Peer Review of “A Full-Scale Agent-Based Model to Hypothetically Explore the Impact of Lockdown, Social Distancing, and Vaccination During the COVID-19 Pandemic in Lombardy, Italy: Model Development” (Preprint)

UNSTRUCTURED COVID-19 outbreak is an awful event. However it gives to the scientists the possibility to test theories about epidemic. The aim of this contribution is to propose a individual-based model of Lombardy COVID-19 outbreak at full-scale, where full-scale means that will be simulated all the 10 millions inhabitant population of Lombardy person by person, in a commercial computer. All this to test the impact of our daily actions in epidemic, investigate social networks connectivity and in the end have an insight on the impact of an hypothetical vaccine.

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Reactive vaccination of workplaces and schools against COVID-19

10.1101/2021.07.26.21261133 ◽

2021 ◽

Author(s):

Benjamin Faucher ◽

Rania Assab ◽

Jonathan Roux ◽

Daniel Levy-Bruhl ◽

Cécile Tran Kiem ◽

...

Keyword(s):

Natural History ◽

Mass Vaccination ◽

Agent Based Model ◽

Agent Based ◽

Social Distancing ◽

To Come ◽

Short Time ◽

The Impact ◽

Reactive Vaccination ◽

Adaptive Approaches

As vaccination against COVID-19 stalls in some countries, increased accessibility and more adaptive approaches may be useful to keep the epidemic under control. Here we study the impact of reactive vaccination targeting schools and workplaces where cases have been detected, with an agent-based model accounting for COVID-19 natural history, vaccine characteristics, individuals' demography and behaviour and social distancing. We study epidemic scenarios ranging from sustained spread to flare-up of cases, and we consider reactive vaccination alone and in combination with mass vaccination. With the same number of doses, reactive vaccination reduces cases more than non-reactive approaches, but may require concentrating a high number of doses over a short time in case of sustained spread. In case of flare-ups, quick implementation of reactive vaccination supported by enhanced test-trace-isolate practices would limit further spread. These results provide key information to promote an adaptive vaccination plan in the months to come.

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INFEKTA—An agent-based model for transmission of infectious diseases: The COVID-19 case in Bogotá, Colombia

PLoS ONE ◽

10.1371/journal.pone.0245787 ◽

2021 ◽

Vol 16 (2) ◽

pp. e0245787

Author(s):

Jonatan Gomez ◽

Jeisson Prieto ◽

Elizabeth Leon ◽

Arles Rodríguez

Keyword(s):

Infectious Diseases ◽

Disease Transmission ◽

Computational Models ◽

Compartmental Model ◽

Transmission Dynamics ◽

Agent Based Model ◽

Agent Based ◽

Social Distancing ◽

The Impact ◽

The Town

The transmission dynamics of the coronavirus—COVID-19—have challenged humankind at almost every level. Currently, research groups around the globe are trying to figure out such transmission dynamics under special conditions such as separation policies enforced by governments. Mathematical and computational models, like the compartmental model or the agent-based model, are being used for this purpose. This paper proposes an agent-based model, called INFEKTA, for simulating the transmission of infectious diseases, not only the COVID-19, under social distancing policies. INFEKTA combines the transmission dynamic of a specific disease, (according to parameters found in the literature) with demographic information (population density, age, and genre of individuals) of geopolitical regions of the real town or city under study. Agents (virtual persons) can move, according to its mobility routines and the enforced social distancing policy, on a complex network of accessible places defined over an Euclidean space representing the town or city. The transmission dynamics of the COVID-19 under different social distancing policies in Bogotá city, the capital of Colombia, is simulated using INFEKTA with one million virtual persons. A sensitivity analysis of the impact of social distancing policies indicates that it is possible to establish a ‘medium’ (i.e., close 40% of the places) social distancing policy to achieve a significant reduction in the disease transmission.

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A full-scale agent-based model of Lombardy COVID-19 dynamics to explore social networks connectivity and vaccine impact on epidemic

10.1101/2020.09.13.20193599 ◽

2020 ◽

Author(s):

Giuseppe Giacopelli

Keyword(s):

Social Networks ◽

Full Scale ◽

Agent Based Model ◽

Individual Based Model ◽

Agent Based ◽

Vaccine Impact ◽

The Impact

COVID-19 outbreak is an awful event. However it gives to the scientists the possibility to test theories about epidemic. The aim of this contribution is to propose a individual-based model of Lombardy COVID-19 outbreak at full-scale, where full-scale means that will be simulated all the 10 millions inhabitant population of Lombardy person by person, in a commercial computer. All this to test the impact of our daily actions in epidemic, investigate social networks connectivity and in the end have an insight on the impact of an hypothetical vaccine.

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Exploring the effectiveness of a COVID-19 contact tracing app using an agent-based model

Scientific Reports ◽

10.1038/s41598-020-79000-y ◽

2020 ◽

Vol 10 (1) ◽

Author(s):

Jonatan Almagor ◽

Stefano Picascia

Keyword(s):

High Rate ◽

Contact Tracing ◽

Agent Based Model ◽

Agent Based ◽

The Social ◽

Behavioural Factors ◽

The Impact ◽

Different Levels ◽

User Rate

AbstractA contact-tracing strategy has been deemed necessary to contain the spread of COVID-19 following the relaxation of lockdown measures. Using an agent-based model, we explore one of the technology-based strategies proposed, a contact-tracing smartphone app. The model simulates the spread of COVID-19 in a population of agents on an urban scale. Agents are heterogeneous in their characteristics and are linked in a multi-layered network representing the social structure—including households, friendships, employment and schools. We explore the interplay of various adoption rates of the contact-tracing app, different levels of testing capacity, and behavioural factors to assess the impact on the epidemic. Results suggest that a contact tracing app can contribute substantially to reducing infection rates in the population when accompanied by a sufficient testing capacity or when the testing policy prioritises symptomatic cases. As user rate increases, prevalence of infection decreases. With that, when symptomatic cases are not prioritised for testing, a high rate of app users can generate an extensive increase in the demand for testing, which, if not met with adequate supply, may render the app counterproductive. This points to the crucial role of an efficient testing policy and the necessity to upscale testing capacity.

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