How disease risk awareness modulates transmission: coupling infectious disease models with behavioural dynamics

Evolutionary analysis of resident groups' intention to participate in green retrofit PPP projects of traditional apartment complexes

Green retrofit PPP projects of traditional apartment complexes play an important role in promoting the green and low-carbon transformation of the construction industry and achieving China's "double carbon" goals. The integrated retrofit of apartment complexes presupposes that the resident groups agree to the retrofit. Therefore, it is necessary to study the evolutionary mechanism of residents' intention to green retrofit and the transformation process of their behavior, and to explore how to enhance residents' intention to participate. First, the dissemination model of residents' intention to green retrofit is constructed. Then, the strategic interaction among government, social capitals and residents under the PPP model is introduced into the dissemination model to define the state transformation probability of resident groups. Finally, the evolution laws of residents' intention to green retrofit are analyzed. The results show that: (1) the behavior of government regulation and social capitals' effort to retrofit can motivate the number of the resident agreeing to green retrofit to meet the proportional limit, (2) the faster the government chooses the strategy of regulation and the social capitals choose the strategy of effort to retrofit, the faster the number of residents agreeing to green retrofit reaches a steady state, (3) when the level of government publicity and education is too low, the cost of government regulation or the subsidy given to residents is too high, the green retrofit of traditional apartment complexes cannot be achieved. The research conclusions can provide a reference for the government to formulate green retrofit policies.

Social dilemmas of sociality due to beneficial and costly contagion

Levels of sociality in nature vary widely. Some species are solitary; others live in family groups; some form complex multi-family societies. Increased levels of social interaction can allow for the spread of useful innovations and beneficial information, but can also facilitate the spread of harmful contagions, such as infectious diseases. It is natural to assume that these contagion processes shape the evolution of complex social systems, but an explicit account of the dynamics of sociality under selection pressure imposed by contagion remains elusive. We consider a model for the evolution of sociality strategies in the presence of both a beneficial and costly contagion. We study the dynamics of this model at three timescales: using a susceptible-infectious-susceptible (SIS) model to describe contagion spread for given sociality strategies, a replicator equation to study the changing fractions of two different levels of sociality, and an adaptive dynamics approach to study the long-time evolution of the population level of sociality. For a wide range of assumptions about the benefits and costs of infection, we identify a social dilemma: the evolutionarily-stable sociality strategy (ESS) is distinct from the collective optimum—the level of sociality that would be best for all individuals. In particular, the ESS level of social interaction is greater (respectively less) than the social optimum when the good contagion spreads more (respectively less) readily than the bad contagion. Our results shed light on how contagion shapes the evolution of social interaction, but reveals that evolution may not necessarily lead populations to social structures that are good for any or all.

How growers make decisions impacts plant disease control

While the spread of plant disease depends strongly on biological factors driving transmission, it also has a human dimension. Disease control depends on decisions made by individual growers, who are in turn influenced by a broad range of factors. Despite this, human behaviour has rarely been included in plant epidemic models. Considering Cassava Brown Streak Disease, we model how the perceived increase in profit due to disease management influences participation in clean seed systems (CSS). Our models are rooted in game theory, with growers making strategic decisions based on the expected profitability of different control strategies. We find that both the information used by growers to assess profitability and the perception of economic and epidemiological parameters influence long-term participation in the CSS. Over-estimation of infection risk leads to lower participation in the CSS, as growers perceive that paying for the CSS will be futile. Additionally, even though good disease management can be achieved through the implementation of CSS, and a scenario where all controllers use the CSS is achievable when growers base their decision on the average of their entire strategy, CBSD is rarely eliminated from the system. These results are robust to stochastic and spatial effects. Our work highlights the importance of including human behaviour in plant disease models, but also the significance of how that behaviour is included.