A method of determining where to target surveillance efforts in heterogeneous epidemiological systems

The spread of pathogens into new environments poses a considerable threat to human, animal, and plant health, and by extension, human and animal wellbeing, ecosystem function, and agricultural productivity, worldwide. Early detection through effective surveillance is a key strategy to reduce the risk of their establishment. Whilst it is well established that statistical and economic considerations are of vital importance when planning surveillance efforts, it is also important to consider epidemiological characteristics of the pathogen in question—including heterogeneities within the epidemiological system itself. One of the most pronounced realisations of this heterogeneity is seen in the case of vector-borne pathogens, which spread between ‘hosts’ and ‘vectors’—with each group possessing distinct epidemiological characteristics. As a result, an important question when planning surveillance for emerging vector-borne pathogens is where to place sampling resources in order to detect the pathogen as early as possible. We answer this question by developing a statistical function which describes the probability distributions of the prevalences of infection at first detection in both hosts and vectors. We also show how this method can be adapted in order to maximise the probability of early detection of an emerging pathogen within imposed sample size and/or cost constraints, and demonstrate its application using two simple models of vector-borne citrus pathogens. Under the assumption of a linear cost function, we find that sampling costs are generally minimised when either hosts or vectors, but not both, are sampled

Comparative Epidemiology of Citrus tristeza virus in Plantings of Various Citrus Species in Costa Rica, and Long Distance Spread by the Brown Citrus Aphid

Copyright 2002, International Organization of Citrus VirologistsFive 400-tree plots were established to compare the virus increase and spread of Citrus tristeza virus (CTV) among grapefruit, orange and lemon plots in San Carlos and Nicoya citrus producing areas of Costa Rica. Tree disease status was assayed semiannually over a 5-yr period via DAS-I ELISA using a monoclonal mixture to detect all CTV isolates, and MCA13 to identify more severe isolates. Aphid population dynamics and species prevalence/diversity were monitored using yellow and green water traps to estimate flying aphid populations. Spatial and spatio-temporal analyses were conducted to determine the dynamics of virus spread. Virus increase was most rapid in the orange plot, much slower in the grapefruit plot and even slower in the lemon plots. Both grapefruit and orange plots in Boca de Arenal demonstrated some tree to adjacent tree associations of CTV-infected trees but none at the scale of groups of trees. This was reversed for the grapefruit plot in Nicoya for which no association existed among adjacent trees but aggregation did exist within groups of trees. Groups of trap trees were planted and maintained every 0.1 km along roadsides radiating away from the edges of a commercial citrus production area in San Carlos to detect long distance spread by events vector. Brown citrus aphid, Toxoptera citricida, colonies formed multiple times in the trap trees, and CTV-infected trap trees were found as far as 4.6 km from the nearest commercial source trees, indicating the ability of T. citricida to traverse and transmit CTV over considerable distances.Universidad de costa Rica/[801-94-905]/UCR/Costa RicaUCR::Vicerrectoría de Investigación::Unidades de Investigación::Ciencias Básicas::Centro de Investigación en Biología Celular y Molecular (CIBCM

Optimising risk-based surveillance for early detection of invasive plant pathogens.

Emerging infectious diseases (EIDs) of plants continue to devastate ecosystems and livelihoods worldwide. Effective management requires surveillance to detect epidemics at an early stage. However, despite the increasing use of risk-based surveillance programs in plant health, it remains unclear how best to target surveillance resources to achieve this. We combine a spatially explicit model of pathogen entry and spread with a statistical model of detection and use a stochastic optimisation routine to identify which arrangement of surveillance sites maximises the probability of detecting an invading epidemic. Our approach reveals that it is not always optimal to target the highest-risk sites and that the optimal strategy differs depending on not only patterns of pathogen entry and spread but also the choice of detection method. That is, we find that spatial correlation in risk can make it suboptimal to focus solely on the highest-risk sites, meaning that it is best to avoid 'putting all your eggs in one basket'. However, this depends on an interplay with other factors, such as the sensitivity of available detection methods. Using the economically important arboreal disease huanglongbing (HLB), we demonstrate how our approach leads to a significant performance gain and cost saving in comparison with conventional methods to targeted surveillance

Effect of varying transmission parameters (<i>β</i>) on the suggested group of sampling for the HLB model (panel (a)) and the tristeza model (panel (b)).

