K. E. Jones, N. G. Patel, M. A. Levy, A. Storeygard, D. Balk et al., Global trends in emerging infectious diseases, Nature, vol.451, p.18288193, 2008.

B. Cazelles and S. Hales, Infectious diseases, climate influences, and nonstationarity, PLoS Med, vol.3, issue.8, p.16903777, 2006.

S. Altizer, A. Dobson, P. Hosseini, P. Hudson, M. Pascual et al., Seasonality and the dynamics of infectious diseases, Ecology letters, vol.9, issue.4, p.16623732, 2006.

D. N. Fisman, Seasonality of infectious diseases, Annu. Rev. Public Health, vol.28, p.17222079, 2007.

B. Cazelles, M. Chavez, A. J. Mcmichael, and S. Hales, Nonstationary influence of El Nino on the synchronous dengue epidemics in Thailand, PLoS Med, vol.2, issue.4, p.15839751, 2005.

C. De-magny, G. C. Cazelles, B. Guégan, and J. F. , Cholera threat to humans in Ghana is influenced by both global and regional climatic variability, EcoHealth, vol.3, issue.4, pp.223-231, 2006.

K. Laneri, A. Bhadra, E. L. Ionides, M. Bouma, R. C. Dhiman et al., Forcing versus feedback: epidemic malaria and monsoon rains in northwest India, PLoS Comput. Biol, vol.6, p.20824122, 2010.

C. Metcalf, K. S. Walter, A. Wesolowski, C. O. Buckee, E. Shevliakova et al., Identifying climate drivers of infectious disease dynamics: Recent advances and challenges ahead, Proc. R. Soc. B, vol.284, p.28814655, 2017.

S. Cauchemez, N. M. Ferguson, C. Wachtel, A. Tegnell, G. Saour et al., Closure of schools during an influenza pandemic. The Lancet Infectious Diseases, vol.9, pp.473-481, 2009.

J. T. Wu, B. J. Cowling, E. H. Lau, D. K. Ip, L. M. Ho et al., School closure and mitigation of pandemic (H1N1) 2009, Hong Kong, Emerging Infectious Diseases, vol.16, issue.3, p.20202441, 2010.

A. Ewing, E. C. Lee, C. Viboud, and S. Bansal, Contact, travel, and transmission: The impact of winter holidays on influenza dynamics in the United States, The Journal of Infectious Diseases, vol.215, issue.5, pp.732-739, 2016.

K. Khan, Z. A. Memish, A. Chabbra, J. Liauw, W. Hu et al., Global public health implications of a mass gathering in Mecca, Saudi Arabia during the midst of an influenza pandemic, Journal of Travel Medicine, vol.17, issue.2, p.20412172, 2010.

M. J. Ferrari, A. Djibo, R. F. Grais, N. Bharti, B. T. Grenfell et al., Rural-urban gradient in seasonal forcing of measles transmission in Niger, Proc. R. Soc. B, vol.277, p.20427338, 2010.

S. Funk, M. Salathé, and V. A. Jansen, Modelling the influence of human behaviour on the spread of infectious diseases: A review, J. R. Soc. Interface, vol.7, p.20504800, 2010.

F. Verelst, L. Willem, and P. Beutels, Behavioural change models for infectious disease transmission: A systematic review (2010-2015), J. R. Soc. Interface, vol.13, p.28003528, 2016.

A. J. Kucharski, K. O. Kwok, V. W. Wei, B. J. Cowling, J. M. Read et al., The contribution of social behavior to the transmission of influenza A in a human population, PLoS Pathogen, vol.10, p.1004206, 2014.

M. J. Keeling and P. Rohani, Modeling infectious diseases in humans and animals, 2008.

H. Heesterbeek, R. M. Anderson, V. Andreasen, S. Bansal, D. Angelis et al., Modeling infectious disease dynamics in the complex landscape of global health, Science, vol.347, issue.6227, p.25766240, 2015.

C. Metcalf and J. Lessler, Opportunities and challenges in modeling emerging infectious diseases. Science, vol.357, p.28706037, 2017.

J. B. Axelsen, R. Yaari, B. T. Grenfell, and L. Stone, Multiannual forecasting of seasonal influenza dynamics reveals climatic and evolutionary drivers, Proc Natl Acad Sci USA, vol.111, issue.26, p.24979763, 2014.

I. Dorigatti, S. Cauchemez, and N. M. Ferguson, Increased transmissibility explains the third wave of infection by the 2009 H1N1 pandemic virus in England, Proc Natl Acad Sci USA, vol.110, issue.33, p.23882078, 2013.

J. Wallinga and P. Teunis, Different epidemic curves for severe acute respiratory syndrome reveal similar impacts of control measures, American Journal of Epidemiology, vol.160, issue.6, pp.509-516, 2014.

S. Cauchemez, P. Y. Boëlle, C. A. Donnelly, N. M. Ferguson, G. Thomas et al., Real-time estimates in early detection of SARS, Emerging Infectious Diseases, vol.12, issue.1, p.16494726, 2006.

H. Nishiura, G. Chowell, H. Heesterbeek, and J. Wallinga, The ideal reporting interval for an epidemic to objectively interpret the epidemiological time course, J. R. Soc. Interface, vol.7, p.19570792, 2009.

A. Cori, N. M. Ferguson, C. Fraser, and S. Cauchemez, A new framework and software to estimate time-varying reproduction numbers during epidemics, American Journal of Epidemiology, vol.178, issue.9, p.24043437, 2013.

E. L. Ionides, C. Bretó, and A. A. King, Inference for nonlinear dynamical systems, Proc Natl Acad Sci USA, vol.103, issue.49, p.17121996, 2006.
DOI : 10.1073/pnas.0603181103
URL : http://www.pnas.org/content/103/49/18438.full.pdf

T. S. Churcher, J. M. Cohen, J. Novotny, N. Ntshalintshali, S. Kunene et al., Measuring the path toward malaria elimination, Science, vol.344, issue.6189, p.24926005, 2014.

F. C. Coelho, D. Carvalho, and L. M. , Estimating the attack ratio of dengue epidemics under time-varying force of infection using aggregated notification data, Scientific report, vol.5, p.18455, 2015.

S. P. Ellner, B. A. Bailey, G. V. Bobashev, A. R. Gallant, B. Grenfell et al., Noise and nonlinearity in measles epidemics: combining mechanistic and statistical approaches to population modeling, The American Naturalist, vol.151, p.18811317, 1998.

A. A. King, E. L. Ionides, M. Pascual, and M. J. Bouma, Inapparent infections and cholera dynamics, Nature, vol.454, p.18704085, 2008.
DOI : 10.1038/nature07084
URL : https://deepblue.lib.umich.edu/bitstream/2027.42/62519/1/nature07084.pdf

A. Bhadra, E. L. Ionides, K. Laneri, M. Pascual, M. Bouma et al., Malaria in Northwest India: Data analysis via partially observed stochastic differential equation models driven by Lévy noise, Journal of the American Statistical Association, vol.106, pp.440-451, 2011.

D. He, J. Dushoff, T. Day, J. Ma, and D. J. Earn, Mechanistic modelling of the three waves of the 1918 influenza pandemic, Theoretical Ecology, vol.4, pp.283-288, 2011.

M. Martinez-bakker, A. A. King, and P. Rohani, Unraveling the transmission ecology of polio, PLoS Biol, vol.13, p.26090784, 2015.

B. Cazelles and N. P. Chau, Adaptive dynamic modelling of HIV/AIDS epidemics using extended Kalman filter, Journal of Biological Systems, vol.3, pp.759-768, 1995.

B. Cazelles and N. P. Chau, Using the Kalman filter and dynamic models to assess the changing HIV/AIDS epidemic, Mathematical Biosciences, vol.140, issue.2, p.9046772, 1997.

J. Dureau, K. Kalogeropoulos, and M. Baguelin, Capturing the time-varying drivers of an epidemic using stochastic dynamical systems, Biostatistics, vol.14, issue.3, p.23292757, 2013.

M. T. Angulo and J. X. Velasco-hernandez, Robust qualitative estimation of time-varying contact rates in uncertain epidemics, Epidemics, p.29567063, 2018.

A. Smirnova, G. Chowell, and L. De-camp, Forecasting epidemics through nonparametric estimation of timedependent transmission rates using the SEIR model, Bulletin of Mathematical Biology, p.28466232, 2017.

B. F. Finkenstädt and B. T. Grenfell, Time series modelling of childhood diseases: a dynamical systems approach, Journal of the Royal Statistical Society: Series C, vol.49, issue.2, pp.187-205, 2000.

O. N. Bjørnstad, B. F. Finkenstädt, and B. T. Grenfell, Dynamics of measles epidemics: estimating scaling of transmission rates using a time series SIR model, Ecological Monographs, vol.72, issue.2, pp.169-184, 2002.

C. Cobelli and J. J. Distefano, Parameter and structural identifiability concepts and ambiguities: a critical review and analysis, Am. J. Physiol.-Regul. Integr. Comp. Physiol, vol.239, pp.7-24, 1980.

H. Miao, X. Xia, A. S. Perelson, and H. Wu, On identifiability of nonlinear ODE models and applications in viral dynamics, SIAM review, vol.53, p.21785515, 2011.

C. Andrieu, A. Doucet, and R. Holenstein, Particle Markov chain Monte Carlo methods, Journal of the Royal Statistical Society: Series B, vol.72, pp.269-342, 2010.

S. Funk, A. Camacho, A. J. Kucharski, R. M. Eggo, and W. J. Edmunds, Real-time forecasting of infectious disease dynamics with a stochastic semi-mechanistic model, Epidemics, vol.22, p.28038870, 2018.

A. Camacho, A. Kucharski, Y. Aki-sawyerr, M. A. White, S. Flasche et al., Temporal changes in Ebola transmission in Sierra Leone and implications for control requirements: a real-time modelling study, PLoS Curr, vol.7, 2015.

J. A. Jacquez and P. Greif, Numerical parameter identifiability and estimability: Integrating identifiability, estimability, and optimal sampling design, Mathematical Biosciences, vol.77, pp.201-227, 1985.

E. Tunali and T. J. Tarn, New results for identifiability of nonlinear systems, IEEE Transactions on Automatic Control, vol.32, pp.146-154, 1987.

S. Audoly, G. Bellu, D. 'angio, L. Saccomani, M. P. Cobelli et al., Global identifiability of nonlinear models of biological systems, IEEE Transactions on Biomedical Engineering, vol.48, p.11235592, 2001.

N. D. Evans, L. J. White, M. J. Chapman, K. R. Godfrey, and M. J. Chappell, The structural identifiability of the susceptible infected recovered model with seasonal forcing, Mathematical Biosciences, vol.194, p.15854675, 2005.

J. D. Chapman and N. D. Evans, The structural identifiability of susceptible-infective-recovered type epidemic models with incomplete immunity and birth targeted vaccination, Biomedical Signal Processing and Control, vol.4, pp.278-284, 2009.

C. Champagne, D. G. Salthouse, R. Paul, V. M. Cao-lormeau, B. Roche et al., Structure in the variability of the basic reproductive number (R 0 ) for Zika epidemics in the Pacific islands, eLife, vol.5, p.27897973, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01487852

T. Bengtsson, P. Bickel, B. Li, and B. , Curse-of-dimensionality revisited: Collapse of the particle filter in very large scale systems, Probability and Statistics: Essays in Honor of David A. Freedman, vol.2, pp.316-334, 2008.

T. Toni, D. Welch, N. Strelkowa, A. Ipsen, and M. Stumpf, Approximate Bayesian computation scheme for parameter inference and model selection in dynamical systems, J R Soc Interface, vol.6, pp.187-202, 2007.

M. Sunnåker, A. G. Busetto, E. Numminen, J. Corander, M. Foll et al., Approximate Bayesian Computation, PLoS Comput Biol, vol.9, p.23341757, 2013.

C. Metcalf, J. Farrar, F. T. Cutts, N. E. Basta, A. L. Graham et al., Use of serological surveys to generate key insights into the changing global landscape of infectious disease, The Lancet, vol.388, pp.30164-30171, 2016.

C. Champagne, R. Paul, S. Ly, V. Duong, R. Leang et al., Dengue modeling in rural Cambodia: statistical performance versus epidemiological relevance, 2017.

C. Bretó, D. He, E. L. Ionides, and A. A. King, Time Series Analysis via Mechanistic Models, The Annals of Applied Statistics, vol.3, pp.319-348, 2009.

J. Dureau, S. Ballesteros, and T. Bogich, SSM: Inference for time series analysis with State Space Models, 2013.

M. Plummer, N. Best, K. Cowles, and K. Vines, Coda: convergence diagnosis and output analysis for, MCMC. R news, vol.6, pp.7-11, 2006.

