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| 研究生： |
廖培志 Liao, Pei-Chih |
|---|---|
| 論文名稱： |
臺灣水生動物疾病流行病學研究：疾病監控系統之建立及應用 Epidemiologic studies of aquatic animals diseases in Taiwan：Establishment and application of disease surveillance system |
| 指導教授： |
陳石柱
Chen, Shih-Chu |
| 學位類別： |
博士 Doctor |
| 系所名稱： |
獸醫學院 - 獸醫學系所 Department of Veterinary Medicine |
| 畢業學年度： | 109 |
| 語文別： | 中文 |
| 論文頁數： | 93 |
| 中文關鍵詞： | 水生動物 、監控系統 、預警 、生產醫學 、氣候因子 |
| 外文關鍵詞： | aquatic, surveillance system, warning, production medicine, climate factor |
| DOI URL： | http://doi.org/10.6346/NPUST202000542 |
| 相關次數： | 點閱：29 下載：0 |
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本研究以流行病學的相關技術，建立一套例行性的水生動物疾病網路監控系統，進行長時間、大規模及跨區域的資訊整合及分析，並透過建置之平台，建立氣象因子與疾病發生關係性之分析模式與生產醫學研究模組。目前已成功即時整合全台灣主要水產養殖縣市之水生動物疾病發生疫情資訊，並應用地理資訊系統，建立相關視覺化統計、分析及預警圖表或儀表板。監測系統中並建置智慧型魚病輔助診斷、水生動物藥物使用查詢與計算及常見水生動物疾病查詢資料庫，提供第一線水生動物獸醫師完整之診療，及後端之疫情分析預警支援。從2006至2017年例行性送檢資料統計中，不同類別好發疾病分別為寄生蟲以車輪蟲症（35.04%）為最高，細菌性疾病以鏈球菌症（27.03%）為主，病毒性疾病則是以虹彩病毒症（47.64%）占最大宗，其中寄生蟲性疾病發生比例近年有增加趨勢。在本研究中，應用所建置的監控系統，進行吳郭魚鏈球菌感染與氣候因子間的相關性分析。結果，當平均溫度高於27.0℃，平均壓力低於1005.1hPa或紫外線指數高於7.2時，於送檢吳郭魚的病例中，陽性場的累積百分比將高於50%。另，在降雨後三日內，降雨因子與吳郭魚鏈球菌的發生有關。因此，使用監控系統作為吳郭魚感染鏈球菌的預警工具，將有助於減少養殖場的經濟損失和人工成本。從監控系統所建立之「生產醫學」研究模組中，首次針對四指馬鮁與赤鰭笛鯛進行接種感染N. seriolae後的病理學研究，在實驗室可控環境下，證實本病原與臨床病灶間之關係性。另，我們發現養殖場須落實生物安全措施，才能有效預防疾病入侵或場內交互感染。由上述結果顯示，此監控系統可即時掌握水生動物疫病趨勢，並找出影響生產之危險因子，透過早期預警機制建立，有效的控制及預防疾病發生。
This research uses epidemiological related technologies to set up a surveillance system for monitoring aquatic animal diseases. This system collects long-term, large-scale and cross-regional informations. From integrating and analyzing the collected datas, we are able to study how the climate changes affect the incidence of diseases. Thus, we establish an analysis model showing the correlation between climatic factors and diseases, and further establish the production medical system. Now, in the main aquaculture counties in Taiwan, the surveillance system not only successfully integrates all the prompt informations on occurrence of aquatic animal diseases, but also simultaneously applies to the geographic information system to provide visual statistics, analysis, warning charts or dashboards. The surveillance system builds in intelligent fish disease auxiliary diagnosis with drug usage query and dosage calculation. It also serves as the database archives of common aquatic animal diseases. This database provides the front-line aquatic animal veterinarians with complete diagnosis and treatment information, and supports the back-end epidemic analysis and create early prevention warning. In the data submitted for inspection from 2006 to 2017, the most common parasitic diseases were caused by Trichodiniasis (35.04%). The bacterial diseases were mostly caused by Streptococcosis (27.03%). As for the viral diseases, Iridovirus infection (47.64%) came to the first place. From the data, we also noticed that the incidence of parasitic diseases has been increasing in recent years. In this research, we applied the surveillance system to analyze the correlation between tilapia Streptococcus infection and climatic factors. The result showed that the cumulated percentage of positive farms from all submitted tilapia cases was more than 50% under the following circumstances; when the average temperature was higher than 27.0℃, when the average pressure was lower than 1005.1hPa, when the UV index was higher than 7.2 or when it rained three days. This result indicates that certain climate factors are highly correlated to the occurrence of Streptococcus in tilapia. We can conclude that this surveillance system serves well as an early warning tool for tilapia Streptococcus infection, thus helps to reduce the labour costs and the economic loss in aquatic indystry. As for the production medical model, we applied the surveillance system to proceed further pathological studies. For the first time, the pathological study of N. seriolae infection was carried out on two fish species; the four-finger threadfin and red snapper. In controlled laboratory tests, the correlation between the pathogen and the lesions was confirmed. In addition, with the implementation of biosecurity measures can effectively prevent disease and cross infection between the farms. In conclusion, from all the results stated, this surveillance system not only provides real-time aquatic animal disease trend and identifies the risk factors affecting production, but also serves as the warning mechanism to effectively control and prevent the occurrence of the aquatic animal diseases.
