Human Population Characteristics population density Examples of town-level aggregation of 2000 census data prop > age 50 etc.

Climatic

Temporal changes in climate

Remotely Sensed Data

The INTREPID/Oxford/NYSDOH Synergy Project Modeled data, maps Surveillance data Satellite data Translated info Federal, State, County, and Local health officials Via HIN

Landscape Epidemiology and RS/GIS -- First expressed by the Russian epidemiologist Pavlovsky, landscape epidemiology involves the identification of geographical areas where disease is transmitted.

Land Cover for New York State derived from 1992 LANDSAT images 30 meter pixel resolution

example measurements for characterizing landscape patterns: Albany area land cover example measurements for characterizing landscape patterns: proportion of - forest cover - wetlands / open water - open herbaceous land amount of “edges” degree of cohesion vs. disbursement - contagion index - interspersion index multi-resolution conditional entropy (Johnson, et al 2001)

How combine all these data for purposes of surveillance? If risk is desired at, say, the town/township level, then modelling may be applied using towns as observational units. So for i = 1, …, n towns, each town is represented by the outcome and covariates (as discussed). Models may then be used to predict risk, which may serve different purposes: - providing a basis for comparison of future observations - use risk predictions themselves to identify hot spots

Example 1: Number of human cases in area i at time t (based on Kleinman, Lazarus and Platt, 2004)

Future observations of cases can be compared to critical Z-values, after adjusting for multiple comparisons, thus providing a hypothesis testing framework for deciding if the number of cases observed in future time t in geographic area i exceeds background expectation.

Example 2: Predicting WNV activity through adjusted dead crow counts Currently, dead crow counts provide weekly indication of WNV activity prior to obtaining lab results. Subject to extreme observation bias, as counts are directly proportional to human population density.

2002 dead crow locations

Breeding Bird Atlas indicates more uniform distribution

Although the risk of at least one person getting infected is also proportional to population density, the risk of any one individual in a rural area may be no less than an individual in a more populated area – but dead crow clusters alone may not indicate this. So, how do we go beyond just identifying dead crows clusters in order to predict risk within small geographic areas (i.e. towns) in a way that adjusts for other variables related to crow and mosquito habitat?

Once the baseline dataset is established, from associating each area (say, town) with fundamental geo-spatial data, then a WNV risk map may be updated weekly by running the model for current week’s crow counts and other covariates that change weekly, like rainfall.

West Nile Virus Risk Mapping: GOALS To gather geo-coded data on the distribution of WNV in the USA. To describe WNV distribution using multi-variate methods with satellite and other (e.g. vector and host distribution) data. To produce risk maps identifying likely areas of infection for state and local authorities. To examine satellite and other archival data for recent changes in the key predictor variables. To develop a web-based data and analysis dissemination system to aid control authorities in New York (and the rest of the USA).

Wake Up! Conclude: Much has been done, but much more can be done Moving from basic cluster detection to modelling may allow incorporation of additional data that is readily available.