Effectiveness of Testing and Contact-Tracing to Counter COVID-19 Pandemic: Designed Experiments of Agent-Based Simulation

The widespread outbreak of the novel coronavirus disease COVID-19 has posed an enormous threat to global public health. A different set of policy interventions has been implemented to mitigate the spread in most countries. While the use of personal protective equipment and social distancing has been specifically emphasized, South Korea has deployed massive testing and contact-tracing program from the early stage of the outbreak. This study aims at investigating the effectiveness of testing and contact-tracing to counter the spread of infectious diseases. Based on the SEICR (susceptible-exposed-infectious-confirmed-recovered) model, an agent-based simulation model is developed to represent the behavior of disease spreading with the consideration of testing and contact-tracing in place. Designed experiments are conducted to verify the effects of testing and contact tracing on the peak number of infections. It has been observed that testing combined with contact tracing may lower the peak infections to a great extent, and it can thus be avoided for the hospital bed capacity to be overwhelmed by infected patients. It is implied that an adequate capability of testing and contact-tracing may enable us to become better prepared for an impending risk of infectious diseases.

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Application of Inherent Risk of Contagion (IRC) framework & modelling to aid local Covid-19 response & mitigation: Viewpoint (Preprint)

10.2196/preprints.19814 ◽

2020 ◽

Author(s):

Helmi Zakariah ◽

Fadzilah bt Kamaluddin ◽

Choo-Yee Ting ◽

Hui-Jia Yee ◽

Shereen Allaham ◽

...

Keyword(s):

Public Health ◽

Public Health Problem ◽

Analytical Framework ◽

Vulnerable Population ◽

Contact Tracing ◽

Global Public Health ◽

Inherent Risk ◽

Transmission Area ◽

Current Outbreak ◽

Novel Coronavirus

UNSTRUCTURED The current outbreak of coronavirus disease 2019 (COVID-19) caused by the novel coronavirus named SARS-CoV-2 has been a major global public health problem threatening many countries and territories. Mathematical modelling is one of the non-pharmaceutical public health measures that plays a crucial role for mitigating the risk and impact of the pandemic. A group of researchers and epidemiologists have developed a machine learning-powered inherent risk of contagion (IRC) analytical framework to georeference the COVID-19 with an operational platform to plan response & execute mitigation activities. This framework dataset provides a coherent picture to track and predict the COVID-19 epidemic post lockdown by piecing together preliminary data on publicly available health statistic metrics alongside the area of reported cases, drivers, vulnerable population, and number of premises that are suspected to become a transmission area between drivers and vulnerable population. The main aim of this new analytical framework is to measure the IRC and provide georeferenced data to protect the health system, aid contact tracing, and prioritise the vulnerable.

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COVID-19 Pandemic Response Simulation in a Large City: Impact of Nonpharmaceutical Interventions on Reopening Society

Medical Decision Making ◽

10.1177/0272989x211003081 ◽

2021 ◽

pp. 0272989X2110030

Author(s):

Serin Lee ◽

Zelda B. Zabinsky ◽

Judith N. Wasserheit ◽

Stephen M. Kofsky ◽

Shan Liu

Keyword(s):

Large City ◽

Face Mask ◽

School Closure ◽

Contact Tracing ◽

The Novel ◽

Agent Based ◽

Social Distancing ◽

Variable Compliance ◽

Novel Coronavirus ◽

Nonpharmaceutical Interventions

As the novel coronavirus (COVID-19) pandemic continues to expand, policymakers are striving to balance the combinations of nonpharmaceutical interventions (NPIs) to keep people safe and minimize social disruptions. We developed and calibrated an agent-based simulation to model COVID-19 outbreaks in the greater Seattle area. The model simulated NPIs, including social distancing, face mask use, school closure, testing, and contact tracing with variable compliance and effectiveness to identify optimal NPI combinations that can control the spread of the virus in a large urban area. Results highlight the importance of at least 75% face mask use to relax social distancing and school closure measures while keeping infections low. It is important to relax NPIs cautiously during vaccine rollout in 2021.

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Agent-Based Simulation for Evaluation of Contact-Tracing Policies Against the Spread of SARS-CoV-2

10.1101/2020.05.12.20098970 ◽

2020 ◽

Cited By ~ 4

Author(s):

Martin Bicher ◽

Claire Rippinger ◽

Christoph Urach ◽

Dominik Brunmeir ◽

Uwe Siebert ◽

...

Keyword(s):

Case Studies ◽

Exploratory Analysis ◽

Contact Tracing ◽

Agent Based Simulation ◽

Effective Measure ◽

Agent Based ◽

The World ◽

Different Characteristics ◽

Second Wave ◽

Restrictive Policy

AbstractThe decline of active COVID-19 cases in many countries in the world has proved that lockdown policies are indeed a very effective measure to stop the exponential spread of the virus. Still, the danger of a second wave of infections is omnipresent and it is clear, that every policy of the lockdown has to be carefully evaluated and possibly replaced by a different, less restrictive policy, before it can be lifted. Tracing of contacts and consequential tracing and breaking of infection-chains is a promising and comparably straightforward strategy to help containing the disease, although its precise impact on the epidemic is unknown. In order to quantify the benefits of tracing and similar policies we developed an agent-based model that not only validly depicts the spread of the disease, but allows for exploratory analysis of containment policies. We will describe our model and perform case studies in which we use the model to quantify impact of contact tracing in different characteristics and draw valuable conclusions about contact tracing policies in general.

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The Use of Agent-Based Simulation to Discover Extreme Cases in Immune-Interactions with Early-Stage Cancer Scenarios

SSRN Electronic Journal ◽

10.2139/ssrn.2828459 ◽

2013 ◽

Author(s):

Grazziela P. Figueredo ◽

Peer-Olaf Siebers ◽

Douglas Augusto ◽

Helio Barbosa ◽

Uwe Aickelin

Keyword(s):

Early Stage ◽

Agent Based Simulation ◽

Early Stage Cancer ◽

Agent Based

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UTLDR: an agent-based framework for modeling infectious diseases and public interventions

Journal of Intelligent Information Systems ◽

10.1007/s10844-021-00649-6 ◽

2021 ◽

Author(s):

Giulio Rossetti ◽

Letizia Milli ◽

Salvatore Citraro ◽

Virginia Morini

Keyword(s):

Infectious Diseases ◽

Data Driven ◽

Contact Tracing ◽

Mobility Patterns ◽

Epidemic Modeling ◽

Specific Data ◽

Agent Based ◽

Software Libraries

AbstractDue to the SARS-CoV-2 pandemic, epidemic modeling is now experiencing a constantly growing interest from researchers of heterogeneous study fields. Indeed, due to such an increased attention, several software libraries and scientific tools have been developed to ease the access to epidemic modeling. However, only a handful of such resources were designed with the aim of providing a simple proxy for the study of the potential effects of public interventions (e.g., lockdown, testing, contact tracing). In this work, we introduce UTLDR, a framework that, overcoming such limitations, allows to generate “what if” epidemic scenarios incorporating several public interventions (and their combinations). UTLDR is designed to be easy to use and capable to leverage information provided by stratified populations of agents (e.g., age, gender, geographical allocation, and mobility patterns…). Moreover, the proposed framework is generic and not tailored for a specific epidemic phenomena: it aims to provide a qualitative support to understanding the effects of restrictions, rather than produce forecasts/explanation of specific data-driven phenomena.

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