Predicting human mobility through the assimilation of social media traces into mobility models

Predicting human mobility flows at different spatial scales is challenged by the heterogeneity of individual trajectories and the multi-scale nature of transportation networks. As vast amounts of digital traces of human behaviour become available, an opportunity arises to improve mobility models by integrating into them proxy data on mobility collected by a variety of digital platforms and location-aware services. Here we propose a hybrid model of human mobility that integrates a large-scale publicly available dataset from a popular photo-sharing system with the classical gravity model, under a stacked regression procedure. We validate the performance and generalizability of our approach using two ground-truth datasets on air travel and daily commuting in the United States: using two different cross-validation schemes we show that the hybrid model affords enhanced mobility prediction at both spatial scales.Comment: 17 pages, 10 figure

High-resolution contact networks of free-ranging domestic dogs Canis familiaris and implications for transmission of infection

This is the final version. Available on open access from Public Library of Science via the DOI in this recordData Availability: All data and code supporting these analyses are available on Dryad doi:10.5061/dryad.7v62484.Contact patterns strongly influence the dynamics of disease transmission in both human and non-human animal populations. Domestic dogs Canis familiaris are a social species and are a reservoir for several zoonotic infections, yet few studies have empirically determined contact patterns within dog populations. Using high-resolution proximity logging technology, we characterised the contact networks of free-ranging domestic dogs from two settlements (n = 108 dogs, covering >80 % of the population in each settlement) in rural Chad. We used these data to simulate the transmission of an infection comparable to rabies and investigated the effects of including observed contact heterogeneities on epidemic outcomes. We found that dog contact networks displayed considerable heterogeneity, particularly in the duration of contacts and that the network had communities that were highly correlated with household membership. Simulations using observed contact networks had smaller epidemic sizes than those that assumed random mixing, demonstrating the unsuitability of homogenous mixing models in predicting epidemic outcomes. When contact heterogeneities were included in simulations, the network position of the individual initially infected had an important effect on epidemic outcomes. The risk of an epidemic occurring was best predicted by the initially infected individual’s ranked degree, while epidemic size was best predicted by the individual’s ranked eigenvector centrality. For dogs in one settlement, we found that ranked eigenvector centrality was correlated with range size. Our results demonstrate that observed heterogeneities in contacts are important for the prediction of epidemiological outcomes in free-ranging domestic dogs. We show that individuals presenting a higher risk for disease transmission can be identified by their network position and provide evidence that observable traits hold potential for informing targeted disease management strategies.Carter Cente

Estimating household contact matrices structure from easily collectable metadata

Contact matrices are a commonly adopted data representation, used to develop compartmental models for epidemic spreading, accounting for the contact heterogeneities across age groups. Their estimation, however, is generally time and effort consuming and model-driven strategies to quantify the contacts are often needed. In this article we focus on household contact matrices, describing the contacts among the members of a family and develop a parametric model to describe them. This model combines demographic and easily quantifiable survey-based data and is tested on high resolution proximity data collected in two sites in South Africa. Given its simplicity and interpretability, we expect our method to be easily applied to other contexts as well and we identify relevant questions that need to be addressed during the data collection procedure

High temperature stability of Terphenyl based thermal oils

Thermal oils are nowadays widely used as heat transfer fluids or cooling media in industrial and energy production plants. Currently, very few data are available about their thermal stability in function of the operating temperatures, which is a crucial parameter to estimate oil structural changes and their possible effects the maximum fluids lifetime. The present work is concerned with ageing tests on a commercially used thermal oil at temperatures higher than the nominal working ones, including a full post-test characterization. At this aim, a dedicated experimental set-up was designed and constructed to study the degradation kinetics, and to qualitatively and quantitatively analyze the released gases. As a result, the kinetic parameters were estimated, along with the related changes in the oil thermos-physical properties

