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Real Time Forecasting of Measles Using Generation-dependent Mathematical Model in Japan, 2018
Andrei R Akhmetzhanov1, Hyojung Lee1, Sung-Mok Jung2
1Hokkaido University.
Background:
Japan experienced a multi-generation outbreak of measles from March to May, 2018. The present study aimed to capture the transmission dynamics of measles by employing a simple mathematical model, and also forecast the future incidence of cases.
Methods:
Epidemiological data that consist of the date of illness onset and the date of laboratory confirmation were analysed. A functional model that captures the generation-dependent growth patterns of cases was employed, while accounting for the time delay from illness onset to diagnosis.
Results:
As long as the number of generations is correctly captured, the model yielded a valid forecast of measles cases, explicitly addressing the reporting delay. Except for the first generation, the effective reproduction number was estimated by generation, assisting evaluation of public health control programs.
Conclusions:
The variance of the generation time is relatively limited compared with the mean for measles, and thus, the proposed model was able to identify the generation-dependent dynamics accurately during the early phase of the epidemic. Model comparison indicated the most likely number of generations, allowing us to assess how effective public health interventions would successfully prevent the secondary transmission.
Insights
A mathematical model accurately forecasted measles cases during Japan's 2018 outbreak by analyzing transmission dynamics and generation intervals. This approach aids in evaluating public health interventions against secondary measles transmission.
Area of Science:
- Epidemiology
- Mathematical Modeling
- Infectious Disease Dynamics
Background:
- A multi-generation measles outbreak occurred in Japan between March and May 2018.
- Understanding transmission dynamics is crucial for controlling infectious diseases.
Purpose of the Study:
- To model the transmission dynamics of measles.
- To forecast future measles case incidence.
- To evaluate public health interventions.
Main Methods:
- Analysis of epidemiological data including illness onset and laboratory confirmation dates.
- Application of a functional model to capture generation-dependent growth patterns.
- Inclusion of time delays from illness onset to diagnosis and reporting.
Main Results:
- The model provided a valid forecast of measles cases, accurately accounting for reporting delays.
- Effective reproduction numbers were estimated per generation, aiding in the assessment of control programs.
- The model successfully identified generation-dependent dynamics, particularly in the early epidemic phase.
Conclusions:
- The limited variance in measles generation time supports the model's accuracy.
- Model comparison determined the most likely number of generations.
- The findings enable assessment of public health intervention effectiveness in preventing secondary transmission.
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