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Field Postmortem Rabies Rapid Immunochromatographic Diagnostic Test for Resource-Limited Settings with Further Molecular Applications
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Early outbreak detection in endemic settings using a novel method applied to sparse Rift Valley fever incidence data.

Alexandros Angelakis1,2, Bryan O Nyawanda1,2, Anna-Sofie Stensgaard3

  • 1Swiss Tropical and Public Health Institute, 4123, Allschwil, Switzerland.

Scientific Reports
|April 17, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a new model to detect Rift Valley Fever (RVF) outbreaks, even with limited data. The method improves accuracy in identifying disease phases for better public health surveillance.

Keywords:
Bayesian analysisHidden Markov modelNegative binomial distributionPublic health surveillanceRift Valley fever (RVF)Zoonotic diseases

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Area of Science:

  • Epidemiology
  • Veterinary Public Health
  • Mathematical Modeling

Background:

  • Rift Valley Fever (RVF) is a significant zoonotic disease transmitted by mosquitoes, with outbreaks influenced by climate.
  • Sparse surveillance data and high zero counts pose challenges for accurate outbreak detection and timing.

Purpose of the Study:

  • To develop and evaluate an improved statistical framework for detecting Rift Valley Fever (RVF) outbreaks.
  • To address data sparsity and zero-inflation issues in surveillance data for enhanced outbreak analysis.

Main Methods:

  • Analysis of monthly RVF surveillance data from Kenya (2015-2022).
  • Comparison of established outbreak detection methods.
  • Development of Negative Binomial models incorporating a hidden Markov process and zero-inflation.

Main Results:

  • The proposed model demonstrated superior performance over existing algorithms, reducing false positives and negatives.
  • The framework effectively estimates monthly transitions between endemic and epidemic phases.
  • Application to Kenyan data indicated a weak negative association with rainfall and weak positive association with temperature, possibly due to underreporting.

Conclusions:

  • The novel statistical model enhances outbreak detection accuracy in settings with sparse surveillance data.
  • This approach offers improved tools for public health decision-making and RVF surveillance.
  • Findings highlight the impact of data sparsity on estimating climate-disease relationships.