This is an Orange-sponsored PhD position on the modeling of the social dimension of disease dynamics based on cell phone data
About the science
The PhD thesis will focus on the identification and study of the social dimension embedded in the dynamics of infectious disease spread through complex dynamical networks generated by high-resolution cell phone data.
Controlling and containing epidemics is an important healthcare priority worldwide, as highlighted by the recent outbreaks of Ebola virus, Zika virus, MERS Coronavirus, and others. Modeling the inherent complexity of disease-spreading processes represents an important field of research aimed at assessing and anticipating the possible implications of an outbreak, and identifying prompt and effective prevention/control strategies. Pathogens spread represents an ever-evolving challenge, requiring continuing efforts at several levels and across a broad range of disciplines. Modern epidemic models recognize the increasing importance of population structure, patterns of interactions and mobility networks, as these can substantially alter the probability of encounters, patterns of exposure, and the likelihood of disease propagation [1-4]. Most importantly, all these factors are often inter-related, with social networks being heavily influenced by geography [5]. While the role of human mobility patterns and contacts in closed settings has been widely addressed in infections transmission [2-4,6-10], also with the use of mobile phone data [11-13], little research has explicitly considered the spatial social dimension of epidemic dynamics [14,15].
Relying on complex networks research, Big Data analytics, and mathematical and computational modeling, the aim of the thesis will be to provide a quantitative description of the aspects of social interactions in space and time that are most relevant to disease transmission, based on the use of high resolution cell phone data. The setting of study will be a region in Africa.
About you
You are a student with a MSc degree (Bac +5 level) in the field of applied mathematics, physics, computational biology, or similar.
You have a background in the following areas of expertise: Big data analysis / mathematical and computational modeling / machine-learning / statistics / complex systems / large-scale networks. You show a strong interest in interdisciplinary research and adaptation to blend into a multidisciplinary team composed by data scientists, infectious disease epidemiologists, modellers, sociologists. You have a demonstrated track record of: (1) manipulating large datasets with advanced machine learning, data mining or big data and complex-network analytics techniques; or (2) developing large-scale mathematical and computational diffusion/contagion processes. Experience in both aspects is highly encouraged.
About the position
You will enter the PhD program of the ED393 Pierre Louis PhD School of Public Health at the Universite Pierre et Marie Curie in Paris, starting the 2017/2018 Academic Year on October 1, 2017. You will be employed by Orange to perform the thesis work in the Orange premises (40-48, avenue de la République 92320 CHATILLON) with a 3-y term contract (30,000 € yearly gross salary).
The subject of the thesis is part of a research program within Orange called 'Digital Society' which seeks to investigate the impact of digital technologies on society as well as to design innovative digital services that meet social expectations through the technologies of the tomorrow’s society. Within this program, a project in particular, ‘Mining social reality with telco data’, aims at extracting from mobile phone data, information useful for individuals’ behavioral analysis and linking it to social phenomena driven by. Based on these data, we infer, for example, clues about social interactions, human mobility and analyse their impact in several research fields such as urban planning (Smart Cities, transport, etc) epidemiology of infectious diseases, education.
You will be supervised by: Dr. Vittoria Colizza, EPIcx lab (Epidemics in complex environments) at Inserm (French National Institute of Health and Medical Research) and Universite Pierre et Marie Curie; Dr. Stefania Rubrichi, SENSE (Sociology and Economics of Networks and Services) lab at Orange XDLab.
Additional information
You will be able to evolve into an R&D department of a telecommunications operator that will give you access to the original data on high-performance infrastructures, ensuring you a unique data processing experience on this scale. The subject of the thesis, at the crossroads of social sciences and the epidemiology of infectious diseases, also offers a rare opportunity for the development of knowledge as well as associated application in the context of an innovative company.
More information on the position on the Orange website:
https://orange.jobs/jobs/offer.do?joid=61895&lang=EN
How to apply
Please submit your application dossier by email to Dr. Vittoria Colizza (vittoria.colizza@inserm.fr) and Dr. Stefania Rubrichi (stefania.rubrichi@orange.com), including:
- your letter of motivation;
- your CV;
- your grades transcript;
- a summary of your Master thesis.
In addition, you will also need to apply online to Orange jobs:Please follow the instructions in the link.
References
[1] Koopman JS and Lynch JW (1996) Emerging Objectives and Methods in Epidemiology. Am J Public Health 86(5)
[2] Keeling MJ, Rohani P. Modeling Infectious Diseases in Humans and Animals. Princeton: Princeton University Press; 2008.
[3] Dorjee S, Poljak Z, Revie CW, Bridgland J, McNab B, Leger E, Sanchez J. A review of simulation modelling approaches used for the spread of zoonotic influenza viruses in animal and human populations. Zoonoses Public Health. 2013; 60(6):383–411.
[4] Wu JT, Cowling BJ. The use of mathematical models to inform influenza pandemic preparedness and response. Exp Biol Med (Maywood). 2011; 236(8):955–61.
[5] Phithakkitnukoon S, Smoreda Z, Olivier P (2012) Socio-Geography of Human Mobility: A Study Using Longitudinal Mobile Phone Data. PLoS ONE 7(6): e39253.
[6] Halloran ME, Ferguson NM, Eubank S, Longini IM, Cummings DAT, Lewis B, Xu S, Fraser C, Vullikanti A, Germann TC, Wagener D, Beckman R, Kadau K, Barrett C, Macken CA, Burke DS, Cooley P. Modeling targeted layered containment of an influenza pandemic in the United States. Proc Natl Acad Sci USA. 2008; 105(12):4639–644.
[7] Ciofi degli Atti ML, Merler S, Rizzo C, Ajelli M, Massari M, Manfredi P, Furlanello C, Scalia Tomba G, Iannelli M. Mitigation measures for pandemic influenza in italy: An individual based model considering different scenarios. PLoS ONE. 2008; 3(3):e1790.
[8] Colizza V, Barrat A, Barthélemy M, Vespignani A (2006) The role of the airline transportation network in the prediction and predictability of global epidemics. Proceedings of the National Academy of Sciences of the United States of America, 103(7): 2015-2020
[9] Bansal S, Grenfell BT, Meyers LA. When individual behaviour matters: homogeneous and network models in epidemiology. J R Soc Interface. 2007; 4(16):879–91.
[10] Stehlé J, Voirin N, Barrat A, Cattuto C, Colizza V, Isella L, Régis C, Pinton JF, Khanafer N, Van den Broeck W, Vanhems P. Simulation of an SEIR infectious disease model on the dynamic contact network of conference attendees. BMC Med. 2011; 9:87.
[11] Wesolowski A, Eagle N, Tatem AJ, Smith DL, Noor AM, et al. (2012) Quantifying the impact of human mobility on malaria. Science 338: 267–270.
[12] Le Menach A, Tatem AJ, Cohen JM, Hay SI, Randell H, et al. (2011) Travel risk, malaria importation and malaria transmission in Zanzibar. Sci Rep 1: 93
[13] Tizzoni M, Bajardi P, Decuyper A, Kon Kam King G, Schneider CM, Blondel V, Smoreda Z, Gonzàlez MC, Colizza V. On the Use of Human Mobility Proxies for Modeling Epidemics. PLoS Comput Biol 10(7): e1003716. doi:10.1371/journal.pcbi.1003716
[14] Toole et al. Coupling human mobility and social ties. J R Soc Interface 12, 20141128 (2015).
[15] Lima et al. Disease Containment Strategies based on Mobility and Information Dissemination. Sci Rep 5, 10650 (2015).
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