[2] The knowledge gained by researchers in the field of disease informatics can be used to aid policymakers' decisions on issues such as spreading public awareness, updating the training of health professionals, and buying vaccines.
[2] Aside from aiding in policymakers' decisions, the goals of disease informatics also include increased identification of biomarkers for transmissibility, improved vaccine design, and a deeper understanding of host-pathogen interactions, and the optimization of antimicrobial development.
Advances with AI and increased accessibility of data aid in predictive modeling and public health surveillance.
[3] AI also provides a valuable avenue by combining its ability of spatial modeling with geographic information system (GIS) data to uncover geographical patterns (for example disease clusters) to support data-driven decision-making for local-level predictions of disease diffusion.
[2] Using these tools, researchers can apply them to data sets (for example genomic data, social media posts, and health records) to make predictions about the potential sources of an outbreak, the likelihood of an individual contracting a certain disease, and forecasting the number of cases of a disease in a given region.
[2] The use of text mining has become a beneficial avenue for querying large amounts of data to aid in gene mapping and the analysis of genomes.
Other important sources that are commonly used synchronically include the following:[4] The accuracy of these AI tools and techniques relies upon providing them with high-quality, comprehensive data.
[3] However, the same issues of different formatting and software to ensure model convergence still affect this approach as well, so algorithmic improvements are needed.
Any models or techniques being used need to be in compliance with local governmental regulations and laws such as HIPAA in the United States.