BackgroundExisting epidemiological models have largely tended to neglect the impact of individual behaviour on the dynamics of diseases. However, awareness of the presence of illness can cause people to change their behaviour by, for example, staying at home and avoiding social contacts. Such changes can be used to control epidemics but they exact an economic cost. Our aim is to study the costs and benefits of using individual-based social distancing undertaken by healthy individuals as a form of control.MethodsOur model is a standard SIR model superimposed on a spatial network, without and with addition of small-world interactions. Disease spread is controlled by allowing susceptible individuals to temporarily reduce their social contacts in response to the presence of infection within their local neighbourhood. We ascribe an economic cost to the loss of social contacts, and weigh this against the economic benefit gained by reducing the impact of the epidemic. We study the sensitivity of the results to two key parameters, the individuals’ attitude to risk and the size of the awareness neighbourhood.ResultsDepending on the characteristics of the epidemic and on the relative economic importance of making contacts versus avoiding infection, the optimal control is one of two extremes: either to adopt a highly cautious control, thereby suppressing the epidemic quickly by drastically reducing contacts as soon as disease is detected; or else to forego control and allow the epidemic to run its course. The worst outcome arises when control is attempted, but not cautiously enough to cause the epidemic to be suppressed. The next main result comes from comparing the size of the neighbourhood of which individuals are aware to that of the neighbourhood within which transmission can occur. The control works best when these sizes match and is particularly ineffective when the awareness neighbourhood is smaller than the infection neighbourhood. The results are robust with respect to inclusion of long-range, small-world links which destroy the spatial structure, regardless of whether individuals can or cannot control them. However, addition of many non-local links eventually makes control ineffective.ConclusionsThese results have implications for the design of control strategies using social distancing: a control that is too weak or based upon inaccurate knowledge, may give a worse outcome than doing nothing.

This version is available at https://strathprints.strath.ac.uk/52750/ Strathprints is designed to allow users to access the research output of the University of Strathclyde. Unless otherwise explicitly stated on the manuscript, Copyright © and Moral Rights for the papers on this site are retained by the individual authors and/or other copyright owners. Please check the manuscript for details of any other licences that may have been applied. You may not engage in further distribution of the material for any profitmaking activities or any commercial gain. You may freely distribute both the url (https://strathprints.strath.ac.uk/) and the content of this paper for research or private study, educational, or not-for-profit purposes without prior permission or charge.Any correspondence concerning this service should be sent to the Strathprints administrator: strathprints@strath.ac.ukThe Strathprints institutional repository (https://strathprints.strath.ac.uk) is a digital archive of University of Strathclyde research outputs. It has been developed to disseminate open access research outputs, expose data about those outputs, and enable the management and persistent access to Strathclyde's intellectual output. Protection Motivation Theory and Social Distancing Behaviour in Response to a Simulated Infectious Disease EpidemicEpidemics of respiratory infectious disease remain one of the most serious health risks facing the population. Non-pharmaceutical interventions (e.g., hand-washing or wearing face masks)can have a significant impact on the course of an infectious disease epidemic. The current study investigated whether protection motivation theory (PMT) is a useful framework for understanding social distancing behaviour (i.e. the tendency to reduce social contacts) in response to a simulated infectious disease epidemic. There were 230 participants (109 males, 121 females, mean age 32.4 years) from the general population who completed self-report measures assessing the components of PMT (Milne, Orbell & Sheeran, 2002). In addition, participants completed a computer game which simulated an infectious disease epidemic in order to provide a measure of social distancing behaviour (Maharaj, McCaldin & Kleczkowski, 2011). The regression analyses revealed that none of the PMT variables were significant predictors of social distancing behaviour during the simulation task. However, fear ( = .218, p<.001), responseefficacy ( = .175, p<.01) and self-efficacy ( = 0.251, p < 0001) were all significant predictors of intention to engage in social distancing behaviour. Overall, the PMT variables (and demographic factors) explain 21.2% of the variance in intention. The findings demonstrated that PMT was a useful framework for understanding intention to engage in social distancing behaviour, but not actual behaviour during the simulated epidemic. These findings may reflect an intentionbehaviour gap in relation to social distancing behaviour.

