Our main aim is to advance the understanding of infectious diseases through mathematical and statistical models. In particular, we focus on developing innovative approaches to address the multifaceted challenges posed by infectious diseases within clinical and epidemiological contexts. Our research encompasses a wide range on infectious diseases including both directly transmitted pathogens (e.g. SARS-CoV-2) and vector-borne diseases (e.g. malaria, schistosomiasis). Specific research activities are in the areas of transmission models and scenario analysis to estimate the impact of control interventions, longitudinal and survival models, Bayesian spatial and latent variable models.
SchiSTOP: Beyond mass drug administration: understanding Schistosomiasis dynamics to STOP transmission.
Whitin this project, we developed SchiSTOP, a hybrid stochastic agent-based and deterministic modelling framework to reproduce S. mansoni transmission in an age-structured human population, including the parasite dynamics in the intermediate host (freshwater snails).
SchiSTOP is flexible in the implementation of the assumptions of regulating mechanisms for S. mansoni transmission. It has been developed to explore the interplay of different regulating mechanisms and their ability to explain observed patterns in S. mansoni epidemiology. However, its formulation is suitable to answer diverse research questions about the epidemiology and control of schistosomiasis. For instance, the presence of a specific module for the dynamics in snails allows for a thorough assessment of the impact of snail control interventions.
Project: https://cordis.europa.eu/project/id/846873
EQUalS: Mathematical modelling to inform an EQUitable and effective response to EpidemicS
The COVID19 pandemic has brought to the fore long-standing inequities that resulted in already-vulnerable groups bearing a disproportionate burden of the disease. Poor and minority groups are more likely to be infected and to experience severe outcomes. This may be due to biological factors like susceptibility and infectiousness or behavioral like contacts rates. Inequalities may have been exacerbated by non-pharmaceutical interventions (NPIs) along with differences in the ability to comply to them. These factors are all likely to differ by socio-economic status (SES). Indistinguishable in the reported disease figures, inequality factors have different implications in terms of expected effectiveness of NPIs. Mathematical models informing epidemic control policies have not accounted for equity as it is hard to disentangle the key drivers.
In this project, we develop a novel approach that builds on existing and novel data sources to i)resolve the relative impact of the key drivers. We use improved stochastic transmission models to ii)estimate the implications of NPIs on the underlying inequalities and design effective and equitable interventions.epidemic while reducing disease inequality.
Veronica Malizia, Jordache Ramijth, Federica Giardina, Kit (C.B.) Roes