Immunization strategies in directed networks

Within-Host Viral Growth and Immune Response Rates Predict Foot-and-Mouth Disease Virus Transmission Dynamics for African Buffalo

AbstractInfectious disease dynamics operate across biological scales: pathogens replicate within hosts but transmit among populations. Functional changes in the pathogen-host interaction thus generate cascading effects across organizational scales. We investigated within-host dynamics and among-host transmission of three strains (SAT-1, -2, -3) of foot-and-mouth disease viruses (FMDVs) in their wildlife host, African buffalo. We combined data on viral dynamics and host immune responses with mathematical models to ask the following questions: How do viral and immune dynamics vary among strains? Which viral and immune parameters determine viral fitness within hosts? And how do within-host dynamics relate to virus transmission? Our data reveal contrasting within-host dynamics among viral strains, with SAT-2 eliciting more rapid and effective immune responses than SAT-1 and SAT-3. Within-host viral fitness was overwhelmingly determined by variation among hosts in immune response activation rates but not by variation among individual hosts in viral growth rate. Our analyses investigating across-scale linkages indicate that viral replication rate in the host correlates with transmission rates among buffalo and that adaptive immune activation rate determines the infectious period. These parameters define the virus's relative basic reproductive number (ℛ0), suggesting that viral invasion potential may be predictable from within-host dynamics.

You are only as safe as your riskiest contact: Effective COVID-19 vaccine distribution using local network information

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Using simulation to evaluate nomination of most popular contacts for vaccination.

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Simulating spread of COVID-19 across two contact networks among high-schoolers.

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Targeting in this way can reduce spread to the susceptible population by 20% or more.

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Results are robust in a synthetic network replicating spread in a small town.

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Results are robust across a wide range of infectiousness, and mistaken nomination.

When vaccines are limited, prior research has suggested it is most protective to distribute vaccines to the most central individuals – those who are most likely to spread the disease. But surveying the population’s social network is a costly and time-consuming endeavour, often not completed before vaccination must begin. This paper validates a local targeting method for distributing vaccines. That is, ask randomly chosen individuals to nominate for vaccination the person they are in contact with who has the most disease-spreading contacts. Even better, ask that person to nominate the next person for vaccination, and so on. To validate this approach, we simulate the spread of COVID-19 along empirical contact networks collected in two high schools, in the United States and France, pre-COVID. These weighted networks are built by recording whenever students are in close spatial proximity and facing one another. We show here that nomination of most popular contacts performs significantly better than random vaccination, and on par with strategies which assume a full survey of the population. These results are robust over a range of realistic disease-spread parameters, as well as a larger synthetic contact network of 3000 individuals.