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Forecasting Human African Trypanosomiasis Prevalences from Population Screening Data Using Continuous Time Models.

Harwin de Vries1, Albert P M Wagelmans1, Epco Hasker2

  • 1Econometric Institute, Erasmus University Rotterdam, Rotterdam, The Netherlands.

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|September 23, 2016
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Summary
This summary is machine-generated.

Predicting human African trypanosomiasis (HAT) prevalence is crucial for effective control. A Logistic Model variant effectively predicts HAT levels, guiding screening strategies for eradication and elimination goals.

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Area of Science:

  • Epidemiology
  • Mathematical Modeling
  • Public Health

Background:

  • Human African trypanosomiasis (gambiense HAT) elimination requires optimized active case finding.
  • Epidemic progression is significantly impacted by the strategic planning of control operations.

Purpose of the Study:

  • To introduce and analyze models for predicting HAT prevalence based on historical data.
  • To identify the most suitable model for HAT prevalence prediction and its application in policy planning.

Main Methods:

  • Analysis of five predictive models using prevalence and screening data from 143 villages in Kwamouth, DRC.
  • Evaluation of model performance based on prediction accuracy and theoretical underpinnings.
  • Application of the chosen Logistic Model variant to simulate screening policy impacts.

Main Results:

  • Variants of the Logistic Model, inspired by the SIS epidemic model, demonstrated superior HAT prevalence prediction.
  • An analytical expression for screening frequency to achieve eradication (zero prevalence) was derived.
  • A method for determining screening frequency for elimination (1 case per 10,000) was developed.
  • Model predictions indicate annual screening may only achieve eradication if over 50% of cases are detected.

Conclusions:

  • The Logistic Model variant is highly suitable for predicting gambiense HAT prevalence and informing control strategies.
  • The derived analytical expressions and methods provide a quantitative basis for planning screening operations.
  • Findings support the need for high detection rates during screening for successful HAT eradication efforts.