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The hosts' susceptibility to infection depends on several factors. The integrity of the skin and mucous membranes helps protect the body against microbial attacks. When the skin is altered, the chance of infection, limb loss, and even death increases.
The integrity and count of the white blood cells help the body resist pathogens and fight infection. When impaired, it reduces the body's resistance to pathogens. The acidic pH levels of the gastrointestinal, genitourinary tracts, and skin...
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Susceptible host availability modulates climate effects on dengue dynamics.

Nicole Nova1, Ethan R Deyle2,3, Marta S Shocket1,4

  • 1Department of Biology, Stanford University, Stanford, CA, USA.

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PubMed
Summary

Climate significantly impacts dengue transmission, but interactions with host susceptibility are complex. Our study reveals temperature and rainfall effects are context-dependent, improving dengue incidence forecasts.

Keywords:
Arbovirusclimatedengueempirical dynamic modellingforecastingrainfallsusceptible population sizetemperaturevector-borne disease

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Area of Science:

  • Epidemiology
  • Climate Science
  • Mathematical Biology

Background:

  • Climate variability is increasingly recognized as a factor influencing the transmission dynamics of mosquito-borne diseases.
  • Understanding the complex, nonlinear interactions between climate variables and host population dynamics is crucial for predicting disease outbreaks.
  • Previous models often struggle to capture these intricate relationships, limiting forecasting accuracy.

Purpose of the Study:

  • To identify key climatic drivers and their interactive effects on dengue transmission dynamics.
  • To develop an empirical model that captures nonlinear and context-dependent relationships between climate, host susceptibility, and dengue incidence.
  • To improve the predictive skill of dengue incidence forecasts.

Main Methods:

  • Utilized nonlinear time series analysis, termed empirical dynamic modeling, to analyze incidence data, susceptible population size, and climate data.
  • Investigated the interactive effects of temperature, rainfall, and susceptible host availability on dengue transmission.
  • Validated the model's performance against existing state-of-the-art forecasting models.

Main Results:

  • Identified that climatic forcing on dengue transmission is significant only when susceptible host populations are abundant.
  • Determined that temperature has a net positive effect, while rainfall has a net negative effect on dengue dynamics.
  • Demonstrated that the model, by incorporating mechanistic, nonlinear, and context-dependent effects, achieved improved forecast skill for dengue incidence.

Conclusions:

  • The interdependence of host population susceptibility and climate critically drives dengue dynamics in a nonlinear and complex manner.
  • Empirical dynamic modeling provides a powerful framework for understanding and predicting climate-sensitive infectious diseases.
  • These findings offer valuable insights for public health interventions and climate adaptation strategies for dengue prevention.