TY - JOUR
T1 - Backward Bifurcation Emerging from a Mathematical Model of African Animal Trypanosomiasis Disease in White Rhino Populations
AU - Aldila, Dipo
AU - Windyhani, Tama
N1 - Funding Information:
This research was financially supported by RistekBRIN, Indonesia through the PDUPT Research Grant Scheme 2021 (ID number: NKB-165/UN2.RST/HKP.05.00/2021).
Publisher Copyright:
© 2021 Published by ITB Institute for Research and Community Services.
PY - 2022/4/1
Y1 - 2022/4/1
N2 - This paper introduces a mathematical model for African animal trypanosomiasis (AAT) in white rhino and tsetse fly populations. The model accommodates two types of interventions, namely infection detection and ground spraying. The dynamical system properties were thoroughly investigated to show the existence of equilibrium points, backward bifurcation, and how they are related to the basic reproduction number. We found that there is a chance that AAT may die out from the population if the basic reproduction number is smaller than one. However, the possible existence of backward bifurcation implies a condition where we may have a stable endemic equilibrium, even when the basic reproduction number is smaller than one. Hence, the basic reproduction number is no longer sufficient to guarantee the disappearance of AAT from the population. Our sensitivity analysis on the basic reproduction number showed that the interventions of infection detection and ground spraying have good potential to eradicate AAT from the population. To analyze the most effective intervention as time-dependent variable, we reconstructed our model as an optimal control problem. Numerical simulations on various scenarios were conducted for the optimal control problem. Cost-effectiveness analysis using the Average Cost-Effectiveness Ratio (ACER) and the Incremental Cost-Effectiveness Ratio (ICER) methods was performed. From the cost-effectiveness analysis, we found that ground spraying is the most cost-effective intervention to combat the spread of AAT in white rhino populations.
AB - This paper introduces a mathematical model for African animal trypanosomiasis (AAT) in white rhino and tsetse fly populations. The model accommodates two types of interventions, namely infection detection and ground spraying. The dynamical system properties were thoroughly investigated to show the existence of equilibrium points, backward bifurcation, and how they are related to the basic reproduction number. We found that there is a chance that AAT may die out from the population if the basic reproduction number is smaller than one. However, the possible existence of backward bifurcation implies a condition where we may have a stable endemic equilibrium, even when the basic reproduction number is smaller than one. Hence, the basic reproduction number is no longer sufficient to guarantee the disappearance of AAT from the population. Our sensitivity analysis on the basic reproduction number showed that the interventions of infection detection and ground spraying have good potential to eradicate AAT from the population. To analyze the most effective intervention as time-dependent variable, we reconstructed our model as an optimal control problem. Numerical simulations on various scenarios were conducted for the optimal control problem. Cost-effectiveness analysis using the Average Cost-Effectiveness Ratio (ACER) and the Incremental Cost-Effectiveness Ratio (ICER) methods was performed. From the cost-effectiveness analysis, we found that ground spraying is the most cost-effective intervention to combat the spread of AAT in white rhino populations.
KW - African animal trypanosomiasis
KW - backward bifurcation
KW - basic reproduction number
KW - cost-effectiveness analysis
KW - optimal control
UR - http://www.scopus.com/inward/record.url?scp=85140968744&partnerID=8YFLogxK
U2 - 10.5614/j.math.fund.sci.2022.54.1.9
DO - 10.5614/j.math.fund.sci.2022.54.1.9
M3 - Article
AN - SCOPUS:85140968744
VL - 54
SP - 151
EP - 189
JO - Journal of Mathematical and Fundamental Sciences
JF - Journal of Mathematical and Fundamental Sciences
SN - 2337-5760
IS - 1
ER -