West Nile virus (WNV) is a globally distributed mosquito-borne virus of great public health concern. The number of WNV human cases and mosquito infection patterns vary in space and time. Many statistical models have been developed to understand and predict WNV geographic and temporal dynamics. However, these modeling efforts have been disjointed with little model comparison and inconsistent validation. In this paper, we describe a framework to unify and standardize WNV modeling efforts nationwide. WNV risk, detection, or warning models for this review were solicited from active research groups working in different regions of the United States. A total of 13 models were selected and described. The spatial and temporal scales of each model were compared to guide the timing and the locations for mosquito and virus surveillance, to support mosquito vector control decisions, and to assist in conducting public health outreach campaigns at multiple scales of decision-making. Our overarching goal is to bridge the existing gap between model development, which is usually conducted as an academic exercise, and practical model applications, which occur at state, tribal, local, or territorial public health and mosquito control agency levels. The proposed model assessment and comparison framework helps clarify the value of individual models for decision-making and identifies the appropriate temporal and spatial scope of each model. This qualitative evaluation clearly identifies gaps in linking models to applied decisions and sets the stage for a quantitative comparison of models. Specifically, whereas many coarse-grained models (county resolution or greater) have been developed, the greatest need is for fine-grained, short-term planning models (m–km, days–weeks) that remain scarce. We further recommend quantifying the value of information for each decision to identify decisions that would benefit most from model input.

We used monthly precipitation and temperature data to give early warning of years with higher West Nile Virus (WNV) risk in Nebraska. We used generalized additive models with a negative binomial distribution and smoothing curves to identify combinations of extremes and timing that had the most influence, experimenting with all combinations of temperature and drought data, lagged by 12, 18, 24, 30, and 36 months. We fit models on data from 2002 through 2011, used Akaike's Information Criterion (AIC) to select the best‐fitting model, and used 2012 as out‐of‐sample data for prediction, and repeated this process for each successive year, ending with fitting models on 2002–2017 data and using 2018 for out‐of‐sample prediction. We found that warm temperatures and a dry year preceded by a wet year were the strongest predictors of cases of WNV. Our models did significantly better than random chance and better than an annual persistence naïve model at predicting which counties would have cases. Exploring different scenarios, the model predicted that without drought, there would have been 26% fewer cases of WNV in Nebraska through 2018; without warm temperatures, 29% fewer; and with neither drought nor warmth, 45% fewer. This method for assessing the influence of different combinations of extremes at different time intervals is likely applicable to diseases other than West Nile, and to other annual outcome variables such as crop yield.

State climatologists and other expert drought observers have speculated about the value of monitoring Twitter for #drought and related hashtags. This study statistically examines the relationships between the rate of tweeting using #drought and related hashtags, within states, accounting for drought status and news coverage of drought. We collected and geolocated tweets, 2017–18, and used regression analysis and a diversity statistic to explain expected and identify unexpected volumes of tweets. This provides a quantifiable means to detect state-weeks with a volume of tweets that exceeds the upper limit of the prediction interval. To filter out instances where a high volume of tweets is related to the activities of one person or very few people, a diversity statistic was used to eliminate anomalous state-weeks where the diversity statistic did not exceed the 75th percentile of the range for that state’s diversity statistic. Anomalous state-weeks in a few cases preceded the onset of drought but more often coincided with or lagged increases in drought. Tweets are both a means of sharing original experience and a means of discussing news and other recent events, and anomalous weeks occurred throughout the course of a drought, not just at the beginning. A sum-to-zero contrast coefficient for each state revealed a difference in the propensity of different states to tweet about drought, apparently reflecting recent and long-term experience in those states, and suggesting locales that would be most predisposed to drought policy innovation.

Zipper, S. C., K. Helm Smith, B. Breyer, J. Qiu, A. Kung, and D. Herrmann. 2017. Socio-environmental drought response in a mixed urban-agricultural setting: synthesizing biophysical and governance responses in the Platte River Watershed, Nebraska, USA. Ecology and Society 22(4):39. https://doi.org/10.5751/ES-09549-220439

This paper reviews previous efforts to assign monetary value to climatic or meteorological information, such as public information on drought, climate, early warning systems, and weather forecast information. Methods and tools that have been explored to examine the benefits of climatic and meteorological information include the avoided cost, contingent valuation, choice experiments, benefit transfer, and descriptive approaches using surveys. The second part of this paper discusses specific considerations related to valuing drought information for public health and the Bureau of Land Management. We found a multitude of connections between drought and the land management and health sectors in the literature. The majority of the papers that we summarized only report biophysical change, because the economic losses of drought are not available. Only a few papers reported economic loss associated with drought. To determine the value of drought information, we need to know more about the role it plays in decision making and what sources of drought information are used in different sectors. This inventory of methods and impacts highlights opportunities for further research in valuing drought information in land management and public health.

Droughts affect recreation and tourism, grazing, forests, and timber, and can have important indirect effects for the ecosystems and species that rely on water. Despite its importance, the effect of drought in the land management sector is less understood than in other water-intensive sectors, such as agriculture and public water supplies. This study presents the first-ever estimates of the economic valuation of the information provided by the U.S. Drought Monitor using the avoided cost method. These estimates are based on the time and labor saved by using the U.S. Drought Monitor rather than compiling drought-related information from other sources, or using other sources for tracking/monitoring droughts, communicating drought conditions, and dealing with drought-related issues. The results reflect rational behavior—the more time needed to compile or collect drought information provided by the U.S. Drought Monitor, the higher the dollar value in avoided cost. This dollar amount also varies by institution and organization, which indicates respondents from different organizations value the information from the U.S. Drought Monitor differently. For example, compared to the state offices, the field offices in the Bureau of Land Management value more of the information provided by the U.S. Drought Monitor. These estimates can be used to estimate the societal benefits and help policy makers evaluate the U.S. Drought Monitor in different sectors.