An Agent-Based Modeling of COVID-19: Validation, Analysis, and Recommendations

Shamil, Salman; Farheen, Farhanaz; Ibtehaz, Nabil; Khan, Irtesam Mahmud; Rahman, M. Sohel

doi:10.1007/s12559-020-09801-w

Cited by 83 publications

(68 citation statements)

References 29 publications

(10 reference statements)

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“…Three of the 15 studies had a high risk of bias due to the lack of use of empirical distributions for variables, the limited number of scenarios analysed and insufficient transparency regarding reporting of the model. 11 20 21 …”

Section: Resultsmentioning

confidence: 99%

“… 15 Another model-based study determined that 60% adoption rate of a CTA could result in an R below 1.0. 11 In one study, adoption rate of 53% resulted in a 47% reduction in R when the complete household of an individual with a positive test result is advised to be quarantined. 14 The last study looking at effect of CTA on R showed that only at 60% adoption rate of the app a significant beneficial effect on R would become apparent.…”

Section: Resultsmentioning

confidence: 99%

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Effectiveness of contact tracing apps for SARS-CoV-2: a rapid systematic review

et al. 2021

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ObjectiveTo systematically review evidence on effectiveness of contact tracing apps (CTAs) for SARS-CoV-2 on epidemiological and clinical outcomes.DesignRapid systematic review.Data sourcesEMBASE (OVID), MEDLINE (PubMed), BioRxiv and MedRxiv were searched up to 28 October 2020.Study selectionStudies, both empirical and model-based, assessing effect of CTAs for SARS-CoV-2 on reproduction number (R), total number of infections, hospitalisation rate, mortality rate, and other epidemiologically and clinically relevant outcomes, were eligible for inclusion.Data extractionEmpirical and model-based studies were critically appraised using separate checklists. Data on type of study (ie, empirical or model-based), sample size, (simulated) time horizon, study population, CTA type (and associated interventions), comparator and outcomes assessed, were extracted. The most important findings were extracted and narratively summarised. Specifically for model-based studies, characteristics and values of important model parameters were collected.Results2140 studies were identified, of which 17 studies (2 empirical, 15 model-based studies) were eligible and included in this review. Both empirical studies were observational (non-randomised) studies and at high risk of bias, most importantly due to risk of confounding. Risk of bias of model-based studies was considered low for 12 out of 15 studies. Most studies demonstrated beneficial effects of CTAs on R, total number of infections and mortality rate. No studies assessed effect on hospitalisation. Effect size was dependent on model parameters values used, but in general, a beneficial effect was observed at CTA adoption rates of 20% or higher.ConclusionsCTAs have the potential to be effective in reducing SARS-CoV-2 related epidemiological and clinical outcomes, though effect size depends on other model parameters (eg, proportion of asymptomatic individuals, or testing delays), and interventions after CTA notification. Methodologically sound comparative empirical studies on effectiveness of CTAs are required to confirm findings from model-based studies.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Effectiveness of contact tracing apps for SARS-CoV-2: a rapid systematic review

et al. 2021

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“…DCTA availability and accessibility must be equitable, and they should not be used in a discriminatory way [ 52 , 56 , 66 , 87 , 175 ]. This is important not only from a human rights perspective but also from an effectiveness perspective because the success of a DCTA depends on factors such as user penetration within the general population [ 88 ] and in high-risk population groups [ 15 , 89 ]. The IDCTA should be disseminated free of charge, so it is accessible by all societal groups but, in particular, those disproportionately affected by COVID-19 such as older adults, people of lower socioeconomic class, and ethnic minorities [ 51 , 52 , 56 , 61 , 90 , 91 ].…”

Section: Resultsmentioning

confidence: 99%

Best Practice Guidance for Digital Contact Tracing Apps: A Cross-disciplinary Review of the Literature