J. Geweke, Evaluating the accuracy of sampling-based approaches to calculating posterior moments, Bayesian Statistics 4, 1992.

P. Heidelberger and P. D. Welch, Simulation run length control in the presence of an initial transient, Opns Res, vol.31, pp.1109-11044, 1983.

C. Torrence and G. P. Compo, A practical guide to wavelet analysis, Bull Am Meteorol Soc, vol.79, pp.61-78, 1998.

B. Cazelles, M. Chavez, D. Berteaux, F. Ménard, J. O. Vik et al., Wavelet analysis of ecological time series, Oecologia, vol.156, p.18322705, 2008.
URL : https://hal.archives-ouvertes.fr/hal-00805449

B. Cazelles, K. Cazelles, and M. Chavez, Wavelet analysis in ecology and epidemiology: impact of statistical tests, J. R. Soc. Interface, vol.11, p.24284892, 2014.
URL : https://hal.archives-ouvertes.fr/hal-00938690

N. H. Saji, B. N. Goswami, P. N. Vinayachandran, and T. Yamagata, A dipole mode in the tropical Indian Ocean, Nature, vol.401, issue.6751, p.16862108, 1999.

B. Adams and M. Boots, Modelling the relationship between antibody-dependent enhancement and immunological distance with application to dengue, Journal of Theoretical Biology, vol.242, issue.2, pp.337-346, 2006.

B. Adams and M. Boots, How important is vertical transmission in mosquitoes for the persistence of dengue? Insights from a mathematical model, Epidemics, vol.2, issue.1, pp.1-10, 2010.

B. Adams and D. D. Kapan, Man Bites Mosquito: Understanding the Contribution of Human Movement to Vector-Borne Disease Dynamics, PLoS ONE, vol.4, issue.8, p.6763, 2009.

B. Adams, E. C. Holmes, C. Zhang, M. P. Mammen, S. Nimmannitya et al., Cross-protective immunity can account for the alternating epidemic pattern of dengue virus serotypes circulating in Bangkok, Proceedings of the National Academy of Sciences, vol.103, pp.14234-14239, 2006.

M. Aguiar, B. Kooi, and N. Stollenwerk, Epidemiology of Dengue Fever: A Model with Temporary Cross-Immunity and Possible Secondary Infection Shows Bifurcations and Chaotic Behaviour in Wide Parameter Regions, Mathematical Modelling of Natural Phenomena, vol.3, issue.4, pp.48-70, 2008.

M. Aguiar, S. Ballesteros, B. W. Kooi, and N. Stollenwerk, The role of seasonality and import in a minimalistic multi-strain dengue model capturing differences between primary and secondary infections: complex dynamics and its implications for data analysis, Journal of Theoretical Biology, vol.289, pp.181-196, 2011.

M. Aguiar, B. W. Kooi, F. Rocha, P. Ghaffari, and N. Stollenwerk, How much complexity is needed to describe the fluctuations observed in dengue hemorrhagic fever incidence data? Ecological Complexity, vol.16, pp.31-40, 2013.

M. Aguiar, R. Paul, A. Sakuntabhai, and N. Stollenwerk, Are we modelling the correct dataset? Minimizing false predictions for dengue fever in Thailand, Epidemiology & Infection, p.142
URL : https://hal.archives-ouvertes.fr/pasteur-02081539

, BIBLIOGRAPHIE, issue.11, pp.2447-2459, 2014.

M. Aguiar, N. Stollenwerk, and S. B. Halstead, The risks behind Dengvaxia recommendation, The Lancet. Infectious Diseases, vol.16, issue.8, pp.30168-30170, 2016.

H. Akaike, A new look at the statistical model identification, IEEE Transactions on Automatic Control, vol.19, issue.6, pp.716-723, 1974.

J. Aldstadt, I. Yoon, D. Tannitisupawong, R. G. Jarman, S. J. Thomas et al., Space-time analysis of hospitalised dengue patients in rural Thailand reveals important temporal intervals in the pattern of dengue virus transmission. Tropical medicine & international health, TM & IH, vol.17, issue.9, pp.1076-1085, 2012.

L. J. Allen, An Introduction to Stochastic Epidemic Models, Mathematical Epidemiology, pp.81-130, 2008.

,

D. Alonso, A. J. Mckane, and M. Pascual, Stochastic amplification in epidemics, Journal of the Royal Society Interface, vol.4, issue.14, pp.575-582, 2007.

N. Alzahrani, P. Neal, S. E. Spencer, T. J. Mckinley, and P. Touloupou, Model selection for time series of count data, Computational Statistics & Data Analysis, vol.122, pp.33-44, 2018.

M. Amaku, F. Azevedo, M. N. Burattini, G. E. Coelho, F. et al., Magnitude and frequency variations of vector-borne infection outbreaks using the Ross-Macdonald model: explaining and predicting outbreaks of dengue fever, Epidemiology and Infection, pp.1-16, 2016.

R. M. Anderson and R. M. May, Directly transmitted infections diseases: control by vaccination

, Science, vol.215, issue.4536, pp.1053-1060, 1982.

R. M. Anderson and R. M. May, Infectious Diseases of Humans: Dynamics and Control, 1992.

H. Andersson and T. Britton, Stochastic Epidemic Models and Their Statistical Analysis, Lecture Notes in Statistics, 2000.

M. Andraud, N. Hens, C. Marais, and P. Beutels, Dynamic Epidemiological Models for Dengue Transmission: A Systematic Review of Structural Approaches, PLoS ONE, vol.7, issue.11, p.49085, 2012.

C. Andrieu, A. Doucet, and R. Holenstein, Particle Markov chain Monte Carlo methods: Particle Markov Chain Monte Carlo Methods, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.72, issue.3, p.14679868, 2010.

A. Andronico, F. Dorléans, J. Fergé, H. Salje, F. Ghawché et al., Real-Time Assessment of Health-Care Requirements During the Zika Virus Epidemic in Martinique, American Journal of Epidemiology, vol.186, issue.10, pp.1194-1203, 2017.
URL : https://hal.archives-ouvertes.fr/inserm-01481405

M. Aubry, J. Finke, A. Teissier, C. Roche, J. Broult et al., Seroprevalence of arboviruses among blood donors in French Polynesia, International journal of infectious diseases: IJID: official publication of the International Society for Infectious Diseases, vol.41, pp.11-12, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01452901

M. Aubry, A. Teissier, C. Roche, S. Teururai, P. Desprès et al., Serosurvey of dengue, Zika and other mosquito-borne viruses in French Polynesia, vol.765, 2015.

. Viewabstract, aspx?sKey=2e5199c4-aceb-4d61-8769-342586917c5a& cKey=74ffe328-3a83-4f28-84c6-5656971501c8&mKey=

M. Aubry, A. Teissier, M. Huart, S. Merceron, J. Vanhomwegen et al., Emerging Infectious Diseases, vol.23, issue.4, pp.669-672, 2014.

K. Auranen, E. Arjas, T. Leino, and A. K. Takala, Transmission of Pneumococcal Carriage in Families: A Latent Markov Process Model for Binary Longitudinal Data, Journal of the American Statistical Association, vol.95, issue.452, p.1044, 2000.

N. T. Bailey, The mathematical theory of infectious diseases and its applications, 1975.

G. Barba-spaeth, W. Dejnirattisai, A. Rouvinski, M. Vaney, I. Medits et al., Structural basis of potent Zika-dengue virus antibody cross-neutralization, Nature, vol.536, issue.7614, pp.48-53, 2016.
URL : https://hal.archives-ouvertes.fr/pasteur-01408100

S. V. Bardina, P. Bunduc, S. Tripathi, J. Duehr, J. J. Frere et al., Enhancement of Zika virus pathogenesis by preexisting antiflavivirus immunity, Science, vol.356, issue.6334, pp.175-180, 2017.

D. H. Barmak, C. O. Dorso, M. Otero, and H. G. Solari, Dengue epidemics and human mobility, Physical Review E, vol.84, issue.1, pp.1550-2376, 2011.
DOI : 10.1103/physreve.84.011901
URL : http://arxiv.org/pdf/1102.3869

J. M. Barrios, W. W. Verstraeten, P. Maes, J. Aerts, J. Farifteh et al., Using the Gravity Model to Estimate the Spatial Spread of Vector-Borne Diseases, International Journal of Environmental Research and Public Health, vol.9, issue.12, pp.4346-4364, 2012.

M. S. Bartlett, Measles Periodicity and Community Size, Journal of the Royal Statistical Society. Series A (General), vol.120, issue.1, pp.48-70, 1957.
DOI : 10.2307/2342553

M. S. Bartlett, The Critical Community Size for Measles in the United States, Journal of the Royal Statistical Society. Series A (General), vol.123, issue.1, pp.37-44, 1960.

M. S. Bartlett, The Relevance of Stochastic Models for Large-Scale Epidemiological Phenomena, Journal of the Royal Statistical Society. Series C (Applied Statistics), vol.13, issue.1, pp.2-8, 1964.

W. G. Bearcroft, Zika virus infection experimentally induced in a human volunteer, Transactions of The Royal Society of Tropical Medicine and Hygiene, vol.50, issue.5, pp.442-448, 1956.

D. Bernoulli, Essai d'une nouvelle analyse de la mortalité causée par la petite vérole et des avantages de l'inoculation pour la prévenir, Mémoires de mathématiques et de physique, Académie royale des sciences, pp.1-45, 1760.

A. Bhadra, ;. Andrieu, A. Doucet, and R. Holenstein, Particle Markov Chain Monte Carlo Methods, Journal of the Royal Statistical Society: Series B (Statistical Methodology), pp.314-315, 2010.

S. Bhatt, P. W. Gething, O. J. Brady, J. P. Messina, A. W. Farlow et al., The global distribution and burden of dengue, Nature, vol.496, issue.7446, pp.504-507, 2013.

P. J. Birrell, G. Ketsetzis, N. J. Gay, B. S. Cooper, A. M. Presanis et al., Bayesian modeling to unmask and predict influenza A/H1n1pdm dynamics in London, Proceedings of the National Academy of Sciences, vol.108, issue.45, pp.18238-18243, 2011.

P. J. Birrell, D. D. Angelis, and A. M. Presanis, Evidence Synthesis for Stochastic Epidemic Models, Statistical Science, vol.33, issue.1, pp.34-43, 2018.
DOI : 10.1214/17-sts631
URL : https://doi.org/10.1214/17-sts631

S. M. Blower and H. Dowlatabadi, Sensitivity and Uncertainty Analysis of Complex Models of Disease Transmission: An HIV Model, as an Example, International Statistical Review / Revue Internationale de Statistique, vol.62, issue.2, pp.229-243, 1994.

L. R. Bowman, S. Runge-ranzinger, and P. J. Mccall, Assessing the Relationship between Vector Indices and Dengue Transmission: A Systematic Review of the Evidence, PLoS Neglected Tropical Diseases, vol.8, issue.5, p.2848, 2014.

L. R. Bowman, S. Donegan, and P. J. Mccall, Is Dengue Vector Control Deficient in Effectiveness or Evidence?: Systematic Review and Meta-analysis, PLOS Neglected Tropical Diseases, vol.10, issue.3, p.4551, 2016.

O. J. Brady, M. A. Johansson, C. A. Guerra, S. Bhatt, N. Golding et al., Modelling adult Aedes aegypti and Aedes albopictus survival at different temperatures in laboratory and field settings, Parasites & Vectors, vol.6, p.351, 2013.

C. Braga, C. F. Luna, C. M. Martelli, W. V. Souza, M. T. Cordeiro et al., Seroprevalence and risk factors for dengue infection in socio-economically distinct areas of Recife, Brazil, Acta Tropica, vol.113, issue.3, pp.234-240, 2010.

F. Brauer, C. Castillo-chavez, A. Mubayi, and S. Towers, Some models for epidemics of vector-transmitted diseases, Infectious Disease Modelling, vol.1, issue.1, pp.79-87, 2016.

C. Bretó, Modeling and Inference for Infectious Disease Dynamics: A Likelihood-Based Approach, Statistical Science, vol.33, issue.1, pp.2168-8745, 2018.

C. Bretó, D. He, E. L. Ionides, and A. A. King, Time series analysis via mechanistic models, The Annals of Applied Statistics, vol.3, issue.1, pp.319-348, 2009.

T. Britton, Stochastic epidemic models: A survey, Mathematical Biosciences, vol.225, issue.1, pp.24-35, 2010.

T. Britton and F. Giardina, Introduction to statistical inference for infectious diseases, Journal de la Société Française de Statistique, vol.157, issue.1, pp.53-70, 2016.

