Covid19: Unless one gets everyone to act, policies may be ineffective or even backfire

Muscillo, Alessio; Pin, Paolo; Razzolini, Tiziano

doi:10.1371/journal.pone.0237057

Cited by 18 publications

(16 citation statements)

References 12 publications

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“…Recent research argues that viral spread is too fast for manual contact tracing in the case of COVID-19 and emphasizes that compliance is critical for the effectiveness of contact-tracing apps 18 . In 21 , the authors point out that compliance is also crucial for policies encouraging social distancing. They find that it is essential that a reduction of social activity is implemented robustly on all social groups, especially on those characterized by intense mixing patterns.…”

Section: Introductionmentioning

confidence: 99%

Extensions of the SEIR model for the analysis of tailored social distancing and tracing approaches to cope with COVID-19

Grimm

Mengel

Schmidt

2021

Sci Rep

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In the context of the COVID-19 pandemic, governments worldwide face the challenge of designing tailored measures of epidemic control to provide reliable health protection while allowing societal and economic activity. In this paper, we propose an extension of the epidemiological SEIR model to enable a detailed analysis of commonly discussed tailored measures of epidemic control—among them group-specific protection and the use of tracing apps. We introduce groups into the SEIR model that may differ both in their underlying parameters as well as in their behavioral response to public health interventions. Moreover, we allow for different infectiousness parameters within and across groups, different asymptomatic, hospitalization, and lethality rates, as well as different take-up rates of tracing apps. We then examine predictions from these models for a variety of scenarios. Our results visualize the sharp trade-offs between different goals of epidemic control, namely a low death toll, avoiding overload of the health system, and a short duration of the epidemic. We show that a combination of tailored mechanisms, e.g., the protection of vulnerable groups together with a “trace & isolate” approach, can be effective in preventing a high death toll. Protection of vulnerable groups without further measures requires unrealistically strict isolation. A key insight is that high compliance is critical for the effectiveness of a “trace & isolate” approach. Our model allows to analyze the interplay of group-specific social distancing and tracing also beyond our case study in scenarios with a large number of groups reflecting, e.g., sectoral, regional, or age differentiation and group-specific behavioural responses.

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Section: Introductionmentioning

confidence: 99%

Extensions of the SEIR model for the analysis of tailored social distancing and tracing approaches to cope with COVID-19

Grimm

Mengel

Schmidt

2021

Sci Rep

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“…4 show that the reach is reduced when all individuals in society face the same constraints, Muscillo et al. ( 2020 ) show that if those individuals who have many neighbours reduce their contacts proportionately less than those who have few, then the disease can take longer to die out. The specification considered by Muscillo et al.…”

Section: Extensions and Related Workmentioning

confidence: 99%

“…The specification considered by Muscillo et al. ( 2020 ) is a special case of our framework where is chosen such that the fraction is increasing in degree.…”

Section: Extensions and Related Workmentioning

confidence: 99%

The effect of social distancing on the reach of an epidemic in social networks

Gutin

Hirano

Hwang

et al. 2021

J Econ Interact Coord

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How does social distancing affect the reach of an epidemic in social networks? We present Monte Carlo simulation results of a susceptible–infected–removed with social distancing model. The key feature of the model is that individuals are limited in the number of acquaintances that they can interact with, thereby constraining disease transmission to an infectious subnetwork of the original social network. While increased social distancing typically reduces the spread of an infectious disease, the magnitude varies greatly depending on the topology of the network, indicating the need for policies that are network dependent. Our results also reveal the importance of coordinating policies at the ‘global’ level. In particular, the public health benefits from social distancing to a group (e.g. a country) may be completely undone if that group maintains connections with outside groups that are not following suit.

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“…This results in a very large search space of possible interventions [13] . Although the necessity of immediate actions in the early days of the pandemic may have resulted in choosing an intervention based on minimal insight, there is now evidence for serious consequences in rolling a sub-optimal 1 INTRODUCTION intervention: lives may be lost, the cost of future interventions may be heightened, or the adherence (hence the impact) of future interventions may be lowered [14,15,16] . Computer simulations using Agent-Based Models (ABMs) can aid officials in making these decisions, by modeling the effects of specific interventions in specific places (e.g., small towns [17] , educational institutions [18,19] , supermarkets [20] ), populations (e.g., targeted vaccinations [21] ), or time windows (e.g., during a yearly mass pilgrimage [22] ).…”

Section: Introductionmentioning

confidence: 99%

When do we need massive computations to perform detailed COVID-19 simulations?

Lutz

Giabbanelli

2021

Preprint

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The COVID-19 pandemic has infected over 200 million people worldwide and killed more than 4 million as of August 2021. Many intervention strategies have been utilized by governments around the world, including masks, social distancing, and vaccinations. However, officials making decisions regarding interventions may have a limited time to act. Computer simulations can aid them by predicting future disease outcomes, but they also have limitations due to requirements on processing power or time. This paper examines whether a machine learning model can be trained on a small subset of simulation runs to inexpensively predict future disease trajectories very close to the original simulation results. Using four previously published agent-based models for COVID-19, this paper analyzes the predictions of decision tree regression machine learning models and compares them to the results of the original simulations. The results indicate that accurate machine learning meta-models can be generated from simulation models with no strong interventions (e.g., vaccines, lockdowns) using small amounts of simulation data. However, meta-models for simulation models that include strong interventions required much more training data to achieve a similar accuracy. This indicates that machine learning meta-models could be used in some scenarios to assist in faster decision making.

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Covid19: Unless one gets everyone to act, policies may be ineffective or even backfire

Cited by 18 publications

References 12 publications

Extensions of the SEIR model for the analysis of tailored social distancing and tracing approaches to cope with COVID-19

Extensions of the SEIR model for the analysis of tailored social distancing and tracing approaches to cope with COVID-19

The effect of social distancing on the reach of an epidemic in social networks

When do we need massive computations to perform detailed COVID-19 simulations?

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