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  # Machine Learning Dataset for Rabies Diagnosis and Outbreak Prediction
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- Contact:
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- - Asa Emmanuel at [email protected] and Kennedy Lushasi at [email protected]
 
 
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  This dataset will help in the real-time and remote diagnosis of rabies disease for humans and animals in low-resource settings. A time series approach can be applied to the outbreak dataset to predict the number of rabies cases likely to occur within an area after a given time interval. This approach can help with resource mobilization, too, such as identifying the number of vaccines required in a specific area at a given time. The number of observations from the two datasets is 12,684. There are three datasets for rabies diagnosis for animals and humans, with 7,081 and 4,585 observations, respectively. In the outbreak prediction dataset, 1,018 observations were accounted for.
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- Authors and Affiliations:
 
 
 
 
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- - Asa Emmanuel, Rebecca Chaula, Deogratias Mzurikwao, Joel Changalucha, Kennedy Lushasi
 
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  # Machine Learning Dataset for Rabies Diagnosis and Outbreak Prediction
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+ ## Contact
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+ Asa Emmanuel at [email protected] and Kennedy Lushasi at [email protected]
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+ ## Dataset
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  This dataset will help in the real-time and remote diagnosis of rabies disease for humans and animals in low-resource settings. A time series approach can be applied to the outbreak dataset to predict the number of rabies cases likely to occur within an area after a given time interval. This approach can help with resource mobilization, too, such as identifying the number of vaccines required in a specific area at a given time. The number of observations from the two datasets is 12,684. There are three datasets for rabies diagnosis for animals and humans, with 7,081 and 4,585 observations, respectively. In the outbreak prediction dataset, 1,018 observations were accounted for.
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+ ## Authors and Affiliations
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+ Asa Emmanuel, Rebecca Chaula, Deogratias Mzurikwao, Joel Changalucha, Kennedy Lushasi
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+ ## How to cite
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+ You can cite all versions by using [DOI 10.5281/zenodo.10068425](https://zenodo.org/doi/10.5281/zenodo.10068425).