O’Connell

Abbas

Beecham

et al. 2021

JMIR Mhealth Uhealth

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Background Digital contact tracing apps have the potential to augment contact tracing systems and disrupt COVID-19 transmission by rapidly identifying secondary cases prior to the onset of infectiousness and linking them into a system of quarantine, testing, and health care worker case management. The international experience of digital contact tracing apps during the COVID-19 pandemic demonstrates how challenging their design and deployment are. Objective This study aims to derive and summarize best practice guidance for the design of the ideal digital contact tracing app. Methods A collaborative cross-disciplinary approach was used to derive best practice guidance for designing the ideal digital contact tracing app. A search of the indexed and gray literature was conducted to identify articles describing or evaluating digital contact tracing apps. MEDLINE was searched using a combination of free-text terms and Medical Subject Headings search terms. Gray literature sources searched were the World Health Organization Institutional Repository for Information Sharing, the European Centre for Disease Prevention and Control publications library, and Google, including the websites of many health protection authorities. Articles that were acceptable for inclusion in this evidence synthesis were peer-reviewed publications, cohort studies, randomized trials, modeling studies, technical reports, white papers, and media reports related to digital contact tracing. Results Ethical, user experience, privacy and data protection, technical, clinical and societal, and evaluation considerations were identified from the literature. The ideal digital contact tracing app should be voluntary and should be equitably available and accessible. User engagement could be enhanced by small financial incentives, enabling users to tailor aspects of the app to their particular needs and integrating digital contact tracing apps into the wider public health information campaign. Adherence to the principles of good data protection and privacy by design is important to convince target populations to download and use digital contact tracing apps. Bluetooth Low Energy is recommended for a digital contact tracing app's contact event detection, but combining it with ultrasound technology may improve a digital contact tracing app's accuracy. A decentralized privacy-preserving protocol should be followed to enable digital contact tracing app users to exchange and record temporary contact numbers during contact events. The ideal digital contact tracing app should define and risk-stratify contact events according to proximity, duration of contact, and the infectiousness of the case at the time of contact. Evaluating digital contact tracing apps requires data to quantify app downloads, use among COVID-19 cases, successful contact alert generation, contact alert receivers, contact alert receivers that adhere to quarantine and testing recommendations, and the number of contact alert receivers who subsequently are tested positive for COVID-19. The outcomes of digital contact tracing apps' evaluations should be openly reported to allow for the wider public to review the evaluation of the app. Conclusions In conclusion, key considerations and best practice guidance for the design of the ideal digital contact tracing app were derived from the literature.

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“…Many ABMs for COVID-19 are in the scale of several hundred agents [78][79][80][81][82][83] to tens of thousands of agents [37,84,85]. Fewer studies have over 100,000 agents [86], and only a paucity of studies has a number of agents that is about equal (eg, the model of Hoertel and colleagues [87] used 500,000 agents) or greater than (eg, one million agents in a February 2021 simulation of Bogota) in this study [38,87,88].…”

Section: Related Work: the Scale Of Agent-based Models For Covid-19mentioning

confidence: 99%

Returning to a Normal Life via COVID-19 Vaccines in the United States: A Large-scale Agent-Based Simulation Study

Li¹,

Giabbanelli²

2021

JMIR Med Inform

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Background In 2020, COVID-19 has claimed more than 300,000 deaths in the United States alone. Although nonpharmaceutical interventions were implemented by federal and state governments in the United States, these efforts have failed to contain the virus. Following the Food and Drug Administration's approval of two COVID-19 vaccines, however, the hope for the return to normalcy has been renewed. This hope rests on an unprecedented nationwide vaccine campaign, which faces many logistical challenges and is also contingent on several factors whose values are currently unknown. Objective We study the effectiveness of a nationwide vaccine campaign in response to different vaccine efficacies, the willingness of the population to be vaccinated, and the daily vaccine capacity under two different federal plans. To characterize the possible outcomes most accurately, we also account for the interactions between nonpharmaceutical interventions and vaccines through 6 scenarios that capture a range of possible impacts from nonpharmaceutical interventions. Methods We used large-scale, cloud-based, agent-based simulations by implementing the vaccination campaign using COVASIM, an open-source agent-based model for COVID-19 that has been used in several peer-reviewed studies and accounts for individual heterogeneity and a multiplicity of contact networks. Several modifications to the parameters and simulation logic were made to better align the model with current evidence. We chose 6 nonpharmaceutical intervention scenarios and applied the vaccination intervention following both the plan proposed by Operation Warp Speed (former Trump administration) and the plan of one million vaccines per day, proposed by the Biden administration. We accounted for unknowns in vaccine efficacies and levels of population compliance by varying both parameters. For each experiment, the cumulative infection growth was fitted to a logistic growth model, and the carrying capacities and the growth rates were recorded. Results For both vaccination plans and all nonpharmaceutical intervention scenarios, the presence of the vaccine intervention considerably lowers the total number of infections when life returns to normal, even when the population compliance to vaccines is as low as 20%. We noted an unintended consequence; given the vaccine availability estimates under both federal plans and the focus on vaccinating individuals by age categories, a significant reduction in nonpharmaceutical interventions results in a counterintuitive situation in which higher vaccine compliance then leads to more total infections. Conclusions Although potent, vaccines alone cannot effectively end the pandemic given the current availability estimates and the adopted vaccination strategy. Nonpharmaceutical interventions need to continue and be enforced to ensure high compliance so that the rate of immunity established by vaccination outpaces that induced by infections.

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An Agent-Based Modeling of COVID-19: Validation, Analysis, and Recommendations

Cited by 83 publications

References 29 publications

Effectiveness of contact tracing apps for SARS-CoV-2: a rapid systematic review

Effectiveness of contact tracing apps for SARS-CoV-2: a rapid systematic review

Best Practice Guidance for Digital Contact Tracing Apps: A Cross-disciplinary Review of the Literature

Returning to a Normal Life via COVID-19 Vaccines in the United States: A Large-scale Agent-Based Simulation Study

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