# data[].attributes.body
Improving situational awareness: Identify 3 to 5 data points our partners could easily collect (without need for additional resources) when they submit specimens for morbidity/mortality investigation that would contribute towards achieving National Surveillance Goals 1 to 4.

# data[].attributes.body
increase the outward communication of whispers data. Actively go to new potential partners and talk to them, present, etc. new partners = beyond the ones who usually send us the dead stuff

# data[].attributes.body
Develop whispers into more flexible data aggregator- different types of disease data (presence/absence, sketchy reports), environmental data, scraped/shared from other sources (ex: influenza research database, inaturalist, NOAA UMEs, etc)

# data[].attributes.body
Situational Awareness. Seek integration of additional data sources. Seek partners to create network of wildlife death and disease reporting. Actively fund partners to input data. Cross talk state DNRs, vet labs and rehab networks.

# data[].attributes.body
Drivers. Create long term, multi-agency funded efforts to jointly investigate health on ecological scales. Many factors are already considered by animal ecologists, we should target partner who are already studying sentinel species that we identify as keystone species for disease.

# data[].attributes.body
Drivers of disease:
With increasing stream temps observe upstream migration of fish species = new species assemblages Thinking here of CO river basin.
Feasible: From crews performing population assessments or translocations obtain species presence/absence, counts, and water for eDNA detection of cryptic presence.

# data[].attributes.body
Add water temp monitors to predict upstream migration (toward newly warming streams) , and perform targetted surveillance of stream reaches for species presence /absence, population, and eDNA.

# data[].attributes.body
for understanding drivers of disease we need systematic: consistent long-term, broad scale surveillance that provides both presence and absence data. This will require focusing on target diseases and investment of considerable resources

# data[].attributes.body
set up national scale surveillance for certain diseases apply management actions in a consistent manner and evaluate the effectiveness of the actions

# data[].attributes.body
Incorporate ability to better house surveillance data collected by NWHC and partners in WHISPers. This would include ability to have # positive / # tested per location, and the ability to add 'neg' events with no disease designation. Then we should incorporate QA/QC and standardization of Center surveillance datasets and upload these to WHISPers (WNS, WNV, Plague, eagle mortalities, etc), so they are available to the public and disease modelers. The goal here is to improve the data stream we collect, so that it would be more useful for evaluating driver's in the future (even if that analysis isn't completed by our in-house statisticians), but where we aren't asking for a major redirection of funds or personnel.

# data[].attributes.body
Situational Awareness: Collect negatives and increased data points via active Partner network and additional questions during submissions/on WHISPers

# data[].attributes.body
Understanding drivers: External partner network to collect long-term datasets needed to understand drivers (nodes or already established sampling areas like ARMI, stream gauges, specific refuges).

# data[].attributes.body
Improving situational awareness: Provide resources to active partner network to initiate a synthesis of designed and scanning wildlife disease surveillance in a targeted region(s) of the US (e.g., the NE or multiple) to achieve National Surveillance Goals 1 to 4.

# data[].attributes.body
Develop a funded partner network, something like ARMI or USFWS refuges, where we develop standardized system sampling - population metrics, environmental cofactors, and paired disease/contaminant diagnostics - to evaluate the health of similar systems across the US. This is a more rigorous way to evaluate driver's and produces a much more robust data set (as the methods are standardized and tests comparable; similar to an LTER or NEON framework). The goal is to better understand driver's of the system - such as wetland health for example.

# data[].attributes.body
Work with DOI sister agencies to set up a coarse network as proposed below.

# data[].attributes.body
Establish key nodes with partners across the continent at which we monitor health, examining various aspects of the system (based on some definition of health) through time.  These biosurveillance efforts would support the overall program by providing the critical information on the mortalities/morbidities that are submitted under specified design principles as one aspect of health of the systems.  The end results are long-term time series of health information linked to auxiliary information that could be used to achieve priority goals 1-4. Situational awareness is a by-product from this effort as processes are tracked through time and reported back to agency nodes.

# data[].attributes.body
If agent is known and mode of spread is known - determine basic reproductive rate R, for directly trasnmitted disease determine wildlife population contact rates, for vector transmitted disease determine how environmental factors affect vector prevalence, reproduction and development.
Relate environmental drivers to vector population and disease.

