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Real time bayesian estimation of the epidemic potential of emerging infectious diseases.

Bettencourt LM, Ribeiro RM - PLoS ONE (2008)

Bottom Line: Currently there is worldwide alert for H5N1 avian influenza becoming as transmissible in humans as seasonal influenza, and potentially causing a pandemic of unprecedented proportions.Violations of these significance tests define statistical anomalies that may signal changes in the epidemiology of emerging diseases and should trigger further field investigation.We apply the methodology to case data from World Health Organization reports to place bounds on the current transmissibility of H5N1 influenza in humans and establish a statistical basis for monitoring its evolution in real time.

View Article: PubMed Central - PubMed

Affiliation: Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America. lmbett@lanl.gov

ABSTRACT

Background: Fast changes in human demographics worldwide, coupled with increased mobility, and modified land uses make the threat of emerging infectious diseases increasingly important. Currently there is worldwide alert for H5N1 avian influenza becoming as transmissible in humans as seasonal influenza, and potentially causing a pandemic of unprecedented proportions. Here we show how epidemiological surveillance data for emerging infectious diseases can be interpreted in real time to assess changes in transmissibility with quantified uncertainty, and to perform running time predictions of new cases and guide logistics allocations.

Methodology/principal findings: We develop an extension of standard epidemiological models, appropriate for emerging infectious diseases, that describes the probabilistic progression of case numbers due to the concurrent effects of (incipient) human transmission and multiple introductions from a reservoir. The model is cast in terms of surveillance observables and immediately suggests a simple graphical estimation procedure for the effective reproductive number R (mean number of cases generated by an infectious individual) of standard epidemics. For emerging infectious diseases, which typically show large relative case number fluctuations over time, we develop a bayesian scheme for real time estimation of the probability distribution of the effective reproduction number and show how to use such inferences to formulate significance tests on future epidemiological observations.

Conclusions/significance: Violations of these significance tests define statistical anomalies that may signal changes in the epidemiology of emerging diseases and should trigger further field investigation. We apply the methodology to case data from World Health Organization reports to place bounds on the current transmissibility of H5N1 influenza in humans and establish a statistical basis for monitoring its evolution in real time.

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Related in: MedlinePlus

Time series of new cases for an emerging infectious disease vs. a standard epidemic.(a) Laboratory confirmed new human H5N1 avian influenza cases, from WHO reports in Vietnam (from January 2004 to June 2006); (b) Number of isolates for seasonal H3N2 influenza in the USA, over the 2004–2005 season. Note the 100-fold difference in case numbers (y-axis) between panel (a) and (b). For an emerging infectious disease such as H5N1 influenza in humans, case numbers are small, very stochastic, and alternate short outbreaks with long quiet periods.
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pone-0002185-g001: Time series of new cases for an emerging infectious disease vs. a standard epidemic.(a) Laboratory confirmed new human H5N1 avian influenza cases, from WHO reports in Vietnam (from January 2004 to June 2006); (b) Number of isolates for seasonal H3N2 influenza in the USA, over the 2004–2005 season. Note the 100-fold difference in case numbers (y-axis) between panel (a) and (b). For an emerging infectious disease such as H5N1 influenza in humans, case numbers are small, very stochastic, and alternate short outbreaks with long quiet periods.

Mentions: Notwithstanding a marked recent increase in systematic surveillance by national and international organizations, and the advent of real time reporting of many public health indicators (syndromics) [24], the epidemiological regime of incipient but evolving transmission has received little attention in terms of quantitative modelling [22], [25]–[28]. The main difficulty is that data in these circumstances tend to be very stochastic, involve small case numbers and may be plagued by uncertainties and inconsistent reporting. As an example, we contrast in Figure 1 the time series of confirmed new human cases of H5N1 avian influenza in Vietnam, reported by the World Health Organization (WHO), with weekly isolate numbers for seasonal H3N2 influenza in the USA during 2004–2005 (see Methods for “Data Sources”). The ultimate objective of this paper is to propose a methodology to extract quantitative inferences and generate epidemiological outlook in real time from time series like that of Figure 1a.


