Effective Social Network-Based Allocation of COVID-19 Vaccines

Chen, Jiangzhuo; Hoops, Stefan; Marathe, Achla; Mortveit, Henning S.; Lewis, Bryan; Venkatramanan, Srinivasan; Haddadan, Arash; Bhattacharya, Parantapa; Adiga, Abhijin; Vullikanti, Anil; Srinivasan, Aravind; Wilson, Mandy; Ehrlich, Gal; Fenster, Maier; Eubank, Stephen; Barrett, Christopher L.; Marathe, Madhav V.

doi:10.1145/3534678.3542673

Cited by 11 publications

(3 citation statements)

References 24 publications

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“…• Desarrollo y efectividad de tratamientos: Debido al tiempo que conlleva el desarrollo de fármacos teniendo en cuenta la velocidad de la enfermedad la reutilización de fármacos (Naasani, 2021), podría ser una estrategia, para ello se utilizó el recurso bioinformático CMap del Broad Institute (Bonnet et al, 2022), además de realizar estudios bioinformáticos entre compuestos como Ciclodextrina e Hidroxitirosol (Paolacci et al, 2021), pero, como era de suma necesidad el desarrollo de tratamientos y medicamentos que contrarrestaran los síntomas y evitar la muerte de los individuos se realizaron pruebas de péptidos de unión a picos del SARS-CoV-2 que ayudaron a la elaboración de vacunas (Thijssen et al, 2023). Luego de la elaboración de las vacunas, se necesitaba la distribución, esto para reducir de alguna manera el impacto de la pandemia, por lo que se utilizó datos de redes sociales con enfoques bioinformáticos, identificando dónde se necesitaba el tratamiento con mayor premura (Chen et al, 2022).…”

Section: Discussionunclassified

Bioinformática utilizada en la detección del covid-19; Análisis del desarrollo bioinformático en el ámbito computacional en América Latina.

Solórzano-Zambrano,

Demera-Ureta

2024

COCIRI

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Los avances en la tecnología son fundamentales para el descubrimiento de las características que poseen los diferentes virus presentados en la actualidad, como es el caso de la bioinformática con respecto a la pandemia por covid-19, los métodos utilizados en la secuenciación genómica del virus han contribuido de manera significativa en el análisis de considerables cantidades de datos obteniendo información relevante durante la evolución del virus, aportando en el desarrollo de plataformas y medicamentos que ayudaron a la disminuir el impacto causado por el SARS-CoV-2. La bioinformática se conoce como una disciplina que combina la biología con la informática y tecnología de información que mediante técnicas aplicadas en los algoritmos de procesamiento de datos, análisis estadísticos y modelado computacional extraen los conocimientos útiles de datos biológicos los mismos que son utilizados en diferentes campos generando cada vez más precisión en sus resultados y sobre todo que los expertos incursionen con esta disciplina en sus diferentes estudios e investigaciones. Este artículo tiene como objetivo realizar una revisión sistemática de la literatura científica a partir de estudios que contengan información relevante respecto a tecnología bioinformática. El desarrollo de esta disciplina es limitado en países que conforman el territorio de América Latina, esto a partir de diversos factores económicos y de infraestructura, sin embargo, existen países que han apostado al desarrollo computacional con la tecnología bioinformática a través del desarrollo de herramientas e investigaciones documentales lo cual les permite establecer un patrón a partir de los datos.

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Section: Discussionunclassified

Bioinformática utilizada en la detección del covid-19; Análisis del desarrollo bioinformático en el ámbito computacional en América Latina.

Solórzano-Zambrano,

Demera-Ureta

2024

COCIRI

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“…In this way, more types of policies, especially those that can not be directly mapped from epidemic prediction results, can be determined by the model. Policies that could be determined by this paradigm of decision models include travel restrictions [18], gathering restrictions [19], and vaccination [20]. Though becoming able to provide valuable support to epidemic policy making, the biggest problem of effect evaluation-based epidemic policy-optimizing is that the outputs of these models generally lack foresight.…”

Section: Epidemic Policy-optimizing Based On Policy Effect Evaluationmentioning

confidence: 99%

Game and Reference: Efficient Policy Making for Epidemic Prevention and Control

Zhiyi,

Bingkun

2024

Preprint

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Epidemic policy-making, as a special data-mining task, is proposed to predict the proper intensities of certain epidemic prevention and control policies based on the spatial-temporal data related to regional epidemics.Previous studies are currently constrained by two issues: First, existing methods are all strongly supervised by policy effect evaluation, since only a small proportion of factors in real-world policy-making are modeled, policies made by the existing models are then easily become extreme or unreasonable. Second, the subjectivity and the cognitive limitation of humans make historical policies not always optimal for the training of decision models. To this end, we present a novel Policy Combination Synthesis (PCS) model for epidemic policy-making. In particular, to prevent extreme decisions, we introduce adversarial learning between the model-made policies and the real policies to force the output policies to be more human-like. On the other hand, to minimize the impact of sub-optimal historical policies, we employ contrastive learning to let the model draw on experience from the best historical policies under similar scenarios. Both adversarial learning and contrastive learning are adaptive to the comprehensive effects of real policies, therefore ensuring that the model always learns useful information.Extensive experiments on real-world data show that policies made by the proposed model outperform the baseline models on both the epidemic containment effect and the economic impact, thereby proving the effectiveness of our work.

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“…We implemented a new agent-based model (EpiHiper) and developed digital twin populations to support the COVID-19 pandemic response. Case studies done in the context of COVID-19 response, using earlier versions of the modeling environment include: ( i ) network-based vaccine allocation ( Chen et al, 2021 , 2022 ); ( ii ) contact tracing ( Hoops et al, 2021 ); ( iii ) economic impact analysis ( Chen et al, 2020a , b ); and ( iv ) the role of basic non-pharmaceutical interventions ( Machi et al, 2021 ).…”

Section: Performance Attributesmentioning

confidence: 99%

Data-driven scalable pipeline using national agent-based models for real-time pandemic response and decision support

Bhattacharya

Chen

Hoops

et al. 2022

The International Journal of High Performance Computing Applica

Self Cite

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This paper describes an integrated, data-driven operational pipeline based on national agent-based models to support federal and state-level pandemic planning and response. The pipeline consists of ( i) an automatic semantic-aware scheduling method that coordinates jobs across two separate high performance computing systems; ( ii) a data pipeline to collect, integrate and organize national and county-level disaggregated data for initialization and post-simulation analysis; ( iii) a digital twin of national social contact networks made up of 288 Million individuals and 12.6 Billion time-varying interactions covering the US states and DC; ( iv) an extension of a parallel agent-based simulation model to study epidemic dynamics and associated interventions. This pipeline can run 400 replicates of national runs in less than 33 h, and reduces the need for human intervention, resulting in faster turnaround times and higher reliability and accuracy of the results. Scientifically, the work has led to significant advances in real-time epidemic sciences.

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Effective Social Network-Based Allocation of COVID-19 Vaccines

Cited by 11 publications

References 24 publications

Bioinformática utilizada en la detección del covid-19; Análisis del desarrollo bioinformático en el ámbito computacional en América Latina.

Bioinformática utilizada en la detección del covid-19; Análisis del desarrollo bioinformático en el ámbito computacional en América Latina.

Game and Reference: Efficient Policy Making for Epidemic Prevention and Control

Data-driven scalable pipeline using national agent-based models for real-time pandemic response and decision support

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