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标题：预报猪病暴发
Forecasting swine disease outbreaks
明尼苏达大学兽医群体医学系助理教授，临床兽医博士（DVM）金?范德瓦尔（Kim VanderWaal）说，研究人员期望美国猪群疾病从监测转向潜在疾病是否以及何时会发生的实际预测。她告诉《今日猪健康》，自2017年以来，范德瓦尔和其他研究人员与美国特定地区的多个养殖企业密切合作，并与莫里森猪健康监测项目（MSHMP）携手。该项目由猪健康信息中心和美国猪业部国家食品和猪业研究资助。
Researchers want to move from monitoring disease in the US pig population to actually forecasting if and when a potential disease will occur, said Kim VanderWaal, DVM, assistant professor in the Department of Veterinary Population Medicine at the University of Minnesota. Since 2017, VanderWaal and other researchers have worked closely with several production systems in a specific region of the US and have partnered with the Morrison Swine Health Monitoring Project (MSHMP), she told Pig Health Today. The work was funded by the Swine Health Information Center and the USDA National Institute of Food and Agriculture.
VanderWaal说：“多年来各养殖企业向MSHMP报告母猪场的感染状况。因此现在我们拥有极大的数据集，可以显示任一猪场在任一周内是否感染了猪流行性腹泻（PED）病毒。我们将这些数据与动物的动向相结合，包括进入母猪场或邻近养殖场的数据，以构建一种具有前瞻性的机器学习算法，预测何时何地将高概率暴发PED。”
“For many years, production companies have been reporting the infection status of their sow farms to the MSHMP. So now we have this incredible dataset showing whether any given farm is infected with porcine epidemic diarrhea (PED) virus in a given week. We combine these data with animal movement data, both into the sow farms as well as into neighboring farms, to build a predictive, machine-learning algorithm that actually forecasts when and where we expect there to be high probability of a PED outbreak,” VanderWaal said.
更好的准备
Better preparedness
她解释说，该模型的总体目标是能够预测未来两周内母猪场暴发PED的可能性。
The overall goal of the model is to be able to project the likelihood that a sow farm is going to break with PED 2 weeks in the future, she explained.
她说：“我们用两种不同的方式来预测。二分法：会暴发或者不会暴发。或是给出一个连续性的概率，从0到1，0代表猪场大概率安全，1代表危险。”
“We forecast this in two different ways. We give a dichotomous yes, it’s going to break, or no, it’s not,” she said. “We also give a continuous probability, ranging from zero-your farm is probably pretty safe-to one, which is danger.
她补充道：“风险因素随时间改变，如涉及动物运输等，我们尝试把这些变量以及随时间推移，其它场发病时空间分布的情况等变化也考虑在内。”
“Risk factors can change through time, especially when you talk about things like animal movements,” she added. “We try to account for those changes, as well as changes in the spatial distribution of the disease in other farms through time.”
范德瓦尔说，该项目的目的是根据动物动向和当前流行病学状况，更好地获知特定猪场当前的风险。
The purpose of the project is to gain a better picture of the current risk for a given farm based on animal movements and current epidemiological landscape, VanderWaal said.
她说：“我们希望这能让养殖者在制定猪场干预或缓解措施时能够做出更有数据支撑的决策。”
“We hope this will enable producers to make better data-informed decisions about interventions or mitigation measures on their farms,” she said.
拼图的其它部分
Other pieces of the puzzle
范德瓦尔指出，模型可就预测疾病是否会暴发，对不同风险因素进行排序。
Models help rank different risk factors in terms of predicting whether or not an outbreak will occur, VanderWaal noted.
她说：“我们将大约20个不同的变量代入模型，然后让机器学习算法对这些变量进行排序，找出与预测最为相关的变量。这些信息还告诉我们很多关于该病的流行病学以及哪些是影响猪场间疾病传播的关键因素。”
“We feed about 20 different variables into the model, and we let the machine-learning algorithm sort those variables and find the ones that are most relevant for predictions,” she said. “That [information] also tells us a lot about the epidemiology of the disease and what factors are important for influencing between-farm disease transmission.”
其中一些信息在2013年PED发生期间，一项早期研究中就披露过。范德瓦尔说，在疫情初期，最重要的因素是动物移动和当地扩散，当猪场彼此毗邻时，这条信息至关重要。
Some of that information was revealed in an earlier study during the emergence of PED in 2013. The most important factors during the initial phase of the epidemic were animal movements as well as local spread, which was critical knowledge when farms were in close proximity to each other, VanderWaal said.
她说：“关于最近的这项研究，这是我们在预测方面的入手之处。我们关注母猪场周边的社区以及邻居的动态。”
“That’s where we started with the forecasting aspect of this [recent study],” she said. “We focus on the spatial neighborhoods around sow farms and what those neighbors are doing.”
存在挑战
Not without challenges
预测的敏感性约为20%，这意味着研究人员可发现五次暴发中的一次。
The forecasting pipeline has a sensitivity of around 20%, which means that researchers can detect one out of every five outbreaks that occur.
