Computational Epidemiology

My interest is related to reaction-diffusion models, characterized by the interdependence between epidemic spreading (reaction) and human behavior (diffusion), the latter including information awareness, memory, human mobility and social integration.

We have proposed a mobility model at country level and we have intertwined it with a model for the diffusion of awareness about the spreading of a disease. We have shown that standard countermeasures, such as quarantine, might be less effective in containing the disease spreading than targeted information campaigns.
Our work has been awarded the first prize at the Orange D4D Challenge in 2012, with an application to developing countries.


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Countermeasures similar to the one proposed in our work have been successfully applied to boost Ebola awareness in some countries of West Africa, to dramatically reduce the spreading of this deadly disease. [link1] [link2]


In a later study, we have developed a higher-order Markov mobility model, that we have named adaptive memory. This model allows to account for the waiting time of individuals conditional to their historical movements while dramatically reducing the number of spurious mobility patterns affecting more traditional Markovian models, with a clear impact on the spatio-temporal spreading patterns of infectious diseases.


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More recently, we have analyzed human flows of 3.5 million (M) Syrian refugees in Turkey inferred from massive mobile-phone data to verify the concern that a massive arrival of people— often from a country with a disrupted healthcare system—can increase the risk of vaccine-preventable disease outbreaks like measles. We used multilayer modeling of interdepen- dent social and epidemic dynamics to demonstrate that the risk of disease reemergence in Turkey, the main host country, can be dramatically reduced by 75 to 90% when the mixing of Turkish and Syrian populations is high. Our results suggested that maximizing the dispersal of refugees in the recipient population contributes to impede the spread of sustained measles epidemics, rather than favoring it. Targeted vaccination campaigns and policies enhancing social integration of refugees are the most effective strategies to reduce epidemic risks for all citizens.

Figure: Model structure and human mobility. (A) Schematic illustration of the model considered in this work. Each prefecture of Turkey is considered as a node of a metapopulation network of geographic patches. Two populations, namely, Turkish and Syrians, are encoded by different colors and move between patches following the inferred interpatch mobility pathways. Turkish and Syrian populations encode two different layers of a multilayer system (31–33), where social dynamics and epidemics spreading happen simultaneously. (B) Mobility of Syrian refugees (Upper) and Turkish citizens (Lower) between the prefectures of Turkey as inferred from CDRs. Different colors are used to indicate the number of individuals moving from a prefecture to another.

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