# data[].attributes.body
forecast spread: meta-analysis on a pathogen or group of pathogens of concern to estimate the effect size of co-variates that we could measure or intergrate at the emergence of a disease. Ideally hypothesis driven.
evaluate management: similar idea to above, meta-analysis or simulation study on the effect of things that could be manipulated in the face of an outbreak.

# data[].attributes.body
forecast spread: Surge capacity to collect data at the beginning of an outbreak that is suitable for a predictive model
evaluate management: find someone who actually has a concrete management plan for a wildlife disease. Butt in Work with them to pro-actively design data collection to evaluate the intended effects of the managemet

# data[].attributes.body
Develop a framework that can be used as a foundation to forecast disease dynamics for emerging to long-endemic diseases.

# data[].attributes.body
Integrate information from surveillance nodes across the nation with structured approaches t to wide-spread issues (e.g., botulism) to examine the spatio-temporal effectiveness of management. Also, conclude a more general look at management in terms of prevention vs. any singular disease.

# data[].attributes.body
Forecast spread:
Disease ecology known- if needed, design diagnostic assays for detection. Where lab assays are available design field deployable assays.
Disease ecology not known- determine agent, experimentally fulfill Koch's postulates, identify mode of spread.

# data[].attributes.body
Forecast spread: collect systematic surveillance data at the beginning of a disease emergence.  Goals of the surveillance should be to track spread through wild and potentially domestic populations. Conduct lab experiments on the environmental factors affecting disease spread and growth. Develop a framework for predicting the spread of emerging diseases.  Determine the information needed to accurately predict spread.
Disease management: evaluate the effects of disease management activities through reviewing literature (meta-analyses) and identifying gaps. Fill gaps using laboratory experiments or small scale field trials.

# data[].attributes.body
Forecast spread: forecast spread as a disease is emerging using limited information (such as host movement ability, pathogen survival in the environment etc. other factors identified above).
Disease management: conduct large-scale geographically distributed studies on the effects of management on disease by coordinating with willing partners.

# data[].attributes.body
Continue to develop diffusion and other EID model frameworks, continue to find new systems to apply it to - eventually do a meta-analysis of the various spread parameters from these models and see if there are generalities that can help simplify and accelerate the use of models (as in, can we apply it quickly when we know very little and have it be somewhat accurate?).

# data[].attributes.body
Have a suite of modeling capabilities and base layers (data lake!) we can pull from quickly that allow us to forecast diseases that still have a lot of uncertainty and very little data. Linked into the EarthMAP or BSVE ecosphere to allow us to do big data inference quickly. OR idea 2 is to have a syndromic google search style surveillance that warns us early of potential unusual mortality events.

# data[].attributes.body
Assess management. Identify system(s) impacted by recurrent wildlife disease that are managed by partners willing to implement mitigation efforts. Obtain 2-3 years of funding to establish a project team to design, implement, and assess a disease management effort.

# data[].attributes.body
Forecast spread. Work with disease modelers to identify data to collect during routine wildlife disease investigations that will be needed to forecast disease spread as new diseases emerge. Initiate collection of these data through an active partner network conducting designed and scanning wildlife disease surveillance. Use these data to forecast spread, to optimize ongoing surveillance, and to implement and assess management of an emerging wildlife disease in real time.

# data[].attributes.body
Pilot systems approach for identifying new interventions for wildlife health issues (Botulism on Hanalei Bay or CWD in WI).  Major impediment is getting the dang thing funded!

# data[].attributes.body
Develop systems mapping and implementation capacity at NWHC. Not sure if this looks like training for our current staff, new partnerships, or new hires?

# data[].attributes.headline
Feasible: Assessment of Management Action

# data[].attributes.subject
Feasible: Assessment of Management Action

# data[].attributes.body
This is an area that is useful but "unsexy". Identification of suitable case studies where the effects of different management action can be compared will be essential. Are the locations and diseases where action (and inaction) can be studied over a time period that is relevant?

# data[].attributes.body
Forecasting spread in a completely naive population, such as when a novel pathogen is introduced is likely to be more feasible than forecasting the re-emergence of an agent where there can be pre-existing immunity. An understanding the population level of susceptibility and innate resistance, the degree of interactions between animals, in combination of topological and environmental factors that affect spread is needed. Are there diseases and locations where all of these factors can be readily obtained given likely funding constraints?