范德瓦尔说，这比我们以前知道得要多，所以这是一个不错的进步。然而，如果我们试图提高敏感性，我们其实就是在制造更多假警报。正预测值为70%，这意味着模型每预测10次疫情，有7次是对的。我们的合作伙伴不会想得到一堆假警报；如果你太频繁地“喊狼来了”，人们就会不作为。我们也在试图平衡这个限制性因素。
“That’s more information than we had before…so it’s a modest improvement,” VanderWaal said. “However, if we try to improve the sensitivity, we basically create more false alarms. The positive predictive value is 70%, which means that for every 10 times the model predicts an outbreak, it’s right seven of those times. Our partners don’t want to get a bunch of false alarms; if you ‘cry wolf’ too often, people stop responding. That’s one of the limitations we’re trying to balance.”
还有另一个挑战，研究人员没有一个标准化的数据库来表明在何时采取了何种生物安全措施，或者饲料车如何在猪场间移动，以及是否存在其它因素，可作为污染物在场间游走。
Another challenge is that researchers don’t have a standardized database of what biosecurity measures are being taken at what point, or how feed trucks are moving between farms, and anything else that could carry fomites between farms.
她说：“我们仍然欠缺一些我们无法量化的部分，因此我们无法将（那些数据）纳入模型中。”
“We certainly have some missing pieces that we’re unable to quantify; therefore, we’re unable to put [that data] into the model,” she said.
下一步
Next steps
范德瓦尔说，增加对MSHMP的参与度是该项目发展的关键，因为感染数据是该模型的重要构成。
Increased participation in the MSHMP is essential for the program to grow, VanderWaal said, because the infection data is a critical component of the model.
她说：“就动物动向数据而言，我们至少需要出发地、目的地、日期和动物数。养殖企业大多都以某种形式记录这些信息。”
“As far as animal movement data is concerned, the minimum of what we need is the origin location of the sites, the destination, the dates and the number of animals moved,” she said. “Most systems are recording that information in some format,” she said.
“自2019年11月以来，我们每周向合作企业提供预测信息，作为该项目试点的一部分。依据在特定时间点风险最高的母猪场进行排名。”
“Since November 2019, we have been delivering forecasts on a weekly basis to the partnering companies that were part of the pilot portion of the project. Their sow farms are ranked according to which ones are most at risk in any given point of time.
范德瓦尔说：“对于参与的猪场，使用这些预测后，我们收集反馈。其中之一是加强生物安全。”
“We’ve had feedback on how [participating farms] are using these forecasts,” VanderWaal said. “One is to basically shore up biosecurity.”
一些养殖体系利用排名列表来提醒员工，对待高风险猪场的生物安全需格外上心。反之，它为风险较低的猪场带来一剂定心丸，让他们腾出手进行成本效益分析，并确定何时需要更多的生物安全措施。
Some systems use the ranked list to help remind staff that they need to be extra careful about biosecurity on the higher-risk farms. Conversely, it provides reassurance for lower-risk farms and gives them time to do a cost-benefit analysis and determine when more biosecurity measures need to be implemented.
未来目标
Future goals
范德瓦尔说，未来的研究包括猪繁殖与呼吸综合征（PRRS）模型，更好地掌握PRRS的免疫机理。
Future research will include a model for porcine reproductive and respiratory syndrome (PRRS), VanderWaal said, and getting a better handle on the immunology of PRRS.
她说：“鉴于猪群内免疫水平有差异，猪过去大概率接触过不同毒株，这些或许会决定了哪些PRRS毒株最有可能成功侵入特定群体。我们正在致力于理解免疫学和流行病学之间的交界点，一个具有特定免疫状态的群体，哪些PRRS毒株最易感染该群体。”
“Given the variable level of immunity across herds and that pigs probably have been exposed to different variants in the past, that might dictate which strains of PRRS may be most successful at invading a particular population,” she said. “We’re doing a lot more work right now to understand the interface between the immunology and the epidemiology to understand which PRRS strains…are most successful, given the immunological profile of the population.
范德瓦尔说：“输入数据，程序执行所有必要的分析步骤，生成数据和我们预测所需的模型。这对于建立新系统[以及]贴合新的流行病学背景非常有用。”
“Given the input data, the pipeline should be able to run through all of the necessary analytical steps to generate the data and the model we need to make the predictions,” VanderWaal said. “That’s useful for onboarding new systems [as well as] adapting to new epidemiological contexts.”
范德瓦尔说，在确保数据格式正确以及如何每周传输猪群动向数据方面，仍有工作要做，但该模型对未来猪群的提升具有重大价值。
There is still work to be done in terms of making sure the data is formatted correctly and figuring out how to transfer movement data on a weekly basis, VanderWaal said, but the model has significant value for future herd improvement.
她说：“如果你有数据，你可以了解很多生产体系中疾病容易暴发的薄弱点，以及找到体系中最易传播疾病的重点猪场。”
“You can learn a lot about the vulnerability of your system to a disease outbreak as well as identify key farms that might be super spreaders in the network, if you have the data,” she said.
原文链接：https://pighealthtoday.com/forecasting-swine-disease-outbreaks/
