Key message: We summarise modelling studies of the most economically important cassava diseases and arthropods, highlighting research gaps where modelling can contribute to the better management of these in the areas of surveillance, control, and host-pest dynamics understanding the effects of climate change and future challenges in modelling. For over 30 years, experimental and theoretical studies have sought to better understand the epidemiology of cassava diseases and arthropods that affect production and lead to considerable yield loss, to detect and control them more effectively. In this review, we consider the contribution of modelling studies to that understanding. We summarise studies of the most economically important cassava pests, including cassava mosaic disease, cassava brown streak disease, the cassava mealybug, and the cassava green mite. We focus on conceptual models of system dynamics rather than statistical methods. Through our analysis we identified areas where modelling has contributed and areas where modelling can improve and further contribute. Firstly, we identify research challenges in the modelling developed for the surveillance, detection and control of cassava pests, and propose approaches to overcome these. We then look at the contributions that modelling has accomplished in the understanding of the interaction and dynamics of cassava and its’ pests, highlighting success stories and areas where improvement is needed. Thirdly, we look at the possibility that novel modelling applications can achieve to provide insights into the impacts and uncertainties of climate change. Finally, we identify research gaps, challenges, and opportunities where modelling can develop and contribute for the management of cassava pests, highlighting the recent advances in understanding molecular mechanisms of plant defence.

Diseases in humans, animals and plants remain an important challenge in our society. Effective control of invasive pathogens often requires coordinated concerted action of a large group of stakeholders. Both epidemiological and human behavioural factors influence the outcome of a disease control campaign. In mathematical models that are frequently used to guide such campaigns, human behaviour is often ill-represented, if at all. Existing models of human, animal and plant disease that do incorporate participation or compliance are often driven by pay-offs or direct observations of the disease state. It is however very well known that opinion is an important driving factor of human decision making. Here we consider the case study of Citrus Huanglongbing disease (HLB), which is an acute bacterial disease that threatens the sustainability of citrus production across the world. We show how by coupling an epidemiological model of this invasive disease with an opinion dynamics model we are able to answer the question: What makes or breaks the effectiveness of a disease control campaign? Frequent contact between stakeholders and advisors is shown to increase the probability of successful control. More surprisingly, we show that informing stakeholders about the effectiveness of control methods is of much greater importance than prematurely increasing their perceptions of the risk of infection. We discuss the overarching consequences of this finding and the effect on human as well as plant disease epidemics. The successful regional control of emerging and invasive diseases often requires that a sufficiently large proportion of the population comply with the control strategy. This is notably the case in diseases such as measles but also applies to epidemics in animals and plants. If insufficient numbers of stakeholders comply with disease control, or if control becomes uncoordinated for some reason, then control fails. Therefore, both epidemiological and human behavioural factors influence the outcome of emerging, endemic, and invasive disease control campaigns. Mathematical models are often used to determine factors that are important for disease control to be successful, but these models tend to focus on the epidemiology and efficacy of control, frequently neglecting human behaviour. A number of mathematical models of human disease, and to some extent animal disease do incorporate participation or compliance behaviours; however, studies looking at human actions and attitudes towards plant disease control are quite rare and almost exclusively driven by pay-offs or direct observations of the disease state. It is however very well known that opinion, for example about how effective control is perceived to be, is also a key driving factor of human decision making. Here we consider the case study of Citrus Huanglongbing disease (HLB), a devastating invasive disease in citrus which threatens production worldwide. We show how by coupling an epidemiological model with an opinion dynamics model it is possible to answer the question: What makes or breaks the effectiveness of a disease control campaign?

Ash dieback is a fungal disease (causal agent Hymenoscyphus fraxineus) infecting Common ash (Fraxinus excelsior) throughout temperate Europe. The disease was first discovered in the UK in 2012 in a nursery in Southern England, in plants which had been imported from the Netherlands. After sampling other recently planted sites across England, more infected trees were found. Tree trade from outside and across the UK may have facilitated the spread of invasive diseases which threaten the sustainability of forestry business, ecological niches and amenity landscapes. Detecting a disease in a nursery at an early stage and knowing how likely it is for the disease to have spread further in the plant trade network, can help control an epidemic. Here, we test two simple sampling rules that 1) inform monitoring strategies to detect a disease at an early stage, and 2) inform the decision of tracking forward the disease after its detection. We apply these expressions to the case of ash dieback in the UK and test them in different scenarios after disease introduction. Our results are useful to inform policy makers’ decisions on monitoring for the control and spread of tree diseases through the nursery trade.