T. Britton and D. Lindenstrand, Epidemic modelling: Aspects where stochasticity matters, Mathematical Biosciences, vol.222, issue.2, pp.109-116, 2009.
DOI : 10.1016/j.mbs.2009.10.001
URL : http://arxiv.org/pdf/0812.3505

C. S. Broussard, C. K. Shapiro-mendoza, G. Peacock, S. A. Rasmussen, C. T. Mai et al., Public Health Approach to Addressing the Needs of Children Affected by Congenital Zika Syndrome, Pediatrics, pp.146-153, 2018.

J. E. Brown, B. R. Evans, W. Zheng, V. Obas, L. Barrera-martinez et al., Human impacts have shaped historical and recent evolution in Aedes aegypti, the dengue and yellow fever mosquito, Evolution, vol.68, issue.2, pp.514-525, 2014.

A. Buchholz and N. Chopin, Improving approximate Bayesian computation via quasi-Monte Carlo, 2017.
DOI : 10.1080/10618600.2018.1497511
URL : http://arxiv.org/pdf/1710.01057

M. Burattini, M. Chen, A. Chow, F. Coutinho, K. Goh et al., Modelling the control strategies against dengue in Singapore, Epidemiology and Infection, vol.136, issue.3, pp.309-319, 2008.

K. P. Burnham and D. R. Anderson, Multimodel Inference: Understanding AIC and BIC in Model Selection, Sociological Methods & Research, vol.33, issue.2, pp.261-304, 2004.

A. Camacho, Approches stochastiques pour la modélisation en épidémiologie : application à la grippe humaine, 2011.

A. Camacho, S. Ballesteros, A. L. Graham, F. Carrat, O. Ratmann et al., Explaining rapid reinfections in multiple-wave influenza outbreaks: Tristan da Cunha 1971 epidemic as a case study, Proceedings of the Royal Society of London B: Biological Sciences, p.20110300, 2011.

A. Camacho, A. Kucharski, Y. Aki-sawyerr, M. A. White, S. Flasche et al., Temporal Changes in Ebola Transmission in Sierra Leone and Implications for Control Requirements: a Real-time Modelling Study, PLoS Currents, vol.7, 2015.

V. Cao-lormeau and D. Musso, Emerging arboviruses in the Pacific, The Lancet, vol.384, issue.9954, pp.61977-61979, 2014.

V. Cao-lormeau, C. Roche, A. Teissier, E. Robin, A. Berry et al., Zika Virus, vol.20, pp.1084-1086, 2013.
URL : https://hal.archives-ouvertes.fr/hal-01521256

V. M. Cao-lormeau, A. Blake, S. Mons, S. Lastere, C. Roche et al., Guillain-Barré Syndrome outbreak associated with Zika virus infection in French Polynesia: a case-control study, Lancet, vol.387, issue.16, pp.562-568, 2016.

R. , lhs: Latin Hypercube Samples, 2016.

M. Y. Carrillo-hernández, J. Ruiz-saenz, L. J. Villamizar, S. Y. Gómez-rangel, and M. Martínez-gutierrez, Co-circulation and simultaneous co-infection of dengue, chikungunya, and zika viruses in patients with febrile syndrome at the Colombian-Venezuelan border, BMC infectious diseases, vol.18, issue.1, p.61, 2018.

S. Cauchemez, F. Carrat, C. Viboud, A. J. Valleron, and P. Y. Boëlle, A Bayesian MCMC approach to study transmission of influenza: application to household longitudinal data, Statistics in Medicine, vol.23, issue.22, pp.3469-3487, 2004.

B. Cazelles and K. Cazelles, Major urban centers have weak influence on the timing of dengue epidemics in Southeast Asia, International Journal of Infectious Diseases, vol.21, 2014.

B. Cazelles and N. P. Chau, Using the Kalman filter and dynamic models to assess the changing HIV/AIDS epidemic, Mathematical Biosciences, vol.140, issue.2, pp.131-154, 1997.

B. Cazelles and S. Hales, Infectious Diseases, Climate Influences, and Nonstationarity, PLOS Med, vol.3, issue.8, p.328, 2006.
DOI : 10.1371/journal.pmed.0030328
URL : https://journals.plos.org/plosmedicine/article/file?id=10.1371/journal.pmed.0030328&type=printable

B. Cazelles, M. Chavez, A. J. Mcmichael, and S. Hales, Nonstationary Influence of El Niño on the Synchronous Dengue Epidemics in Thailand, PLoS Medicine, vol.2, issue.4, 2005.

B. Cazelles, C. Champagne, and J. Dureau, Accounting for non-stationarity in epidemiology by embedding time-varying parameters in stochastic models, PLOS Computational Biology, vol.14, issue.8, p.1006211, 2018.
DOI : 10.1371/journal.pcbi.1006211
URL : https://hal.archives-ouvertes.fr/hal-01880099

G. Celeux, F. Forbes, C. P. Robert, and D. M. Titterington, Deviance information criteria for missing data models, Bayesian Analysis, vol.1, issue.4, pp.651-673, 2006.
DOI : 10.1214/06-ba122
URL : https://hal.archives-ouvertes.fr/inria-00071724

C. Champagne, D. G. Salthouse, R. Paul, V. Cao-lormeau, B. Roche et al., Structure in the variability of the basic reproductive number (R0) for Zika epidemics in the Pacific islands. eLife, 5:e19874, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01487852

C. Champagne, R. Paul, S. Ly, V. Duong, R. Leang et al., Dengue modeling in rural Cambodia: Statistical performance versus epidemiological relevance, Epidemics, 2018.
DOI : 10.1101/208876
URL : https://www.biorxiv.org/content/biorxiv/early/2017/10/27/208876.full.pdf

M. Chan and M. A. Johansson, The incubation periods of Dengue viruses, PloS One, vol.7, issue.11, p.50972, 2012.

O. Chareonsook, H. M. Foy, A. Teeraratkul, and N. Silarug, Changing epidemiology of dengue hemorrhagic fever in Thailand, Epidemiology and Infection, vol.122, issue.1, pp.950-2688, 1999.

V. Charu, S. Zeger, J. Gog, O. N. Bjørnstad, S. Kissler et al., Human mobility and the spatial transmission of influenza in the United States, PLOS Computational Biology, vol.13, issue.2, p.1005382, 2017.

C. Chen, N. S. Chong, and R. Smith, A Filippov model describing the effects of media coverage and quarantine on the spread of human influenza, Mathematical Biosciences, vol.296, pp.98-112, 2018.

Q. Cheng, Q. Jing, R. C. Spear, J. M. Marshall, Z. Yang et al., The interplay of climate, intervention and imported cases as determinants of the 2014 dengue outbreak in Guangzhou, PLOS Neglected Tropical Diseases, vol.11, issue.6, p.5701, 2017.

E. Chikaki and H. Ishikawa, A dengue transmission model in Thailand considering sequential infections with all four serotypes, Journal of Infection in Developing Countries, vol.3, issue.9, pp.711-722, 2009.

N. Chopin, P. E. Jacob, and O. Papaspiliopoulos, SMC2: an efficient algorithm for sequential analysis of state space models, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.75, issue.3, pp.397-426, 2013.

T. Chouin-carneiro, A. Vega-rua, M. Vazeille, A. Yebakima, R. Girod et al., Differential Susceptibilities of Aedes aegypti and Aedes albopictus from the Americas to Zika Virus, PLoS neglected tropical diseases, vol.10, issue.3, p.4543, 2016.
URL : https://hal.archives-ouvertes.fr/pasteur-01491874

G. Chowell, C. A. Torre, C. Munayco-escate, L. Suárez-ognio, R. López-cruz et al., Spatial and temporal dynamics of dengue fever in Peru, vol.136, pp.1667-1677, 2008.

C. Centre-d'hygiène and . De-salubrité-publique, Surveillance et Veille Sanitaire en Polynesie Française, 2014.

H. Clapham, D. A. Cummings, A. Nisalak, S. Kalayanarooj, B. Thaisomboonsuk et al., Epidemiology of Infant Dengue Cases Illuminates Serotype-Specificity in the Interaction between Immunity and Disease, and Changes in Transmission Dynamics, PLOS Neglected Tropical Diseases, vol.9, issue.12, p.4262, 2015.

L. Coudeville and G. P. Garnett, Transmission Dynamics of the Four Dengue Serotypes in Southern Vietnam and the Potential Impact of Vaccination, PLOS ONE, vol.7, issue.12, p.51244, 2012.

K. Cowles and B. P. Carlin, Markov Chain Monte Carlo Convergence Diagnostics: A Comparative Review, Journal of the American Statistical Association, p.91, 1995.
DOI : 10.2307/2291683

H. Q. Cuong, N. T. Vu, B. Cazelles, M. F. Boni, K. T. Thai et al., Spatiotemporal Dynamics of Dengue Epidemics, Southern Vietnam. Emerging Infectious Diseases, vol.19, pp.945-953, 2013.

. Dass, Direction des Affaires Sanitaires et Sociales. Situation sanitaire en Nouvelle Calédonie. Les arboviroses : dengue, chikungunya, zika, 2014.

, Bairros e Favelas do Município do Rio de Janeiro-Tabela 2248-Fontes: dados-IBGE, Índice de Desenvolvimento Social (IDS) por Áreas de Planejamento (AP), 2010.

, Regiões Administrativas (RA) e Bairros no Município do Rio de Janeiro em, Número de empregados por atividade econômica segundo as Áreas de Planejamento (AP), 2000.

. Bibliographie,

E. Daudens, S. Lastere, C. Hirschauer, V. M. Cao-lormeau, R. Louette et al., Épidémiologie de la dengue et stratégies de lutte en Polynésie française, 2006.

D. De-angelis, A. M. Presanis, P. J. Birrell, G. S. Tomba, and T. House, Four key challenges in infectious disease modelling using data from multiple sources, Epidemics, vol.10, pp.83-87, 2015.

T. S. De-simone, R. M. Nogueira, E. S. Araújo, F. R. Guimarães, F. B. Santos et al., Dengue virus surveillance: the co-circulation of DENV-1, DENV-2 and DENV-3 in the State of Rio de Janeiro, Brazil. Transactions of The Royal Society of Tropical Medicine and Hygiene, vol.98, pp.553-562, 2004.

N. B. Defelice, E. Little, S. R. Campbell, and J. Shaman, Ensemble forecast of human West Nile virus cases and mosquito infection rates, Nature Communications, vol.8, p.14592, 2017.

W. Dejnirattisai, P. Supasa, W. Wongwiwat, A. Rouvinski, G. Barba-spaeth et al., Dengue virus sero-cross-reactivity drives antibody-dependent enhancement of infection with zika virus, Nature Immunology, vol.17, issue.9, pp.1102-1108, 2016.
URL : https://hal.archives-ouvertes.fr/pasteur-02003950

E. Delisle, C. Rousseau, B. Broche, I. Leparc-goffart, G. L'ambert et al., Euro Surveillance: Bulletin Europeen Sur Les Maladies Transmissibles, vol.20, issue.17, 2014.

O. Diekmann, J. , .. P. Heesterbeek, and M. G. Roberts, The construction of next-generation matrices for compartmental epidemic models, Journal of The Royal Society Interface, vol.7, issue.47, pp.1742-5662, 2010.

K. Dietz, The estimation of the basic reproduction number for infectious diseases. Statistical methods in medical research, vol.2, pp.23-41, 1993.

A. Doucet and A. M. Johansen, A tutorial on particle filtering and smoothing: fifteen years later, 2011.

A. Doucet, N. Freitas, and N. Gordon, Sequential Monte Carlo Methods in Practice, 2001.

. Bibliographie,

A. Doucet, N. D. Freitas, and N. Gordon, An Introduction to Sequential Monte Carlo Methods. In Sequential Monte Carlo Methods in Practice, Statistics for Engineering and Information Science, pp.3-14, 2001.

