Testing, tracing and isolation in compartmental models

Sturniolo, Simone; Waites, William; Colbourn, Tim; Manheim, David; Panovska‐Griffiths, Jasmina

doi:10.1371/journal.pcbi.1008633

Cited by 42 publications

(47 citation statements)

References 77 publications

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“…Second, they are unsuitable for answering questions that depend on details of behavior at the individual level, such as superspreading events, transmission within multigenerational households, school classroom cohorting, and contact tracing. While it is possible to approximate some of these phenomena in compartmental models [ 91 , 92 ], these approximations typically exclude important factors such as time delays. Some of the issues regarding compartmental models’ predictive performance [ 93 – 95 ] may be partly a consequence of their inability to capture key mechanisms of epidemic spread.…”

Section: Discussionmentioning

confidence: 99%

Covasim: An agent-based model of COVID-19 dynamics and interventions

et al. 2021

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The COVID-19 pandemic has created an urgent need for models that can project epidemic trends, explore intervention scenarios, and estimate resource needs. Here we describe the methodology of Covasim (COVID-19 Agent-based Simulator), an open-source model developed to help address these questions. Covasim includes country-specific demographic information on age structure and population size; realistic transmission networks in different social layers, including households, schools, workplaces, long-term care facilities, and communities; age-specific disease outcomes; and intrahost viral dynamics, including viral-load-based transmissibility. Covasim also supports an extensive set of interventions, including non-pharmaceutical interventions, such as physical distancing and protective equipment; pharmaceutical interventions, including vaccination; and testing interventions, such as symptomatic and asymptomatic testing, isolation, contact tracing, and quarantine. These interventions can incorporate the effects of delays, loss-to-follow-up, micro-targeting, and other factors. Implemented in pure Python, Covasim has been designed with equal emphasis on performance, ease of use, and flexibility: realistic and highly customized scenarios can be run on a standard laptop in under a minute. In collaboration with local health agencies and policymakers, Covasim has already been applied to examine epidemic dynamics and inform policy decisions in more than a dozen countries in Africa, Asia-Pacific, Europe, and North America.

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Section: Discussionmentioning

confidence: 99%

Covasim: An agent-based model of COVID-19 dynamics and interventions

et al. 2021

Self Cite

View full text Add to dashboard Cite

show abstract

“…Second, they are unsuitable for answering questions that depend on details of behavior at the individual level, such as superspreading events, transmission within multigenerational households, school classroom cohorting, and contact tracing. While it is possible to approximate some of these phenomena in compartmental models (91,92), these approximations typically exclude important factors such as time delays. Some of the issues regarding compartmental models' predictive performance (93-95) may be partly a consequence of their inability to capture key mechanisms of epidemic spread.…”

Section: Discussionmentioning

confidence: 99%

Covasim: an agent-based model of COVID-19 dynamics and interventions

Kerr

Stuart

Mistry

et al. 2020

Preprint

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144

192

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The COVID-19 pandemic has created an urgent need for models that can project epidemic trends, explore intervention scenarios, and estimate resource needs. Here we describe the methodology of Covasim , an open-source model developed to help address these questions. Covasim includes demographic information on age structure and population size; realistic transmission networks in different social layers, including households, schools, workplaces, and communities; age-specific disease outcomes; and intrahost viral dynamics, including viral-load-based transmissibility. Covasim also supports an extensive set of interventions, including non-pharmaceutical interventions, such as physical distancing, hygiene measures, and protective equipment; and testing interventions, such as symptomatic and asymptomatic testing, isolation, contact tracing, and quarantine. These interventions can incorporate the effects of delays, loss-to-follow-up, micro-targeting, and other factors. In collaboration with local health agencies and policymakers, Covasim has already been applied to examine disease dynamics and policy options in Africa, Europe, Oceania, and North America.

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“…Among these, contact tracing applications (CTAs) seem to be the most widely used technology, implemented in many countries striving to curb the pandemic spread [1,2]. CTAs are digitalized versions of contact tracing, a traditional and manual public health practice commonly deployed during infectious outbreaks as part of a track, trace, and isolation strategy [3,4]. The process of contact tracing is to track and identify an infected individual's contacts to break chains of transmission [3].…”

Section: Introductionmentioning

confidence: 99%

Digital Contact Tracing Applications during COVID-19: A Scoping Review about Public Acceptance

2021

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Digital contact tracing applications (CTAs) have been one of the most widely discussed technical methods of controlling the COVID-19 outbreak. The effectiveness of this technology and its ethical justification depend highly on public acceptance and adoption. This study aims to describe the current knowledge about public acceptance of CTAs and identify individual perspectives, which are essential to consider concerning CTA acceptance and adoption. In this scoping review, 25 studies from four continents across the globe are compiled, and critical topics are identified and discussed. The results show that public acceptance varies across national cultures and sociodemographic strata. Lower acceptance among people who are mistrusting, socially disadvantaged, or those with low technical skills suggest a risk that CTAs may amplify existing inequities. Regarding determinants of acceptance, eight themes emerged, covering both attitudes and behavioral perspectives that can influence acceptance, including trust, privacy concerns, social responsibility, perceived health threat, experience of and access to technologies, performance expectancy and perceived benefits, and understanding. Furthermore, widespread misconceptions about the CTA function are a topic in need of immediate attention to ensure the safe use of CTAs. The intention-action gap is another topic in need of more research.

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Testing, tracing and isolation in compartmental models

Cited by 42 publications

References 77 publications

Covasim: An agent-based model of COVID-19 dynamics and interventions

Covasim: An agent-based model of COVID-19 dynamics and interventions

Covasim: an agent-based model of COVID-19 dynamics and interventions

Digital Contact Tracing Applications during COVID-19: A Scoping Review about Public Acceptance

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