Real time bayesian estimation of the epidemic potential of emerging infectious diseases.

Bettencourt LM, Ribeiro RM - PLoS ONE (2008)

Time series of new cases for an emerging infectious disease vs. a standard epidemic.(a) Laboratory confirmed new human H5N1 avian influenza cases, from WHO reports in Vietnam (from January 2004 to June 2006); (b) Number of isolates for seasonal H3N2 influenza in the USA, over the 2004–2005 season. Note the 100-fold difference in case numbers (y-axis) between panel (a) and (b). For an emerging infectious disease such as H5N1 influenza in humans, case numbers are small, very stochastic, and alternate short outbreaks with long quiet periods.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC2366072&req=5

pone-0002185-g001: Time series of new cases for an emerging infectious disease vs. a standard epidemic.(a) Laboratory confirmed new human H5N1 avian influenza cases, from WHO reports in Vietnam (from January 2004 to June 2006); (b) Number of isolates for seasonal H3N2 influenza in the USA, over the 2004–2005 season. Note the 100-fold difference in case numbers (y-axis) between panel (a) and (b). For an emerging infectious disease such as H5N1 influenza in humans, case numbers are small, very stochastic, and alternate short outbreaks with long quiet periods.
Mentions: Notwithstanding a marked recent increase in systematic surveillance by national and international organizations, and the advent of real time reporting of many public health indicators (syndromics) [24], the epidemiological regime of incipient but evolving transmission has received little attention in terms of quantitative modelling [22], [25]–[28]. The main difficulty is that data in these circumstances tend to be very stochastic, involve small case numbers and may be plagued by uncertainties and inconsistent reporting. As an example, we contrast in Figure 1 the time series of confirmed new human cases of H5N1 avian influenza in Vietnam, reported by the World Health Organization (WHO), with weekly isolate numbers for seasonal H3N2 influenza in the USA during 2004–2005 (see Methods for “Data Sources”). The ultimate objective of this paper is to propose a methodology to extract quantitative inferences and generate epidemiological outlook in real time from time series like that of Figure 1a.

Bottom Line: Currently there is worldwide alert for H5N1 avian influenza becoming as transmissible in humans as seasonal influenza, and potentially causing a pandemic of unprecedented proportions.Violations of these significance tests define statistical anomalies that may signal changes in the epidemiology of emerging diseases and should trigger further field investigation.We apply the methodology to case data from World Health Organization reports to place bounds on the current transmissibility of H5N1 influenza in humans and establish a statistical basis for monitoring its evolution in real time.

View Article: PubMed Central - PubMed

Affiliation: Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America. lmbett@lanl.gov

ABSTRACT

Background: Fast changes in human demographics worldwide, coupled with increased mobility, and modified land uses make the threat of emerging infectious diseases increasingly important. Currently there is worldwide alert for H5N1 avian influenza becoming as transmissible in humans as seasonal influenza, and potentially causing a pandemic of unprecedented proportions. Here we show how epidemiological surveillance data for emerging infectious diseases can be interpreted in real time to assess changes in transmissibility with quantified uncertainty, and to perform running time predictions of new cases and guide logistics allocations.

Methodology/principal findings: We develop an extension of standard epidemiological models, appropriate for emerging infectious diseases, that describes the probabilistic progression of case numbers due to the concurrent effects of (incipient) human transmission and multiple introductions from a reservoir. The model is cast in terms of surveillance observables and immediately suggests a simple graphical estimation procedure for the effective reproductive number R (mean number of cases generated by an infectious individual) of standard epidemics. For emerging infectious diseases, which typically show large relative case number fluctuations over time, we develop a bayesian scheme for real time estimation of the probability distribution of the effective reproduction number and show how to use such inferences to formulate significance tests on future epidemiological observations.

Conclusions/significance: Violations of these significance tests define statistical anomalies that may signal changes in the epidemiology of emerging diseases and should trigger further field investigation. We apply the methodology to case data from World Health Organization reports to place bounds on the current transmissibility of H5N1 influenza in humans and establish a statistical basis for monitoring its evolution in real time.

Show MeSH
Related in: MedlinePlus