M. R. Duffy, T. Chen, W. T. Hancock, A. M. Powers, J. L. Kool et al., Zika virus outbreak on Yap Island, federated states of Micronesia, New England Journal of Medicine, vol.360, issue.24, pp.2536-2543, 2009.
URL : https://hal.archives-ouvertes.fr/pasteur-00734543

V. Duong, L. Lambrechts, R. E. Paul, S. Ly, R. S. Lay et al., Asymptomatic humans transmit dengue virus to mosquitoes, Proceedings of the National Academy of Sciences, 2015.
URL : https://hal.archives-ouvertes.fr/pasteur-01239113

M. Dupont-rouzeyrol, O. O'connor, E. Calvez, M. Daurès, M. John et al., Co-infection with Zika and Dengue Viruses in 2 Patients, Emerging Infectious Diseases, vol.21, issue.2, pp.381-382, 2014.
URL : https://hal.archives-ouvertes.fr/pasteur-01114780

A. P. Durbin, Dengue Antibody and Zika: Friend or Foe?, Trends in Immunology, vol.37, issue.10, pp.30096-30101, 2016.

J. Dureau, S. Ballesteros, and T. Bogich, ssm: Inference for State Space Models like playing with duplo blocks, 2013.

J. Dureau, K. Kalogeropoulos, and M. Baguelin, Capturing the time-varying drivers of an epidemic using stochastic dynamical systems, Biostatistics, vol.14, issue.3, pp.541-555, 2013.

C. Dye and G. Hasibeder, Population dynamics of mosquito-borne disease: effects of flies which bite some people more frequently than others, Transactions of the Royal Society of Tropical Medicine and Hygiene, vol.80, issue.1, pp.69-77, 1986.

C. Dye, B. Williams-;-grenfell, B. T. Dobson, and A. P. , Nonlinearities in the Dynamics of Indirectly-Transmitted Infections (or, does having a Vector make a Difference?), Ecology of Infectious Diseases in Natural Populations, pp.260-279, 1995.

O. Dyer, Philippines halts dengue immunisation campaign owing to safety risk, BMJ, vol.359, p.5759, 2017.

. Bibliographie,

M. C. Eisenberg, S. L. Robertson, and J. H. Tien, Identifiability and estimation of multiple transmission pathways in cholera and waterborne disease, Journal of Theoretical Biology, vol.324, pp.84-102, 2013.

S. P. Ellner, B. A. Bailey, G. V. Bobashev, A. R. Gallant, B. T. Grenfell et al., Noise and Nonlinearity in Measles Epidemics: Combining Mechanistic and Statistical Approaches to Population Modeling, The American Naturalist, vol.151, issue.5, pp.425-440, 1998.

R. A. Erickson, S. M. Presley, L. J. Allen, K. R. Long, and S. B. Cox, A dengue model with a dynamic Aedes albopictus vector population, Ecological Modelling, vol.221, issue.24, pp.2899-2908, 2010.

N. Evans, M. Chapman, M. Chappell, and K. Godfrey, The structural identifiability of a general epidemic (SIR) model with seasonal forcing, IFAC Proceedings Volumes, vol.35, pp.109-114, 2002.

G. Evensen, The Ensemble Kalman Filter: theoretical formulation and practical implementation, Ocean Dynamics, vol.53, issue.4, pp.343-367, 2003.

R. C. Fares, K. P. Souza, G. Añez, and M. Rios, Epidemiological Scenario of Dengue in Brazil, BioMed Research International, p.321873, 2015.

N. R. Faria, R. D. Azevedo, M. U. Kraemer, R. Souza, M. S. Cunha et al., Zika virus in the Americas: Early epidemiological and genetic findings, pp.345-349, 2016.

M. Fasiolo, N. Pya, and S. N. Wood, A Comparison of Inferential Methods for Highly Nonlinear State Space Models in Ecology and Epidemiology, Statistical Science, vol.31, issue.1, pp.96-118, 2016.

C. Favier, D. Schmit, C. D. Müller-graf, B. Cazelles, N. Degallier et al., Influence of spatial heterogeneity on an emerging infectious disease: the case of dengue epidemics, Proceedings of the Royal Society B: Biological Sciences, vol.272, pp.1171-1177, 1568.
URL : https://hal.archives-ouvertes.fr/hal-00086944

C. Favier, N. Degallier, M. G. Rosa-freitas, J. P. Boulanger, J. R. Costa-lima et al., Early determination of the reproductive number for vector-borne diseases: the case of dengue in Brazil, Tropical Medicine and International Health, vol.11, issue.3, pp.1365-3156, 2006.
URL : https://hal.archives-ouvertes.fr/hal-00158732

N. Ferguson, R. Anderson, and S. Gupta, The effect of antibody-dependent enhancement on the transmission dynamics and persistence of multiple-strain pathogens, Proceedings of the National Academy of Sciences of the United States of America, vol.96, pp.790-794, 1999.

N. M. Ferguson, Z. M. Cucunubá, I. Dorigatti, G. L. Nedjati-gilani, C. A. Donnelly et al., Countering Zika in Latin America, Science, p.219, 2016.

E. Fernandez, W. Dejnirattisai, B. Cao, S. M. Scheaffer, P. Supasa et al., Human antibodies to the dengue virus E-dimer epitope have therapeutic activity against Zika virus infection, Nature Immunology, vol.18, issue.11, pp.1261-1269, 2017.

B. F. Finkenstädt and B. T. Grenfell, Time series modelling of childhood diseases: a dynamical systems approach, Journal of the Royal Statistical Society: Series C (Applied Statistics), vol.49, issue.2, pp.187-205, 2002.

,

W. E. Fitzgibbon, J. J. Morgan, and G. F. Webb, An outbreak vector-host epidemic model with spatial structure: the 2015-2016 Zika outbreak in Rio De Janeiro, Theoretical Biology & Medical Modelling, vol.14, 2017.

S. Flasche, M. Jit, I. Rodríguez-barraquer, L. Coudeville, M. Recker et al., The Long-Term Safety, Public Health Impact, and Cost-Effectiveness of Routine Vaccination with a Recombinant, Live-Attenuated Dengue Vaccine (Dengvaxia): A Model Comparison Study, PLOS Medicine, vol.13, issue.11, p.1002181, 2016.

S. Funk, M. Salathé, and V. A. Jansen, Modelling the influence of human behaviour on the spread of infectious diseases: a review, Journal of The Royal Society Interface, vol.7, issue.50, pp.1247-1256, 2010.

. Bibliographie,

S. Funk, S. Bansal, C. T. Bauch, K. T. Eames, W. J. Edmunds et al., Nine challenges in incorporating the dynamics of behaviour in infectious diseases models, Epidemics, vol.10, pp.21-25, 2015.

S. Funk, A. J. Kucharski, A. Camacho, R. M. Eggo, L. Yakob et al., Comparative analysis of dengue and Zika outbreaks reveals differences by setting and virus, 2016.

D. Gao, Y. Lou, D. He, T. C. Porco, Y. Kuang et al., Prevention and Control of Zika as a Mosquito-Borne and Sexually Transmitted Disease: A Mathematical Modeling Analysis, Scientific Reports, vol.6, p.28070, 2016.

A. Gelman, How to think about "identifiability" in Bayesian inference?, 2014.

A. Gelman and D. B. Rubin, Inference from Iterative Simulation Using Multiple Sequences, Statistical Science, vol.7, issue.4, pp.457-472, 1992.
DOI : 10.1214/ss/1177011136
URL : https://doi.org/10.1214/ss/1177011136

A. Gelman, J. B. Carlin, H. S. Stern, D. B. Dunson, A. Vehtari et al., Bayesian Data Analysis, Third Edition, 2013.

J. George, W. G. Valiant, M. J. Mattapallil, M. Walker, Y. S. Huang et al., Prior Exposure to Zika Virus Significantly Enhances Peak Dengue-2 Viremia in Rhesus Macaques, Scientific Reports, vol.7, issue.1, p.10498, 2017.

J. Geweke, Evaluating the Accuracy of Sampling-Based Approaches to the Calculation of Posterior Moments, Bayesian Statistics, pp.169-193, 1992.

G. J. Gibson and E. Renshaw, Estimating parameters in stochastic compartmental models using Markov chain methods, Mathematical Medicine and Biology: A Journal of the IMA, vol.15, issue.1, pp.19-40, 1998.

G. J. Gibson, G. Streftaris, and D. Thong, Comparison and Assessment of Epidemic Models, Statistical Science, vol.33, issue.1, pp.19-33, 2018.

D. T. Gillespie, A general method for numerically simulating the stochastic time evolution of coupled chemical reactions, Journal of Computational Physics, vol.22, issue.4, pp.403-434, 1976.

. Bibliographie,

D. T. Gillespie, Approximate accelerated stochastic simulation of chemically reacting systems, The Journal of Chemical Physics, vol.115, issue.4, pp.1716-1733, 2001.
DOI : 10.1063/1.1378322

J. R. Gog, S. Ballesteros, C. Viboud, L. Simonsen, O. N. Bjornstad et al., , 2009.

, PLoS Comput Biol, vol.10, issue.6, p.1003635, 2014.

E. Gould, J. Pettersson, S. Higgs, R. Charrel, and X. De-lamballerie, Emerging arboviruses: Why today? One Health, vol.4, pp.1-13, 2017.

R. R. Graham, M. Juffrie, R. Tan, C. G. Hayes, I. Laksono et al., A prospective seroepidemiologic study on dengue in children four to nine years of age in Yogyakarta, Indonesia I. studies in 1995-1996, The American Journal of Tropical Medicine and Hygiene, vol.61, issue.3, pp.2-9637, 1999.

L. Grange, E. Simon-loriere, A. Sakuntabhai, L. Gresh, R. Paul et al., Epidemiological Risk Factors Associated with High Global Frequency of Inapparent Dengue Virus Infections, Frontiers in Immunology, vol.5, 2014.

D. Guha-sapir and B. Schimmer, Dengue fever: new paradigms for a changing epidemiology, Emerging Themes in Epidemiology, vol.2, issue.1, 2005.

M. G. Guzman, S. B. Halstead, H. Artsob, P. Buchy, J. Farrar et al., Dengue: a continuing global threat, Nature Reviews Microbiology, vol.8, pp.7-16, 2010.

H. Haario, E. Saksman, and J. Tamminen, An Adaptive Metropolis Algorithm, Bernoulli, vol.7, issue.2, p.223, 2001.

S. B. Halstead and . Dengue, Lancet, vol.370, issue.9599, pp.1644-1652, 2007.

S. B. Halstead, Dengue Virus-Mosquito Interactions, Annual Review of Entomology, vol.53, issue.1, pp.273-291, 2008.

S. B. Halstead, Biologic Evidence Required for Zika Disease Enhancement by Dengue Antibodies, Emerging Infectious Diseases, vol.23, issue.4, pp.569-573, 2017.

S. N. Hammond, A. Balmaseda, L. Pérez, Y. Tellez, S. I. Saborío et al., Differences in dengue severity in infants, children, and adults in a 3-year hospital-based study in Nicaragua, The American Journal of Tropical Medicine and Hygiene, vol.73, issue.6, pp.2-9637, 2005.

. Bibliographie,

L. C. Harrington, A. Fleisher, D. Ruiz-moreno, F. Vermeylen, C. V. Wa et al., Heterogeneous feeding patterns of the dengue vector, Aedes aegypti, on individual human hosts in rural Thailand, PLoS neglected tropical diseases, vol.8, issue.8, p.3048, 2014.

I. Harris, P. Jones, and . Cru-ts4, Climatic Research Unit (CRU) Time-Series (TS) version 4.01 of high-resolution gridded data of month-by-month variation in climate, vol.01, 1901.

I. Harris, P. Jones, T. Osborn, and D. Lister, Updated high-resolution grids of monthly climatic observations-The CRU TS3.10 Dataset, International Journal of Climatology, vol.34, 2014.

E. B. Hayes, Zika Virus Outside Africa, Emerging Infectious Diseases, vol.15, issue.9, pp.1347-1350, 2009.

D. He, E. L. Ionides, and A. A. King, Plug-and-play inference for disease dynamics: measles in large and small populations as a case study, Journal of The Royal Society Interface, vol.7, issue.43, pp.271-283, 2010.

D. He, D. Gao, Y. Lou, S. Zhao, and S. Ruan, A comparison study of Zika virus outbreaks in French Polynesia, Colombia and the State of Bahia in Brazil, Scientific Reports, vol.7, issue.1, p.273, 2017.

H. Heesterbeek, R. M. Anderson, V. Andreasen, S. Bansal, D. De-angelis et al., Viboud, and Isaac Newton Institute IDD Collaboration. Modeling infectious disease dynamics in the complex landscape of global health, Science, vol.347, issue.6227, pp.4339-4339, 2015.

M. Heringer, T. M. Souza, M. D. Lima, P. C. Nunes, N. R. Faria et al., Dengue type 4 in Rio de Janeiro, Brazil: case characterization following its introduction in an endemic region, BMC Infectious Diseases, vol.17, issue.1, p.410, 2017.

K. S. Hickmann, G. Fairchild, R. Priedhorsky, N. Generous, J. M. Hyman et al., Forecasting the 2013-2014 Influenza Season Using Wikipedia, PLOS Computational Biology, vol.11, issue.5, p.1004239, 2015.

