Agrometeorology and models for the parasite cycle forecast

Area of Science:

• Agricultural Science
• Epidemiology
• Environmental Science

Background:

• Insects and vector-borne diseases are significantly influenced by meteorological variables.
• Mathematical models are crucial for forecasting pest cycles and disease vector abundance using climate data.
• The Sicilian Agrometeorological Information System (SIAS) utilizes agrometeorological models for integrated pest management in citrus orchards.

Purpose of the Study:

• To leverage SIAS's expertise in agrometeorological modeling.
• To develop a deductive model for Phlebotomus perniciosus, a primary vector of leishmaniasis in Sicily.
• To aid in the control of leishmaniasis by understanding vector population dynamics.

Main Methods:

• Utilizing a network of 95 GSM meteorological stations in Sicily.
• Adapting existing mathematical models developed for agricultural pests (Aonidiella aurantii).
• Developing a deductive model specifically for the phenological phases of Phlebotomus perniciosus.

Main Results:

• Successfully adapted SIAS's modeling experience for a new vector-borne disease context.
• Established a framework for a deductive model to predict Phlebotomus perniciosus populations.
• Provided a basis for integrated vector management strategies against leishmaniasis.

Conclusions:

• Agrometeorological modeling offers a viable approach for managing disease vectors.
• The developed model for Phlebotomus perniciosus can support leishmaniasis control efforts in Sicily.
• This interdisciplinary approach highlights the potential of climate data in public health management.