BackgroundStudies of social distancing during epidemics have found that the strength of the response can have a decisive impact on the outcome. In previous work we developed a model of social distancing driven by individuals’ risk attitude, a parameter which determines the extent to which social contacts are reduced in response to a given infection level. We showed by simulation that a strong response, driven by a highly cautious risk attitude, can quickly suppress an epidemic. However, a moderately cautious risk attitude gives weak control and, by prolonging the epidemic without reducing its impact, may yield a worse outcome than doing nothing. In real societies, social distancing may arise spontaneously from individual choices rather than being imposed centrally. There is little data available about this as opportunistic data collection during epidemics is difficult. Our study uses a simulated epidemic in a computer game setting to measure the social distancing response.MethodsTwo hundred thirty participants played a computer game simulating an epidemic on a spatial network. The player controls one individual in a population of 2500 (with others controlled by computer) and decides how many others to contact each day. To mimic real-world trade-offs, the player is motivated to make contact by being rewarded with points, while simultaneously being deterred by the threat of infection. Participants completed a questionnaire regarding psychological measures of health protection motivation. Finally, simulations were used to compare the experimentally-observed response to epidemics with no response.ResultsParticipants reduced contacts in response to infection in a manner consistent with our model of social distancing. The experimentally observed response was too weak to halt epidemics quickly, resulting in a somewhat reduced attack rate and a substantially reduced peak attack rate, but longer duration and fewer social contacts, compared to no response. Little correlation was observed between participants’ risk attitudes and the psychological measures.ConclusionsOur cognitive model of social distancing matches responses to a simulated epidemic. If these responses indicate real world behaviour, spontaneous social distancing can be expected to reduce peak attack rates. However, additional measures are needed if it is important to stop an epidemic quickly.

Water hyacinth (Pontederia crassipes, also referred to as Eichhornia crassipes) is one of the most invasive weed species in the world, causing significant adverse economic and ecological impacts, particularly in tropical and sub-tropical regions. Large scale real-time monitoring of areas of chronic infestation is critical to formulate effective control strategies for this fast spreading weed species. Assessment of revenue generation potential of the harvested water hyacinth biomass also requires enhanced understanding to estimate the biomass yield potential for a given water body. Modern remote sensing technologies can greatly enhance our capacity to understand, monitor, and estimate water hyacinth infestation within inland as well as coastal freshwater bodies. Readily available satellite imagery with high spectral, temporal, and spatial resolution, along with conventional and modern machine learning techniques for automated image analysis, can enable discrimination of water hyacinth infestation from other floating or submerged vegetation. Remote sensing can potentially be complemented with an array of other technology-based methods, including aerial surveys, ground-level sensors, and citizen science, to provide comprehensive, timely, and accurate monitoring. This review discusses the latest developments in the use of remote sensing and other technologies to monitor water hyacinth infestation, and proposes a novel, multi-modal approach that combines the strengths of the different methods.

A B S T R A C TThere is increasing interest in the use of serious games in STEM education. Interactive simulations and serious games can be used by students to explore systems where it would be impractical or unethical to perform real world studies or experiments. Simulations also have the capacity to reveal the internal workings of systems where these details are hidden in the real world. However, there is still much to be investigated about the best methods for using these games in the classroom so as to derive the maximum educational benefit. We report on an experiment to compare two different methods of using a serious game for teaching a complex concept in marine ecology, in a university setting: expert demonstration versus exploration-based learning. We created an online game based upon a mathematical simulation of fishery management, modelling how fish populations grow and shrink in the presence of stock removal through fishing. The player takes on the role of a fishery manager, who must set annual catch quotas, making these as high as possible to maximise profit, without exceeding sustainable limits and causing the stock to collapse. There are two versions of the game. The "whitebox" or "teaching" game gives the player full information about all model parameters and actual levels of stock in the ocean, something which is impossible to measure in reality. The "black-box" or "testing" game displays only the limited information that is available to fishery managers in the real world, and is used to test the player's understanding of how to use that information to solve the problem of estimating the optimal catch quota.Our study addresses the question of whether students are likely to learn better by freely exploring the teaching game themselves, or by viewing a demonstration of the game being played expertly by the lecturer. We conducted an experiment with two groups of students, one using free, self-directed exploration and the other viewing an expert demonstration. Both groups were then assessed using the black box testing game, and completed a questionnaire. Our results show a statistically significant benefit for expert demonstration over free exploration. Qualitative analysis of the responses to the questionnaire demonstrates that students saw benefits to both teaching approaches, and many would have preferred a combination of expert demonstration with exploration of the game. The research was carried out among a mix of undergraduate and taught postgraduate science students. Future research challenges include extending the current study to larger cohorts and exploring the potential effectiveness of serious games and interactive simulation-based teaching methods in a range of STEM subjects in both university and school settings.