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The Vespa mandarinia disaster has been a problem all over the world. In the prevention and control of Vespa mandarinia, the efficiency is often low and a lot of social resources are wasted because of the inability to effectively predict the spread of Vespa mandarinia. Therefore, this paper proposes a propagation model based on independent cascade. Based on the traditional propagation model, the habit factor and propagation index are added, which makes the habit and reproduction of wasps fully considered. Vespa mandarinia tend to migrate to the water edge, and the nodes closer to the water edge in the pro- pagation model are more likely to be activated. The longer the Vespa mandarinia nests exist, the higher the probability of having more Vespa mandarinia, and the longer the nodes exist in the propagation model, the stronger the propagation ability. The model is validated by the real Vespa mandarinia disaster data in the United States in 2019, which proves that the propagation model considering Vespa mandarinia habits and reproduction will have better performance.

Vespa mandarinia is the largest species of hornet in the world, and the occurrence of the nest was alarming. Additionally, the giant hornet is a predator of European honeybees, invading and destroying their nests. A small number of the hornets are capable of destroying a whole colony of European honeybees in a short time. At the same time, they are voracious predators of other insects that are considered agricultural pests [

Classical propagation models include linear threshold model [

Place 1000 randomly distributed nodes within 11,000 square kilometers of the Vespa mandarinia infestation area as checkpoints, and place these nodes in the node graph along with the nodes that have been identified as having Vespa mandarinia. When the distance between two nodes is less than the set threshold, an edge is established between the random node and the random node, the confirmed node and the random node. The weight on the edge is obtained by the weight formula between nodes.

In predicting Vespa mandarinia propagation, we use a directed graph to record the nodes that have been propagated and their propagation directions. When initializing that digraph, we put all the nodes that have been confirmed to appear Vespa mandarinia into a digraph. Searched for nodes that appeared in Vespa mandarinia on a directed graph, and conducted propagation operations for each of their neighboring nodes that did not appear in Vespa mandarinia.

A node that has been propagated at t has only a single opportunity to propagate its neighbors at t + 1 .

Assuming that node V is activated at t, for any neighbor w of V, the probability that w is activated at t + 1 is P v m .

The propagation diagram is shown below (

In the design of P v m probabilities, We first need to consider the effect of distance on Vespa mandarinia migration [

L = 2 R arcsin ( sin 2 ( x 1 − x 2 2 ) + cos x 1 cos x 2 sin 2 ( y 1 − y 2 2 ) )

d i s t S c o r e = L max − L L max

We need not only to consider the d i s t S c o r e obtained by the distance L between two nodes, but also the life habit of the Vespa mandarinia [

h a b i t S c o r e = S v + B i a s S w + B i a s

We believe that the areas where Vespa mandarinia have been identified earlier will have more Vespa mandarinia than the areas where Vespa mandarinia have been identified later due to the continuous reproduction of Vespa mandarinia [

P v m = min ( ( S v + B i a s S w + B i a s × L max − L L max E f f ) , 1 )

Propagation prediction for each round was performed as above, with nodes

that were successfully propagated each round placed into a directed graph. In the next round, these nodes that were propagated successfully also performed a propagation operation on neighboring nodes to which they were not propagated.

Our data set comes from the real Vespa mandarinia disaster in the United States in 2020 [

It can be seen from the

We do sensitivity analysis on the threshold of node connection distance. By changing the threshold value, we can see whether the model is stable. The comparison is shown below:

As can be seen from

No. | Label | Prediction | Distance/mile | ||
---|---|---|---|---|---|

Latitude | Longitude | Latitude | Longitude | ||

1 | 48.993892 | −122.702242 | 48.98 | −122.7 | 0.903 |

2 | 48.927519 | −122.745016 | 48.99 | −122.7 | 4.775 |

3 | 48.984269 | −122.574809 | 48.93 | −122.65 | 5.069 |

4 | 48.979497 | −122.581335 | 49.03 | −122.8 | 10.507 |

5 | 48.983375 | −122.582465 | 49.01 | −122.8 | 10.032 |

6 | 48.984172 | −122.57472 | 49.07 | −122.62 | 6.286 |

7 | 48.98422 | −122.574726 | 49.01 | −122.77 | 9.029 |

relatively stable. And the parameters we choose are almost optimal.

We also explore whether the communication index has a positive effect on the model prediction. The blue curve in the figure below is the actual distance between the predicted result and the real result of the model considering the propagation index, while the orange curve is the actual distance between the predicted result and the real result of the model not considering the propagation index.

From

In our independent cascade propagation model. After the deduction and prediction of the model, the prediction results after 100 days and 200 days are obtained respectively. The results are shown in the

As can be seen from the above result diagram, Vespa mandarinia spread and migrated to the nearby areas to a certain extent. There are some clusters of Vespa mandarinia in the picture. This is because if there are many Vespa mandarinia in the vicinity of an area, it will have more chances to be spread. Because the life habits of Vespa mandarinia are used to guide the propagation model, we can see that the Vespa mandarinia migrate slowly to the water.

As can be seen from the diagram above (

migrating to the water side continues to move to the water side, which makes its migration path form a line in the picture.

We also draw the spread trend chart, we can see the trend of Vespa mandarinia spread more intuitively.

From the

In this paper, we propose an independent cascade propagation model to predict the propagation of Vespa mandarinia. It fully considered the habits and reproduction of Vespa mandarinia, and provided guidance for the prediction of propagation model. The effectiveness of this guidance is verified by experiments. This model can also be applied to the propagation of other pests to make the control measures more effective.

The authors declare no conflicts of interest regarding the publication of this paper.

Zhou, T., Gao, J., Liu, T.Z. and Jiang, Y.F. (2021) An Inde- pendent Cascade Propagation Model of Vespa mandarinia Population with Propa- gation Index. Open Access Library Journal, 8: e7345. https://doi.org/10.4236/oalib.1107345