Economics of Robust Surveillance on Exotic Animal Diseases: the Case of Bluetongue

Control of emerging animal diseases critically depends on their early detection. However, designing surveillance programs for exotic and emerging diseases is very challenging because of knowledge gaps on the probability of incursion and mechanisms of spread. Using the example of Bluetongue Virus, which is exotic to the UK, we develop a metapopulation epidemic-economic modelling framework that considers the incursion, detection, spread and control of a disease in a livestock production system composed of heterogeneous subpopulations. The model is then embedded in an information gap (info-gap) framework to assess the robustness of surveillance and vaccination policies to unacceptable outbreaks losses and applied to the case of Bluetongue in the UK. The results show that active reporting of suspect clinical signs by farmers is a very robust way to reduce unacceptable outcomes. Vaccination of animals in high risk regions led to robustly protective programs. If vaccines are not available, surveillance targeted to the high risk region is very robust even if the extent of the high risk region is not known and effectiveness of detection is very low. Surveillance programs focusing in all regions with the same intensity are in general not robust unless the dispersal of the vector connecting both regions is very high.compartmental epidemic model, emergent animal disease, Knightian uncertainty, sentinel surveillance system, Livestock Production/Industries,

Seasonal and spatial heterogeneities in host and vector abundances impact the spatiotemporal spread of bluetongue

Bluetongue (BT) can cause severe livestock losses and large direct and indirect costs for farmers. To propose targeted control strategies as alternative to massive vaccination, there is a need to better understand how BT virus spread in space and time according to local characteristics of host and vector populations. Our objective was to assess, using a modelling approach, how spatiotemporal heterogeneities in abundance and distribution of hosts and vectors impact the occurrence and amplitude of local and regional BT epidemics. We built a reaction–diffusion model accounting for the seasonality in vector abundance and the active dispersal of vectors. Because of the scale chosen, and movement restrictions imposed during epidemics, host movements and wind-induced passive vector movements were neglected. Four levels of complexity were addressed using a theoretical approach, from a homogeneous to a heterogeneous environment in abundance and distribution of hosts and vectors. These scenarios were illustrated using data on abundance and distribution of hosts and vectors in a real geographical area. We have shown that local epidemics can occur earlier and be larger in scale far from the primary case rather than close to it. Moreover, spatial heterogeneities in hosts and vectors delay the epidemic peak and decrease the infection prevalence. The results obtained on a real area confirmed those obtained on a theoretical domain. Although developed to represent BTV spatiotemporal spread, our model can be used to study other vector-borne diseases of animals with a local to regional spread by vector diffusion

Inferences about the transmission of Schmallenberg virus within and between farms

In the summer of 2011 Schmallenberg virus (SBV), a Culicoides-borne orthobunyavirus, emerged in Germany and The Netherlands and subsequently spread across much of Europe. To draw inferences about the transmission of SBV we have developed two models to describe its spread within and between farms. The within-farm model was fitted to seroprevalence data for cattle and sheep farms in Belgium and The Netherlands, with parameters estimated using approximate Bayesian computation. Despite the short duration of viraemia in cattle and sheep (mean of 3–4 days) the within-farm seroprevalence can reach high levels (mean within-herd seroprevalence >80%), largely because the probability of transmission from host to vector is high (14%) and SBV is able to replicate quickly (0.03 per day-degree) and at relatively low temperatures (threshold for replication: 12.3 °C). Parameter estimates from the within-farm model were then used in a separate between-farm model to simulate the regional spread of SBV. This showed that the rapid spread of SBV at a regional level is primarily a consequence of the high probability of transmission from host to vector and the temperature requirements for virus replication. Our results, obtained for a region of the UK in a typical year with regard to animal movements, indicate that there is no need to invoke additional transmission mechanisms to explain the observed patterns of rapid spread of SBV in Europe. Moreover, the imposition of movement restrictions, even a total movement ban, has little effect on the spread of SBV at this scale

Global stability of an SIS epidemic model with a finite infectious period

Assuming a general distribution for the sojourn time in the in- fectious class, we consider an SIS type epidemic model formulated as a scalar integral equation. We prove that the endemic equilibrium of the model is globally asymptotically stable whenever it exists, solving the conjecture of Hethcote and van den Driessche (1995) for the case of nonfatal diseases

