A susceptible-infected model of early detection of respiratory infection outbreaks on a background of influenza

Abstract

The threat of biological warfare and the emergence of new infectious agents spreading at a global scale have highlighted the need for major enhancements to the public health infrastructure. Early detection of epidemics of infectious diseases requires both real-time data and real-time interpretation of data. Despite moderate advancements in data acquisition, the state of the practice for real-time analysis of data remains inadequate. We present a nonlinear mathematical framework for modeling the transient dynamics of influenza, applied to historical data sets of patients with influenza-like illness. We estimate the vital time-varying epidemiological parameters of infections from historical data, representing normal epidemiological trends. We then introduce simulated outbreaks of different shapes and magnitudes into the historical data, and estimate the parameters representing the infection rates of anomalous deviations from normal trends. Finally, a dynamic threshold-based detection algorithm is devised to assess the timeliness and sensitivity of detecting the irregularities in the data, under a fixed low false-positive rate. We find that the detection algorithm can identify such designated abnormalities in the data with high sensitivity with specificity held at 97%, but more importantly, early during an outbreak. The proposed methodology can be applied to a broad range of influenza-like infectious diseases, whether naturally occurring or a result of bioterrorism, and thus can be an integral component of a realtime surveillance system.

FluSiM Simulation for Malaysia: Toward sImproved Pandemic Surveillance

“…The A H1N1 influenza emerged through cross species transmission and has been shown to have arisen due to recombination of swine, avian and human strains [16,17]. Avian influenza is already endemic particularly in Asian poultry [18,19].…”

Graph Matching Based Decision Support Tools For Mitigating Spread Of Infectious Diseases Like H1N1

“…One: preparedness by simulating hypothetical scenarios to estimate expected cases and necessary resources required (Hagenaars et al (2004), Longini et al (2005), Feighner et al (2008), Pan-InfORM (2009)). And two: timely detection of flu virus by studying the effect of size of infected population on the rate of infection (Mohtashemi et al (2006), Reis et al (2007)). Though the existing research provides some assistance to public health departments in decision making, it has the following shortcomings.…”

“…The model used survey based contact data and mortality data from influenza pandemics to determine the allocation for five outcome measures: deaths, infections, years of life lost, contingent valuation and economic costs. Mohtashemi et al (2006) modeled the short-term dynamic interaction between different subpopulations with respect to an infectious disease using a nonlinear system of difference equations. They used the model to detect anomalous deviations from historically observed events to estimate the rate of infection.…”

Multi‐syndrome analysis of time series using PCA: A new concept for outbreak investigation

“…Temporal anomaly detection is a key component of real-time syndromic surveillance [1][2][3][4]. Although space-time detection schemes may sometimes prove to be more powerful at anomaly detection than purely temporal analysis [5,6], spatial data remains scarce.…”

“…First, it has become more evident in recent years that due to intrinsic variation in the daily counts of health-related data, performance measures are greatly improved if detection thresholds are defined dynamically per time window of analysis [4,23], instead of applying a fixed numeric threshold for monitoring the entire data [3]. In the multi-syndrome framework presented here, we showed how different features of the embedded, and often hidden, dynamics in the NDP data can be captured and manifested through different components.…”