Difference between revisions of "Team:NYMU-Taipei/Project-Model"

Line 310: Line 310:
  
 
</div>
 
</div>
 
+
<br>
 
<div class="fund">
 
<div class="fund">
 
<font size=36px>另一頁</font>
 
<font size=36px>另一頁</font>
  
 +
<h2>Purpose</h2><hr />
 +
 +
<p>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Our model aims to figure out the efficacy of the IOS system by comparing the population of B. dorsalis with and without this system. Written in NetLogo<sub>(1),</sub> , a software designed for modeling complex situations, the model combines both stochastic and deterministic methods. The stochastic part of the model was used to estimate the <em>Metarhizium Anisopliae</em> infection by our traps, whereas the deterministic part was used to simulate natural population growth of the flies. The efficiency of our IOS system predicted by this model is further used to analyze the cost-effectiveness of our prototype.</p>
 +
 +
<h2>Rules</h2><hr />
 +
 +
<h3>Assumption</h3>
 +
 +
<h3>Natural Condition</h3>
 +
 +
<h3>With IOS system</h3>
 +
 +
<h3>Parameters</h3>
 +
 +
<h3>Results</h3>
 +
<h3>Code</h3>
 +
<h3>Reference</h3>
  
<h2 style="margin-top:30px; margin-bottom:10px;">Purpose</h2>
 
  
 
</div>
 
</div>

Revision as of 19:43, 19 October 2016


第一頁

Epidemic


B. dorsalis is a major cause of annual agricultural losses due to oviposition in fruits. We hereby introduce our prototype trap and M. anisopliae, our genetically engineered fungi, to address this issue.
Our model aims to demonstrate that combining our prototype with the fungi can reduce the population of B. dorsalis. We selected and revised the SEIR model, which fits the ideal assumption in epidemiology, to make it more practical for our purpose.

Symbol

Description

Susceptible

B. dorsalis is slightly or moderately resistant to M. anisopliae

Exposed

B. dorsalis is exposed to a low amount of M. anisopliae in our trap; disease will be spread further during mating.

Infected

M. anisopliae enters B. dorsalis’ hemolymph and initiates cell division.

Death

B. dorsalis dies from the infection of M. anisopliae.

Based on our assumptions, we developed a set of differential equations that characterizes the nature of the epidemic in our model:


Parameter



Type

Name

Meaning (Value)

Reference

Constant

Mating rate

0.8635

 

daily_capture

40

 

daily_move_in

0

 

K

adult death rate (0.05)

[1]

k0

larval death rate (0.23)

[2]

Variable

ms

susceptible male

 

Fs

susceptible female

me

exposed male

Fe

exposed female

mi

infected male

Fi

infected female

D

Death


Result



The graph above shows the initial increase in population due to the attraction of B. dorsalis from the surrounding environment. However, the population subsequently drops when the M. anisopliae infection begin to spread. The results indicate that the use of prototype combined with the fungi yields a 70% decrease in the B. dorsalis population. Thus, we deduce that our product can be deployed in orchards and farms a few weeks prior to harvest to minimize crop damage.





Reference


1. Life History and Demographic Parameters of Three Laboratory-reared Tephritids (Diptera: Tephritidae) R. Vargas - D. Miyashita - T. Nishida - Annals of the Entomological Society of America - 1984

2. Effect of Temperature on the Development and Survival of Immature Stages of the Carambola Fruit Fly,Bactrocera carambolae, and the Asian Papaya Fruit Fly,Bactrocera papayae, Reared On Guava Diet Solomon Danjuma - Narit Thaochan - Surakrai Permkam - Chutamas Satasook - Journal of Insect Science - 2014


另一頁

Purpose


        Our model aims to figure out the efficacy of the IOS system by comparing the population of B. dorsalis with and without this system. Written in NetLogo(1), , a software designed for modeling complex situations, the model combines both stochastic and deterministic methods. The stochastic part of the model was used to estimate the Metarhizium Anisopliae infection by our traps, whereas the deterministic part was used to simulate natural population growth of the flies. The efficiency of our IOS system predicted by this model is further used to analyze the cost-effectiveness of our prototype.

Rules


Assumption

Natural Condition

With IOS system

Parameters

Results

Code

Reference