Research Project:
Preventive health behaviors, such as physical distancing, mask-wearing, and vaccination, are all effective measures to counter the propagation of respiratory infectious diseases. Their adoption by individuals is generally the result of internal and external factors. These factors include risk perception, fear or awareness, past healthcare experiences, and exposure to social and virtual groups. Understanding the underlying mechanisms affecting behavioral response is thus pivotal to maximizing public health efforts during an outbreak and prompting compliance with those measures.
The BEHAVE-MOD project aims to integrate these behavioral aspects into the study of disease dynamics to generate a novel unified modelling framework.
Risk and behavioral data will be collected during the seasonal peak via online surveys within the Italian research project BEHAVE-MOD. These data will be complemented with both traditional socio-demographic data and unconventional data on social media and internet usage. This will allow us to: i) investigate the determinants influencing compliance with health preventive behaviors and vaccine hesitancy to infectious diseases in the post-pandemic scenario; ii) simulate disease dynamics with an increased level of realism while assessing the impact of behavioral components on epidemic trajectories.The findings will enable us to design a portfolio of preventive and control interventions targeting the most negatively affected subgroups and enhance their effectiveness.

Job description:
A combination of quantitative approaches and research methodologies will be required to extend the current state-of-the-art of the epidemiological models integrating behavior. Data collected through surveys will be used to study the interplay between risk perception, peer influence, and adoption of preventive behaviors or vaccine hesitancy during the seasonal peak. Statistical and machine learning techniques will be used to achieve this goal. Agent-based and compartmental models will then be developed to unravel the complex relationship between preventive behaviors, vaccination choices, and disease burden.
The successful candidate should have a quantitative background with an already solid experience in mathematical modeling of infection transmission, and related statistical methods. She/he should moreover demonstrate previous involvement in research projects relevant to the BEHAVE-MOD framework, should be able to follow projects through from conception to publication in high impact factor journals, be a strong writer, have experience presenting work to scientific audiences from diverse fields and enjoy working collaboratively. The successful candidate will be part of a team of epidemiologists, statisticians, demographers, sociologists and computational social scientists. Supervision of more junior team members might be required.

Candidates are required to have a PhD or equivalent, carried out in Italy or abroad, on one of the following: physics, computer science, informatics, mathematics, demography, mathematical-biology/quantitative epidemiology/statistics/data science/network sciences. High quantitative skills with statistical software (Stata, R) and at least one programming language (Python/C/C++) as well as excellence proficiency in English will be required.