The impact of DEmographic Changes on Infectious DisEases transmission and control in middle and low income countries (DECIDE)
Principal Investigator: Alessia Melegaro
Dondena Researchers: Piero Poletti, Emanuele Del Fava
Associate Researchers: Piero Manfredi (Università di Pisa), Stefano Merler (Fondazione Bruno Kessler, Trento), Simon Gregson (Imperial College London & BRTI, Harare), James Nokes (Kenya Medical Research Institute, Kilifi), John Williams (Imperial College London)
Contract Type: ERC Starting Grant 2011 (grant agreement no.: 283955)
Project Funding: 1,210,000€
Start Date: 1 April 2012
End Date: 31 March 2017
General Field of Research
DECIDE assesses the impact of demographic changes on infectious diseases transmission and control in middle/low income countries.
1. To evaluate the short and medium term impact that demographic changes, such as fertility decline, urbanization, mortality decline and the additional demographic impact of the HIV/AIDS epidemic, may have on infection transmission and, consequently, on morbidity and mortality of critical childhood diseases.
2. To advance the traditional mathematical modeling framework used to evaluate the impact of public health interventions for infectious diseases to incorporate realistic processes of population transition.
3. To produce a new generation of epidemiological models capable of studying the potential implications of these demographic changes on long-term trajectories of infectious diseases and of control strategies.
1. Gathering of social contact data: To assess how past, ongoing and future changes in key demographic processes (i.e., fertility and mortality) and structures (i.e., households, schools) affect infection transmission, we will gather social contact data that are of critical importance to the spread of infections and to identify the most optimal intervention to put in place. Social contact data will be collected in two Sub-Saharan countries (Kenya and Zimbabwe) and in demographic settings representative of the different phases of the ongoing demographic transition.
2. Development of an individual based model: To predict the impact of forecasted population changes on relevant socio-demographic structures, we are developing an individual-based-model (IBM) parameterized and validated through the use of DHS data. One of the main outputs of the model will be a set of time–dependent “synthetic” contact matrices by age and settings in each of the sites under study.
3. Statistical modeling of demographic data: To investigate the association between life course trajectories, HIV/AIDS prevalence, and sexual behaviors, we will make use of innovative statistical models based on the analysis of life events sequences, taking therefore into account the strong dependence between all the important life events.
4. Modeling for policy: To identify the optimal intervention policies, a set of infection-specific transmission dynamic models will be developed with realistic and time-changing population structures (age distribution, mortality, family size, space) and contact patterns.
European Research Council
Last updated 03 March 2014 - 13:12:30
Dondena - Università Bocconi