Rift Valley Fever Epidemiology, Surveillance, and Control: What Have Models Contributed?

Métras, Raphaëlle; Collins, Lisa; White, Richard G.; Alonso, Silvia; Chevalier, Véronique; Thuranira-McKeever, Christine; Pfeiffer, Dirk U.

doi:10.1089/vbz.2010.0200

Cited by 45 publications

(49 citation statements)

References 46 publications

(49 reference statements)

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“…Rift Valley Fever was first reported among livestock in Kenya around 1915, but the virus was not isolated until 1931 (Daubney et al, 1931). RVFV is a USDA and Human Health and Services select agent (Boshra et al, 2011; Hartley et al, 2011; Métras et al, 2011), because it can cause fatal hemorrhagic disease in infected humans and has a high mortality rate among livestock (Boshra et al, 2011; Hartley et al, 2011). This mosquito-transmitted virus is endemic to Africa, but has recently spread to the Arabian Peninsula (Métras et al, 2011; Niu et al, 2012).…”

Section: Virusesmentioning

confidence: 99%

“…RVFV is a USDA and Human Health and Services select agent (Boshra et al, 2011; Hartley et al, 2011; Métras et al, 2011), because it can cause fatal hemorrhagic disease in infected humans and has a high mortality rate among livestock (Boshra et al, 2011; Hartley et al, 2011). This mosquito-transmitted virus is endemic to Africa, but has recently spread to the Arabian Peninsula (Métras et al, 2011; Niu et al, 2012). In other locations, including the US, endemic mosquito species can carry and transmit RVFV (Konrad and Miller, 2012; Métras et al, 2011; Niu et al, 2012).…”

Section: Virusesmentioning

confidence: 99%

“…This mosquito-transmitted virus is endemic to Africa, but has recently spread to the Arabian Peninsula (Métras et al, 2011; Niu et al, 2012). In other locations, including the US, endemic mosquito species can carry and transmit RVFV (Konrad and Miller, 2012; Métras et al, 2011; Niu et al, 2012). …”

Section: Virusesmentioning

confidence: 99%

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Viruses and antiviral immunity in Drosophila

Cherry

2014

Developmental & Comparative Immunology

121

123

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Viral pathogens present many challenges to organisms, driving the evolution of a myriad of antiviral strategies to combat infections. A wide variety of viruses infect invertebrates, including both natural pathogens that are insect-restricted, and viruses that are transmitted to vertebrates. Studies using the powerful tools available in the model organism Drosophila have expanded our understanding of antiviral defenses against diverse viruses. In this review, we will cover three major areas. First, we will describe the tools used to study viruses in Drosophila. Second, we will survey the major viruses that have been studied in Drosophila. And lastly, we will discuss the well-characterized mechanisms that are active against these diverse pathogens, focusing on non-RNAi mediated antiviral mechanisms. Antiviral RNAi is discussed in another paper in this issue.

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Section: Virusesmentioning

confidence: 99%

Section: Virusesmentioning

confidence: 99%

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Viruses and antiviral immunity in Drosophila

Cherry

2014

Developmental & Comparative Immunology

121

123

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“…Models of RVF have been aimed at addressing two primary concerns [34]. First, what is the risk of disease resurgence and spread in endemic regions?…”

Section: Introductionmentioning

confidence: 99%

Coupling Vector-host Dynamics with Weather Geography and Mitigation Measures to Model Rift Valley Fever in Africa

McMahon

Manore

Hyman

et al. 2014

Math. Model. Nat. Phenom.

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We present and characterize a multi-host epidemic model of Rift Valley fever (RVF) virus in East Africa with geographic spread on a network, rule-based mitigation measures, and mosquito infection and population dynamics. Susceptible populations are depleted by disease and vaccination and are replenished with the birth of new animals. We observe that the severity of the epidemics is strongly correlated with the duration of the rainy season and that even severe epidemics are abruptly terminated when the rain stops. Because naturally acquired herd immunity is established, total mortality across 25 years is relatively insensitive to many mitigation approaches. Strong reductions in cattle mortality are expected, however, with sufficient reduction in population densities of either vectors or susceptible (ie. unvaccinated) hosts. A better understanding of RVF epidemiology would result from serology surveys to quantify the importance of herd immunity in epidemic control, and sequencing of virus from representative animals to quantify the realative importance of transportation and local reservoirs in nucleating yearly epidemics. Our results suggest that an effective multi-layered mitigation strategy would include vector control, movement control, and vaccination of young animals yearly, even in the absence of expected rainfall.

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“…Different users have different needs according to their missions. For example, in the case of surveillance models, some will need to see everything reported by event-based surveillance systems (i.e., they are unconcerned with specificity but sensitivity is of high value to them), while other users may demand low false alarm rates (i.e., specificity is important for their needs) [71], [72]. The Operational Readiness Level rating of any given model will thus depend upon the diverse questions and purposes to which any given model is applied.…”

Section: Results and Analysismentioning

confidence: 99%

Disease Prediction Models and Operational Readiness

et al. 2014

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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 http://BioCat.pnnl.gov. 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.

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Rift Valley Fever Epidemiology, Surveillance, and Control: What Have Models Contributed?

Abstract: Enhanced outbreak prediction and control and increased knowledge on RVF epidemiology would benefit from additional field data, continued development, and refinement of modeling techniques for exploring plausible disease transmission mechanisms and the impact of intervention strategies.

Cited by 45 publications

References 46 publications

Viruses and antiviral immunity in Drosophila

Viruses and antiviral immunity in Drosophila

Coupling Vector-host Dynamics with Weather Geography and Mitigation Measures to Model Rift Valley Fever in Africa

Disease Prediction Models and Operational Readiness

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