Socio-demographic and health factors drive the epidemic progression and should guide vaccination strategies for best COVID-19 containment

Markovič, Rene; Šterk, Marko; Marhl, Marko; Perc, Matjaž; Gosak, Marko

doi:10.1016/j.rinp.2021.104433

Cited by 77 publications

(50 citation statements)

References 97 publications

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“…COVID-19 vaccine prioritization strategies have been studied in many ways, by using deterministic differential equation models [ 3 – 8 ], agent-based models [ 9 , 10 ], network models [ 11 , 12 ], and various other approaches [ 13 , 14 ], as well as by considering ethical factors [ 15 , 16 ]. Most studies focused solely on age and age-dependent disease behavior [ 6 – 8 , 14 ].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of the United States COVID-19 vaccine allocation strategy

et al. 2021

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Background Anticipating an initial shortage of vaccines for COVID-19, the Centers for Disease Control (CDC) in the United States developed priority vaccine allocations for specific demographic groups in the population. This study evaluates the performance of the CDC vaccine allocation strategy with respect to multiple potentially competing vaccination goals (minimizing mortality, cases, infections, and years of life lost (YLL)), under the same framework as the CDC allocation: four priority vaccination groups and population demographics stratified by age, comorbidities, occupation and living condition (congested or non-congested). Methods and findings We developed a compartmental disease model that incorporates key elements of the current pandemic including age-varying susceptibility to infection, age-varying clinical fraction, an active case-count dependent social distancing level, and time-varying infectivity (accounting for the emergence of more infectious virus strains). The CDC allocation strategy is compared to all other possibly optimal allocations that stagger vaccine roll-out in up to four phases (17.5 million strategies). The CDC allocation strategy performed well in all vaccination goals but never optimally. Under the developed model, the CDC allocation deviated from the optimal allocations by small amounts, with 0.19% more deaths, 4.0% more cases, 4.07% more infections, and 0.97% higher YLL, than the respective optimal strategies. The CDC decision to not prioritize the vaccination of individuals under the age of 16 was optimal, as was the prioritization of health-care workers and other essential workers over non-essential workers. Finally, a higher prioritization of individuals with comorbidities in all age groups improved outcomes compared to the CDC allocation. Conclusion The developed approach can be used to inform the design of future vaccine allocation strategies in the United States, or adapted for use by other countries seeking to optimize the effectiveness of their vaccine allocation strategies.

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Section: Introductionmentioning

confidence: 99%

Evaluation of the United States COVID-19 vaccine allocation strategy

et al. 2021

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“…The number of cases and deaths from COVID-19 is associated with social, demographic, and health factors (Drefahl et al, 2020 ; Karmakar et al, 2021 ; Markovič et al, 2021 ). Although this multidimensionality is widely recognized, not many studies propose to build composite indicators for the analysis of COVID-19.…”

Section: Multidimensional Analysis Of Covid-19mentioning

confidence: 99%

Factors that most expose countries to COVID-19: a composite indicators-based approach

et al. 2021

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Studies carried out in different countries correlate social, economic, environmental, and health factors with the number of cases and deaths from COVID-19. However, such studies do not reveal which factors make one country more exposed to COVID-19 than other. Based on the composite indicators approach, this research identifies the factors that most impact the number of cases and deaths of COVID-19 worldwide and measures countries’ exposure to COVID-19. Three composite indicators of exposure to COVID-19 were constructed through Principal Component Analysis, Simple Additive Weighting, and k-means clustering. The number of cases and deaths from COVID-19 is strongly correlated ( R > 0.60) with composite indicator scores and moderately concordant ( K > 0.4) with country clusters. Factors directly or indirectly associated with the age of the population are the ones that most expose countries to COVID-19. The population of countries most exposed to COVID-19 is 12 years older on average. The proportion of the elderly population in these countries is at least twice that of countries less exposed to COVID-19. Factors that can increase the population’s life expectancy, such as Gross Domestic Product per capita and the Human Development Index, are four times and 1.3 times higher in more exposed countries to COVID-19. Providing better living conditions increases both the population’s life expectancy and the country’s exposure to COVID-19.

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“…This stands in contrast to more traditional compartmental models that track the evolution of the pandemic at the level of compartments containing large numbers of people. Although network models are typically more computationally expensive than well-mixed compartmental models, their incorporation of the physical contact network allows them to better model heterogeneous populations and their dynamics [ 1 ].…”

Section: Introductionmentioning

confidence: 99%

Modelling the impact of social distancing and targeted vaccination on the spread of COVID-19 through a real city-scale contact network

Hartnett

Parker

Gulden

et al. 2021

Journal of Complex Networks

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We use mobile device data to construct empirical interpersonal physical contact networks in the city of Portland, Oregon, both before and after social distancing measures were enacted during the COVID-19 pandemic. These networks reveal how social distancing measures and the public’s reaction to the incipient pandemic affected the connectivity patterns within the city. We find that as the pandemic developed there was a substantial decrease in the number of individuals with many contacts. We further study the impact of these different network topologies on the spread of COVID-19 by simulating an SEIR epidemic model over these networks and find that the reduced connectivity greatly suppressed the epidemic. We then investigate how the epidemic responds when part of the population is vaccinated, and we compare two vaccination distribution strategies, both with and without social distancing. Our main result is that the heavy-tailed degree distribution of the contact networks causes a targeted vaccination strategy that prioritizes high-contact individuals to reduce the number of cases far more effectively than a strategy that vaccinates individuals at random. Combining both targeted vaccination and social distancing leads to the greatest reduction in cases, and we also find that the marginal benefit of a targeted strategy as compared to a random strategy exceeds the marginal benefit of social distancing for reducing the number of cases. These results have important implications for ongoing vaccine distribution efforts worldwide.

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Socio-demographic and health factors drive the epidemic progression and should guide vaccination strategies for best COVID-19 containment

Cited by 77 publications

References 97 publications

Evaluation of the United States COVID-19 vaccine allocation strategy

Evaluation of the United States COVID-19 vaccine allocation strategy

Factors that most expose countries to COVID-19: a composite indicators-based approach

Modelling the impact of social distancing and targeted vaccination on the spread of COVID-19 through a real city-scale contact network

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