Veterinary Epidemiology and Zoonosis

EMPIRICAL SIMULATION MODELING

• Simulation models can be used to forecast disease incidence accurately.
• These forecasts are of value in selecting suitable prophylactic measures,
  • Empirical model utilize indicators that are obtained by analyzing the relationship between morbidity and any associated variables.
  • Frequently used variables are those relating to climate.
  • These models are not strictly mathematical models because they do not attempt to analyze the dynamics of agent’s life cycles, but simply quantify associated phenomena.
  • They are sometimes refers to as “black-box’ models because the relationship between data that are fed into the model and the results that are generated cannot be satisfactorily explained.

Example: Forecasting the incidence of Fascioliosis

• The lifecycle of Fasciola hepatica and Fasciola gigantica is complex, involved in stages inside a final and intermediate host and on herbage.
• Two important meteorological factors in the development of a parasite are temperature above 10˚C and the presence of free water. This is the basis of the ‘Mt’ forecasting system for fascioliosis.
• ‘Mt’ is a monthly index of witness given by,

                                  ‘Mt’ = (R-p+5) n

• R - Rainfall in inches
• p - Potential transpiration
• n - Number of rain days

• Seasonal summation of ‘Mt’ indices (Σ Mt) can be calculated by adding the ‘Mt’ values for the six months period.
• This sum simulates the progression of the disease in relation to changing meteorological conditions and so can be used to predict losses owing to Fascioliasis, so that suitable prophylactic measures can be undertaken.