We enhance prediction of occurrence and spread of viral epidemics and AMR in the human and animal populations as well as the design of managing risk attributed to these epidemics by integrating data from community-based approaches to national surveillance, genomic and traditional epidemiology through the use of digital and data sciences
The COVID-19 pandemic, the Ebola Virus Disease epidemic in West Africa and the relative high burden of communicable diseases in Africa, have emphasized the necessity for having, in situ in Africa, well-coordinated national action plans for health security, regional and continental coordination. For efficiency, all should be supported by relevant One Health expertise for disease surveillance and pathogen detection/identification especially at the human, animal and environment interface.
The existing opportunities in availability of data from multiple sources like climate, human and animal demographics, occurrence of endemic and epidemic diseases etc., we continue to face the challenge of exploiting integrative modelling to predict future occurrence of disease epidemics
Digital and data sciences is an evolving field with huge potential to provide quick and reliable epidemiological insight to trends and identify patterns that may support national authorities in effective and coordinated preparedness and response to disease events, especially viral epidemics and AMR. This programme focus to build such regional capacity, based on these four pillars
How could the integration of data from a community-based approach to national disease surveillance, genomic and traditional epidemiology plus sociological approaches with those data from geo-spatial and climatic studies enhance the prediction of occurrence, spread and design of risk management of viral epidemics and AMR in the human and animal populations, when the analyses and modelling are enhanced by the inclusion of Artificial Intelligence and Geospatial Analyses?. The research topics include
Using integrative technology-driven disease surveillance tools to enhance the efficiency of epidemic intelligence
Applying AI to predict trends and provide real-time monitoring, control and management of infectious disease outbreaks
To design, develop, validate and pilot One Health-based epidemic intelligence tools
To design and develop tailored courses adapted to specific data management needs of key actors at different levels of human and animal health systems
To operationalize One Health disease surveillance at community level
To assess how intelligent monitoring tools that will employ Geospatial science could efficiently monitor environmental and climate changes related to infectious disease in various seasons of the year.
To develop a disease modelling which can predict and forecast the future spread of disease for the public health (human and animal health systems) in the community areas.
To identify and analyze the risk concerned with environmental health while considering the influencing factors (i.e. exposure of hazardous wastes, pollutions, proximity to industrial sites, and healthcare).
To determine socio-ecological drivers of infectious diseases at different levels of human and animal health systems, including community
To develop and evaluate the potential effect of different multi-level health system interactive models on the transmission dynamics of infectious diseases
To determine the interactive effect of socioecological factors and environmental factors on transmission dynamics of climate sensitive zoonotic diseases
To determine high-risk areas that could be targeted for preparedness and monitoring of endemic and emergence of viral epidemics and AMR;
To build capacity of front liners in respect to risk assessment, communication and management of viral epidemics and AMR;
To determine the drivers of endemic and emerging viral epidemics and AMR
Develop a smart digital platform (SDP) for One Health in Africa for quick response and resilience.
Develop prediction models for early warning and immediate response of infectious diseases in Africa
Develop artificial intelligence -based management practices for ethical, legal, and societal implications in health sector