Research
It is still poorly known to what extent the human behavior and its changes over time can affect the spread of an epidemic in a complex modern society as the one we live in today. I aim to give insight into this crucial question, by analyzing the effects of mobility patterns, population heterogeneity, individual behavior (e.g., spontaneous behavioral changes as a protective response to an epidemic, vaccination choices), demographic changes, immigration processes on the spread of an epidemic.
Go to the EpiMod page, the page of the group I lead (Marco Ajelli, Laura Fumanelli, Piero Poletti and myself).
Selected publications
- P. Poletti et a. Risk perception and effectiveness of uncoordinated behavioral responses in an emerging epidemic. Mathematical Biosciences, In press, 2012.
- S. Merler et al. Determinants of the spatiotemporal dynamics of the 2009 H1N1 pandemic in Europe: implications for real-time modelling. PLoS Computational Biology, 7(9): e1002205, 2011.
- G. Guzzetta et al. Modeling socio-demography to capture tuberculosis transmission dynamics in a low burden setting. Journal of Theoretical Biology, 289:197-205, 2011.
- P. Poletti et al. Transmission potential of Chikungunya virus and control measures: the case of Italy. PLoS ONE, 6(5): e18860, 2011.
- C. Rizzo et al. Epidemiology and transmission dynamics of the 1918-19 pandemic influenza in Florence, Italy. Vaccine, 29:27-32, 2011.
- M. Ajelli et al. Evaluation of model prediction during the early phase of the 2009 influenza pandemic in Italy. Influenza and Other Respiratory Viruses, 5:202-229, 2011
- P. Poletti et al. The effect of risk perception on the 2009 H1N1 pandemic influenza dynamics. PLoS ONE, 6(2): e16460, 2011.
- M. Ajelli et al. Spatiotemporal dynamics of viral hepatitis A in Italy. Theoretical Population Biology, 79:1-11, 2011.
- M. Ajelli et al. Model predictions and evaluation of possible control strategies for the 2009 A/H1N1v influenza pandemic in Italy. Epidemiology and Infection, 139: 68-79, 2011.
- F. Iozzi et al. Little Italy: an agent-based approach to the estimation of contact patterns. Fitting predicted matrices to serological data. PLoS Computational Biology, 6(12): e1001021, 2010.
- M. Ajelli et al. Comparing large-scale computational approaches to epidemic modeling: agent-based versus structured metapopulation models. BMC Infectious Diseases, 10:190, 2010.
- S. Merler and M. Ajelli. The role of population heterogeneity and human mobility in the spread of pandemic influenza. Proceedings of the Royal Society B, 277: 557-565, 2010.
- S. Merler et al. Age-prioritized use of antivirals during an influenza pandemic, BMC Infectious Diseases 9:117, 2009
- M. Ajelli and S. Merler. An individual-based model of hepatitis A transmission. Journal of Theoretical Biology, 259(3):478-488, 2009.
- P. Poletti et al. Spontaneous behavioural changes in response to epidemics. Journal of Theoretical Biology, 260: 31-40, 2009.
- S. Merler et al. Coinfection can trigger multiple pandemic waves. Journal of Theoretical Biology, 254(2):499-507, 2008.
- M. Ciofi degli Atti et al. Mitigation measures for pandemic influenza in Italy: an individual based model considering different scenarios. PLoS ONE, 3(3): e1790, 2008.
- M. Ajelli and S. Merler. The impact of the unstructured contacts component in influenza pandemic modeling . PLoS ONE, 3(1): e1519, 2008.
- A. Barla et al. Machine learning methods for predictive proteomics. Briefings in Bioinformatics, 9:119-128, 2008.
- G. Jurman et al. Algebraic stability indicators for ranked lists in molecular profiling. Bioinformatics, 24: 258 - 264, 2008.
- S. Paoli et al. Integrating gene expression profiling and clinical data. Int. J of Approximate Reasoning, 47(1):58-69, 2008.
- M. Ciofi degli Atti et al. Modelling scenarios of diffusion and control of pandemic influenza, italy. Eurosurveillance, 12(1):E070104.2, 2007.
- S. Merler et al. Parallelizing AdaBoost by weights dynamics. Computational Statistics and Data Analysis, 51: 2487-2498, 2007.
- S. Riccadonna et al. Supervised classification of combined copy number and gene expression data. Journal of Integrative Bioinformatics, 4(3):74, 2007.
- M. Cannataro et al. A grid environment for high-throughput proteomics. IEEE Transactions on Nanobioscience, 6(2):117-123, 2007.
- C. Furlanello et al. Combining feature selection and DTW for time-varying functional genomics. IEEE Transactions on Signal Processing, 54 (6): 2436-2443, 2006.
- S. Merler and G. Jurman. Terminated Ramp - Support Vector Machine: a nonparametric data dependent kernel. Neural Network, 19: 1597-1611, 2006.
- C. Furlanello et al. Semi-supervised learning for molecular profiling. IEEE Transactions on Computational Biology and Bioinformatics, 2(2): 110-118, 2005.
- S. Merler et al. Bias-variance control via hard points shaving. International Journal of Pattern Recognition and Artificial Intelligence, 18(5):891-903, 2004.
- C. Furlanello et al. An accelerated procedure for recursive feature ranking on microarray data. Neural Networks, 16(5-6): 641-648, 2003.
- C. Furlanello et al. Entropy-Based Gene ranking without selection bias for the predictive classification of microarray data. BMC Bioinformatics, (4):54, 2003.
- S. Merler et al. Automatic model selection in cost-sensitive boosting. Information Fusion, 4(1):3-10, 2003.
- A. Rizzoli et al. Geographical Information Systems and bootstrap aggregation (bagging) of tree-based classifiers for Lyme disease risk assessment in Trentino, Italian Alps. Journal of Medical Entomology, 39(3):485-492, 2002.
- I. Cattadori et al. Searching for mechanisms of synchrony in spatially structured gamebird populations. Journal of Animal Ecology, 69:620-638, 2000.
- I. Cattadori et al. Synchrony, scale and temporal dynamics of rock partridge populations in the dolomite. Journal of Animal Ecology, 68:540-549, 1999.
- S. Merler and C. Furlanello. Selection of tree-based classifiers with the bootstrap 632+ rule. Biometrical Journal, 39(2):1-14, 1997
- C. Furlanello et al. Speaker normalization and model selection of combined neural nets. Connection Science, 9(1):31-50, 1997
- S. Merler et al. Classification tree methods for analysis of mesoscale distribution of ixodes ricinus (acari: Ixodidae) in Trentino, Italian Alps. Journal of Medical Entomology, 33(6):888-893, 1996.
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