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Ordinal regression models for zero-inflated and/or over-dispersed count data.

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A new ordinal regression model (MN-MS) effectively models challenging count data, outperforming traditional methods. This model reveals how environmental factors influence malaria-carrying mosquito biting rates in the Peruvian Amazon.

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Area of Science:

  • Ecology
  • Environmental Science
  • Biostatistics

Background:

  • Count data present modeling challenges due to zero-inflation and over-dispersion.
  • Existing parametric models lack a consensus for optimal selection.
  • Accurate modeling is crucial for understanding ecological processes and disease vectors.

Purpose of the Study:

  • To propose an ordinal regression model (MN) as a default for count data.
  • To extend the MN model with automatic model selection (MN-MS) for improved inference.
  • To apply the MN-MS model to analyze mosquito biting rates and environmental factors in the Peruvian Amazon.

Main Methods:

  • Development of a novel ordinal regression model (MN).
  • Extension to an automatic model selection framework (MN-MS).
  • Application of MN-MS to analyze mosquito biting rates in relation to environmental variables.

Main Results:

  • The MN-MS model demonstrated superior inference compared to traditional approaches.
  • MN-MS achieved excellent fit and predictive skill in the Peruvian Amazon study.
  • Distinct mosquito species showed varied responses to landscape features, with implications for malaria transmission.

Conclusions:

  • The MN and MN-MS models offer valuable tools for environmental and ecological modeling.
  • These models provide robust methods for analyzing complex count data.
  • Findings highlight the impact of landscape on mosquito ecology and disease vector dynamics.