Bighorn Sheep Demographics Following Pneumonia Die-Off Events

Bighorn sheep populations across the Intermountain West are subject to disease pressure from the respiratory bacteria Mycoplasma ovipneumoniae. Although the effects of M. ovipneumoniae-associated disease die-offs are well documented, less is known about the factors driving long-term differences in post-die-off population responses. While many herds experience years to decades in which recruitment is less than 20 lambs per 100 ewes, some herds’ lamb survival rates are able to rebound rapidly following die-off events. The reason why these herds recover quickly while others do not is currently unknown. Here, we assess the roles environmental, demographic, and pathogen-associated factors could play in shaping bighorn sheep herd recovery. Our analysis relies on more than 30 years of data from over 58 bighorn sheep herds across the state of Nevada. Our results suggest that herd demographic responses to M. ovipneumoniae vary dramatically across subspecies, and that environmental factors may be more important in shaping those demographic responses in desert bighorn than in Rocky Mountain or California bighorn herds. Our results could have important implications on prioritization of bighorn sheep recovery efforts throughout the Intermountain West

Global dynamics of periodic infectious disease models with time-dependent delays

Many infectious diseases have seasonal trends and exhibit variable periods of peak seasonality. Understanding the population dynamics due to seasonal changes becomes very important for predicting and controlling disease transmission risks. For some directly transmitted and vector-borne diseases, the length of the incubation period strongly depends on the temperature. This thesis is devoted to the study of the global dynamics of some periodic epidemic models with periodic incubation periods. We start with a classical SEIRS epidemic model with a time-dependent latent period in Chapter 2. Moreover, vector-borne diseases, such as West Nile virus, bluetongue, and malaria, are always highly dependent on seasonal change, especially the temperature. To investigate the seasonal effects and temperature-dependent delays on West Nile virus, we present a periodic functional differential equations model with the vertical transmission, the periodic maturation delay, and the periodic extrinsic incubation period in Chapter 3. In Chapter 4, we propose a bluetongue model with seasonality and temperature-dependent incubation period, which describes the dynamics of bluetongue transmission via cattle and sheep as hosts and midges as vectors. To explore the effects of the spatial and temporal heterogeneity in hosts and vectors, and only vector movements on the spread of bluetongue, we develop a nonlocal periodic reaction-diffusion model of bluetongue disease with periodic time delays in Chapter 5. Based on the theory of the basic reproduction ratio, we derive and numerically compute the basic reproduction ratio for our models. By the theory of dynamical systems, we show that the basic reproduction ratio acts as a threshold parameter for the global dynamics for each model. Numerical simulations or case studies are carried out to illustrate the analytic results and help us provide some new findings. At the end of this thesis, we present a brief summary and some interesting future works

Using combined diagnostic test results to hindcast trends of infection from cross-sectional data

Infectious disease surveillance is key to limiting the consequences from infectious pathogens and maintaining animal and public health. Following the detection of a disease outbreak, a response in proportion to the severity of the outbreak is required. It is thus critical to obtain accurate information concerning the origin of the outbreak and its forward trajectory. However, there is often a lack of situational awareness that may lead to over- or under-reaction. There is a widening range of tests available for detecting pathogens, with typically different temporal characteristics, e.g. in terms of when peak test response occurs relative to time of exposure. We have developed a statistical framework that combines response level data from multiple diagnostic tests and is able to ‘hindcast’ (infer the historical trend of) an infectious disease epidemic. Assuming diagnostic test data from a cross-sectional sample of individuals infected with a pathogen during an outbreak, we use a Bayesian Markov Chain Monte Carlo (MCMC) approach to estimate time of exposure, and the overall epidemic trend in the population prior to the time of sampling. We evaluate the performance of this statistical framework on simulated data from epidemic trend curves and show that we can recover the parameter values of those trends. We also apply the framework to epidemic trend curves taken from two historical outbreaks: a bluetongue outbreak in cattle, and a whooping cough outbreak in humans. Together, these results show that hindcasting can estimate the time since infection for individuals and provide accurate estimates of epidemic trends, and can be used to distinguish whether an outbreak is increasing or past its peak. We conclude that if temporal characteristics of diagnostics are known, it is possible to recover epidemic trends of both human and animal pathogens from cross-sectional data collected at a single point in time