15 research outputs found

    An Assessment of Epidemiology Capacity in a One Health Team at the Provincial Level in Thailand

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    A multi-sectoral core epidemiology capacity assessment was conducted in provinces that implemented One Health services in order to assess the efficacy of a One Health approach in Thailand. In order to conduct the assessment, four provinces were randomly selected as a study group from a total of 19 Thai provinces that are currently using a One Health approach. As a control group, four additional provinces that never implemented a One Health approach were also sampled. The provincial officers were interviewed on the epidemiologic capacity of their respective provinces. The average score of epidemiologic capacity in the provinces implementing the One Health approach was 66.45%, while the provinces that did not implement this approach earned a score of 54.61%. The epidemiologic capacity of surveillance systems in provinces that utilized the One Health approach earned higher scores in comparison to provinces that did not implement the approach (75.00% vs. 53.13%, p-value 0.13). Although none of the capacity evaluations showed significant differences between the two groups, we found evidence that provinces implementing the One Health approach gained higher scores in both surveillance and outbreak investigation capacities. This may be explained by more efficient capacity when using a One Health approach, specifically in preventing, protecting, and responding to threats in local communities

    Real-time Forecasting of the 2014 Dengue Fever Season in Thailand

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    Real-time surveillance of an infectious disease in a third world country poses many problems that are not conventionally confronted by statistical researchers. As the first ones - to our knowledge - to attempt real-time forecasts of dengue fever in Thailand, we have faced these problems head-on in our quest to build a model that accurately predicts case counts in the presence of erratic reporting, shifting population dynamics, and potential climate change

    Real-time Forecasting of the 2014 Dengue Fever Season in Thailand

    No full text
    Real-time surveillance of an infectious disease in a third world country poses many problems that are not conventionally confronted by statistical researchers. As the first ones - to our knowledge - to attempt real-time forecasts of dengue fever in Thailand, we have faced these problems head-on in our quest to build a model that accurately predicts case counts in the presence of erratic reporting, shifting population dynamics, and potential climate change

    Comparison of province-level prediction accuracy between full-data and real-time predictions, by prediction horizon.

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    <p>These results were computed comparing predictions as if the full data was available at the analysis time with the real-time predictions that build in a 6-biweek (approximately 3 month) buffer to account for delayed case data. The table shows the 5th percentile (<i>Q</i><sub>5</sub>), 25th percentile (<i>Q</i><sub>25</sub>), median (<i>Q</i><sub>50</sub>), 75th percentile (<i>Q</i><sub>75</sub>), and 95th percentile (<i>Q</i><sub>95</sub>) value of the relative MAE from each province at the given horizon. The relative MAEs were calculated as the MAE from the real-time predictions divided by the MAE from the full-data predictions, i.e. values larger than 1 indicate that the real-time models showed more absolute error on average than the full-data models.</p
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