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The detail of the EMD method [21] and its application to epidemiological time series was previously described [23, 25 27].
Thus, combining both epidemiological time series data with genetic data may be a powerful approach.
EPIPOI does not, however, cover all analytical possibilities that can be of relevance in the investigation of epidemiological time series.
The Analytical Software for Epidemiological Time Series (Epipoi) was used to show the seasonality of ILI cases over years (19).
This time step can be considered as a host epidemiological time step, corresponding to the expected interval between bacterial transmission events.
It is hoped that EPIPOI can address this imbalance and make available hitherto specialist tools in an intuitive format, so that a wider array of public health practitioners can explore their own epidemiological time series.
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In the context of forecasts, epidemiological time-series (TS) often i) exhibit inter-annual plus seasonal fluctuations; ii) require disease-specific tailoring of forecasting methods; and, iii) suffer from non-stationarity.
The monthly aggregated DF data were merged with the monthly mean weather data for the epidemiological time-series analysis.
Therefore, this is a limitation of most studies on disease burden that make use of epidemiological time-series.
Epidemiological time-series studies have found associations between daily concentrations of ambient nitrogen dioxide (NO2) and daily mortality and morbidity outcomes.
As process-based air pollution models become more widely used in epidemiological time-series analysis, assessments of error impact that include statistical simulation may be useful.
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