The tool was developed through collaboration between researchers at AfriAI Lab and veterinary experts. It leverages data based on clinical symptoms, and machine learning techniques.

The tool uses machine learning algorithms (supervised learning models) to analyze disease patterns and clinical data.

Clinical data records of infected and healthy livestocks.

Data was cross referenced with veterinary expert to ensure accuracy and categorized into positive and negative cases to train the model.

Field vet input observable clinical symptoms of livestock (e.g., fever, nasal discharge) through a web-based interface.

The tool cleans and processes the input data to ensure consistency.

The AI tool applies a trained machine learning model to analyze the symptoms and combines results with a python function to predict the likelihood of PPR.

The tool provides a prediction score of PPR or likeliness of other similar diseases like Blue tongue or CBPP.

Surveillance of PPR detected points.

Veterinarians and farmers.

Observable clinical symptoms (temperature, nasal discharge, mouth sores, difficult breathing, diarhhea, eye discharge, oral nasal lesion).

The prediction whether positive or negative PPR. Positive PPR has the prediction score while negative is accompanied with the indication of presence of Blue tongue or CPBB.

Not to the farmer.

Unlike traditional methods, which requires sending samples to distant laboratories, the AI tool offers immediate predictions in the field hence proving to more efficient.

Traditionally it can take more than 3 days because of sampling, transportation and clinical testing but the AI tool takes less than 1 minute.

The AI tool can be used by all vets and farmers even those with limitations/shortage in diagnostic facilities.

Yes

https://drive.google.com/drive/folders/151ovuvJyk4N6QW-7g6zWQKBRdCiRmhZF?usp=sharing 

Yes

Availability of sufficient and high quality data to retrain the model so that it performs better.

It requires Internet connectivity to be used.

Not yet available in other languages that are common to farmers like Swahili and vernacular languages.

Conducting field trials across multiple regions.

More domain experts to be engaged in testing and validation.

A feedback loop to improve the model.

The current accuracy rate is 91% although it may vary depending on how the system is inputted or reported.

Symptoms variability when the different breeds of sheep and goats manifest the symptoms differently

Software or hardware bugs can impact functionality.

Environmental factors being dominant such that the model will need retraining.

How accurately does the AI tool predict compared to traditional methods?

How reliable is the tool across different regions?

What is the rate of false positive and false negatives in the field conditions?

An experienced veterinarian can interpret a vague symptom better than an AI tool.

Environmental factors can highly impact AI accuracy than human diagnosis.

Model training data

Algorithmic limitations

Retraining with new data

Incorporating feedback to refine the tool.

Disease prevention through timely detection and surveillance. 

Facilitate data driven insights so that the policies and strategies can be enforced well.

Allow for interventions to be sent to the needed areas.

Cost reduction for the diagnosis of the disease.

Food security improved by mitigating disease impact.

Contribute to facilitate global eradication of PPR.

Recommendations feature

Multilingual feature

Research collaborations to collect diverse datasets and improve the AI tool.

Government support to incorporate the tool in DVS disease management systems