<p>We estimate the relative sampling efforts required from vectors compared to that from hosts when using the current model parameters (located at the intersection of the dashed lines) using the ratio , and assume that the relative cost of sampling hosts compared to vectors is equal to this threshold (8 for HLB, 6 for Tristeza)—indicating the ‘equivalence point’ as described in the text. The numbers in the key on the right describe the relative vector sampling effort for different transmission rates, but the colour gradient relates to the ratio of the relative vector sampling effort to the relative host sampling cost , and is shown on the log scale in order to better discriminate values less than 1. Regions shown in red have a sampling effort ratio greater than the cost ratio (suggesting that sampling hosts would minimise the total cost) and those in blue have a ratio less than the cost ratio (suggesting that sampling vectors would minimise the total cost). The frontier between these two (indicating a ratio equal to the cost ratio) is shown in white.</p

Effect of varying sampling effort on the mean prevalence at first detection for the HLB model (panels (a) and (b) and the tristeza model (panels (c) and (d).

<p>The estimated prevalence at first detection in hosts is shown in the graphs on the left, and that in vectors is shown in the graphs on the right. The dashed line indicates a host (vertical line) and a vector (horizontal line) sampling effort of 800 samples per 28 days, with the intersection of these dashed lines indicating a theoretical scenario in which a total of 800 hosts and 800 vectors were sampled.</p

Parameter values for different models used in the current study.

<p>Parameter values for different models used in the current study.</p

Prediction of Phyllosticta citricarpa using an hourly infection model and validation with prevalence data from South Africa and Australia

An hourly infection model was used for a risk assessment of citrus black spot (CBS) caused by Phyllosticta citricarpa. The infection model contained a temperature-moisture response function and also included functions to simulate ascospore release and dispersal of pycnidiospores. A validation data set of 18 locations from South Africa and Australia was developed based on locations with known citrus black spot prevalence. An additional 67 sites from Europe and the United States with unknown prevalence were also identified. The model was run for each location with 9 years of hourly weather data from the National Centers for Environmental Prediction (NCEP) Climate Forecast System Reanalysis (CFSR) database. The infection scores for the sites with known prevalence where ranked and a threshold for suitability in a given year was derived from the average score of the lowest ranked moderate prevalence site. The results of the simulation confirm that locations in Florida were high risk while most locations in California and Europe were not at risk. The European location with the highest risk score was Andravida, Greece which had 67% of years suitable for ascosporic infection but only 11% of years were suitable for pycnidiosporic infection. There were six other sites in Europe that had frequency of years suitable for ascosporic infection greater than 22% including Pontecagnano, Italy; Kekrya, Greece; Reggio Calabria, Italy; Cozzo Spadaro, Italy; Messina, Italy; and Siracusa, Italy. Of these six sites only Reggio Calabria had a frequency of years suitable for pycnidiosporic infection greater than 0%. These six sites are predicted to have prevalence similar or less than Messina, South Africa, i.e. low and occasional. Other sites in Europe would best be described as likely to have no prevalence based on very low simulated scores for both spore types. Although Andravida had a similar risk of infection to moderate locations in South Africa there was a difference in the seasonality of infection periods. The ascosporic infection period score was similar between the two sites, but Andravida had a much lower pycnidiosporic infection score in the middle of the period of fruit susceptibility than Addo, South Africa. In Europe favorable climatic conditions are discontinuous, i.e., there is a low frequency of suitable seasons. This raises doubts about the ability of the pathogen to persist at a location and cause disease loss when favorable seasons reoccur. These results suggest that Europe is less suitable for CBS than suggested by an earlier study produced by the European Food Safety Authority using a similar model. The findings from our model simulations suggest that only a few isolated locations in the extreme south of Europe are likely to have a low to marginal risk of P.citricarpa establishment

Parameter values used in the estimation of the transmission parameters (<i>β</i>) for the two models in the current study.

<p>Parameter values used in the estimation of the transmission parameters (<i>β</i>) for the two models in the current study.</p