N. A. Honório, R. M. Nogueira, C. T. Codeço, M. S. Carvalho, O. G. Cruz et al., Spatial Evaluation and Modeling of Dengue Seroprevalence and Vector Density in Rio de Janeiro, Brazil, PLOS Neglected Tropical Diseases, vol.3, issue.11, 2009.

R. Huy, P. Buchy, A. Conan, C. Ngan, S. Ong et al., National dengue surveillance in Cambodia 1980-2008: epidemiological and virological trends and the impact of vector control, Bulletin of the World Health Organization, vol.88, issue.9, pp.650-657, 2010.
URL : https://hal.archives-ouvertes.fr/pasteur-00808323

N. Imai, I. Dorigatti, S. Cauchemez, and N. M. Ferguson, Estimating Dengue Transmission Intensity from Sero-Prevalence Surveys in Multiple Countries, PLoS Neglected Tropical Diseases, vol.9, issue.4, 2015.

N. Imai, I. Dorigatti, S. Cauchemez, and N. M. Ferguson, Estimating Dengue Transmission Intensity from Case-Notification Data from Multiple Countries, PLOS Negl Trop Dis, vol.10, issue.7, p.4833, 2016.

. Insee, Populations légales dans les collectivités d'outre-mer et Mayotte-Populations légales au recensement de la population 2012 de Polynésie française, 2012.

. Insee, Populations légales dans les collectivités d'outre-mer et Mayotte-Populations légales au recensement de la population 2014 de, 2014.

E. L. Ionides, C. Breto, and A. A. King, Inference for nonlinear dynamical systems, Proceedings of the National Academy of Sciences, vol.103, issue.49, pp.18438-18443, 2006.

E. L. Ionides, A. Bhadra, Y. Atchadé, and A. King, Iterated filtering, The Annals of Statistics, vol.39, issue.3, pp.1776-1802, 2011.
DOI : 10.1214/11-aos886
URL : https://doi.org/10.1214/11-aos886

E. L. Ionides, D. Nguyen, Y. Atchadé, S. Stoev, and A. A. King, Inference for dynamic and latent variable models via iterated, perturbed Bayes maps, Proceedings of the National Academy of Sciences, vol.112, issue.3, pp.719-724, 2015.

P. E. Jacob, L. M. Murray, C. C. Holmes, and C. P. Robert, Better together? Statistical learning in models made of modules, 2017.

M. A. Johansson, J. Hombach, and D. A. Cummings, Models of the impact of dengue vaccines: a review of current research and potential approaches, Vaccine, vol.29, issue.35, pp.5860-5868, 2011.

T. Jombart, D. M. Aanensen, M. Baguelin, P. Birrell, S. Cauchemez et al., OutbreakTools: A new platform for disease outbreak analysis using the R software, Epidemics, vol.7, pp.28-34, 2014.

T. Jombart, A. Cori, X. Didelot, S. Cauchemez, C. Fraser et al., Bayesian Reconstruction of Disease Outbreaks by Combining Epidemiologic and Genomic Data, PLoS Computational Biology, vol.10, issue.1, p.1003457, 2014.

K. E. Jones, N. G. Patel, M. A. Levy, A. Storeygard, D. Balk et al., Global trends in emerging infectious diseases, Nature, vol.451, issue.7181, pp.990-993, 2008.

J. Kang and J. Aldstadt, The Influence of Spatial Configuration of Residential Area and Vector Populations on Dengue Incidence Patterns in an Individual-Level Transmission Model, International Journal of Environmental Research and Public Health, vol.14, issue.7, p.792, 2017.

Y. Kao and M. C. Eisenberg, Practical unidentifiability of a simple vector-borne disease model: implications for parameter estimation and intervention assessment, Epidemics, 2018.

S. Karl, N. Halder, J. K. Kelso, S. A. Ritchie, and G. J. Milne, A spatial simulation model for dengue virus infection in urban areas, BMC Infectious Diseases, vol.14, p.447, 2014.

R. E. Kass and A. E. Raftery, Bayes Factors, Journal of the American Statistical Association, 1995.

L. C. Katzelnick, L. Gresh, M. E. Halloran, J. C. Mercado, G. Kuan et al., Antibody-dependent enhancement of severe dengue disease in humans

, Science, pp.1095-9203, 2017.

A. B. Kawiecki and R. C. Christofferson, Zika Virus-Induced Antibody Response Enhances Dengue Virus Serotype 2 Replication In Vitro, The Journal of Infectious Diseases, vol.214, issue.9, pp.1357-1360, 2016.

M. J. Keeling and P. Rohani, Modelling infectious diseases in humans and animals, 2008.

W. O. Kermack and A. G. Mckendrick, A contribution to the mathematical theory of epidemics, Proc. R. Soc. Lond. A, vol.115, pp.700-721, 1927.

A. A. King, E. L. Ionides, M. Pascual, and M. J. Bouma, Inapparent infections and cholera dynamics, Nature, issue.7206, pp.877-880, 2008.

A. A. King, M. Domenech-de-celles, F. M. Magpantay, and P. Rohani, Avoidable errors in the modelling of outbreaks of emerging pathogens, with special reference to Ebola, Proceedings of the Royal Society B: Biological Sciences, vol.282, pp.20150347-20150347, 1806.

A. A. King, D. Nguyen, and E. L. Ionides, Statistical Inference for Partially Observed Markov Processes via the R Package pomp, Journal of Statistical Software, 2016.

J. Knape, N. Jonzén, and M. Sköld, On observation distributions for state space models of population survey data: Observation models for population data, Journal of Animal Ecology, vol.80, issue.6, pp.1269-1277, 2011.

J. Koopman, Modeling Infection Transmission, Annual Review of Public Health, vol.25, issue.1, pp.303-326, 2004.

G. P. Kouri, M. G. Guzmán, J. R. Bravo, and C. Triana, Dengue haemorrhagic fever/dengue shock syndrome: lessons from the Cuban epidemic, Bulletin of the World Health Organization, vol.67, issue.4, pp.375-380, 1981.

M. U. Kraemer, M. E. Sinka, K. A. Duda, A. Q. Mylne, F. M. Shearer et al., The global distribution of the arbovirus vectors Aedes aegypti and Ae. albopictus. eLife, vol.4, p.8347, 2015.

M. U. Kraemer, D. Bisanzio, R. C. Reiner, R. Zakar, J. B. Hawkins et al., Inferences about spatiotemporal variation in dengue virus transmission are sensitive to assumptions about human mobility: a case study using geolocated tweets from Lahore, Pakistan. EPJ Data Science, vol.7, issue.1, 2018.

. Bibliographie,

A. J. Kucharski, S. Funk, R. M. Eggo, H. Mallet, W. J. Edmunds et al., Transmission Dynamics of Zika Virus in Island Populations: A Modelling Analysis of the 2013-14 French Polynesia Outbreak, PLOS Neglected Tropical Diseases, vol.10, issue.5, p.4726, 2016.

A. J. Kucharski, M. Kama, C. H. Watson, M. Aubry, S. Funk et al., Using paired serology and surveillance data to quantify dengue transmission and control during a large outbreak in Fiji. bioRxiv, p.246116, 2018.

G. Kuno, Review of the factors modulating dengue transmission, Epidemiologic Reviews, vol.17, issue.2, pp.321-335, 1995.

L. Lambrechts, T. W. Scott, and D. J. Gubler, Consequences of the Expanding Global Distribution of Aedes albopictus for Dengue Virus Transmission, PLOS Neglected Tropical Diseases, vol.4, issue.5, p.646, 2010.
URL : https://hal.archives-ouvertes.fr/pasteur-02011027

L. Lambrechts, K. P. Paaijmans, T. Fansiri, L. B. Carrington, L. D. Kramer et al., Impact of daily temperature fluctuations on dengue virus transmission by Aedes aegypti, Proceedings of the National Academy of Sciences, vol.108, pp.7460-7465, 2011.
URL : https://hal.archives-ouvertes.fr/pasteur-00587940

K. Laneri, A. Bhadra, E. L. Ionides, M. Bouma, R. C. Dhiman et al., Forcing Versus Feedback: Epidemic Malaria and Monsoon Rains in Northwest India, PLoS Computational Biology, vol.6, issue.9, p.1000898, 2010.

M. S. Lau, G. Marion, G. Streftaris, and G. J. Gibson, New model diagnostics for spatio-temporal systems in epidemiology and ecology, Journal of The Royal Society Interface, vol.11, issue.93, pp.20131093-20131093, 2014.

M. Azou, A. Moureau, E. Sarti, J. Nealon, B. Zambrano et al., Symptomatic Dengue in Children in 10 Asian and Latin American Countries, New England Journal of Medicine, vol.374, issue.12, pp.1533-4406, 2016.

M. Azou, J. Assoukpa, K. Fanouillere, E. Plennevaux, M. Bonaparte et al., Dengue seroprevalence: data from the clinical development of a tetravalent dengue vaccine in 14 countries, 2005.

, Transactions of the Royal Society of Tropical Medicine and Hygiene, 2018.

. Bibliographie,

J. P. Ledermann, L. Guillaumot, L. Yug, S. C. Saweyog, M. Tided et al., Aedes hensilli as a Potential Vector of Chikungunya and Zika Viruses, PLOS Negl Trop Dis, vol.8, issue.10, p.3188, 2014.

E. C. Lee, M. R. Kelly, B. M. Ochocki, S. M. Akinwumi, K. E. Hamre et al., Model distinguishability and inference robustness in mechanisms of cholera transmission and loss of immunity, Journal of Theoretical Biology, vol.420, pp.68-81, 2017.

J. Legrand, R. Grais, P. Boelle, A. Valleron, and A. Flahault, Understanding the dynamics of Ebola epidemics, Epidemiology and Infection, vol.135, issue.4, pp.610-621, 2007.

S. Lequime and L. Lambrechts, Vertical transmission of arboviruses in mosquitoes: A historical perspective, Infection, Genetics and Evolution, vol.28, pp.681-690, 2014.
URL : https://hal.archives-ouvertes.fr/pasteur-01976254

J. Lessler, C. T. Ott, A. C. Carcelen, J. M. Konikoff, J. Williamson et al., Times to key events in Zika virus infection and implications for blood donation: a systematic review, Bulletin of the World Health Organization, vol.94, issue.11, pp.841-849, 2016.

J. Liu-helmersson, H. Stenlund, A. Wilder-smith, and J. Rocklöv, Vectorial Capacity of Aedes aegypti: Effects of Temperature and Implications for Global Dengue Epidemic Potential, PLoS ONE, vol.9, issue.3, p.89783, 2014.

A. L. Lloyd, Destabilization of epidemic models with the inclusion of realistic distributions of infectious periods, Proceedings. Biological Sciences / The Royal Society, vol.268, pp.985-993, 1470.

A. L. Lloyd, J. Zhang, and A. M. Root, Stochasticity and heterogeneity in host-vector models, Journal of The Royal Society Interface, vol.4, issue.16, pp.851-863, 2007.

W. P. London and J. A. Yorke, Recurrent outbreaks of measles, chickenpox and mumps. I. Seasonal variation in contact rates, American Journal of Epidemiology, vol.98, issue.6, pp.2-9262, 1973.

J. Lourenço and M. Recker, Viral and Epidemiological Determinants of the Invasion Dynamics of Novel Dengue Genotypes, PLOS Neglected Tropical Diseases, vol.4, issue.11, p.894, 2010.

. Bibliographie,

J. Lourenço and M. Recker, Natural, Persistent Oscillations in a Spatial Multi-Strain Disease System with Application to Dengue, PLoS Comput Biol, vol.9, issue.10, p.1003308, 2013.

J. Lourenço and M. Recker, The 2012 Madeira Dengue Outbreak: Epidemiological Determinants and Future Epidemic Potential, PLoS Neglected Tropical Diseases, vol.8, issue.8, p.3083, 2014.

J. Lourenço, M. M. Lima, N. R. Faria, A. Walker, M. U. Kraemer et al., Epidemiological and ecological determinants of Zika virus transmission in an urban setting. eLife, 6:e29820, 2017.

R. Lowe, B. Cazelles, R. Paul, and X. Rodó, Quantifying the added value of climate information in a spatio-temporal dengue model. Stochastic Environmental Research and Risk Assessment, pp.1436-3259, 2015.

,

P. M. Luz, C. T. Codeço, E. Massad, and C. J. Struchiner, Uncertainties regarding dengue modeling in Rio de Janeiro, Brazil. Memórias do Instituto Oswaldo Cruz, vol.98, pp.871-878, 2003.