Evaluation of a candidate breast cancer associated SNP in ERCC4 as a risk modifier in BRCA1 and BRCA2 mutation carriers. Results from the Consortium of Investigators of Modifiers of BRCA1/BRCA2 (CIMBA)

Background: In this study we aimed to evaluate the role of a SNP in intron 1 of the ERCC4 gene (rs744154), previously reported to be associated with a reduced risk of breast cancer in the general population, as a breast cancer risk modifier in BRCA1 and BRCA2 mutation carriers. Methods: We have genotyped rs744154 in 9408 BRCA1 and 5632 BRCA2 mutation carriers from the Consortium of Investigators of Modifiers of BRCA1/2 (CIMBA) and assessed its association with breast cancer risk using a retrospective weighted cohort approach. Results: We found no evidence of association with breast cancer risk for BRCA1 (per-allele HR: 0.98, 95% CI: 0.93–1.04, P=0.5) or BRCA2 (per-allele HR: 0.97, 95% CI: 0.89–1.06, P=0.5) mutation carriers. Conclusion: This SNP is not a significant modifier of breast cancer risk for mutation carriers, though weak associations cannot be ruled out. A Osorio1, R L Milne2, G Pita3, P Peterlongo4,5, T Heikkinen6, J Simard7, G Chenevix-Trench8, A B Spurdle8, J Beesley8, X Chen8, S Healey8, KConFab9, S L Neuhausen10, Y C Ding10, F J Couch11,12, X Wang11, N Lindor13, S Manoukian4, M Barile14, A Viel15, L Tizzoni5,16, C I Szabo17, L Foretova18, M Zikan19, K Claes20, M H Greene21, P Mai21, G Rennert22, F Lejbkowicz22, O Barnett-Griness22, I L Andrulis23,24, H Ozcelik24, N Weerasooriya23, OCGN23, A-M Gerdes25, M Thomassen25, D G Cruger26, M A Caligo27, E Friedman28,29, B Kaufman28,29, Y Laitman28, S Cohen28, T Kontorovich28, R Gershoni-Baruch30, E Dagan31,32, H Jernström33, M S Askmalm34, B Arver35, B Malmer36, SWE-BRCA37, S M Domchek38, K L Nathanson38, J Brunet39, T Ramón y Cajal40, D Yannoukakos41, U Hamann42, HEBON37, F B L Hogervorst43, S Verhoef43, EB Gómez García44,45, J T Wijnen46,47, A van den Ouweland48, EMBRACE37, D F Easton49, S Peock49, M Cook49, C T Oliver49, D Frost49, C Luccarini50, D G Evans51, F Lalloo51, R Eeles52, G Pichert53, J Cook54, S Hodgson55, P J Morrison56, F Douglas57, A K Godwin58, GEMO59,60,61, O M Sinilnikova59,60, L Barjhoux59,60, D Stoppa-Lyonnet61, V Moncoutier61, S Giraud59, C Cassini62,63, L Olivier-Faivre62,63, F Révillion64, J-P Peyrat64, D Muller65, J-P Fricker65, H T Lynch66, E M John67, S Buys68, M Daly69, J L Hopper70, M B Terry71, A Miron72, Y Yassin72, D Goldgar73, Breast Cancer Family Registry37, C F Singer74, D Gschwantler-Kaulich74, G Pfeiler74, A-C Spiess74, Thomas v O Hansen75, O T Johannsson76, T Kirchhoff77, K Offit77, K Kosarin77, M Piedmonte78, G C Rodriguez79, K Wakeley80, J F Boggess81, J Basil82, P E Schwartz83, S V Blank84, A E Toland85, M Montagna86, C Casella87, E N Imyanitov88, A Allavena89, R K Schmutzler90, B Versmold90, C Engel91, A Meindl92, N Ditsch93, N Arnold94, D Niederacher95, H Deißler96, B Fiebig97, R Varon-Mateeva98, D Schaefer99, U G Froster100, T Caldes101, M de la Hoya101, L McGuffog49, A C Antoniou49, H Nevanlinna6, P Radice4,5 and J Benítez1,3 on behalf of CIMB

