Research team

Global Health Institute (GHI)

Expertise

In his research, Steven Abrams uses and develops novel statistical and mathematical methodology for the analysis of infectious disease data with the aim of improving the understanding of infectious disease dynamics and transmission, the evaluation of intervention strategies and surveillance for vaccine-preventable diseases.

Statistical methods for the estimation of age- and time-dependent epidemiological malaria parameters and the analysis of social network data as a novel approach to design malaria elimination strategies. 01/10/2020 - 30/09/2024

Abstract

The aim of this research project is the development of new advanced, state-of-the-art methodology for epidemiologists, mathematical modellers and biostatisticians interested in modelling vector-borne infectious disease transmission. More specifically, we focus on the estimation of age- and time-dependent epidemiological malaria parameters, correcting for other attribute data, governing the spread and transmission of malaria. In addition, interest is in the identification of key individuals responsible for sustained malaria transmission in low transmission settings. Based on social network analysis techniques, we aim at gaining insights relevant for the development of malaria elimination strategies. In conclusion, the main objectives in this proposal are (1) the development of novel methodology to integrate mathematical and statistical models to estimate time- and age-varying malaria epidemiological parameters in the presence of unobserved heterogeneity; (2) the development of approaches to deal with doubly interval censored observations in combination with outcome-dependent sampling and heterogeneity; and (3) the study of heterogeneity in household conditions and individual attribute data using social network data. Although special attention is directed to malaria, the methodology developed in this project is more widely applicable in the context of vector-borne infections in both human and animal populations.

Researcher(s)

Research team(s)

StatUA, a forum for applied statistics. 01/01/2014 - 31/12/2021

Abstract

This project represents a research contract awarded by the University of Antwerp. The supervisor provides the Antwerp University research mentioned in the title of the project under the conditions stipulated by the university.

Researcher(s)

Research team(s)

  • Social Epidemiology & Health Policy (SEHPO)

Realistic forecasting, control and preparedness for coming COVID-19 waves (RESTORE). 22/05/2020 - 21/05/2021

Abstract

Over the following months Belgian society will be asked to adapt its behavior in order to keep the COVID-19 outbreak under control and not to jeopardise our healthcare system. This calls for an expertbased policy that is supported by accurate medium-term model forecasts on the further COVID-19 progression to assess the impacts of possible control and containment measures. For that purpose, we will start from established population-based SEIR-models and extend these to account for age- and space-dependent disease dynamics, the stochasticity intrinsic to disease spread and hospital bed capacity. These extended models will be complemented with a socioeconomic model to quantify the effects of measures on consumption, labor supply and economic activity. Finally, a controller will be developed that interacts with the aforementioned models to identify the optimal set of control and mitigation measures. The extended and holistic SEIR modeling framework offers a solid basis for providing Belgian healthcare institutions and policy makers with more accurate predictions. Currently, the consortium already provides short-term forecasts to policy makers, and hence it has all connections and simple models in place to guarantee a swift delivery of improved tools.

Researcher(s)

Research team(s)