Artificial Intelligence and One Health: Knowledge Bases for Causal Modeling
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Artificial Intelligence and One Health: Knowledge Bases for Causal Modeling Nitin Pandit1* and Abi T. Vanak1,2,3 Abstract | Scientists all over the world are moving toward building database systems based on the One Health concept to prevent and manage outbreaks of zoonotic diseases. An appreciation of the process of discovery with incomplete information and a recognition of the role of observations gathered painstakingly by scientists in the field shows that simple databases will not be sufficient to build causal models of the complex relationships between human health and ecosystems. Rather, it is important also to build knowledge bases which complement databases using non-monotonic logic based artificial intelligence techniques, so that causal models can be improved as new, and sometimes contradictory, information is found from field studies.
1 Background The recently launched National Mission on Biodiversity and Human Well-Being (NMBH)1 aims to conserve and restore the rich but rapidly degrading biodiversity of India. Launched by the Prime Minister’s Science, Technology and Innovation Advisory Council in 2019, the NMBH is designed to bring together several disciplines which impact and are impacted by biodiversity. Driven by respected research institutions in India, the NMBH is the first step toward developing the science and for building the capacity needed for the integration of biodiversity in the areas of agriculture, disaster management, climate change, bioeconomy, ecosystem services and health. Post its launch, COVID-19 happened, providing a fillip to the component on biodiversity and health, as the role of zoonotic diseases came into limelight2. The world over, the scientific community is focused on the emerging trans-disciplinary approaches of One Health, a discipline that characterizes the relationships of biodiversity vis-a-vis human and public health3. The glue that binds the components of the ambitious mission is a geospatial database for cataloguing and mapping life (CML). The design of the CML is largely based on the experience of the India Biodiversity Portal (IBP4), which has
J. Indian Inst. Sci. | VOL xxx:x | xxx–xxx 2020 | journal.iisc.ernet.in
been designed to support researchers and interested citizens in collection and collation of biodiversity related data sets. Concurrently, many other systems for biodiversity data have been created around the world, such as G BIF5, with applications ranging from species identification6 to reintroduction7. Modern algorithms using big data driven machine learning (ML)8 and neural networks (NN)9, coupled with sensors with new capabilities such as b ioacoustics10 and analytical approaches such as genomics11, are used to complement traditional approaches of biodiversity conservation in situ and in vivo. Meanwhile, data and models about human health are also becoming increasingly complex, as medical discoveries utilize new computation assisted approaches for health management from prevention to c
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