Optimal allocation of limited vaccine to minimize the effective reproduction number

Rao, Isabelle J.; Brandeau, Margaret L.

doi:10.1016/j.mbs.2021.108654

Cited by 14 publications

(9 citation statements)

References 24 publications

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“…Bertsimas et al proposed a model that combines DELPHI epidemiological model with a bilinear, non-convex optimization to minimize exposure, mortality, and distance between vaccination centers and population centers [60]. Rao et al present an analytical model for vaccine allocation with the aim of minimizing the effective reproduction number [61]. Shim et al learn the parameters of the age-structured model of COVID-19 spread in South Korea to design vaccine deployment policies such that infections, deaths could be mitigated, given the parameters of age distribution and social contact [62], while Meehan et al [63] and Buckner et al [64] use the age-structured model and epidemiological model, respectively, to suss out high-priority individuals to be vaccinated based on their contact rates and risk of infection.…”

Section: Optimization Formulationmentioning

confidence: 99%

“…The module may distribute vaccines based on the disease transmissibility, gauged in terms of reproduction number (see Sec. 2.2), among the zonal population [61]. The proportion of the elderly individuals with comorbidities may be a determinant of vaccine distribution [63].…”

Section: Details Of Intelligent Computation Modulesmentioning

confidence: 99%

“…Moreover, these frameworks may utilize varying subset of factors as inputs. For example, Rao et al solve vaccine allocation based on effective reproduction number [61], whereas Shim et al address the same problem based on age and contact pattern [62]. Evidently, consensus algorithms are necessary to aggregate the solutions proffered by myriad computational frameworks.…”

Section: Data Integrationmentioning

confidence: 99%

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Towards a Unified Pandemic Management Architecture: Survey, Challenges and Future Directions

Roy¹,

Ghosh²,

Uplavikar³

et al. 2022

Preprint

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The pandemic caused by SARS-CoV-2 has left an unprecedented impact on health, economy and society worldwide. Emerging strains are making pandemic management increasingly challenging. There is an urge to collect epidemiological, clinical, and physiological data to make an informed decision on mitigation measures. Advances in the Internet of Things (IoT) and edge computing provide solutions for pandemic management through data collection and intelligent computation. While existing data-driven architectures attempt to automate decision-making, they do not capture the multifaceted interaction among computational models, communication infrastructure, and the generated data. In this paper, we perform a survey of the existing approaches for pandemic management, including online data repositories and contact-tracing applications. We then envision a unified pandemic management architecture that leverages the IoT and edge computing to automate recommendations on vaccine distribution, dynamic lockdown, mobility scheduling and pandemic prediction. We elucidate the flow of data among the layers of the architecture, namely, cloud, edge and end device layers. Moreover, we address the privacy implications, threats, regulations, and existing solutions that may be adapted to optimize the utility of health data with security guarantees. The paper ends with a lowdown on the limitations of the architecture and research directions to enhance its practicality.

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Section: Optimization Formulationmentioning

confidence: 99%

Section: Details Of Intelligent Computation Modulesmentioning

confidence: 99%

Section: Data Integrationmentioning

confidence: 99%

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Towards a Unified Pandemic Management Architecture: Survey, Challenges and Future Directions

Roy¹,

Ghosh²,

Uplavikar³

et al. 2022

Preprint

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show abstract

“…(1) * = 0 with * ∈ {H, S, W, O} in eq. (11). However, second level interventions with face masks and distancing are in place, and we assume a reduced contact rate by r…”

Section: Mitigation and Opening Strategiesmentioning

confidence: 99%

Regional opening strategies with commuter testing and containment of new SARS-CoV-2 variants in Germany

