About this Research Topic
The volume and complexity of agricultural and livestock data available to support disease prevention and control, and production has grown rapidly over the last decade. This rapid increase in the quantity of data has not necessarily resulted in a consequent ability to improve the quality of production and to inform policy. There is an urgent need for increasing our ability to apply computational tools to mine big agro-ecosystems data through an interdisciplinary team of agricultural, medical and social scientists in order to improve efficiency of food production systems and provide science based policy inputs, with the ultimate objective of improving health and well-being of local and global communities. The research topic here includes presentation at a workshop on Big Data-related research applied to disease prevention and control under the umbrella of one medicine, one science. The workshop was held as part of the iCOMOS conference in Minnesota, April 2016. iCOMOS is one of the leading conferences worldwide in the field of one medicine, one science. Spontaneous submissions related with the analysis of big data with application to human, plant, or animal health, contributing to improve the efficiency of food production systems, are also welcome.
Keywords: Big data, disease prevention and control, analysis, policy
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