SV2 Mediates Entry of Tetanus Neurotoxin into Central Neurons

Tetanus neurotoxin causes the disease tetanus, which is characterized by rigid paralysis. The toxin acts by inhibiting the release of neurotransmitters from inhibitory neurons in the spinal cord that innervate motor neurons and is unique among the clostridial neurotoxins due to its ability to shuttle from the periphery to the central nervous system. Tetanus neurotoxin is thought to interact with a high affinity receptor complex that is composed of lipid and protein components; however, the identity of the protein receptor remains elusive. In the current study, we demonstrate that toxin binding, to dissociated hippocampal and spinal cord neurons, is greatly enhanced by driving synaptic vesicle exocytosis. Moreover, tetanus neurotoxin entry and subsequent cleavage of synaptobrevin II, the substrate for this toxin, was also dependent on synaptic vesicle recycling. Next, we identified the potential synaptic vesicle binding protein for the toxin and found that it corresponded to SV2; tetanus neurotoxin was unable to cleave synaptobrevin II in SV2 knockout neurons. Toxin entry into knockout neurons was rescued by infecting with viruses that express SV2A or SV2B. Tetanus toxin elicited the hyper excitability in dissociated spinal cord neurons - due to preferential loss of inhibitory transmission - that is characteristic of the disease. Surprisingly, in dissociated cortical cultures, low concentrations of the toxin preferentially acted on excitatory neurons. Further examination of the distribution of SV2A and SV2B in both spinal cord and cortical neurons revealed that SV2B is to a large extent localized to excitatory terminals, while SV2A is localized to inhibitory terminals. Therefore, the distinct effects of tetanus toxin on cortical and spinal cord neurons are not due to differential expression of SV2 isoforms. In summary, the findings reported here indicate that SV2A and SV2B mediate binding and entry of tetanus neurotoxin into central neurons

Limited role of spatial selfstructuring in emergent trade-offs during pathogen evolution

Pathogen transmission and virulence are main evolutionary variables broadly assumed to be linked through trade-offs. In well-mixed populations, these trade-offs are often ascribed to physiological restrictions, while populations with spatial self-structuring might evolve emergent trade-offs. Here, we reexamine a spatially-explicit, SIR model of the latter kind proposed by Ballegooijen and Boerlijst with the aim of characterising the mechanisms causing the emergence of the trade-off and its structural robustness. Using invadability criteria, we establish the conditions under which an evolutionary feedback between transmission and virulence mediated by pattern formation can poise the system to a critical boundary separating a disordered state (without emergent trade-off) from a self-structured phase (where the trade-off emerges), and analytically calculate the functional shape of the boundary in a certain approximation. Beyond evolutionary parameters, the success of an invasion depends on the size and spatial structure of the invading and invaded populations. Spatial self-structuring is often destroyed when hosts are mobile, changing the evolutionary dynamics to those of a well-mixed population. In a metapopulation scenario, the systematic extinction of the pathogen in the disordered phase may counteract the disruptive effect of host mobility, favour pattern formation and therefore recover the emergent trade-off.This work has been supported by the Spanish Ministerio de Economía, Industria y Competitividad and FEDER funds of the EU through grants ViralESS (FIS2014-57686-P and FIS2017-84256-P). The internship of VB was financed by the Severo Ochoa Centers of Excellence Program (SEV-2013-0347)

