Dynamics of COVID-19 under social distancing measures are driven by transmission network structure

Nande, Anjalika; Adlam, Ben; Sheen, Justin; Levy, Michael Z.; Hill, Alison L.

doi:10.1101/2020.06.04.20121673

Cited by 37 publications

(73 citation statements)

References 94 publications

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“…Our trajectories show that lowering a of transmission rates by more than 50–70% compared with its maximum, intrinsic value, is required to stop epidemic and to turn it into the decaying regime. A recent modelling study of COVID-19 clinical progression and transmission network structure on the outcomes of social distancing interventions shows that the strength of within-household transmission is a critical determinant of success, governing the timing and size of the epidemic peak, and also gives rise to a delay between the date when measures were taken and the decay of infections [ 43 ].…”

Section: Discussionmentioning

confidence: 99%

Covid-19 Transmission Trajectories–Monitoring the Pandemic in the Worldwide Context

Loeffler‐Wirth

Schmidt

Binder

2020

Viruses

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The Covid-19 pandemic is developing worldwide with common dynamics but also with marked differences between regions and countries. These are not completely understood, but presumably, provide a clue to find ways to mitigate epidemics until strategies leading to its eradication become available. We describe an iteractive monitoring tool available in the internet. It enables inspection of the dynamic state of the epidemic in 187 countries using trajectories that visualize the transmission and removal rates of the epidemic and in this way bridge epi-curve tracking with modelling approaches. Examples were provided which characterize state of epidemic in different regions of the world in terms of fast and slow growing and decaying regimes and estimate associated rate factors. The basic spread of the disease is associated with transmission between two individuals every two-three days on the average. Non-pharmaceutical interventions decrease this value to up to ten days, whereas ‘complete lock down’ measures are required to stop the epidemic. Comparison of trajectories revealed marked differences between the countries regarding efficiency of measures taken against the epidemic. Trajectories also reveal marked country-specific recovery and death rate dynamics. The results presented refer to the pandemic state in May to July 2020 and can serve as ‘working instruction’ for timely monitoring using the interactive monitoring tool as a sort of ‘seismometer’ for the evaluation of the state of epidemic, e.g., the possible effect of measures taken in both, lock-down and lock-up directions. Comparison of trajectories between countries and regions will support developing hypotheses and models to better understand regional differences of dynamics of Covid-19.

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Section: Discussionmentioning

confidence: 99%

Covid-19 Transmission Trajectories–Monitoring the Pandemic in the Worldwide Context

Loeffler‐Wirth

Schmidt

Binder

2020

Viruses

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“…On the other hand, a too early lock-down would have only postponed the outbreak, but not its strength [ 27 ]. While these restrictions were adopted at the beginning of March, we started seeing their effectiveness around April due to the clinical and epidemiological characteristics of COVID-19 [ 11 ]. Indeed, both the natural progression of the disease and the residual transmission during the lock-down phase, such as household transmission, have generated long delays between the beginning of restriction measures and the observation of their efficacy.…”

Section: Discussionmentioning

confidence: 99%

“…Given this request, in order to represent the COVID-19 epidemic dynamics in Italy, we use a compartmental epidemiological model taken from [ 10 ], based on the classic SEIR model with lock-down (L) measures (SEIRL). A simplified version of the model is presented in [ 11 ], where it is used to analyze the consequences of household transmission in the evolution of the pandemic. In order to make the model representative of the Umbria scenario during the initial phase of the pandemic, we estimate model parameters against the public COVID-19 data of Umbria using our Bayesian method for model calibration, called Conditional Robust Calibration (CRC) [ 12 , 13 , 14 ].…”

Section: Introductionmentioning

confidence: 99%

Mathematical Modeling and Robustness Analysis to Unravel COVID-19 Transmission Dynamics: The Italy Case

Antonini¹,

Calandrini

Stracci

et al. 2020

Biology

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This study started from the request of providing predictions on hospitalization and Intensive Care Unit (ICU) rates that are caused by COVID-19 for the Umbria region in Italy. To this purpose, we propose the application of a computational framework to a SEIR-type (Susceptible, Exposed, Infected, Removed) epidemiological model describing the different stages of COVID-19 infection. The model discriminates between asymptomatic and symptomatic cases and it takes into account possible intervention measures in order to reduce the probability of transmission. As case studies, we analyze not only the epidemic situation in Umbria but also in Italy, in order to capture the evolution of the pandemic at a national level. First of all, we estimate model parameters through a Bayesian calibration method, called Conditional Robust Calibration (CRC), while using the official COVID-19 data of the Italian Civil Protection. Subsequently, Conditional Robustness Analysis (CRA) on the calibrated model is carried out in order to quantify the influence of epidemiological and intervention parameters on the hospitalization rates. The proposed pipeline properly describes the COVID-19 spread during the lock-down phase. It also reveals the underestimation of new positive cases and the need of promptly isolating asymptomatic and presymptomatic cases. The results emphasize the importance of the lock-down timeliness and provide accurate predictions on the current evolution of the pandemic.

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“…The stochastic nature of network models also allowed for projection of the range of outcomes within scenarios, especially useful for estimating the probability of outbreaks. Several network-based models for COVID-19 have been developed ( 7 , 20 , 28 , 29 ), including models for social contact restriction in community-based settings and ship environments. One strength of our specific network modeling approach with TERGMs is the representation of dynamic networks in which both the node set and edge set are responsive to epidemic dynamics in statistically principled ways ( 30 ).…”

Section: Discussionmentioning

confidence: 99%

Modeling Dynamic Network Strategies for SARS-CoV-2 Control on a Cruise Ship

Jenness

Willebrand

Malik

et al. 2020

Preprint

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SARS-CoV-2 outbreaks have occurred on several nautical vessels, driven by the high-density contact networks on these ships. Optimal strategies for prevention and control that account for realistic contact networks are needed. We developed a network-based transmission model for SARS-CoV-2 on the Diamond Princess outbreak to characterize transmission dynamics and to estimate the epidemiological impact of outbreak control and prevention measures. This model represented the dynamic multi-layer network structure of passenger-passenger, passenger-crew, and crew-crew contacts, both before and after the large-scale network lockdown imposed on the ship in response to the disease outbreak. Model scenarios evaluated variations in the timing of the network lockdown, reduction in contact intensity within the sub-networks, and diagnosis-based case isolation on outbreak prevention. We found that only extreme restrictions in contact patterns during network lockdown and idealistic clinical response scenarios could avert a major COVID-19 outbreak. Contact network changes associated with adequate outbreak prevention were the restriction of passengers to their cabins, with limited passenger-crew contacts. Clinical response strategies required for outbreak prevention included early mass screening with an ideal PCR test (100% sensitivity) and immediate case isolation upon diagnosis. Public health restrictions on optional leisure activities like these should be considered until longer-term effective solutions such as a COVID-19 vaccine become widely available.

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Dynamics of COVID-19 under social distancing measures are driven by transmission network structure

Cited by 37 publications

References 94 publications

Covid-19 Transmission Trajectories–Monitoring the Pandemic in the Worldwide Context

Covid-19 Transmission Trajectories–Monitoring the Pandemic in the Worldwide Context

Mathematical Modeling and Robustness Analysis to Unravel COVID-19 Transmission Dynamics: The Italy Case

Modeling Dynamic Network Strategies for SARS-CoV-2 Control on a Cruise Ship

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