Hospitalization records for Salmonella infections were abstracted from the Centers for Medicare and Medicaid Services (CMS) for all Medicare recipients aged 65 or above in the contiguous U.S. for 2002 (Alaska, Hawaii, Virgin Islands and Puerto Rico were excluded from the analysis). In the U.S., more than 95% of the population aged 65 or above are covered by Medicare; hence the CMS records are nationally representative. Each hospitalization record contains information on the date of admission, ZIP code and state of residence, age and up to 10 diagnostic codes based on the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM). Records containing any diagnosis starting with "003", indicating "other Salmonella infections", were included in this analysis. Conditions starting with "002", indicating "typhoid and paratyphoid fevers" caused by Salmonella typhi infection, were excluded due to their low frequencies.

Temporally, all records were aggregated to a monthly level; spatially, to a county level. Monthly hospitalization rates were computed using county population aged 65 or above as the denominator. Population counts were obtained from the Census 2000, Summary File 1. To minimize spurious high rates caused by extremely low denominators, a spatial re-aggregation scheme [see Figure 1] was applied to incorporate counties with a low number of elderly into the adjacent counties until the total number of elderly exceeded 1,000, a parameter that was selected based upon the population of elderly per county and the frequency of diseases. The re-aggregation collapsed the number of counties within the continental U.S. from 3,109 to 2,794. On the maps, the boundaries between two aggregated counties were removed to form one larger area. For each aggregated county, the elderly population and monthly Salmonella hospitalization counts were summed together. Monthly hospitalization rates per 10,000 elderly, R _ij , were calculated for each aggregated county i (i = 1:2,794) and month j (j = 1:12) as follows:

Ambient temperature data were obtained from the PRISM Group at Oregon State University for average maximum monthly temperature, average maximum annual temperature, and average maximum temperature anomaly for 2002. Data were downloaded in ESRI ArcInfo Ascii Grid format with a spatial resolution of 4 kilometer grid cells and imported into ESRI's ArcMap software. The aggregated county polygons were overlaid onto each temperature grid and the mean temperature or temperature anomaly was calculated for each county. Choropleth maps for the average monthly and annual maximum temperature were created by considering the full range of county-level temperature values with the classification emphasizing temperatures above freezing shown in shades of yellow to dark orange. The monthly temperature anomaly maps show the deviation in temperature on a monthly basis from the 30-year norm for each month. The choropleth maps for the temperature anomaly emphasize temperature deviations greater than +3°C in pink and less than -2°C in purple.

Exposure data on livestock were obtained from the 2002 quinquennial Census of Agriculture gathered by the U.S. Department of Agriculture's National Agricultural Statistics Service. County-level data on the number of broiler and other meat-type chickens sold from livestock farms to food distributors were downloaded and mapped by matching the county FIPS codes with county-level polygons. A choropleth map of the number of broiler chickens sold in 2002 was created by first performing a logarithmic transformation on the data to alleviate the problematic skewness, followed by assigning the data into five categories using a natural breaks classification.

Twelve monthly static maps for 2002 were created. To facilitate visual comparison between months, the bin sizes adopted in the legend were unified across the 12 maps. Countywide Salmonella hospitalization data for the elderly were extremely skewed due to a disproportionately high amount of low rates and a small amount of high rate outliers. Seasonal changes further exaggerated the problem since there was a two to three-fold increase in salmonellosis during the summer time. To alleviate the problems caused by the skewness, a logarithmic transformation was applied. The logarithmic transformation helps to optimally assign categories into high and low brackets (Figure 2). The unified bin sizes were derived by assigning a natural break classification scheme to the month with the highest amount of hospitalization cases. The resulting classification scheme was then applied to the other maps.

Hospitalization rates were represented on the map with graduated dot symbols. The graduated colour and symbol size of the dots allow for a quick visual comparison between high and low rates; larger and darker dots indicate higher rates and smaller and lighter dots indicate lower rates. The sizes of the graduated dot symbols are proportional to the outcome rates and hence are preferred over polygons with graduating shades, which are harder to interpret due to the wide range of county sizes. The size of the largest graduated dot symbol was determined using the average county size so that overlapping of dots and county boundaries were minimized.

The monthly maps of county-level Salmonella-related hospitalizations were superimposed onto maps of environmental exposures including: average monthly maximum temperature, the number of broiler chickens sold in 2002, and monthly temperature deviation from the 30-year norm. Maps were exported from ArcMap and imported into Adobe Flash software as an individual key frame. The frames were sequentially incorporated into a movie.

The speed for playing the map movie was optimized so that that the viewers can have enough time to identify clusters and commit the frame to short memory, allowing them to distinguish changes when the next frame appears. Herein, the dynamic maps are shown at a frame rate of one frame per second and the Salmonella infection data span two key frames. An interactive interface consisting of control buttons for stop, play, move forward or back one frame, and replay are added to help the viewers investigate the dynamic map at their own pace.

To indicate the calendar time, a label with the year and month was incorporated into the maps. For maps that span more than one year, a sliding calendar bar was added to help the viewer track time duration. The sliding calendar bar could also be interactive so that the viewer could easily navigate to a specific point in time.

Narrations were incorporated into the movies to help the viewers appreciate the dynamics of Salmonella hospitalizations with respect to the exposures.

