01456nas a2200169   4500008004100000245008400041210006900125260003400194300000900228490000700237520091600244100001701160700001801177700001901195700002301214856004901237       2019                        eng d00aEfficient sentinel surveillance strategies for preventing epidemics on networks0 aEfficient sentinel surveillance strategies for preventing epidem  bPublic Library of Sciencec11  a1-190 v153 aAuthor summary In a network of individuals susceptible to some infectious disease, what are the best locations to monitor in order to detect the infection before most damage can be done? In this paper we address this question by considering various heuristic strategies for sentinel placement that can potentially be implemented in real-world situations without requiring excessive amounts of computation, or even having perfect data about the structure of the network. We find that strategies that attempt to distribute sentinels over different regions of the network perform best in highly modular or spatially embedded networks, whereas the strategy of targeting the most well connected individuals works best when there is a considerable amount of contact heterogeneity between individuals. Our results may be used as a guideline to help decide when certain strategies should, or should not, be implemented.1 aColman, Ewan1 aHolme, Petter1 aSayama, Hiroki1 aGershenson, Carlos  uhttps://doi.org/10.1371/journal.pcbi.100751701973nas a2200229   4500008004100000022001400041245005100055210005100106300000700157490000600164520131800170100002301488700001701511700002101528700002801549700001901577700002201596700002001618700001801638700002301656856006401679       2018                        eng d  a2296-424X00aRank Dynamics of Word Usage at Multiple Scales0 aRank Dynamics of Word Usage at Multiple Scales  a450 v63 aThe recent dramatic increase in online data availability has allowed researchers to explore human culture with unprecedented detail, such as the growth and diversification of language. In particular, it provides statistical tools to explore whether word use is similar across languages, and if so, whether these generic features appear at different scales of language structure. Here we use the Google Books $N$-grams dataset to analyze the temporal evolution of word usage in several languages. We apply measures proposed recently to study rank dynamics, such as the diversity of $N$-grams in a given rank, the probability that an $N$-gram changes rank between successive time intervals, the rank entropy, and the rank complexity. Using different methods, results show that there are generic properties for different languages at different scales, such as a core of words necessary to minimally understand a language. We also propose a null model to explore the relevance of linguistic structure across multiple scales, concluding that $N$-gram statistics cannot be reduced to word statistics. We expect our results to be useful in improving text prediction algorithms, as well as in shedding light on the large-scale features of language use, beyond linguistic and cultural differences across human populations.1 aMorales, José, A.1 aColman, Ewan1 aSánchez, Sergio1 aSánchez-Puig, Fernanda1 aPineda, Carlos1 aIñiguez, Gerardo1 aCocho, Germinal1 aFlores, Jorge1 aGershenson, Carlos  uhttps://www.frontiersin.org/article/10.3389/fphy.2018.00045