中文摘要 Ⅰ
英文摘要 Ⅱ
謝 誌 Ⅳ
目 錄 Ⅴ
圖表目錄 Ⅶ
第1章 前言 1
第2章 文獻回顧 3
2.1台灣水生動物養殖概況及挑戰 3
2.2水生動物疾病控制發展趨勢 5
2.3流行病學在疾病控制上應用 6
2.4動物監控系統 15
2.5吳郭魚鏈球菌症簡介 18
第2章 材料與方法 21
3.1建立水生動物疾病診療系統之原理與方法 21
3.2養魚場數據與氣象資料數據收集 26
3.3水生動物疾病診療系統資料統計及分析模式建立 29
3.4新興養殖魚種細菌性疾病檢測方法 29
第4章 結果 30
4.1水生動物疾病診療系統之操作模式 30
4.2水生動物疾病診療系統之統計分析 41
4.3水生動物疾病發生因子之分析與預測 48
4.4新興養殖魚種病例報告 54
第5章 討論 61
5.1監控系統開發 61
5.2監控系統應用 65
5.3監控系統發展 68
5.4結論 69
5.5未來發展方向 70
參考文獻 71
附錄 85
附錄1、水生動物疾病監控系統操作頁面功能程式設計 85
附錄2、水生動物疾病監控系統統計分析與程式設計 88
附錄3、專利證書：水生動物疾病病菌與疫苗主種子管控系統及其方法，發明第I686762號 89
附錄4、專利證書：錦鯉生產供應鏈管理系，發明第I521460號 90
作者簡介 91
期刊發表 93
圖目錄
Fig. 1 History of aquatic animal diseases in Taiwan 4
Fig. 2 Production medical system 6
Fig. 3 Data collection object 22
Fig. 4 Aquatic animal disease diagnosis procedures 23
Fig. 5 System structure 24
Fig. 6 System function summary 24
Fig. 7 Collect six climate factors from 8 different weather stations 27
Fig. 8 The structural diagram of TDS shows how the system analyzes data and provides information for clinical diagnosis 28
Fig. 9 The operation process of the aquatic animal disease diagnosis and treatment system 33
Fig. 10 Create new cases 33
Fig. 11 Farmer database 34
Fig. 12 Case inquire 34
Fig. 13 Pond database 35
Fig. 14 Current data 35
Fig. 15 Water quality parameter 36
Fig. 16 History 36
Fig. 17 Used drugs 37
Fig. 18 Signs 37
Fig. 19 Smear examination 38
Fig. 20 Examination 38
Fig. 21 Diagnosis 39
Fig. 22 Suggestion 39
Fig. 23 Remarks 40
Fig. 24 Report 40
Fig. 25 Different categories of disease occurrence trend 43
Fig. 26 Single and complex infection analysis 43
Fig. 27 The cumulated infection rate streptococcosis and vibriosis in Taiwan during 2006-2017 44
Fig. 28 The cumulated infection rate of tilapia streptococcosis in each month 44
Fig. 29 Gross lesions of streptococcosis in tilapia 49
Fig. 30 The cumulated infection rate of tilapia streptococcosis in each month throughout a decade (2006-2015) 50
Fig. 31 The infection rate and cumulative infection rate of tilapia streptococcosis in Taiwan during 2006-2015 50
Fig. 32 The relations between the percentage of cumulated positive farms from all submitted tilapia cases and climate factors 51
Fig. 33 The tilapia streptococcosis hot zone at different times 51
Fig. 34 Nocardiosis infected East Asian four finger threadfin gross lesions 56
Fig. 35 Nocardiosis infected East Asian four finger threadfin pathological lesions 57
Fig. 36 Nocardiosis infected red snapper pathological lesions 57
Fig. 37 Acid-fast staining of infected spleen of red snapper 58
Fig. 38 16S rRNA sequence-based phylogenetic tree of NM107152 and NM108007 isolates from our study and from GenBank 58
Fig. 39 Analysis and design of aquatic animal disease surveillance dashboard 63
Fig. 40 System structure and process 64
Fig. 41 This text cloud is drawn based on the content of the plan of this query result, and extracts the more representative words with higher word frequency 65
表目錄
Table 1 Standard form 25
Table 2 Total cases of fish diseases from different counties 45
Table 3 Statistics from fish species 45
Table 4 Statistical parasitic disease 46
Table 5 Statistical bacterial disease 46
Table 6 Statistical viral disease 47
Table 7 Streptococcus often occurs in fish species analysis 47
Table 8 Identification Gram positive cocci isolated from tilapia from 2013 to 2015 52
Table 9 Univariate logistic regression analysis on tilapia streptococcosis and climatic factors 52
Table 10 Multivariate logistic regression analysis on tilapia streptococcosis and climatic factors 53
Table 11 Univariate logistic regression analysis of the rainfall in different days before the occurrence of tilapia streptococcosis 53
Table 12 API-ZYM result for isolate and reference strains of Nocardia seriolae 59
Table 13 Sensitivities to antibiotic disks for strains isolated from diseased East Asian four finger threadfin and red snapper 59
Table 14 Cumulative mortality (%) of red snapper with Nocardia seriolae 60
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校外公開2025/11/19