scholarly journals Estimating the impact of interventions against COVID-19: From lockdown to vaccination

PLoS ONE ◽  
2021 ◽  
Vol 16 (12) ◽  
pp. e0261330
Author(s):  
James Thompson ◽  
Stephen Wattam
Keyword(s):  
Spatial Clustering ◽  
Herd Immunity ◽  
Contact Tracing ◽  
Agent Based Model ◽  
Seir Model ◽  
Agent Based ◽  
Resident Population ◽  
First Case ◽  
Equivalent Equation ◽  
The Impact

Coronavirus disease 2019 (COVID-19) is an infectious disease of humans caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Since the first case was identified in China in December 2019 the disease has spread worldwide, leading to an ongoing pandemic. In this article, we present an agent-based model of COVID-19 in Luxembourg, and use it to estimate the impact, on cases and deaths, of interventions including testing, contact tracing, lockdown, curfew and vaccination. Our model is based on collation, with agents performing activities and moving between locations accordingly. The model is highly heterogeneous, featuring spatial clustering, over 2000 behavioural types and a 10 minute time resolution. The model is validated against COVID-19 clinical monitoring data collected in Luxembourg in 2020. Our model predicts far fewer cases and deaths than the equivalent equation-based SEIR model. In particular, with R0 = 2.45, the SEIR model infects 87% of the resident population while our agent-based model infects only around 23% of the resident population. Our simulations suggest that testing and contract tracing reduce cases substantially, but are less effective at reducing deaths. Lockdowns are very effective although costly, while the impact of an 11pm-6am curfew is relatively small. When vaccinating against a future outbreak, our results suggest that herd immunity can be achieved at relatively low coverage, with substantial levels of protection achieved with only 30% of the population fully immune. When vaccinating in the midst of an outbreak, the challenge is more difficult. In this context, we investigate the impact of vaccine efficacy, capacity, hesitancy and strategy. We conclude that, short of a permanent lockdown, vaccination is by far the most effective way to suppress and ultimately control the spread of COVID-19.

scholarly journals Estimating the impact of interventions against COVID-19: from lockdown to vaccination

2021 ◽  
Author(s):  
James Thompson ◽  
Stephen Wattam
Keyword(s):  
Spatial Clustering ◽  
Herd Immunity ◽  
Contact Tracing ◽  
Agent Based Model ◽  
Seir Model ◽  
Agent Based ◽  
Resident Population ◽  
First Case ◽  
Equivalent Equation ◽  
The Impact

AbstractCoronavirus disease 2019 (COVID-19) is an infectious disease of humans caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Since the first case was identified in China in December 2019 the disease has spread worldwide, leading to an ongoing pandemic. In this article, we present a detailed agent-based model of COVID-19 in Luxembourg, and use it to estimate the impact, on cases and deaths, of interventions including testing, contact tracing, lockdown, curfew and vaccination.Our model is based on collation, with agents performing activities and moving between locations accordingly. The model is highly heterogeneous, featuring spatial clustering, over 2000 behavioural types and a 10 minute time resolution. The model is validated against COVID-19 clinical monitoring data collected in Luxembourg in 2020.Our model predicts far fewer cases and deaths than the equivalent equation-based SEIR model. In particular, with R0 = 2.45, the SEIR model infects 87% of the resident population while our agent-based model results, on average, in only around 23% of the resident population infected. Our simulations suggest that testing and contract tracing reduce cases substantially, but are much less effective at reducing deaths. Lockdowns appear very effective although costly, while the impact of an 11pm-6am curfew is relatively small. When vaccinating against a future outbreak, our results suggest that herd immunity can be achieved at relatively low levels, with substantial levels of protection achieved with only 30% of the population immune. When vaccinating in midst of an outbreak, the challenge is more difficult. In this context, we investigate the impact of vaccine efficacy, capacity, hesitancy and strategy.We conclude that, short of a permanent lockdown, vaccination is by far the most effective way to suppress and ultimately control the spread of COVID-19.