H. P. Mallet, A. L. Vial, and D. Musso, Bilan de l'épidémie à virus Zika en Polynésie française, Bulletin d'Informations Sanitaires, Epidemiologiques et Statistiques, 2015.

M. P. Mammen, C. Pimgate, C. J. Koenraadt, A. L. Rothman, J. Aldstadt et al., Ypil-Butac, and others. Spatial and temporal clustering of dengue virus transmission in Thai villages, PLoS Med, vol.5, issue.11, p.205, 2008.

J. Marin, P. Pudlo, C. P. Robert, and R. J. Ryder, Approximate Bayesian computational methods, Statistics and Computing, vol.22, issue.6, pp.1573-1375, 2012.
DOI : 10.1007/s11222-011-9288-2
URL : https://hal.archives-ouvertes.fr/hal-00567240

M. A. Martín-acebes, J. Saiz, and N. Jiménez-de-oya, Antibody-Dependent Enhancement and Zika: Real Threat or Phantom Menace?, Frontiers in Cellular and Infection Microbiology, vol.8, 2018.

E. Massad, M. N. Burattini, F. A. Coutinho, and L. F. Lopez, Dengue and the risk of urban yellow fever reintroduction in Sao Paulo State, Brazil. Revista de Saúde Pública, vol.37, pp.477-484, 2003.

A. P. Masucci, J. Serras, A. Johansson, and M. Batty, Gravity versus radiation models: On the importance of scale and heterogeneity in commuting flows, Physical Review E, vol.88, issue.2, p.22812, 2013.

T. Mckinley, A. R. Cook, and R. Deardon, Inference in Epidemic Models without Likelihoods, The International Journal of Biostatistics, vol.5, issue.1, 2009.

J. P. Messina, O. J. Brady, T. W. Scott, C. Zou, D. M. Pigott et al., Global spread of dengue virus types: mapping the 70 year history, Trends in Microbiology, vol.22, issue.3, pp.138-146, 2014.

J. P. Messina, O. J. Brady, D. M. Pigott, N. Golding, M. U. Kraemer et al., The many projected futures of dengue, Nature Reviews

, Microbiology, vol.13, issue.4, pp.230-239, 2015.

C. J. Metcalf, J. Farrar, F. T. Cutts, N. E. Basta, A. L. Graham et al., Use of serological surveys to generate key insights into the changing global landscape of infectious disease. The Lancet, 2016.

H. Miao, X. Xia, A. S. Perelson, and H. Wu, On identifiability of nonlinear ODE models and applications in viral dynamics, SIAM review. Society for Industrial and Applied Mathematics, vol.53, issue.1, pp.3-39, 2011.

H. L. Mills and S. Riley, The Spatial Resolution of Epidemic Peaks, PLOS Computational Biology, vol.10, issue.4, p.1003561, 2014.

D. Moulay, N. Verdière, and L. Denis-vidal, Identifiablility of parameters in an epidemiologic model modeling the transmission of the Chikungunya, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00728680

N. A. Muhammad-azami, S. A. Salleh, H. Neoh, S. Z. Syed-zakaria, and R. Jamal, Dengue epidemic in Malaysia: Not a predominantly urban disease anymore, BMC Research Notes, vol.4, p.216, 2011.

L. M. Murray, Bayesian State-Space Modelling on High-Performance Hardware Using LibBi, 2013.

D. Musso and D. J. Gubler, Zika Virus, Clinical Microbiology Reviews, vol.29, issue.3, pp.487-524, 2016.
URL : https://hal.archives-ouvertes.fr/hal-02004359

Y. Nagao and K. Koelle, Decreases in dengue transmission may act to increase the incidence of dengue hemorrhagic fever, Proceedings of the National Academy of Sciences of the United States of America, vol.105, pp.2238-2243, 2008.

S. Naish, P. Dale, J. S. Mackenzie, J. Mcbride, K. Mengersen et al., Climate change and dengue: a critical and systematic review of quantitative modelling approaches, BMC Infectious Diseases, vol.14, p.167, 2014.

I. , Stochastic models of some endemic infections, Mathematical Biosciences, 2002.

P. P. , National Institute of Statistics, Ministry of Planning. General Population Census of Cambodia, National report on final census results, 2008.

E. M. Netto, A. Moreira-soto, C. Pedroso, C. Höser, S. Funk et al., High Zika Virus Seroprevalence in Salvador, Northeastern Brazil Limits the Potential for Further Outbreaks, mBio, vol.8, issue.6, pp.2150-7511, 2017.

A. L. Nevai and E. Soewono, A model for the spatial transmission of dengue with daily movement between villages and a city, vol.31, pp.150-178, 2014.

N. M. Nguyen, D. T. Kien, T. V. Tuan, N. T. Quyen, C. N. Tran et al., Host and viral features of human dengue cases shape the population of infected and infectious Aedes aegypti mosquitoes, Proceedings of the National Academy of Sciences, vol.110, issue.22, pp.9072-9077, 2013.

A. Nisalak, H. E. Clapham, S. Kalayanarooj, C. Klungthong, B. Thaisomboonsuk et al., Forty Years of Dengue Surveillance at a Tertiary Pediatric Hospital in, The American Journal of Tropical Medicine and Hygiene, vol.94, issue.6, pp.1342-1347, 1973.

H. Nishiura, R. Kinoshita, K. Mizumoto, Y. Yasuda, and K. Nah, Transmission potential of Zika virus infection in the South Pacific, International Journal of Infectious Diseases, vol.45, pp.95-97, 2016.

H. Nishiura, K. Mizumoto, W. E. Villamil-gómez, and A. J. Rodríguez-morales, Preliminary estimation of the basic reproduction number of Zika virus infection during Colombia epidemic, Travel Medicine and Infectious Disease, vol.0, issue.0, pp.2015-2016, 2016.

R. M. Nogueira and A. L. Eppinghaus, Dengue virus type 4 arrives in the state of Rio de Janeiro: a challenge for epidemiological surveillance and control. Memórias do Instituto Oswaldo Cruz, BIBLIOGRAPHIE, vol.106, issue.3, pp.255-256, 2011.

R. M. Nogueira, H. G. Schatzmayr, A. M. Bispo-de-filippis, F. Barreto-dos-santos, R. Venâncio-da-cunha et al., Dengue Virus Type 3, Brazil, Emerging Infectious Diseases, vol.11, issue.9, pp.1376-1381, 2002.

M. R. Nunes, N. R. Faria, J. M. De-vasconcelos, N. Golding, M. U. Kraemer et al., Emergence and potential for spread of Chikungunya virus in Brazil, BMC Medicine, vol.13, issue.1, p.102, 2015.

P. D. O'neill, Introduction and snapshot review: Relating infectious disease transmission models to data, Statistics in Medicine, vol.29, issue.20, pp.2069-2077, 2010.

P. D. O'neill and G. O. Roberts, Bayesian Inference for Partially Observed Stochastic Epidemics, Journal of the Royal Statistical Society. Series A (Statistics in Society, vol.162, issue.1, pp.121-129, 1999.

J. Ong, G. Yap, C. Hapuarachchi, C. S. Tang, S. Liang et al., Dengue review of 2016 and outlook for 2017, Singapore Epidemiol News Bull, vol.43, issue.2, pp.47-56, 2017.

, Apr%202017%20Vol%2043%20No%202%20(Final).pdf

L. Opatowski, E. Varon, C. Dupont, L. Temime, S. Werf et al., Assessing pneumococcal meningitis association with viral respiratory infections and antibiotics: insights from statistical and mathematical models, Proceedings of the Royal Society of London B: Biological Sciences, vol.280, 1764.

L. Opatowski, M. Baguelin, and R. M. Eggo, Influenza interaction with cocirculating pathogens and its impact on surveillance, pathogenesis, and epidemic profile: A key role for mathematical modelling, PLOS Pathogens, vol.14, issue.2, p.1006770, 2018.
URL : https://hal.archives-ouvertes.fr/inserm-02171126

K. O'reilly, R. Lowe, J. Edmunds, P. Mayaud, A. Kucharski et al., Projecting the end of the Zika virus epidemic BIBLIOGRAPHIE in Latin America: a modelling analysis, 2018.

,

J. Owen, D. J. Wilkinson, and C. S. Gillespie, Likelihood free inference for Markov processes: a comparison, Statistical Applications in Genetics and Molecular Biology, vol.14, issue.2, 2015.

J. Owen, D. J. Wilkinson, and C. S. Gillespie, Scalable inference for Markov processes with intractable likelihoods, Statistics and Computing, vol.25, issue.1, pp.1573-1375, 2015.

A. Pandey, A. Mubayi, and J. Medlock, Comparing vector-host and SIR models for dengue transmission, Mathematical Biosciences, vol.246, issue.2, pp.252-259, 2013.

S. Pant, Information sensitivity functions to assess parameter information gain and identifiability of dynamical systems, Journal of The Royal Society Interface, vol.15, issue.142, p.20170871, 2018.

P. Pantoja, E. X. Pérez-guzmán, I. V. Rodríguez, L. J. White, O. González et al., Zika virus pathogenesis in rhesus macaques is unaffected by pre-existing immunity to dengue virus, Nature Communications, vol.8, p.15674, 2017.

M. Pascual and A. Dobson, Seasonal Patterns of Infectious Diseases, PLOS Medicine, vol.2, issue.1, p.5, 2005.

S. R. Passos, B. D. Santos, M. A. , J. Cerbino-neto, S. N. Buonora et al., Detection of Zika Virus in April 2013 Patient Samples, Brazil. Emerg Infect Dis, vol.23, 2017.

T. A. Perkins, R. C. Reiner, I. Rodriguez-barraquer, D. L. Smith, T. W. Scott et al., A review of transmission models of dengue: a quantitative and qualitative analysis of model features, Dengue and dengue hemorrhagic fever, pp.99-114, 2014.

T. A. Perkins, V. A. Paz-soldan, S. T. Stoddard, A. C. Morrison, B. M. Forshey et al., Calling in sick: impacts of fever on intra-urban human mobility, Proc. R. Soc. B, vol.283, pp.1471-2954, 1834.

T. A. Perkins, A. S. Siraj, C. W. Ruktanonchai, M. U. Kraemer, and A. J. Tatem, Model-based projections of Zika virus infections in childbearing women in the Americas, Nature Microbiology, vol.1, issue.9, p.16126, 2016.

L. R. Petersen, D. J. Jamieson, A. M. Powers, and M. A. Honein, Zika Virus, New England Journal of Medicine, vol.374, issue.16, pp.1552-1563, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01465099

P. Pongsumpun and I. Tang, Transmission of dengue hemorrhagic fever in an age structured population, Mathematical and Computer Modelling, vol.37, issue.9, pp.949-961, 2003.

A. Prayitno, A. Taurel, J. Nealon, H. I. Satari, M. R. Karyanti et al., Dengue seroprevalence and force of primary infection in a representative population of urban dwelling Indonesian children, PLOS Neglected Tropical Diseases, vol.11, issue.6, p.5621, 2017.

. Prefeiturario, Cobertura Vegetal e Uso da Terra, 2010.

. Prefeiturario, Limite Bairro-Prefeitura da Cidade do Rio de Janeiro-IPP, 2016.

P. Pudlo, J. Marin, A. Estoup, J. Cornuet, M. Gautier et al., Reliable ABC model choice via random forests, Bioinformatics, vol.32, issue.6, pp.859-866, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01067925

G. Pujol, B. Iooss, and A. Janon, Package 'sensitivity, 2016.

H. Putter, S. H. Heisterkamp, J. M. Lange, and F. D. Wolf, A Bayesian approach to parameter estimation in HIV dynamical models, Statistics in Medicine, vol.21, issue.15, pp.2199-2214, 2002.

A. E. Raftery, T. Gneiting, F. Balabdaoui, and M. Polakowski, Using Bayesian Model Averaging to Calibrate Forecast Ensembles, Monthly Weather Review, vol.133, issue.5, pp.1155-1174, 2005.

B. Randuineau, Interactions between pathgogens, 2015.
URL : https://hal.archives-ouvertes.fr/tel-01237856

D. A. Rasmussen, O. Ratmann, and K. Koelle, Inference for Nonlinear Epidemiological Models Using Genealogies and Time Series, PLoS Computational Biology, vol.7, issue.8, p.1002136, 2011.

. Bibliographie,

D. A. Rasmussen, M. F. Boni, and K. Koelle, Reconciling Phylodynamics with Epidemiology: The Case of Dengue Virus in Southern Vietnam, Molecular Biology and Evolution, vol.31, issue.2, pp.258-271, 2014.

