A population data-driven workflow for COVID-19 modeling and learning

Ozik, Jonathan; Wozniak, Justin M.; Collier, Nicholson; Macal, Charles M.; Binois, Mickaël

doi:10.1177/10943420211035164

Cited by 33 publications

(18 citation statements)

References 58 publications

(68 reference statements)

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“…An important strategic challenge is how to address them. The computational epidemiology literature includes many large-scale, parameter-rich models (for instance, the CityCOVID model from Argonne National Laboratory; [ 128 ]). Phylodynamic and phylogeographic methods take a retrospective approach (see, for instance, [ 68 , 129 , 130 ]).…”

Section: Discussionmentioning

confidence: 99%

Population genetic models for the spatial spread of adaptive variants: A review in light of SARS-CoV-2 evolution

Steiner

Novembre²

2022

PLoS Genet

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Theoretical population genetics has long studied the arrival and geographic spread of adaptive variants through the analysis of mathematical models of dispersal and natural selection. These models take on a renewed interest in the context of the COVID-19 pandemic, especially given the consequences that novel adaptive variants have had on the course of the pandemic as they have spread through global populations. Here, we review theoretical models for the spatial spread of adaptive variants and identify areas to be improved in future work, toward a better understanding of variants of concern in Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) evolution and other contemporary applications. As we describe, characteristics of pandemics such as COVID-19—such as the impact of long-distance travel patterns and the overdispersion of lineages due to superspreading events—suggest new directions for improving upon existing population genetic models.

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Section: Discussionmentioning

confidence: 99%

Population genetic models for the spatial spread of adaptive variants: A review in light of SARS-CoV-2 evolution

Steiner

Novembre²

2022

PLoS Genet

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“…In a school environment setting, a study proposed an artificial intelligence (AI)‐powered ABM (Valtchev et al, 2021 ) to examine the challenges anticipated for preventative testing of COVID‐19. Two studies combined machine‐learning algorithms with ABM to model the COVID‐19 transmission (Ozik et al, 2021 ) and calculated the effects of the COVID‐19 pandemic on the banking system and the real economy (Polyzos et al, 2021 ), respectively. Six studies integrated geospatial data with ABM, which adds spatial‐temporal characteristics of COVID‐19 transmission to improve containment policy.…”

Section: Resultsmentioning

confidence: 99%

Using simulation modelling and systems science to help contain COVID‐19: A systematic review

et al. 2022

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This study systematically reviews applications of three simulation approaches, that is, system dynamics model (SDM), agent‐based model (ABM) and discrete event simulation (DES), and their hybrids in COVID‐19 research and identifies theoretical and application innovations in public health. Among the 372 eligible papers, 72 focused on COVID‐19 transmission dynamics, 204 evaluated both pharmaceutical and non‐pharmaceutical interventions, 29 focused on the prediction of the pandemic and 67 investigated the impacts of COVID‐19. ABM was used in 275 papers, followed by 54 SDM papers, 32 DES papers and 11 hybrid model papers. Evaluation and design of intervention scenarios are the most widely addressed area accounting for 55% of the four main categories, that is, the transmission of COVID‐19, prediction of the pandemic, evaluation and design of intervention scenarios and societal impact assessment. The complexities in impact evaluation and intervention design demand hybrid simulation models that can simultaneously capture micro and macro aspects of the socio‐economic systems involved.

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“… GQ1, SQ1 Ozik et al. ( 2021 ) USA, France Present CityCOVID, which is a detailed agent-based model that enables the creation of efficient, urban-scale MPI-distributed agent-based models (ABMs). GQ1, GQ2, SQ1, SQ3, SQ4 Pan et al.…”

Section: Resultsmentioning

confidence: 99%

Tracking machine learning models for pandemic scenarios: a systematic review of machine learning models that predict local and global evolution of pandemics

Palermo

Policarpo

Costa

et al. 2022

Netw Model Anal Health Inform Bioinforma

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This systematic review aims to study and classify machine learning models that predict pandemics’ evolution within affected regions or countries. The advantage of this systematic review is that it allows the health authorities to decide what prediction model fits best depending upon the region’s criticality and optimize hospitals’ approaches to preparing and anticipating patient care. We searched ACM Digital Library, Biomed Central, BioRxiv+MedRxiv, BMJ, Computers and Applied Sciences, IEEEXplore, JMIR Medical Informatics, Medline Daily Updates, Nature, Oxford Academic, PubMed, Sage Online, ScienceDirect, Scopus, SpringerLink, Web of Science, and Wiley Online Library between 1 January 2020 and 31 July 2022. We divided the interventions into similarities between cumulative COVID-19 real cases and machine learning prediction models’ ability to track pandemics trending. We included 45 studies that rated low to high risk of bias. The standardized mean differences (SMD) for the two groups were 0.18, 95% CI, with interval of [0.01, 0.35], =0, and p value=0.04. We built a taxonomic analysis of the included studies and determined two domains: pandemics trending prediction models and geolocation tracking models. We performed the meta-analysis and data synthesis and got low publication bias because of missing results. The level of certainty varied from very low to high. By submitting the 45 studies on the risk of bias, the levels of certainty, the summary of findings, and the statistical analysis via the forest and funnel plots assessments, we could determine the satisfactory statistical significance homogeneity across the included studies to simulate the progress of the pandemics and help the healthcare authorities to take preventive decisions.

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A population data-driven workflow for COVID-19 modeling and learning

Cited by 33 publications

References 58 publications

Population genetic models for the spatial spread of adaptive variants: A review in light of SARS-CoV-2 evolution

Population genetic models for the spatial spread of adaptive variants: A review in light of SARS-CoV-2 evolution

Using simulation modelling and systems science to help contain COVID‐19: A systematic review

Tracking machine learning models for pandemic scenarios: a systematic review of machine learning models that predict local and global evolution of pandemics

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