expensive yet state-of-the-art Agent-Based Models (ABMs). Insight obtained from designing novel GMs calibrated to ABMs furnishes reduced, yet reliable surrogates for the end-to-end public health challenge of community contact tracing and transmission. Further, this novel research transcends and synergizes information integration and informatics, leading to an advance in the science of GMs. Cognizance into the data lifecycle using properly coarse-grained modeling will broaden the toolkit available to public health specialists, and hopefully empower governments and health agencies, here and abroad, in addressing the profound challenges in disease and vaccination campaigns confronting us by COVID and future pandemics. In this proof of principle study, focusing on the GM methodology development, we show, first, how static GM of the Ising model type (characterised by pair-wise interaction between nodes related to traffic and communications between nodes representing communities, or census tracts within a given city, and with local infection bias) emerge from a dynamic GM of the Independent Cascade type, introduced and studied in Computer and Networks sciences mainly in the context of the spread of social influences. Second, we formulate the problem of inference in epidemiology as inference problems in the Ising model setting. Specifically, we pose the challenge of computing Conditional A-posteriori Level of Infection (CALI), which provides a quantitative answer to the questions: What is the probability that a given node in the GM (given census tract within the city) becomes infected in the result of injection of the infection at another node, e.g. due to arrival of a super-spreader agent or occurrence of the super-spreader event in the area. To answer the question exactly is not feasible for any realistic size (larger than 30-50 nodes) model. We therefore adopt and develop approximate inference techniques, of the variational and variable elimination types, developed in the GM literature. To demonstrate utility of the methodology, which seems new for the public health application, we build a 123-node model of Seattle, as well as its 10-node and 20-node coarse-grained variants, and then conduct the proof of principles experimental studies. The experiments on the coarse-grained models have helped us to validate the approximate inference by juxtaposing it to the exact inference. The experiments also lead to discovery of interesting and most probably universal phenomena. In particular, we observe (a) a strong sensitivity of CALI to the location of the initial infection, and (b) strong alignment of the resulting infection probability (values of CALI) observed at different nodes in the regimes of moderate interaction between the nodes. We then speculate how these, and other observations drawn from the synthetic experiments, can be extended to a more realistic, data driven setting of actual operation importance. We conclude the manuscript with an extensive discussion of how the methodology should be developed further, both at the level of devising realistic GMs from observational data (and also enhancing it with microscopic ABM modeling and simulations) and also regarding utilisation of the GM inference methodology for more complex problems of the pandemic mitigation and control.

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“…Despite the fact that pandemics have been encountered before in history, it turned out that the world was not prepared for it. One of the main reasons for that is the rapid spread of the threat due to the disappeared borders and ease of travelling to all parts of the world with today's technology [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

“…The realization that the virus was spreading rapidly and that it was transmitted between people [4], immediately followed by the realization that its negative effects on human health were more harmful than the commonly encountered viral infections (and even fatal) [7][8][9], caused a global panic. People all over the world have begun to ask and seek answers to many questions about the nature of this disease [10,11].…”

Section: Introductionmentioning

confidence: 99%

Novel additive manufacturing applications for communicable disease prevention and control: focus on recent COVID-19 pandemic

et al. 2021

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COVID-19 disease caused by the SARS-CoV-2 virus has had serious adverse effects globally in 2020 which are foreseen to extend in 2021, as well. The most important of these effects was exceeding the capacity of the healthcare infrastructures, and the related inability to meet the need for various medical equipment especially within the first months of the crisis following the emergence and rapid spreading of the virus. Urgent global demand for the previously unavailable personal protective equipment, sterile disposable medical supplies as well as the active molecules including vaccines and drugs fueled the need for the coordinated efforts of the scientific community. Amid all this confusion, the rapid prototyping technology, 3D printing, has demonstrated its competitive advantage by repositioning its capabilities to respond to the urgent need. Individual and corporate, amateur and professional all makers around the world with 3D printing capacity became united in effort to fill the gap in the supply chain until mass production is available especially for personal protective equipment and other medical supplies. Due to the unexpected, everchanging nature of the COVID-19 pandemic-like all other potential communicable diseases-the need for rapid design and 3D production of parts and pieces as well as sterile disposable medical equipment and consumables is likely to continue to keep its importance in the upcoming years. This review article summarizes how additive manufacturing technology can contribute to such cases with special focus on the recent COVID-19 pandemic.

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Inference of person-to-person transmission of COVID-19 reveals hidden super-spreading events during the early outbreak phase

Cited by 92 publications

References 33 publications

Inferencing superspreading potential using zero-truncated negative binomial model: exemplification with COVID-19

Inferencing superspreading potential using zero-truncated negative binomial model: exemplification with COVID-19

Graphical Models of Pandemic

Novel additive manufacturing applications for communicable disease prevention and control: focus on recent COVID-19 pandemic

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