Data-Driven Modeling to Assess Receptivity for Rift Valley Fever Virus

<div><p>Rift Valley Fever virus (RVFV) is an enzootic virus that causes extensive morbidity and mortality in domestic ruminants in Africa, and it has shown the potential to invade other areas such as the Arabian Peninsula. Here, we develop methods for linking mathematical models to real-world data that could be used for continent-scale risk assessment given adequate data on local host and vector populations. We have applied the methods to a well-studied agricultural region of California with 1 million dairy cattle, abundant and competent mosquito vectors, and a permissive climate that has enabled consistent transmission of West Nile virus and historically other arboviruses. Our results suggest that RVFV outbreaks could occur from February–November, but would progress slowly during winter–early spring or early fall and be limited spatially to areas with early increases in vector abundance. Risk was greatest in summer, when the areas at risk broadened to include most of the dairy farms in the study region, indicating the potential for considerable economic losses if an introduction were to occur. To assess the threat that RVFV poses to North America, including what-if scenarios for introduction and control strategies, models such as this one should be an integral part of the process; however, modeling must be paralleled by efforts to address the numerous remaining gaps in data and knowledge for this system.</p></div

Study area.

<p>Map showing the location of the study area within California (left panel), and a map of the study area depicting the dominant land use within each 5-km grid cell (right panel).</p

Seasonal temperature pattern within the study area.

<p>Graph showing daily mean temperatures (dark line) and – percentiles (shaded area) for the study area.</p

Seasonal mosquito abundance patterns.

<p>Realistic annual patterns for <i>Cx. tarsalis</i> and <i>Ae. melanimon</i> defined using trap data for each of the dominant land use categories within the study area. Traps collected <i>Ae. melanimon</i> only in 2 land uses, with the largest numbers occurring in seasonally flooded wetlands.</p

Spatio-temporal patterns of epidemicity, .

<p>Maps (upper panel) showing an estimate of the maximal transmission potential, , by month, and a graph of median daily values (lower panel) by land use class. Dashed lines in the lower panel indicate the and percentiles for the land use class of the same color. Wetlands and other grid cells without competent hosts (i.e., dairy cows) are mapped in gray and were not included in the analysis because transmission would not be expected in those locations. December is omitted from the maps because it did not differ meaningfully from January, with universally 1.</p

Diagram of the model.

<p>Schematic of the SEIR model constructed for Rift Valley fever virus circulation in California. Mosquitoes are categorized as capable of vertical transmission (<i>Aedes</i>) or not (<i>Culex</i>). For <i>Aedes</i>, adult mosquitoes emerge from uninfected (P) or vertically infected (Q) eggs. Hosts are categorized as highly competent (livestock) or incompetent (dead-end hosts) for RVFV transmission. See the text for a complete explanation.</p

Initial Definitions of Operational Readiness Levels for Disease Prediction Models.

<p>Initial Definitions of Operational Readiness Levels for Disease Prediction Models.</p

A Sampling of Keywords and Phrases.

<p>+ Is used to link phrases or keywords with the Boolean operator “and”.</p><p>* Is used as truncation to search for words that begin with the same letters or to replace any number of characters.</p

The citations placed in each mode of transmission group.

<p>If a model involved multiple agents in different categories, the paper was placed in multiple groups.</p

Disease Prediction Models and Operational Readiness

<div><p>The objective of this manuscript is to present a systematic review of biosurveillance models that operate on select agents and can forecast the occurrence of a disease event. We define a disease event to be a biological event with focus on the One Health paradigm. These events are characterized by evidence of infection and or disease condition. We reviewed models that attempted to predict a disease event, not merely its transmission dynamics and we considered models involving pathogens of concern as determined by the US National Select Agent Registry (as of June 2011). We searched commercial and government databases and harvested Google search results for eligible models, using terms and phrases provided by public health analysts relating to biosurveillance, remote sensing, risk assessments, spatial epidemiology, and ecological niche modeling. After removal of duplications and extraneous material, a core collection of 6,524 items was established, and these publications along with their abstracts are presented in a semantic wiki at <a href="http://BioCat.pnnl.gov" target="_blank">http://BioCat.pnnl.gov</a>. As a result, we systematically reviewed 44 papers, and the results are presented in this analysis. We identified 44 models, classified as one or more of the following: event prediction (4), spatial (26), ecological niche (28), diagnostic or clinical (6), spread or response (9), and reviews (3). The model parameters (e.g., etiology, climatic, spatial, cultural) and data sources (e.g., remote sensing, non-governmental organizations, expert opinion, epidemiological) were recorded and reviewed. A component of this review is the identification of verification and validation (V&V) methods applied to each model, if any V&V method was reported. All models were classified as either having undergone Some Verification or Validation method, or No Verification or Validation. We close by outlining an initial set of operational readiness level guidelines for disease prediction models based upon established Technology Readiness Level definitions.</p></div