Cost-optimal Seeding Strategy During a Botanical Pandemic in Domesticated Fields

TL;DR

This paper introduces a novel epidemiological-economic model and an algorithm for optimizing seeding strategies in domesticated fields during botanical pandemics to maximize economic profit.

Contribution

It presents a new combined epidemiological-economic model and an optimal seeding algorithm tailored for botanical pandemics in agricultural fields.

Findings

Recovery and infection rates similarly impact economic outcomes.

Larger farms do not necessarily yield higher profits.

Proposed strategy significantly improves economic gains.

Abstract

Botanical pandemics cause enormous economic damage and food shortages around the globe. However, since botanical pandemics are here to stay in the short-medium term, domesticated field owners can strategically seed their fields to optimize each session's economic profit. In this work, we propose a novel epidemiological-economic mathematical model that describes the economic profit from a field of plants during a botanical pandemic. We describe the epidemiological dynamics using a spatio-temporal extended Susceptible-Infected-Recovered epidemiological model with a non-linear output economic model. We provide an algorithm to obtain an optimal grid-formed seeding strategy to maximize economic profit, given field and pathogen properties. We show that the recovery and basic infection rates have a similar economic influence. Unintuitively, we show that a larger farm does not promise higher economic profit. Our results demonstrate a significant benefit of using the proposed seeding strategy and shed more light on the dynamics of the botanical pandemic.