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

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.

scholarly journals A data driven agent-based model that recommends non-pharmaceutical interventions to suppress Coronavirus disease 2019 resurgence in megacities

2021 ◽  
Vol 18 (181) ◽  
pp. 20210112
Author(s):  
Ling Yin ◽  
Hao Zhang ◽  
Yuan Li ◽  
Kang Liu ◽  
Tianmu Chen ◽  
...  
Keyword(s):  
Census Data ◽  
Herd Immunity ◽  
Risk Tolerance ◽  
Contact Tracing ◽  
Agent Based Model ◽  
Agent Based ◽  
Shenzhen City ◽  
Other World ◽  
Work Class ◽  
Pharmaceutical Interventions

Before herd immunity against Coronavirus disease 2019 (COVID-19) is achieved by mass vaccination, science-based guidelines for non-pharmaceutical interventions are urgently needed to reopen megacities. This study integrated massive mobile phone tracking records, census data and building characteristics into a spatially explicit agent-based model to simulate COVID-19 spread among 11.2 million individuals living in Shenzhen City, China. After validation by local epidemiological observations, the model was used to assess the probability of COVID-19 resurgence if sporadic cases occurred in a fully reopened city. Combined scenarios of three critical non-pharmaceutical interventions (contact tracing, mask wearing and prompt testing) were assessed at various levels of public compliance. Our results show a greater than 50% chance of disease resurgence if the city reopened without contact tracing. However, tracing household contacts, in combination with mandatory mask use and prompt testing, could suppress the probability of resurgence under 5% within four weeks. If household contact tracing could be expanded to work/class group members, the COVID resurgence could be avoided if 80% of the population wear facemasks and 40% comply with prompt testing. Our assessment, including modelling for different scenarios, helps public health practitioners tailor interventions within Shenzhen City and other world megacities under a variety of suppression timelines, risk tolerance, healthcare capacity and public compliance.

scholarly journals Exploring the effectiveness of a COVID-19 contact tracing app with an agent-based model

2020 ◽  
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

Abstract A 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.

scholarly journals Examining the Impact of the Walking School Bus With an Agent-Based Model

2014 ◽  
Vol 104 (7) ◽  
pp. 1196-1203 ◽  
Author(s):  
Yong Yang ◽  
Ana Diez-Roux ◽  
Kelly R. Evenson ◽  
Natalie Colabianchi
Keyword(s):  
Agent Based Model ◽  
School Bus ◽  
Agent Based ◽  
Walking School Bus ◽  
The Impact

scholarly journals A Computational Study of the Effect of Experience on Problem/Solution Space Exploration in Teams

2019 ◽  
Vol 1 (1) ◽  
pp. 11-20
Author(s):  
Marija Majda Perisic ◽  
Tomislav Martinec ◽  
Mario Storga ◽  
John S Gero
Keyword(s):  
Computational Study ◽  
Space Exploration ◽  
Solution Space ◽  
Problem Solution ◽  
Design Team ◽  
Design Teams ◽  
Agent Based Model ◽  
Problem Space ◽  
Agent Based ◽  
The Impact

AbstractThis paper presents the results of computational experiments aimed at studying the effect of experience on design teams’ exploration of problem-solution space. An agent-based model of a design team was developed and its capability to match theoretically-based predictions is tested. Hypotheses that (1) experienced teams need less time to find a solution and that (2) in comparison to the inexperienced teams, experienced teams spend more time exploring the solution-space than the problem-space, were tested. The results provided support for both of the hypotheses, demonstrating the impact of learning and experience on the exploration patterns in problem and solution space, and verifying the system's capability to produce the reliable results.

scholarly journals Development of an agent-based model to assess the impact of substandard and falsified anti-malarials: Uganda case study

2019 ◽  
Vol 18 (1) ◽  
Author(s):  
Sachiko Ozawa ◽  
Daniel R. Evans ◽  
Colleen R. Higgins ◽  
Sarah K. Laing ◽  
Phyllis Awor
Keyword(s):  
Agent Based Model ◽  
Agent Based ◽  
The Impact
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