DEmographic Changes on Infectious DisEases transmission and control in middle and low income countries (DECIDE)

DECIDE assesses the impact of demographic changes on infectious diseases transmission and control in middle/low income countries.

Project Aims

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 modelling 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.

Project Components

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 modelling of demographic data: To investigate the association between life course trajectories, HIV/AIDS prevalence, and sexual behaviours, 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. Modelling 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.

Contact person: Alessia Melegaro (University of Bocconi) and John Williams (Imperial College London) 

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