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Reflexiones sobre el pasado, el presente y el futuro de la epidemiología cuantitativa de las enfermedades de las

Laurence V Madden¹

¹Department of Plant Pathology, The Ohio State University, Wooster, Ohio, USA;

Annual review of phytopathology

|September 3, 2025

Resumen

La epidemiología de enfermedades de las plantas ha avanzado significativamente desde 1963, integrando más matemáticas y computación. Los principios clave permanecen, con el progreso impulsado por las estadísticas, la IA y el aprendizaje automático para un mejor modelado y pronóstico de enfermedades.

Área de la Ciencia:

Patología vegetal
Epidemiología
Biología cuantitativa

Sus antecedentes:

La epidemiología moderna de las enfermedades de las plantas comenzó en 1963 con una reunión internacional y el libro de Vanderplank.
El campo ha evolucionado durante más de 60 años, incorporando métodos cuantitativos avanzados.

Objetivo del estudio:

Reflexionar sobre los principales desarrollos en la epidemiología de las enfermedades de las plantas en las últimas seis décadas.
Resaltar la integración de las matemáticas, las estadísticas y los métodos computacionales.

Principales métodos:

Revisión de la evolución histórica de la epidemiología de las enfermedades de las plantas.
Análisis de la evolución de los enfoques cuantitativos en el modelado y la predicción de enfermedades.

Principales resultados:

La epidemiología de enfermedades de las plantas se ha expandido con un mayor uso de herramientas matemáticas, estadísticas y computacionales.
Dos áreas principales: el modelado teórico/semiempírico y la predicción de epidemias en tiempo real.
El progreso está vinculado a los avances en las estadísticas, la computación, el aprendizaje automático y la IA.

Conclusiones:

Los principios fundamentales de Vanderplank en la epidemiología de enfermedades vegetales siguen siendo relevantes.
El campo continúa avanzando a través de la integración interdisciplinaria y la innovación tecnológica.
El progreso futuro depende del desarrollo continuo de enfoques computacionales y basados en datos.

Palabras clave:

Pronóstico Aprendizaje automático Los modelos Pronóstico las estadísticas Teoría

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