Lack of SARS-CoV-2 RNA environmental contamination in a tertiary referral hospital for infectious diseases in Northern Italy

none140noNAnoneColaneri M.; Seminari E.; Piralla A.; Zuccaro V.; Di Filippo A.; Baldanti F.; Bruno R.; Mondelli M.U.; Brunetti E.; Di Matteo A.; Maiocchi L.; Pagnucco L.; Mariani B.; Ludovisi S.; Lissandrin R.; Parisi A.; Sacchi P.; Patruno S.F.A.; Michelone G.; Gulminetti R.; Zanaboni D.; Novati S.; Maserati R.; Orsolini P.; Vecchia M.; Sciarra M.; Asperges E.; Sambo M.; Biscarini S.; Lupi M.; Roda S.; Chiara Pieri T.; Gallazzi I.; Sachs M.; Valsecchi P.; Perlini S.; Alfano C.; Bonzano M.; Briganti F.; Crescenzi G.; Giulia Falchi A.; Guarnone R.; Guglielmana B.; Maggi E.; Martino I.; Pettenazza P.; Pioli di Marco S.; Quaglia F.; Sabena A.; Salinaro F.; Speciale F.; Zunino I.; De Lorenzo M.; Secco G.; Dimitry L.; Cappa G.; Maisak I.; Chiodi B.; Sciarrini M.; Barcella B.; Resta F.; Moroni L.; Vezzoni G.; Scattaglia L.; Boscolo E.; Zattera C.; Michele Fidel T.; Vincenzo C.; Vignaroli D.; Bazzini M.; Iotti G.; Mojoli F.; Belliato M.; Perotti L.; Mongodi S.; Tavazzi G.; Marseglia G.; Licari A.; Brambilla I.; Daniela B.; Antonella B.; Patrizia C.; Giulia C.; Giuditta C.; Marta C.; Rossana D.; Milena F.; Bianca M.; Roberta M.; Enza M.; Stefania P.; Maurizio P.; Elena P.; Antonio P.; Francesca R.; Antonella S.; Maurizio Z.; Guy A.; Laura B.; Ermanna C.; Giuliana C.; Luca D.; Gabriella F.; Gabriella G.; Alessia G.; Viviana L.; Claudia L.; Valentina M.; Simona P.; Marta P.; Alice B.; Giacomo C.; Irene C.; Alfonso C.; Di Martino R.; Di Napoli A.; Alessandro F.; Guglielmo F.; Loretta F.; Federica G.; Alessandra M.; Federica N.; Giacomo R.; Beatrice R.; Maria S.I.; Monica T.; Nepita Edoardo V.; Calvi M.; Tizzoni M.; Nicora C.; Triarico A.; Petronella V.; Marena C.; Muzzi A.; Lago P.; Comandatore F.; Bissignandi G.; Gaiarsa S.; Rettani M.; Bandi C.Colaneri, M.; Seminari, E.; Piralla, A.; Zuccaro, V.; Di Filippo, A.; Baldanti, F.; Bruno, R.; Mondelli, M. U.; Brunetti, E.; Di Matteo, A.; Maiocchi, L.; Pagnucco, L.; Mariani, B.; Ludovisi, S.; Lissandrin, R.; Parisi, A.; Sacchi, P.; Patruno, S. F. A.; Michelone, G.; Gulminetti, R.; Zanaboni, D.; Novati, S.; Maserati, R.; Orsolini, P.; Vecchia, M.; Sciarra, M.; Asperges, E.; Sambo, M.; Biscarini, S.; Lupi, M.; Roda, S.; Chiara Pieri, T.; Gallazzi, I.; Sachs, M.; Valsecchi, P.; Perlini, S.; Alfano, C.; Bonzano, M.; Briganti, F.; Crescenzi, G.; Giulia Falchi, A.; Guarnone, R.; Guglielmana, B.; Maggi, E.; Martino, I.; Pettenazza, P.; Pioli di Marco, S.; Quaglia, F.; Sabena, A.; Salinaro, F.; Speciale, F.; Zunino, I.; De Lorenzo, M.; Secco, G.; Dimitry, L.; Cappa, G.; Maisak, I.; Chiodi, B.; Sciarrini, M.; Barcella, B.; Resta, F.; Moroni, L.; Vezzoni, G.; Scattaglia, L.; Boscolo, E.; Zattera, C.; Michele Fidel, T.; Vincenzo, C.; Vignaroli, D.; Bazzini, M.; Iotti, G.; Mojoli, F.; Belliato, M.; Perotti, L.; Mongodi, S.; Tavazzi, G.; Marseglia, G.; Licari, A.; Brambilla, I.; Daniela, B.; Antonella, B.; Patrizia, C.; Giulia, C.; Giuditta, C.; Marta, C.; D'Alterio, Rossana; Milena, F.; Bianca, M.; Roberta, M.; Enza, M.; Stefania, P.; Maurizio, P.; Elena, P.; Antonio, P.; Francesca, R.; Antonella, S.; Maurizio, Z.; Guy, A.; Laura, B.; Ermanna, C.; Giuliana, C.; Luca, D.; Gabriella, F.; Gabriella, G.; Alessia, G.; Viviana, L.; Meisina, Claudia; Valentina, M.; Simona, P.; Marta, P.; Alice, B.; Giacomo, C.; Irene, C.; Alfonso, C.; Di Martino, R.; Di Napoli, A.; Alessandro, F.; Guglielmo, F.; Loretta, F.; Federica, G.; Albertini, Alessandra; Federica, N.; Giacomo, R.; Beatrice, R.; Maria, S. I.; Monica, T.; Nepita Edoardo, V.; Calvi, M.; Tizzoni, M.; Nicora, C.; Triarico, A.; Petronella, V.; Marena, C.; Muzzi, A.; Lago, P.; Comandatore, F.; Bissignandi, G.; Gaiarsa, S.; Rettani, M.; Bandi, C

