Real-Time Epidemic Monitoring and Forecasting of H1N1-2009 Using Influenza-Like Illness from General Practice and Family Doctor Clinics in Singapore

Abstract

BackgroundReporting of influenza-like illness (ILI) from general practice/family doctor (GPFD) clinics is an accurate indicator of real-time epidemic activity and requires little effort to set up, making it suitable for developing countries currently experiencing the influenza A (H1N1 -2009) pandemic or preparing for subsequent epidemic waves.Methodology/Principal FindingsWe established a network of GPFDs in Singapore. Participating GPFDs submitted returns via facsimile or e-mail on their work days using a simple, standard data collection format, capturing: gender; year of birth; “ethnicity”; residential status; body temperature (°C); and treatment (antiviral or not); for all cases with a clinical diagnosis of an acute respiratory illness (ARI). The operational definition of ILI in this study was an ARI with fever of 37.8°C or more. The data were processed daily by the study co-ordinator and fed into a stochastic model of disease dynamics, which was refitted daily using particle filtering, with data and forecasts uploaded to a website which could be publicly accessed. Twenty-three GPFD clinics agreed to participate. Data collection started on 2009-06-26 and lasted for the duration of the epidemic. The epidemic appeared to have peaked around 2009-08-03 and the ILI rates had returned to baseline levels by the time of writing.Conclusions/SignificanceThis real-time surveillance system is able to show the progress of an epidemic and indicates when the peak is reached. The resulting information can be used to form forecasts, including how soon the epidemic wave will end and when a second wave will appear if at all.

Influenza A(H1N1)pdm09 during air travel

“…28 Assessing surveillance tools during the early pH1N1 epidemic period in Singapore, investigators concluded that the high levels of "background noise" within acute respiratory illness surveillance data provide poorer resolution of influenza epidemic activity than does influenza-like illness. 30 Just as the number of secondary cases of acute respiratory illness considerably over-represents the true incidence of pH1N1 infection among passengers, it is also likely that some asymptomatic or mildly symptomatic passengers with pH1N1 infection, or those with atypical symptoms such as diarrhea, were not identified. From our investigation of travel contacts, serologic testing identified 3 individuals with evidence of asymptomatic pH1N1 infection.…”

Retrospective forecasting of the 2010–2014 Melbourne influenza seasons using multiple surveillance systems

“…Other studies have used a single data source [1,19,20] or a primary data source that was modulated by a secondary data source ('ILI+') [21,22]; in both cases single datasets were used to generate and evaluate the forecasts.…”

Forecasting influenza outbreak dynamics in Melbourne from Internet search query surveillance data

“…A variety of recursive Bayesian estimation methods (‘filters’) have been used for such forecasting purposes,1, 2 often in combination with Internet search query surveillance data and mechanistic models of infection 3, 4, 5, 6, 7, 8…”