S. A. Rasmussen, D. J. Jamieson, M. A. Honein, and L. R. Petersen, Zika Virus and Birth Defects-Reviewing the Evidence for Causality, The New England Journal of Medicine, vol.374, issue.20, pp.1981-1987, 2016.

A. Raue, C. Kreutz, T. Maiwald, J. Bachmann, M. Schilling et al., Structural and practical identifiability analysis of partially observed dynamical models by exploiting the profile likelihood, Bioinformatics, vol.25, issue.15, pp.1923-1929, 2009.

L. Raynal, J. Marin, P. Pudlo, M. Ribatet, C. P. Robert et al., ABC random forests for Bayesian parameter inference. Peer Community in Evolutionary Biology, p.100036, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01337189

M. Recker, K. B. Blyuss, C. P. Simmons, T. T. Hien, B. Wills et al., Immunological serotype interactions and their effect on the epidemiological pattern of dengue, Proceedings. Biological Sciences, vol.276, pp.2541-2548, 1667.

N. G. Reich, S. Shrestha, A. A. King, P. Rohani, J. Lessler et al., Interactions between serotypes of dengue highlight epidemiological impact of cross-immunity, Journal of The Royal Society Interface, vol.10, issue.86, pp.1742-5662, 2013.

N. G. Reich, S. A. Lauer, K. Sakrejda, S. Iamsirithaworn, S. Hinjoy et al., Challenges in Real-Time Prediction of Infectious Disease: A Case Study of Dengue in Thailand, PLOS Neglected Tropical Diseases, vol.10, issue.6, p.4761, 2016.

R. C. Reiner, T. A. Perkins, C. M. Barker, T. Niu, L. F. Chaves et al., A systematic review of mathematical models of mosquito-borne pathogen transmission: 1970-2010, Journal of The Royal Society Interface, vol.10, issue.81, pp.20120921-20120921, 2013.

R. C. Reiner, S. T. Stoddard, and T. W. Scott, Socially structured human movement shapes dengue transmission despite the diffusive effect of mosquito dispersal, Epidemics, vol.6, pp.30-36, 2014.

P. Reiter, S. Lathrop, M. Bunning, B. Biggerstaff, D. Singer et al., Texas Lifestyle Limits Transmission of Dengue Virus, Emerging Infectious Diseases, vol.9, issue.1, pp.86-89, 2003.

M. E. Reller, C. Bodinayake, A. Nagahawatte, V. Devasiri, W. Kodikara-arachichi et al., Unsuspected Dengue and Acute Febrile Illness in Rural and Semi-Urban Southern Sri Lanka, Emerging Infectious Diseases, vol.18, issue.2, pp.256-263, 2012.

P. Renault, J. Solet, D. Sissoko, E. Balleydier, S. Larrieu et al., A major epidemic of chikungunya virus infection on Reunion Island, The American Journal of Tropical Medicine and Hygiene, vol.77, issue.4, pp.2-9637, 2005.

F. A. Rey, K. Stiasny, M. Vaney, M. Dellarole, and F. X. Heinz, The bright and the dark side of human antibody responses to flaviviruses: lessons for vaccine design, EMBO reports, vol.19, issue.2, pp.206-224, 2018.

G. Rezza, Dengue and other Aedes-borne viruses: a threat to Europe? Eurosurveillance, vol.21, 2016.

G. Rezza, L. Nicoletti, R. Angelini, R. Romi, A. C. Finarelli et al., A. Cassone, and CHIKV study group. Infection with chikungunya virus in Italy: an outbreak in a temperate region, Lancet, vol.370, issue.9602, pp.61779-61785, 2007.
URL : https://hal.archives-ouvertes.fr/hal-00686085

G. S. Ribeiro, M. Kikuti, L. B. Tauro, L. C. Nascimento, C. W. Cardoso et al., Does immunity after Zika virus infection cross-protect against dengue? The Lancet Global Health, vol.6, pp.140-141, 2018.

J. Riou, C. Poletto, and P. Boëlle, A comparative analysis of Chikungunya and Zika transmission, Epidemics, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01446320

J. Riou, C. Poletto, and P. Boëlle, Improving early epidemiological assessment of emerging Aedes-transmitted epidemics using historical data, PLOS Neglected Tropical Diseases, vol.12, issue.6, p.6526, 2018.

C. Robert and G. Casella, Monte Carlo Statistical Methods. Springer Texts in Statistics, 2004.

G. Roberts and R. Tweedie, Geometric convergence and central limit theorems for multidimensional Hastings and Metropolis algorithms, Biometrika, vol.83, issue.1, pp.95-110, 1996.

G. O. Roberts and J. S. Rosenthal, Optimal scaling for various Metropolis-Hastings algorithms, Statistical Science, vol.16, issue.4, pp.2168-8745, 2001.

G. O. Roberts and J. S. Rosenthal, Examples of Adaptive MCMC, Journal of Computational and Graphical Statistics, vol.18, issue.2, pp.349-367, 2009.

M. G. Roberts and J. A. Heesterbeek, A new method for estimating the effort required to control an infectious disease, Proceedings of the Royal Society B: Biological Sciences, vol.270, pp.1471-2954, 1522.

F. Rocha, M. Aguiar, M. Souza, and N. Stollenwerk, Time-scale separation and centre manifold analysis describing vector-borne disease dynamics, International Journal of Computer Mathematics, vol.90, issue.10, pp.2105-2125, 2013.

F. Rocha, L. Mateus, U. Skwara, M. Aguiar, and N. Stollenwerk, Understanding dengue fever dynamics: a study of seasonality in vector-borne disease models, International Journal of Computer Mathematics, vol.93, issue.8, pp.1405-1422, 2016.

B. Roche, B. Gaillard, L. Léger, R. Pélagie-moutenda, T. Sochacki et al., An ecological and digital epidemiology analysis on the role of human behavior on the 2014 Chikungunya outbreak in Martinique, Scientific Reports, vol.7, issue.1, p.5967, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01581824

I. Rodriguez-barraquer, M. T. Cordeiro, C. Braga, W. V. Souza, E. T. Marques et al., From Re-Emergence to Hyperendemicity: The Natural History of the Dengue Epidemic in Brazil, PLOS Neglected Tropical Diseases, vol.5, issue.1, p.935, 2011.

A. J. Rodriguez-morales, W. E. Villamil-gómez, and C. Franco-paredes, The arboviral burden of disease caused by co-circulation and co-infection of dengue, chikungunya and Zika in the Americas, Travel Medicine and Infectious Disease, vol.14, issue.3, pp.177-179, 2016.

P. Rohani, D. J. Earn, B. Finkenstadt, and B. T. Grenfell, Population dynamic interference among childhood diseases, Proceedings of the Royal Society B: Biological Sciences, vol.265, pp.1471-2954, 1410.

P. Rohani, M. J. Keeling, and B. T. Grenfell, The interplay between determinism and stochasticity in childhood diseases, The American Naturalist, vol.159, issue.5, pp.469-481, 2002.

D. Rojas, N. Dean, Y. Yang, E. Kenah, J. Quintero et al., The epidemiology and transmissibility of Zika virus in Girardot and San Andres island, Colombia, European communicable disease bulletin, vol.21, issue.28, 2015.

V. Romeo-aznar, R. Paul, O. Telle, and M. Pascual, Mosquito-borne transmission in urban landscapes: the missing link between vector abundance and human density, Proc. R. Soc

, B, vol.285, pp.1471-2954, 1884.

M. G. Rosa-freitas, P. Tsouris, I. C. Reis, M. D. Magalhães, T. F. Nascimento et al., Dengue and land cover heterogeneity in Rio de Janeiro, Oecologia Australis, vol.14, issue.3, pp.641-667, 2010.

M. Roy, M. J. Bouma, E. L. Ionides, R. C. Dhiman, and M. Pascual, The Potential Elimination of Plasmodium vivax Malaria by Relapse Treatment: Insights from a Transmission Model and Surveillance Data from NW India, PLOS Negl Trop Dis, vol.7, issue.1, p.1979, 2013.

H. Salje, J. Lessler, T. P. Endy, F. C. Curriero, R. V. Gibbons et al., Revealing the microscale spatial signature of dengue transmission and immunity in an urban population, Proceedings of the National Academy of Sciences of the United States of America, vol.109, issue.24, pp.9535-9538, 2012.

H. Salje, J. Lessler, I. M. Berry, M. C. Melendrez, T. Endy et al., Dengue diversity across spatial and temporal scales: Local structure and the effect of host population size, Science, vol.355, issue.6331, pp.1302-1306, 2017.

K. Sallah, R. Giorgi, L. Bengtsson, X. Lu, E. Wetter et al., Mathematical models for predicting human mobility in the context of infectious disease spread: introducing the impedance model, International Journal of Health Geographics, vol.16, issue.1, p.42, 2017.
URL : https://hal.archives-ouvertes.fr/inserm-01985990

N. Sangkawibha, S. Rojanasuphot, S. Ahandrik, S. Viriyapongse, S. Janatasen et al., Risk factors in dengue shock syndrome: a prospective epidemiologic study in Rayong, Thailand I. The 1980 outbreak, American journal of epidemiology, vol.120, issue.5, pp.653-669, 1984.

S. Sarkka, Bayesian Filtering and Smoothing, 2013.

M. Sarzynska, O. Udiani, and N. Zhang, A study of gravity-linked metapopulation models for the spatial spread of dengue fever, 2013.

E. Saulnier, O. Gascuel, and S. Alizon, Inferring epidemiological parameters from phylogenies using regression-ABC: A comparative study, PLOS Computational Biology, vol.13, issue.3, p.1005416, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01567904

L. Schuler-faccini, Possible Association Between Zika Virus Infection and Microcephaly-Brazil, MMWR. Morbidity and Mortality Weekly Report, vol.65, 2015.

T. M. Sharp, K. M. Tomashek, J. S. Read, H. S. Margolis, and S. H. Waterman, A New Look at an Old Disease: Recent Insights into the Global Epidemiology of Dengue, Current Epidemiology Reports, vol.4, issue.1, pp.11-21, 2017.

D. S. Shepard, E. A. Undurraga, Y. A. Halasa, and J. D. Stanaway, The global economic burden of dengue: a systematic analysis, The Lancet. Infectious Diseases, vol.16, issue.8, pp.146-154, 2016.

Z. Shousheng, L. Denis-vidal, and N. Verdière, Identifiability study in a model describing the propagation of the chikungunya to the human population, MOSIM 2014, 10ème Conférence Francophone de Modélisation, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01166654

S. Shrestha, A. A. King, and P. Rohani, Statistical Inference for Multi-Pathogen Systems, PLOS Computational Biology, vol.7, issue.8, p.1002135, 2011.

S. Shrestha, B. Foxman, D. M. Weinberger, C. Steiner, C. Viboud et al., Identifying the interaction between influenza and pneumococcal pneumonia using incidence data, Science translational medicine, vol.5, pp.191-84, 2013.

D. P. Shutt, C. A. Manore, S. Pankavich, A. T. Porter, and S. Y. Valle, Estimating the reproductive number, total outbreak size, and reporting rates for Zika epidemics in South and Central America, Epidemics, vol.21, pp.63-79, 2017.

F. Simini, M. C. González, A. Maritan, and A. Barabási, A universal model for mobility and migration patterns, Nature, vol.484, issue.7392, p.96, 2012.

D. L. Smith, K. E. Battle, S. I. Hay, C. M. Barker, T. W. Scott et al., Macdonald, and a Theory for the Dynamics and Control of Mosquito-Transmitted Pathogens

, PLoS Pathogens, vol.8, issue.4, 2012.

D. L. Smith, T. A. Perkins, R. C. Reiner, C. M. Barker, T. Niu et al.,

J. Singh, A. J. Stoller, U. Tatem, H. C. Kitron, J. M. Godfray et al., Recasting the theory of mosquito-borne pathogen transmission dynamics and control, Transactions of The Royal Society of Tropical Medicine and Hygiene, vol.108, issue.4, pp.1878-3503, 2014.

, Infected cases of Dengue per neighborhood and period (Casos de Dengue por bairro e período), 2016.

, Epidemiological data (Dado Epidemiológicos), Zika, 2016.

H. E. Soper, The Interpretation of Periodicity in Disease Prevalence, Journal of the Royal Statistical Society, vol.92, issue.1, pp.34-73, 1929.

D. J. Spiegelhalter, N. G. Best, B. P. Carlin, and A. Van-der-linde, Bayesian measures of model complexity and fit, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.64, issue.4, pp.583-639, 2002.

,

. {stan-development-team}, Stan Modeling Language: User's Guide and Reference Manual, 2017.

M. Stefanouli and S. Polyzos, Gravity vs radiation model: two approaches on commuting in Greece, Transportation Research Procedia, vol.24, pp.65-72, 2017.