Seasonal transmission potential and activity peaks of the new influenza A(H1N1): a Monte Carlo likelihood analysis based on human mobility

On 11 June the World Health Organization officially raised the phase of pandemic alert (with regard to the new H1N1 influenza strain) to level 6. We use a global structured metapopulation model integrating mobility and transportation data worldwide in order to estimate the transmission potential and the relevant model parameters we used the data on the chronology of the 2009 novel influenza A(H1N1). The method is based on the maximum likelihood analysis of the arrival time distribution generated by the model in 12 countries seeded by Mexico by using 1M computationally simulated epidemics. An extended chronology including 93 countries worldwide seeded before 18 June was used to ascertain the seasonality effects. We found the best estimate R0 = 1.75 (95% CI 1.64 to 1.88) for the basic reproductive number. Correlation analysis allows the selection of the most probable seasonal behavior based on the observed pattern, leading to the identification of plausible scenarios for the future unfolding of the pandemic and the estimate of pandemic activity peaks in the different hemispheres. We provide estimates for the number of hospitalizations and the attack rate for the next wave as well as an extensive sensitivity analysis on the disease parameter values. We also studied the effect of systematic therapeutic use of antiviral drugs on the epidemic timeline. The analysis shows the potential for an early epidemic peak occurring in October/November in the Northern hemisphere, likely before large-scale vaccination campaigns could be carried out. We suggest that the planning of additional mitigation policies such as systematic antiviral treatments might be the key to delay the activity peak inorder to restore the effectiveness of the vaccination programs.Comment: Paper: 29 Pages, 3 Figures and 5 Tables. Supplementary Information: 29 Pages, 5 Figures and 7 Tables. Print version: http://www.biomedcentral.com/1741-7015/7/4

Clinical characteristics of coronavirus disease (COVID-19) early findings from a teaching hospital in Pavia, North Italy, 21 to 28 February 2020

We describe clinical characteristics, treatments and outcomes of 44 Caucasian patients with coronavirus disease (COVID-19) at a single hospital in Pavia, Italy, from 21\u201328 February 2020, at the beginning of the outbreak in Europe. Seventeen patients developed severe disease, two died. After a median of 6 days, 14 patients were discharged from hospital. Predictors of lower odds of discharge were age>65 years, antiviral treatment and for severe disease, lactate dehydrogenase >300 mg/dL