Version 1
: Received: 26 November 2020 / Approved: 30 November 2020 / Online: 30 November 2020 (14:15:33 CET)
How to cite:
Ghosh, G.; Bandyopadhyay, S.; Chattopadhyay, A. Agroclimatic Impact on Gastrointestinal Infection: A Mathematical Model. Preprints2020, 2020110731. https://doi.org/10.20944/preprints202011.0731.v1
Ghosh, G.; Bandyopadhyay, S.; Chattopadhyay, A. Agroclimatic Impact on Gastrointestinal Infection: A Mathematical Model. Preprints 2020, 2020110731. https://doi.org/10.20944/preprints202011.0731.v1
Ghosh, G.; Bandyopadhyay, S.; Chattopadhyay, A. Agroclimatic Impact on Gastrointestinal Infection: A Mathematical Model. Preprints2020, 2020110731. https://doi.org/10.20944/preprints202011.0731.v1
APA Style
Ghosh, G., Bandyopadhyay, S., & Chattopadhyay, A. (2020). Agroclimatic Impact on Gastrointestinal Infection: A Mathematical Model. Preprints. https://doi.org/10.20944/preprints202011.0731.v1
Chicago/Turabian Style
Ghosh, G., Subhasish Bandyopadhyay and Amit Chattopadhyay. 2020 "Agroclimatic Impact on Gastrointestinal Infection: A Mathematical Model" Preprints. https://doi.org/10.20944/preprints202011.0731.v1
Abstract
Identifying the correct dosage and time are key factors to successful implementation of anthelmintic. Comparing differential evolution of infection between anthelmintic treated animals against untreated ones, we present a mathematical model that first calibrates data collected and analyzed over an extended period of 10 years (2011-2019), and then predicts the dynamical evolution of gastrointestinal parasites in livestock, focusing specifically on Stronglye \& Coccidia oocysts, the two prime negative contributors to cattle health, measured using the standard Eggs-Per-Gram (EPG) index. The model incorporates information about all three critical regimes of infection - low infection regime ($< 50$ EPG), medium infection regime ($50-100$ EPG) and high infection regime ($>100$ EPG), including fatally large doses of infection ($> 500$), and probabilistically estimates the variation in animal weight due to infection propagation. A key success of our model is its ability to accurately predict the appropriate anthelmintic treatment times for cattle from a numerical solution of the model presented. The generic model can be applied to other agroclimatic conditions and can serve as a major diagnostic tool for anthelmintic strategizing.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