T. Stocks, T. Britton, and M. Höhle, pomp-Astic Inference For Epidemic Models: Simple Vs. Complex. bioRxiv, p.125880, 2017.

T. Stocks, T. Britton, and M. Höhle, Model selection and parameter estimation for dynamic epidemic models via iterated filtering: application to rotavirus in Germany, Biostatistics, 2018.

S. T. Stoddard, A. C. Morrison, G. M. Vazquez-prokopec, V. P. Soldan, T. J. Kochel et al., The Role of Human Movement in the Transmission of Vector-Borne Pathogens, PLOS Neglected Tropical Diseases, vol.3, issue.7, p.481, 2009.

S. T. Stoddard, B. M. Forshey, A. C. Morrison, V. A. Paz-soldan, G. M. Vazquez-prokopec et al., House-to-house human movement drives dengue virus transmission, Proceedings of the National Academy of Sciences of the United States of America, vol.110, issue.3, pp.994-999, 2013.

L. M. Stolerman, D. Coombs, and S. Boatto, SIR-Network Model and Its Application to Dengue Fever, SIAM Journal on Applied Mathematics, 2015.

D. Strickman, R. Sithiprasasna, P. Kittayapong, and B. L. Innis, Distribution of dengue and Japanese encephalitis among children in rural and suburban Thai villages, The American Journal of Tropical Medicine and Hygiene, vol.63, issue.1-2, pp.2-9637, 2000.

J. A. Swanstrom, J. A. Plante, K. S. Plante, E. F. Young, E. Mcgowan et al., Dengue Virus Envelope Dimer Epitope Monoclonal Antibodies Isolated from Dengue Patients Are Protective against Zika Virus, mBio, vol.7, issue.4, pp.2150-7511, 2016.

R. D. Team, R: A Language and Environment for Statistical Computing, 2015.

M. G. Teixeira, J. B. Siqueira, G. L. Ferreira, L. Bricks, and G. Joint, Epidemiological Trends of Dengue Disease in Brazil, A Systematic Literature Search and Analysis, vol.7, 2000.

O. Telle, A. Vaguet, N. K. Yadav, B. Lefebvre, E. Daudé et al., The Spread of Dengue in an Endemic Urban Milieu-The Case of Delhi, India. PLOS ONE, vol.11, issue.1, p.146539, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01562023

Q. A. Bosch, B. K. Singh, M. R. Hassan, D. D. Chadee, and E. Michael, The Role of Serotype Interactions and Seasonality in Dengue Model Selection and Control: Insights from a Pattern Matching Approach, PLOS Neglected Tropical Diseases, vol.10, issue.5, p.4680, 2016.

Q. A. Bosch, H. E. Clapham, L. Lambrechts, V. Duong, P. Buchy et al., Contributions from the silent majority dominate dengue virus transmission, PLoS Pathogens, vol.14, issue.5, 2018.
URL : https://hal.archives-ouvertes.fr/pasteur-02011812

A. C. Terzian, A. S. Schanoski, M. T. Mota, R. A. Silva, C. F. Estofolete et al., Viral Load and Cytokine Response Profile Does Not Support Antibody-Dependent Enhancement in Dengue-Primed Zika Virus-Infected Patients, Clinical Infectious Diseases, vol.65, issue.8, pp.1260-1265, 2017.

M. Teurlai, R. Huy, B. Cazelles, R. Duboz, C. Baehr et al., Can Human Movements Explain Heterogeneous Propagation of Dengue Fever in Cambodia?, PLOS Neglected Tropical Diseases, vol.6, issue.12, p.1957, 2012.
URL : https://hal.archives-ouvertes.fr/hal-01495165

K. T. Thai, T. Q. Binh, P. T. Giao, H. L. Phuong, L. Q. Hung et al., Seroprevalence of dengue antibodies, annual incidence and risk factors among children in southern Vietnam, Tropical Medicine & International Health, vol.10, issue.4, pp.379-386, 2005.

K. T. Thai, T. T. Nga, N. Van-nam, H. L. Phuong, P. T. Giao et al., Incidence of primary dengue virus infections in Southern Vietnamese children and reactivity against other flaviviruses, Tropical Medicine & International Health, vol.12, issue.12, pp.1553-1557, 2007.

R. N. Thompson, C. A. Gilligan, and N. J. Cunniffe, Detecting Presymptomatic Infection Is Necessary to Forecast Major Epidemics in the Earliest Stages of Infectious Disease Outbreaks

, PLOS Comput Biol, vol.12, issue.4, p.1004836, 2016.

N. T. Tien, C. Luxemburger, N. T. Toan, L. Pollissard-gadroy, V. T. Huong et al., A prospective cohort study of dengue infection in schoolchildren in Long Xuyen, Viet Nam. Transactions of the Royal Society of Tropical Medicine and Hygiene, vol.104, issue.9, pp.592-600, 2010.

T. Toni, D. Welch, N. Strelkowa, A. Ipsen, and M. P. Stumpf, Approximate Bayesian computation scheme for parameter inference and model selection in dynamical systems, Journal of The Royal Society Interface, vol.6, issue.31, pp.187-202, 2009.

P. Touloupou, N. Alzahrani, P. Neal, S. E. Spencer, and T. J. Mckinley, Efficient Model Comparison Techniques for Models Requiring Large Scale Data Augmentation, Bayesian Analysis, vol.13, issue.2, pp.437-459, 2018.

S. Towers, F. Brauer, C. Castillo-chavez, A. K. Falconar, A. Mubayi et al., Estimate of the reproduction number of the 2015 Zika virus outbreak in Barranquilla, Colombia, and estimation of the relative role of sexual transmission, Epidemics, vol.17, pp.50-55, 2016.

S. Towers, F. Brauer, C. Castillo-chavez, A. K. Falconar, A. Mubayi et al., Estimation of the reproduction number of the 2015 Zika virus outbreak in Barranquilla, Colombia, and a first estimate of the relative role of sexual transmission, 2016.

J. Truscott and N. M. Ferguson, Evaluating the Adequacy of Gravity Models as a Description of Human Mobility for Epidemic Modelling, PLOS Computational Biology, vol.8, issue.10, p.1002699, 2012.

P. Van-den-driessche and J. Watmough, Reproduction numbers and sub-threshold endemic equilibria for compartmental models of disease transmission, Mathematical Biosciences, vol.180, pp.25-5564, 2002.

D. A. Vasco, H. J. Wearing, and P. Rohani, Tracking the dynamics of pathogen interactions: Modeling ecological and immune-mediated processes in a two-pathogen single-host system, Journal of Theoretical Biology, vol.245, issue.1, pp.9-25, 2007.

F. Verelst, L. Willem, and P. Beutels, Behavioural change models for infectious disease transmission: a systematic review, Journal of the Royal Society Interface, vol.13, issue.125, 2010.

C. Viboud, O. N. Bjørnstad, D. L. Smith, L. Simonsen, M. A. Miller et al., Synchrony, Waves, and Spatial Hierarchies in the Spread of Influenza, Science, issue.5772, pp.447-451, 2006.

C. Viboud, K. Sun, R. Gaffey, M. Ajelli, L. Fumanelli et al., The RAPIDD ebola forecasting challenge: Synthesis and BIBLIOGRAPHIE lessons learnt, Epidemics, vol.22, pp.13-21, 2018.

D. , .. M. Villela, L. S. Bastos, L. M. De-carvalho, O. G. Cruz et al., Zika in Rio de Janeiro: Assessment of basic reproduction number and comparison with dengue outbreaks, Epidemiology and Infection, vol.145, issue.8, pp.1649-1657, 2017.

S. Vong, V. Khieu, O. Glass, S. Ly, V. Duong et al., Dengue Incidence in Urban and Rural Cambodia: Results from Population-Based Active Fever Surveillance, PLoS Neglected Tropical Diseases, vol.4, issue.11, p.903, 2006.
URL : https://hal.archives-ouvertes.fr/pasteur-00808145

,

S. Vongpunsawad, D. Intharasongkroh, T. Thongmee, and Y. Poovorawan, Seroprevalence of antibodies to dengue and chikungunya viruses in Thailand, PLOS ONE, vol.12, issue.6, p.180560, 2017.

E. Walter and L. Pronzato, On the identifiability and distinguishability of nonlinear parametric models, Mathematics and Computers in Simulation, vol.42, issue.2, pp.125-134, 1996.

L. Wang, S. Valderramos, A. Wu, S. Ouyang, C. Li et al., From Mosquitos to Humans: Genetic Evolution of Zika Virus, Cell Host & Microbe, vol.19, issue.5, pp.561-565, 2016.

L. Wang, X. Zhang, and Z. Liu, An SEIR Epidemic Model with Relapse and General Nonlinear Incidence Rate with Application to Media Impact. Qualitative Theory of Dynamical Systems, pp.1-21, 2017.

H. J. Wearing and P. Rohani, Ecological and immunological determinants of dengue epidemics, Proceedings of the National Academy of Sciences, vol.103, issue.31, pp.11802-11807, 2006.

. Weatherunderground, Weather Forecast & Reports-Long Range & Local, 2016.

R. A. Weiss and A. J. Mcmichael, Social and environmental risk factors in the emergence of infectious diseases, Nature Medicine, vol.10, issue.12s, pp.70-76, 2004.

A. Wesolowski, T. Qureshi, M. F. Boni, P. R. Sundsøy, M. A. Johansson et al., Impact of human mobility on the emergence of dengue epidemics in Pakistan, Proceedings of the National Academy of Sciences, vol.112, 2015.

, Dengue vaccine: WHO position paper. Weekly epidemiological record 30, World Health Organization, 2016.

, IHR 2005) Emergency Committee on Zika virus and observed increase in neurological disorders and neonatal malformations, 2005.

W. Dengue,

O. Wichmann, I. Yoon, S. Vong, K. Limkittikul, R. V. Gibbons et al., Dengue in Thailand and Cambodia: An Assessment of the Degree of Underrecognized Disease Burden Based on Reported Cases, PLoS Neglected Tropical Diseases, vol.5, issue.3, p.996, 2011.
URL : https://hal.archives-ouvertes.fr/pasteur-00805501

H. Wickham, ggplot2: Elegant Graphics for Data Analysis, 2009.

H. Wu, H. Zhu, H. Miao, and A. S. Perelson, Parameter Identifiability and Estimation of HIV/AIDS Dynamic Models, Bulletin of Mathematical Biology, vol.70, issue.3, pp.785-799, 2008.

,

D. R. Xavier, M. D. Magalhães, R. Gracie, I. C. Reis, V. P. Matos et al., Spatial-temporal diffusion of dengue in the municipality of Rio de Janeiro, 2000.

, Cadernos De Saude Publica, vol.33, issue.2, p.186615, 2017.

Y. Xia, O. N. Bjørnstad, and B. T. Grenfell, Measles metapopulation dynamics: a gravity model for epidemiological coupling and dynamics, The American Naturalist, vol.164, issue.2, pp.267-281, 2004.

X. Yan, C. Zhao, Y. Fan, Z. Di, and W. Wang, Universal predictability of mobility patterns in cities, Journal of the Royal Society Interface, vol.11, issue.100, 2014.

J. Yang and J. S. Rosenthal, Automatically tuned general-purpose MCMC via new adaptive diagnostics, Computational Statistics, vol.32, issue.1, pp.315-348, 2017.

W. Yang, A. Karspeck, and J. Shaman, Comparison of Filtering Methods for the Modeling and Retrospective Forecasting of Influenza Epidemics, PLoS Computational Biology, vol.10, issue.4, p.1003583, 2014.

,

. Bibliographie,

G. Yap, S. Liang, C. Hapuarachchi, C. Chong, L. Tan et al., Dengue outlook for Singapore in 2016, Singapore Epidemiol News Bull, vol.42, issue.1, pp.3-10, 2016.

I. Yoon, A. L. Rothman, D. Tannitisupawong, A. Srikiatkhachorn, R. G. Jarman et al., Underrecognized Mildly Symptomatic Viremic Dengue Virus Infections in Rural Thai Schools and Villages, The Journal of Infectious Diseases, vol.206, issue.3, pp.389-398, 2012.

D. Yu, Q. Lin, A. P. Chiu, and D. He, Effects of reactive social distancing on the 1918 influenza pandemic, PLOS ONE, vol.12, issue.7, p.180545, 2017.

Q. Zhang, K. Sun, M. Chinazzi, A. P. Piontti, N. E. Dean et al., Spread of Zika virus in the Americas, Proceedings of the National Academy of Sciences, vol.114, issue.22, pp.4334-4343, 2017.