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Ross-Macdonald models: Which one should we use?

Mario Ignacio Simoy1, Juan Pablo Aparicio2

  • 1Instituto de Investigaciones en Energía no Convencional (INENCO), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Universidad Nacional de Salta, Av. Bolivia 5100, Salta 4400, Argentina; Instituto Multidisciplinario sobre Ecosistemas y Desarrollo Sustentable, Universidad Nacional del Centro de la Provincia de Buenos Aires (UNICEN), Facultad de Ciencias Exactas, Paraje Arroyo Seco s/n, Tandil 7000, Argentina.

Acta Tropica
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Summary
This summary is machine-generated.

This study compares various Ross-Macdonald models for vector-borne diseases, revealing how different assumptions about disease periods significantly impact epidemic dynamics and outcomes.

Keywords:
Agent based modelDelayed modelEpidemiologyRoss-Macdonald model

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Area of Science:

  • Mathematical modeling
  • Epidemiology
  • Vector-borne diseases

Background:

  • Ross-Macdonald models are foundational for vector-borne disease dynamics.
  • Existing models often use varied formulations without exploring dynamical consequences.
  • A comprehensive comparison of different Ross-Macdonald model assumptions is lacking.

Purpose of the Study:

  • To present and analyze diverse Ross-Macdonald model formulations.
  • To investigate the dynamical impact of varying assumptions on disease periods.
  • To compare model outputs with empirical data and highlight key drivers.

Main Methods:

  • Developed an agent-based model with arbitrary latency/infectious period distributions.
  • Created a deterministic Volterra integral equations model with arbitrary waiting times.
  • Compared model solutions using epidemic statistics (peak, final size) and basic reproduction number (R0).

Main Results:

  • Different distributions for latent and infectious periods significantly alter epidemic curves.
  • Agent-based models provided empirical estimations for R0.
  • Seasonality was identified as a critical factor influencing epidemic dynamics and duration.

Conclusions:

  • Realistic distributions for latent and infectious periods are crucial for accurate modeling.
  • Model formulation choices profoundly affect vector-borne disease dynamics.
  • Seasonality plays a key role in shaping epidemic patterns.