Fusion of Biosurveillance with Epidemiological Modeling

Usually, classical epidemiology is considered to be largely retrospective while biosurveillance should be prospective by definition. Recently, there have been published a few papers which can be viewed as a first step to the so-called real time epidemiology with the possibility of predicting further development of the epidemics (see the corresponding references in [1]). Still, those papers use weekly reporting, which is typical for epidemiology of infection diseases as a whole. In contrast, modern syndromic surveillance operates with daily counts (typically of emergency departments / clinics / doctor offices visits), and relying on weekly data is considered obsolete. And yet, the most important difference between epidemiological approach and syndromic surveillance is in information quality of the data used. Epidemiology uses weekly counts of cases with confirmed diagnosis whereas syndromic surveillance works with pre-diagnostic data. At the first glance, it is unclear whether these two types of data can be connected to each other, and consequently, whether epidemiological modeling can be of any use in syndromic surveillance. If the answer is ‘yes’, then we can use all the wealth of epidemiologic analytics for early detection and situational awareness goals. Below we will see the benefits syndromic surveillance can potentially get from its fusion with epidemiological modeling. The simplest and the most popular epidemiological model is the so-called susceptible-Infectious-recovered (SIR) model. SIR model is one of the early triumphs of mathematical epidemiology (since 1927!), and still it is a workhorse of modern epidemiology. We have also used SIR in [1]. There is a fundamental parameter in SIR, so-called basic reproductive number, R₀, which has a very simple epidemiological interpretation and can answer many questions regarding both early detection and situational awareness. First of all, R₀ is a threshold parameter, determining whether or not there is an epidemic: if R₀ is greater than 1 then the epidemic has started, otherwise there is no epidemic. Thus, R₀ can be used as a natural litmus test for early detection. Secondly, in terms of R₀  one can determine such important characteristics of the epidemic as: (1) the initial rate of increase of the epidemic during its exponential growth phase; (2) the number of infected at the epidemic peak; (3) the total number of infected over the course of the outbreak; (4) the critical vaccination threshold (=1/R₀.) etc. All these characteristics are very important for situational awareness. Therefore, using basic reproductive number R₀ allows us to timely detect an outbreak and simultaneously to evaluate the parameters of the rising epidemic in order to develop measures for timely response and consequence management. See technical details in [1]. However, all these results can be used in syndromic surveillance only if we can connect two different types of data: daily numbers of people with confirmed diagnoses and daily pre-diagnostic counts. Fortunately, it is possible at least in some very important cases (influenza epidemics and pandemics among them). It can be done thru the assumption that the overall number of infected on each day can be approximated by the sum of the number of visits to the emergency department (clinic or doctor office) during the past d days where d is an average number of days of infectivity. This is a reasonable approximation if parameter d is adequately estimated or chosen. In [1] (see also references therein) it has been supposed that for influenza, the mean duration of infectivity d is equal to 7 days. Indeed, most epidemiologists agree that influenza infectivity begins the day before illness onset and can persist for up to 7 days, although some persons may shed virus for longer periods, particularly young children and severely immuno-compromised patients. This assumption is clinically realistic and plays a fundamental role in our approach. It transforms visit counts, which are basic observable variables in syndromic surveillance, into the main epidemiological SIR variable – the number of infected. It is precisely the point where integration of syndromic surveillance with epidemiological predictive modeling is taking place. In this fusion, syndromic surveillance is an information provider (in terms of daily visit counts) and real-time epidemiology contributes to analysis and decision-making. As an additional bonus, 7-day summation allows to compensate for the day-of-week (DOW) effect in number of visits variability. Note that the DOW effect is a primary systematic feature of the data in all recent biosurveillance systems, which drastically influences their performance. The simple 7-day summation procedure is very effective in removing weekly patterns.

Thus, the above mentioned integration of syndromic surveillance and epidemiology can be effectively done in the important case of influenza.

But why influenza is so important?

References

[1] Shtatland, E. and Shtatland, T. (2011). Statistical approach to biosurveillance in crisis: what is next. NESUG Proceedings. [full text]