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Michiel van Boven

Researcher at Utrecht University

Publications -  94
Citations -  3907

Michiel van Boven is an academic researcher from Utrecht University. The author has contributed to research in topics: Population & Vaccination. The author has an hindex of 29, co-authored 82 publications receiving 3020 citations. Previous affiliations of Michiel van Boven include Wageningen University and Research Centre & Oklahoma State University Center for Health Sciences.

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Impact of delays on effectiveness of contact tracing strategies for COVID-19: a modelling study.

TL;DR: The impact of timeliness and completeness in various steps of a contact tracing strategy is evaluated using a stochastic mathematical model with explicit time delays between time of infection and symptom onset and between symptom onset, diagnosis by testing, and isolation.
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Avian Influenza A Virus (H7N7) Epidemic in The Netherlands in 2003: Course of the Epidemic and Effectiveness of Control Measures

TL;DR: The observation that Rh remained >1 suggests that the containment of the epidemic was probably due to the reduction in the number of susceptible flocks by complete depopulation of the infected areas rather than to the reduce of the transmission by the other control measures.
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Risk Maps for the Spread of Highly Pathogenic Avian Influenza in Poultry

TL;DR: The analyses provide an estimate of the spatial range over which highly pathogenic avian influenza viruses spread between farms, and emphasize that control measures aimed at controlling such outbreaks need to take into account the local density of farms.
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Herd immunity to Newcastle disease virus in poultry by vaccination

TL;DR: In this article, the requirements of an epidemiologically effective vaccination program against Newcastle disease in poultry, based on data from experimental transmission studies, were studied and the implications for the control of Newcastle disease by vaccination were discussed.
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Optimizing infectious disease interventions during an emerging epidemic.

TL;DR: First principles for allocating scarce resources with limited data are presented and it is shown that under a broad class of assumptions, the simple rule of targeting intervention measures at the group with the highest risk of infection per individual will achieve the largest reduction in the transmission potential of a novel infection.