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Anticipating epidemic transitions with imperfect data.

Tobias S Brett1,2, Eamon B O'Dea1,2, Éric Marty1

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Early-warning signals (EWS) can predict epidemic transitions even with imperfect epidemiological data. Most tested EWS remain reliable indicators of disease emergence despite reporting errors and data aggregation.

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Area of Science:

  • Epidemiology
  • Mathematical Biology
  • Public Health

Background:

  • Epidemic transitions mark shifts in infectious disease spread, from limited transmission to large outbreaks.
  • Early-warning signals (EWS) are proposed to anticipate these transitions by detecting characteristic changes in disease dynamics.
  • The reliability of EWS is uncertain due to the nature of epidemiological data, including aggregation and reporting errors.

Purpose of the Study:

  • To evaluate the performance of various early-warning signals (EWS) in predicting epidemic transitions using imperfect epidemiological data.
  • To determine how data aggregation and reporting errors affect the accuracy of different EWS.
  • To identify which EWS remain viable for detecting emerging infectious diseases under realistic data conditions.

Main Methods:

  • Simulated numerical models of disease emergence and stationary disease dynamics.
  • Calculated the area under the curve (AUC) to quantify the performance of EWS in distinguishing between emerging and stationary diseases.
  • Assessed EWS performance across various data reporting scenarios, including different levels of reporting error and data aggregation periods.

Main Results:

  • Most EWS can predict disease emergence even with imperfect data.
  • The mean, variance, and first differenced variance perform well unless reporting error is highly overdispersed.
  • Autocorrelation, autocovariance, and decay time are reliable if the data aggregation period exceeds the serial interval and reporting error is not highly overdispersed.
  • Coefficient of variation, skewness, and kurtosis were found to be unreliable indicators.

Conclusions:

  • Seven out of ten evaluated EWS demonstrate good performance across most realistic reporting scenarios.
  • Imperfect epidemiological data, including reporting errors and aggregation, does not fundamentally hinder the use of EWS for many emerging diseases.
  • EWS remain a valuable tool for anticipating epidemic transitions in public health surveillance.