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.Author summary: Emerging pathogens are an increasing threat to human, animal, and plant health. In areas where these pathogens have not yet become established, surveillance is needed to detect incursions early enough to implement control measures. However, most epidemiological systems are heterogeneous in nature, and it is unclear how finite surveillance resources should be divided between constituent groups (such as hosts and vectors in the case of vector-borne pathogens). We use mathematical and statistical methods to address this issue. Taking the example of vector-borne pathogens, we show how to estimate the proportion of infected hosts or vectors at the time of first detection for any combination of host and vector sampling rates, given some knowledge of the characteristics of pathogen spread within and between hosts and vectors. We predict that the required total sampling effort and cost for early detection will be lowest when either hosts or vectors are sampled, with the optimal group to sample being the one with the highest estimated prevalence during initial exponential growth (which has clear parallels with ‘targeted surveillance’). We demonstrate the use of our framework by applying it to two vector-borne diseases of citrus and evaluate its predictions using a simple simulation model of sampling.