A Model for the Early Identification of Sources of Airborne Pathogens in an Outdoor Environment

<div><p>Background</p><p>Source identification in areas with outbreaks of airborne pathogens is often time-consuming and expensive. We developed a model to identify the most likely location of sources of airborne pathogens.</p><p>Methods</p><p>As a case study, we retrospectively analyzed three Q fever outbreaks in the Netherlands in 2009, each with suspected exposure from a single large dairy goat farm. Model input consisted only of case residential addresses, day of first clinical symptoms, and human population density data. We defined a spatial grid and fitted an exponentially declining function to the incidence-distance data of each grid point. For any grid point with a fit significant at the 95% confidence level, we calculated a measure of risk. For validation, we used results from abortion notifications, voluntary (2008) and mandatory (2009) bulk tank milk sampling at large (i.e. >50 goats and/or sheep) dairy farms, and non-systematic vaginal swab sampling at large and small dairy and non-dairy goat/sheep farms. In addition, we performed a two-source simulation study.</p><p>Results</p><p>Hotspots – areas most likely to contain the actual source – were identified at early outbreak stages, based on the earliest 2–10% of the case notifications. Distances between the hotspots and suspected goat farms varied from 300–1500 m. In regional likelihood rankings including all large dairy farms, the suspected goat farms consistently ranked first. The two-source simulation study showed that detection of sources is most clear if the distance between the sources is either relatively small or relatively large.</p><p>Conclusions</p><p>Our model identifies the most likely location of sources in an airborne pathogen outbreak area, even at early stages. It can help to reduce the number of potential sources to be investigated by microbial testing and to allow rapid implementation of interventions to limit the number of human infections and to reduce the risk of source-to-source transmission.</p></div

Most common STs in human and chicken isolates in The Netherlands in two time periods.

<p>Only the contributions of those CCs including STs that were found in the human data of 2002–2003 in proportions over 0.01 are represented. The contributions of less frequent STs within these CCs are summed and presented by the “+” symbol; the contributions of other CCs are omitted. For the human data of 2002–2003 the presented CCs make up for 83% of all isolates. For the chicken data of 2000–2007 and the human and chicken data of 2010–2011, these CCs make up for 65%, 67% and 52% of all data, respectively.</p

Statistics of the self-attributed proportions of 250 chicken isolates for reduced source datasets of size <i>n</i> (on <i>x</i>-axis).

<p>Every reduced dataset is generated from the original dataset by randomly removing isolates from an original set of 150. The boxes indicate variability in the mean attributed proportions over the 10 different reduced datasets per model and per reduction factor. Indicated are the minimal, maximal and average means. The whiskers indicate the average 2.5% and 97.5% confidence limits over the different reduced datasets. The star-symbols represent the minimum 2.5% limit and the maximum 97.5% limit.</p

Overview of the data selection in outbreak area C. The PC4-polygons are indicated by the green lines.

<p>The center of the case cluster is indicated by the green star. This star is also the center of the spatial grid with a resolution of 250×250 m (black squares). Around each of the grid points (example indicated by the large red square) the distance <i>r</i> to all PC6's (small blue dots) within <i>Z</i> = 5000 m is determined, as well as the number of cases <i>k</i> and inhabitants <i>n</i> in these PC6's.</p

Overall mean probability (%) and 95% confidence interval for human <i>C. jejuni</i> and <i>C. coli</i> infections to originate from chicken, cattle, pig, sheep, and the environment.

<p>A. Baseline attribution results (see main text); B. Attribution results with Dutch chicken isolates replaced by chicken isolates from Scotland, the UK and Switzerland; C. Attribution results with Dutch chicken isolates replaced by chicken isolates from New Zealand, Finland and USA; D. Attribution results with Dutch, Scottish, English and Swiss chicken isolates as separate <i>Campylobacter</i> reservoirs.</p

Cumulative number of cases per week (solid lines) and the temporal nMR-fraction (“tf-nMR”) (i.e. the spatial cumulative nMR-values per week as fraction of spatial cumulative nMR-values using all cases of 2009) (dashed lines) for the areas A (green), B (red), and C (blue).

<p>Cumulative number of cases per week (solid lines) and the temporal nMR-fraction (“tf-nMR”) (i.e. the spatial cumulative nMR-values per week as fraction of spatial cumulative nMR-values using all cases of 2009) (dashed lines) for the areas A (green), B (red), and C (blue).</p

Two source simulations with the distance from the two sources to their closest hotspot as function of the distance between the two sources.

<p>The one source is indicated in red; the other in blue. The distance to all grid cells with nMR>0.9 is listed by small open circles; the grid cell with the maximum nMR-value in the hotspot is indicated by a large closed circle. If a local maximum with nMR-values<0.9 appeared, then triangle symbols are used, taking into account all grid cells with nMR-values of 0.10 lower than the local maximum. If a source could not be attributed to a single hotspot, then the distance to both hotspots was indicated (e.g., at x = 3.2 km). Results with just one hotspot are indicated by the grey rectangles at the x-axis.</p

Similarity of STs in chicken and in human isolates from samples collected in different years.

<p>The <i>x</i>-axis gives the absolute difference between years in which the isolates from human cases and chicken were obtained. To enhance the size of the sample subsets, chicken isolates collected between 2000 and 2004 were aggregated and assigned to be collected in 2002, those collected between 2005 and 2007 were assigned to be collected in 2006, and those collected in 2010–2011 were assigned to be collected in 2010. Human isolates were arranged in three groups: 2002, 2003, and 2010–2011. The <i>y</i>-axis represents the PSI between those isolates collections.</p

Bar chart of frequency distributions of the most prevalent CCs in 12 human datasets.

<p>Only CCs of which a prevalence higher than 10% was found are plotted.</p

Number of isolates in published human (h) and source (s) datasets and (last column) bootstrapped similarities of the human data with the human data in the NL1 dataset.

a<p>The datasets are ordered in decreasing similarity of the human isolates with the NL1 human data. Numbers of isolates that are written in bold were used in the baseline source attribution analysis.</p>b<p>Proportional Similarity Index, with 95% confidence intervals.</p