CaDDiS A Diagnostic Tool for African Cattle Diseases

CaDDiSA Diagnostic Tool forAfrican Cattle DiseasesIain McKendrick(Yiqun Gu, George Gettinby,Crawford Revie)

JustificationIn Sub-Saharan Africa:• Population growth of 3.1% p.a.• Major losses through livestock disease– East Coast Fever alone kills >1,000,000 cattlep.a. in eastern, central and southern Africa– Costing > US$ 165,000,000 p.a.• Shortage of qualified veterinary surgeons

Expert Systems• Rule-Based Systems• Bayesian Belief Networks

Sample Rule from ECFImmunisation ModuleIF animal age < 1 month THEN“ECFiM can only be successfullycarried out in cattle older than 1month. Immunise the calves oncethey reach the age of 1 month,but preferably before they reachthe age of 4 months.”

Expert Systems• Rule-Based Systems– inflexible– difficulty in incorporating uncertainty• Bayesian Belief Networks– flexible– based on a probabilistic paradigm

A ‘Simple’ Example• In Edinburgh, I overhear a womanspeaking Finnish. What country doesshe probably come from?• What if she was speaking French?• What if we were in Brussels?• What if she then said something inEnglish, with a Scandinavian accent?

Bayes’ TheoremDisease: East Coast FeverOcular Lesions: ‘TRUE’ ‘FALSE’‘TRUE’ 5 6‘FALSE’ 5 970P(ECF)=0.01P(ECF | Ocular Lesions)=0.45P(A | B) = P(B | A) x P(A) / P(B)

Bayesian Belief….• Bayesian updating of probabilitydistributions• Joint distribution of all variables andparameters difficult to handle• Simplify using conditional independence todefine an acyclical directed propagationnetwork

…NetworksChangein urineFeverSwollenlymphnodesAnaplasmosis ECF Babesiosis

Materials• Questionnaire-based data• Derived from veterinary experts• Impossible to collect information relatingto conditionals between clinical signs

Anaplasmosis• Intraerythrocytic parasites of ruminants,usually Anaplasma marginale• Spread by 16 species of tick• Resistance in young gives rise to endemicstability

Anaplasmosis Signs• Anaemia• Change in urine• Depression/Lethargy• Transient febrile response• Loss of condition• Drop in milk yield• Inappetance• Laboured breathing• Pale/yellow mucous membranes

Babesiosis (Redwater)• Intraerythrocytic protozoan parasites ofruminants, Babesia bigeminaor Babesia bovis• Largely spread by one host Boophilus spp ticks• Resistance in young gives rise to endemicstability

Babesiosis Signs• Anaemia• Change in urine• Nervous Signs• Fever• Loss of condition• Drop in milk yield• Inappetance• Depression/Lethargy• Jaundice• Accelerated breathing• Muscle tremors

East Coast Fever• Protozoan parasite of ruminants, Theileriaparva• Spread by 3 species of tick, but primarilyRhipicephalus appendiculatus• Resistance in young gives rise to endemicstability

Rhipicephalusappendiculatus

T. parva sporozoites(Michael Shaw,ILRAD)

ECF Signs• Anaemia (??) or Sudden death• Coughing• Swollen lymph nodes• Fever• Inappetance• Loss of condition• Drop in milk yield• Depression/Lethargy• Laboured breathing• Nasal discharge• Diarrhoea• Nervous symptoms

CaDDiS in Action• http://vie.dis.strath.ac.uk/vie/CaDDiS/

Strengths andWeaknesses: 1• Interesting results• Simple network structure means thatsystem not good at ‘writing off’ redundantinformation

Strengths andWeaknesses: 2• Not based on consensus• Allows for variation and uncertainty acrosstime, space and users

Strengths andWeaknesses: 3• Easy to use• Users may misinterpret results– over-interpretation of results– misunderstanding of ‘belief’ statements

Strengths andWeaknesses: 4• Generic• Allows probabilistic inputs• Sometimes difficult to follow the logic...

Conclusions• A successful pilot• Scope to develop useful expert systems• Needs more and better data• A useful teaching tool?• Clinicians don’t need to worry……..

Acknowledgements• Work carried out within VeterinaryInformatics Group, Universities ofGlasgow/Strathclyde• Financed by ODA/DFID• In association with ILRAD/ILRI, Nairobi• Many other experts, but especially TonyIrvin & Susanna Williamson• ILRI/VETAid for most of the pictures

Reference• McKendrick, I.J., Gettinby, G., Gu, Y., Reid,S. & Revie, C. (2000) Using a BayesianBelief Network to aid differential diagnosisof tropical bovine diseases. PreventiveVeterinary Medicine, 47, 141-156.