Dynamic maps provide additional insight into temporal disease dynamics that is not obtainable from static maps. In an exploratory context, static maps can be used to identify and compare patterns in a spatial context only. Static maps allow us to determine what patterns exist, where they exist, how they exist, and identify how they compare to other spatial patterns. Dynamic mapping deepens our understanding of the data by adding the temporal domain 6 11 , through which researchers can further investigate when and where diseases emerge, when and to where they spread, for how long or where they persist, and identify differences in their spatio-temporal patterns. Percolation of Salmonella infections was demonstrated in the dynamic maps where Salmonella-related hospitalizations showed up in northern Mississippi in May and June and spread to southern Mississippi in July. A persistent cluster of Salmonella-related hospitalizations occurred in the New York metropolitan area from May through October with an apparent peak in August.

Dynamic maps allow us to investigate processes, such as disease dynamics, rather than simply recognize static patterns: it is useful for modeling disease transmission, place of exposure and probabilities of hospitalization. By overlaying disease outcome with exposure variables, multiple data sets can be integrated to identify trends, degree of synchronization 6 between exposures and outcomes, or even synchronization between two outcomes such as mortality and co-morbidity. Furthermore, dynamic mapping allows for the visualization of disease incidences and seasonal patterns at conventional temporal scales such as daily, weekly or monthly, which would be difficult to convey through traditional static maps. Dynamic Map 2 [Additional file 2] illustrates the seasonal peaks in temperature during the summer months with the hottest temperatures occurring in the South and moving northward. In concurrence with the temperature upsurge, Salmonella hospitalizations spread northward from July to August, demonstrating a travelling wave with a potentially strong influence from environmental factors such as temperature and humidity.

Studies have shown that peaks in ambient temperature correspond with peaks in Salmonella incidence with a lag of one to five weeks 4 5 . By superimposing monthly Salmonella hospitalizations onto maps of ambient temperatures and livestock abundances, dynamic maps can be used to generate hypotheses about routes of exposure, spatial patterns of transmission, and diseases clustering. When viewing the dynamic maps of monthly Salmonella infections superimposed onto monthly temperatures the rationale for exploration of a lagged relationship between exposure and outcome emerges. If a lag relationship does exist, dynamic maps offer channel to observe the synchronization between the outcome and exposure.

Dynamic maps allow viewers to track multiple locations of disease occurrence simultaneously. The U.S. Centers for Disease Control and Prevention (CDC) reported minor outbreaks of Salmonella during 2002. From January through April of 2002, there were 47 cases of multi-drug resistant Salmonella Newport in five states: New York, Michigan, Pennsylvania, Ohio and Connecticut, causing 17 hospitalizations 12 . From March through May of 2002, tainted cantaloupe from Mexico caused 58 cases of Salmonella Serotype Poona infections in 10 states: California, Washington, Oregon, Colorado, Nevada, Missouri, Texas, Arkansas, Minnesota, and Vermont. Eleven cases were in individuals aged > 60 years and there were 10 hospitalizations reported 13 . In June 2002, an outbreak of Salmonella Serotype Javiana infected people who attended an athletic competition in Orlando, Florida 14 . There were 141 ill people dispersed across 32 states with 3 known hospitalizations. These outbreak examples illustrate that one strain of Salmonella may result in a multistate outbreak that affects nonadjacent states. Also, the outbreak may originate in one location but be reported in multiple states depending upon where the infected patient lives. Dynamic maps provide users with a visual-analytic capability that allows more careful consideration of the nuances within the data.

It is important to recognize that the major strength of the proposed dynamic mapping methodology is its visual-analytic capabilities to assist as an exploratory tool to catalyze the scientific process by propagating potential research questions and future directions. While the methodology is not yet ready to test hypotheses, this capability would be the next step for methodological development.

Determining class breaks for dynamic maps poses a significant challenge because a single scheme must work for multiple maps and the placement of a class break can either emphasize small fluctuations in the data or mask large changes 15 . Monmonier 7 uses a bin scoring method to assign category breaks to dynamic spatio-temporal choropleth maps in order to avoid creating a visually noisy and constantly changing display that could confound the visualization of major trends in the data. Harrower 15 puts forth a methodology that avoids classification altogether by placing the highest and lowest values at each end of a continuous colour ramp and placing the remaining data proportionally along that continuum for each map frame. His study shows that this unclassed choropleth dynamic mapping method can improve our ability to see gradual changes in the mapped data which more realistically reflect the underlying process. After the class break schema is determined, dynamic maps are built by producing individual map frames one-by-one for each point in time 16 using GIS and animation software 17 .

When we developed our principles for effective dynamic mapping, we heavily borrowed from Harrower's 18 principles including: (1) create a simple base map with only a few data classes or features so that it is quickly readable, (2) provide map controls to stop and playback the movie frame-by-frame, (3) direct the viewer's attention with voice over or dynamic map symbols, (4) generalize the map data to highlight important trends, (5) use categorical data legends such as "low", "medium" and "high", and (6) provide a short introduction to the interface before showing the data to break up the learning curve.

Many factors should be considered when composing effective dynamic maps for disease incidence data. The following seven principles were suggested for consideration.