Speaker
Description
Plant diseases have a major impact on crop production worldwide. However, the success of plant disease control methods depends on their adoption by growers (\cite{MW22}). In the present work, we propose a behavioral epidemiology model, in which a classical SI-model is intertwined with growers’ behavior in terms of roguing (removal of) infected plants or not. This ODE model is built on \cite{M26} with variables representing infected and uninfected fields on which roguing is performed or not. The transition of a field from the non-roguing to the roguing pool (and vice-versa) occurs at a rate depending on the perceived payoff difference. Two kinds of perceived payoff differences are compared: {\it poor information}, in which growers believe roguing can fully control the disease and only know the overall disease prevalence, and {\it quality information}, in which they assess correctly the effects of roguing and precisely know the prevalences associated with both strategies. Bifurcation analyses show that quality of information is crucial in understanding expected growers’ behavior and resulting disease dynamics. The main difference is that, when $R_0$ is large, poor-information growers keep roguing, while they stop roguing with quality information, realizing that roguing does not improve their revenue enough to compensate for its cost. In the latter case, the transition between universal adoption and no-adoption occurs through an $R_0$ range where both pure strategies are stable.
Bibliography
@article{MW22,
title = {How growers make decisions impacts plant disease control},
volume = {18},
issn = {1553-7358},
url = {https://dx.plos.org/10.1371/journal.pcbi.1010309},
doi = {10.1371/journal.pcbi.1010309},
abstract = {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.},
language = {en},
number = {8},
urldate = {2026-03-11},
journal = {PLOS Computational Biology},
author = {Murray-Watson, Rachel E. and Hamelin, Frédéric M. and Cunniffe, Nik J.},
editor = {Struchiner, Claudio José},
month = aug,
year = {2022},
pages = {e1010309},
}
@article{M26,
title = {Wearing face masks to protect oneself and/or others: {Counter}-intuitive results from a simple epidemic model accounting for selfish and altruistic human behavior},
volume = {624},
issn = {00225193},
shorttitle = {Wearing face masks to protect oneself and/or others},
url = {https://linkinghub.elsevier.com/retrieve/pii/S0022519326000202},
doi = {10.1016/j.jtbi.2026.112395},
language = {en},
urldate = {2026-03-11},
journal = {Journal of Theoretical Biology},
author = {Martin, Hugo and Castella, François and Hamelin, Frédéric},
month = may,
year = {2026},
pages = {112395},
}