Kuehn

Abele

Binder

et al. 2021

Preprint

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Background: Despite the accelerating vaccination process, a large majority of the population is still susceptible to SARS-CoV-2. In addition, we face the spread of novel variants. Until we overcome the pandemic, reasonable mitigation and opening strategies are crucial to balance public health and economic interests. Methods: We model the spread of SARS-CoV-2 over the German counties by a graph-SIR-type model with particular focus on commuter testing. We account for political interventions by varying contact reduction values in private and public locations such as homes, schools, workplaces, and other. We consider different levels of lockdown strictness, commuter testing strategies, or the delay of intervention implementation. We conduct numerical simulations to assess the effectiveness of the different intervention strategies after one month. The virus dynamics in the counties are initialized randomly with incidences between 75-150 weekly new cases per 100,000 inhabitants (red zones) or below 10 (green zones) and consider 25 different initial scenarios of randomly distributed red zones (between 2 and 20 % of all counties). To account for uncertainty, we consider an ensemble set of 500 Monte Carlo runs for each scenario. Results: We find that the strength of the lockdown in regions with out of control virus dynamics is most important to avoid the spread into neighboring regions. With very strict lockdowns in red zones, commuter testing rates of twice a week can substantially contribute to the safety of adjacent regions. In contrast, less strict lockdowns with the same commuter testing rate quickly and substantially lead to overall higher infection dynamics. A further key contributor is the potential delay of the intervention implementation. In order to keep the spread of the virus under control, strict regional lockdowns with minimum delay and commuter testing of at least twice a week are advisable.

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“…In the context of the SARS-CoV-2 pandemic, ODE-SIR-type models with focus on vaccination were used by [ 11 , 12 ] while [ 13 ] focused on hospitalization and ICU demands. The authors of [ 14 , 15 ] considered similar models with an additional differentiation of confirmed and unconfirmed infections.…”

Section: Introductionmentioning

confidence: 99%

Regional opening strategies with commuter testing and containment of new SARS-CoV-2 variants in Germany

et al. 2022

View full text Add to dashboard Cite

Background Despite the vaccination process in Germany, a large share of the population is still susceptible to SARS-CoV-2. In addition, we face the spread of novel variants. Until we overcome the pandemic, reasonable mitigation and opening strategies are crucial to balance public health and economic interests. Methods We model the spread of SARS-CoV-2 over the German counties by a graph-SIR-type, metapopulation model with particular focus on commuter testing. We account for political interventions by varying contact reduction values in private and public locations such as homes, schools, workplaces, and other. We consider different levels of lockdown strictness, commuter testing strategies, or the delay of intervention implementation. We conduct numerical simulations to assess the effectiveness of the different intervention strategies after one month. The virus dynamics in the regions (German counties) are initialized randomly with incidences between 75 and 150 weekly new cases per 100,000 inhabitants (red zones) or below (green zones) and consider 25 different initial scenarios of randomly distributed red zones (between 2 and 20% of all counties). To account for uncertainty, we consider an ensemble set of 500 Monte Carlo runs for each scenario. Results We find that the strength of the lockdown in regions with out of control virus dynamics is most important to avoid the spread into neighboring regions. With very strict lockdowns in red zones, commuter testing rates of twice a week can substantially contribute to the safety of adjacent regions. In contrast, the negative effect of less strict interventions can be overcome by high commuter testing rates. A further key contributor is the potential delay of the intervention implementation. In order to keep the spread of the virus under control, strict regional lockdowns with minimum delay and commuter testing of at least twice a week are advisable. If less strict interventions are in favor, substantially increased testing rates are needed to avoid overall higher infection dynamics. Conclusions Our results indicate that local containment of outbreaks and maintenance of low overall incidence is possible even in densely populated and highly connected regions such as Germany or Western Europe. While we demonstrate this on data from Germany, similar patterns of mobility likely exist in many countries and our results are, hence, generalizable to a certain extent.

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Optimal allocation of limited vaccine to minimize the effective reproduction number

Cited by 14 publications

References 24 publications

Towards a Unified Pandemic Management Architecture: Survey, Challenges and Future Directions

Towards a Unified Pandemic Management Architecture: Survey, Challenges and Future Directions

Regional opening strategies with commuter testing and containment of new SARS-CoV-2 variants in Germany

Regional opening strategies with commuter testing and containment of new SARS-CoV-2 variants in Germany

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