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- Thompson, Robin N., et al.
(author)
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Key questions for modelling COVID-19 exit strategies
- 2020
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In: Proceedings of the Royal Society of London. Biological Sciences. - : The Royal Society. - 0962-8452 .- 1471-2954. ; 287:1932
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Journal article (peer-reviewed)abstract
- Combinations of intense non-pharmaceutical interventions (lockdowns) were introduced worldwide to reduce SARS-CoV-2 transmission. Many governments have begun to implement exit strategies that relax restrictions while attempting to control the risk of a surge in cases. Mathematical modelling has played a central role in guiding interventions, but the challenge of designing optimal exit strategies in the face of ongoing transmission is unprecedented. Here, we report discussions from the Isaac Newton Institute 'Models for an exit strategy' workshop (11-15 May 2020). A diverse community of modellers who are providing evidence to governments worldwide were asked to identify the main questions that, if answered, would allow for more accurate predictions of the effects of different exit strategies. Based on these questions, we propose a roadmap to facilitate the development of reliable models to guide exit strategies. This roadmap requires a global collaborative effort from the scientific community and policymakers, and has three parts: (i) improve estimation of key epidemiological parameters; (ii) understand sources of heterogeneity in populations; and (iii) focus on requirements for data collection, particularly in low-to-middle-income countries. This will provide important information for planning exit strategies that balance socio-economic benefits with public health.
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- Ball, Frank G., et al.
(author)
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EPIDEMICS ON RANDOM INTERSECTION GRAPHS
- 2014
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In: The Annals of Applied Probability. - 1050-5164 .- 2168-8737. ; 24:3, s. 1081-1128
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Journal article (peer-reviewed)abstract
- In this paper we consider a model for the spread of a stochastic SIR (Susceptible -> Infectious -> Recovered) epidemic on a network of individuals described by a random intersection graph. Individuals belong to a random number of cliques, each of random size, and infection can be transmitted between two individuals if and only if there is a clique they both belong to. Both the clique sizes and the number of cliques an individual belongs to follow mixed Poisson distributions. An infinite-type branching process approximation (with type being given by the length of an individual's infectious period) for the early stages of an epidemic is developed and made fully rigorous by proving an associated limit theorem as the population size tends to infinity. This leads to a threshold parameter R-*, so that in a large population an epidemic with few initial infectives can give rise to a large outbreak if and only if R-*>1. A functional equation for the survival probability of the approximating infinite-type branching process is determined; if R-*<= 1, this equation has no nonzero solution, while if R-*>1, it is shown to have precisely one nonzero solution. A law of large numbers for the size of such a large outbreak is proved by exploiting a single-type branching process that approximates the size of the susceptibility set of a typical individual.
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- Hermans, Karin G, et al.
(author)
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Overexpression of prostate-specific TMPRSS2(exon 0)-ERG fusion transcripts corresponds with favorable prognosis of prostate cancer.
- 2009
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In: Clinical cancer research : an official journal of the American Association for Cancer Research. - 1078-0432. ; 15:20, s. 6398-403
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Journal article (peer-reviewed)abstract
- To gain insight in the mechanism and clinical relevance of TMPRSS2-ERG expression in prostate cancer, we determined the specific characteristics of fusion transcripts starting at TMPRSS2 exon 1 and at a more upstream and less characterized exon 0.We used quantitative PCR analysis to investigate expression of wild-type TMPRSS2(exon 0) and TMPRSS2(exon 1) and of ERG fusion transcripts. Expression was tested in normal tissue samples, in prostate cancer cell lines and xenografts, and in fresh-frozen clinical prostate cancer samples (primary tumors and recurrences). Expression in clinical samples was correlated with disease progression.TMPRSS2(exon 0) and TMPRSS2(exon 1) transcripts were similarly androgen regulated in prostate cancer cell lines, but the expression levels of TMPRSS2(exon 1) were much higher. Comparison of expression in different tissues showed TMPRSS2(exon 0) expression to be much more prostate specific. In androgen receptor-positive prostate cancer xenografts, TMPRSS2(exon 1) transcripts were expressed at similar levels, but TMPRSS2(exon 0) transcripts were expressed at very variable levels. The same phenomenon was observed for TMPRSS2-ERG fusion transcripts. In clinical prostate cancers, the expression of TMPRSS2(exon 0)-ERG was even more variable. Expression of TMPRSS2(exon 0)-ERG transcripts was detected in 55% (24 of 44) of gene fusion-positive primary tumors but only in 15% (4 of 27) of gene fusion-positive recurrences and at much lower levels. Furthermore, in primary tumors, expression of TMPRSS2(exon 0)-ERG transcripts was an independent predictor of biochemical progression-free survival.The expression of TMPRSS2(exon 0)-ERG fusion transcripts in prostate cancer is associated with a less-aggressive biological behavior.
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