資料分析如何進行預測分析_可以進行預測分析來防止未來的大流行

資料分析如何進行預測分析

As science gets more advanced, we realise how interconnected our world is. The smallest things can have the biggest impacts. For instance, researchers are today looking at how the sand that every year is blown from Africa through the Atlantic, is actually impacting the number of hurricanes that end up hitting the East coast of the US. Turns out, the small grains of sand ‘pierce’ through the air currents, which are growing hurricanes, absorbing the moisture and consequently cutting off the ‘fuel’ that would allow the hurricane to grow.

随着科學的進步，我們意識到世界之間的互相聯系。 最小的事物可能産生最大的影響。 例如，今天的研究人員正在研究每年非洲從大西洋經大西洋吹來的沙子實際上如何影響最終襲擊美國東海岸的飓風數量。 事實證明，沙粒中的細小顆粒通過氣流“刺穿”，進而形成飓風，吸收了水分，是以切斷了使飓風生長的“燃料”。

That starts to give you an appreciation of the big picture. Things that are happening in one part of the world, don’t just stay in that part of the world.

這開始使您對全局有一個了解。 在世界某個地區發生的事情，不僅停留在世界的這一部分。

In an ever more globalised world, this is even more true for diseases, to the point where the question of a global disease is not “if”, but “when”. Aside from the evident life and health casualties, a pandemic has also an elevated economic impact, costing on average between $5 and $8 trillion to the global economy.

在一個日益全球化的世界中，疾病更是如此，以至于全球疾病的問題不是“如果”，而是“何時”。 除了明顯的生命和健康傷亡之外，大流行還對經濟産生了更高的影響，全球經濟平均為此付出5到8萬億美元。

More specifically, and according to the World Economic Forum, fighting COVID-19 could cost 500 times as much as pandemic prevention measures. Some experts estimated that COVID-19 pandemic could end up costing between $8.1 and $15.8 trillion globally.

更具體地說，根據世界經濟論壇的說法，與COVID-19作戰的費用可能是防大流行措施的500倍。 一些專家估計，COVID-19大流行可能最終在全球造成8.1至15.8萬億美元的損失。

Right now, data is simply not being effectively collected, analysed or deployed to drive decisions to stop outbreaks on their tracks and this year’s pandemic is a live example of that. However, if this has taught us something is that, we are in the age of an information revolution where Big data & Artificial Intelligence (AI) can help us do more informed decisions every day. Shifting from reaction to prevention, reducing the impacts of shocks and thus improve the resilience of our systems.

目前，根本沒有有效地收集，分析或部署資料來推動決策以阻止疾病爆發，而今年的流行就是一個活生生的例子。 但是，如果這告訴我們的是事實，那麼我們正處于資訊革命的時代，大資料與人工智能(AI)可以幫助我們每天做出更明智的決策。 從React轉向預防，減少沖擊的影響，進而提高我們系統的彈性。

There is growing optimism that newer approaches, including mobile-phone location tracking and data mining of search engines and social media, can help deliver a faster, more refined picture of where diseases are unfolding and might head to next.

人們越來越樂觀地認為，更新的方法(包括手機位置跟蹤以及搜尋引擎和社交媒體的資料挖掘)可以幫助您更快，更精确地了解疾病正在發生的地方，并可能下一步發展。

依靠AI預防下一次爆發 (Leaning on AI to Prevent the Next Outbreak)

According to some of the most recent reports, AI had detected this coronavirus at its very early stages. The company BlueDot, which uses machine learning to monitor the spread of contagious diseases around the world, had alerted about the rapid increase in pulmonary disease in Wuhan late last year.

根據一些最新報告，AI在早期就檢測到了這種冠狀病毒。 BlueDot公司使用機器學習來監測全球傳染病的傳播，該公司去年年底曾對武漢市肺病的Swift增長表示警覺。

More specifically, BlueDot gathers data on over 150 diseases and syndromes around the world searching every 15 minutes, 24 hours a day, including official data from organisations like the Center for Disease Control or the World Health Organization but also data from less official sources like worldwide travel patterns, environmental and animal data, social media sensing. It then classifies this data into a taxonomy and applies machine learning to identify relevant flagged cases for further analysis.

更具體地說，BlueDot每天24小時每15分鐘收集一次有關全球150多種疾病和綜合征的資料，其中包括疾病控制中心或世界衛生組織等組織的官方資料，也包括來自全球的不太官方的資料出行方式，環境和動物資料，社交媒體感覺。 然後，它将這些資料分類為一個分類法，并應用機器學習來識别相關的标記案例以進行進一步分析。

While these are still early stages for AI, if enough trust was given to such a model, it could have helped authorities prepare, alert and take the necessary measures which could have perhaps prevented the outbreak in the first place. It is not unwarranted to think that going forward more attention may be given to these signals.

盡管這些仍是AI的早期階段，但如果對這種模型給予足夠的信任，它可能會幫助當局準備，發出警報并采取必要的措施，而這些措施可能首先可以阻止爆發。 并非毫無疑問地認為，可以對這些信号給予更多關注。

One can logically expect for these systems to only improve with time, more so since every single day there is more and more publicly available data that can be taken into account to more accurately pinpoint the beginnings of what could derive into an outbreak, or even to sense conditions or potential hotspots of what could lead to a brewing disease. Analysing such vast pools of data can be possible due to the amount and the speed at which AI systems can go through the data to detect patterns.

從邏輯上可以預期，随着時間的推移，這些系統隻會有所改善，是以，由于每天都有越來越多的公開資料可供考慮，以便更準确地查明爆發原因的起點，甚至可以感覺可能導緻釀造疾病的條件或潛在熱點。 由于AI系統可以通過資料來檢測模式的數量和速度，是以可以分析如此龐大的資料池。

Although anticipating the appearance of a virus will carry some margin of error, especially at the beginning, since the AI is as good as the quality and volume of data it is fed, over time it could still detect conditions instantly and more accurately than the experts who initially fed it.

盡管預測病毒的出現會帶來一些誤差，尤其是在開始時，由于AI與其所饋送的資料的品質和數量一樣好，但是随着時間的推移，它仍然可以比專家更快，更準确地檢測到狀況。最初喂它的人。

This might be only the beginning of an era where applying predictive modelling to millions of data points can help detect the danger before it appears. For example, one way of doing it could be using probabilistic forecasting, data mining and scenario planning, then improving the models over time with deep reinforcement learning so that the system eventually becomes autonomous.

這可能隻是一個時代的開始，在該時代中，将預測性模組化應用于數百萬個資料點可以幫助在危險出現之前進行檢測。 例如，一種實作方法是使用機率預測，資料挖掘和方案規劃，然後通過深度強化學習随時間改進模型，以使系統最終變得自治。

從React型轉變為預測型 (Shifting from Reactive to Predictive)

While the ability to predict the future with certainty is not something that can be said lightly, this uncertainty could be reduced through predictive analysis. Predicting the course of an epidemic, even after it has started, can still help authorities plan better to contain its spread.

雖然不能輕易說出确定未來的能力，但可以通過預測分析來減少這種不确定性。 即使已經開始流行，對流行的過程進行預測仍可以幫助當局更好地計劃以控制流行。

However, the true potential will come from being able to accurately identify areas with high degree of probability for a potential viral disease to be born, which has the ability to easily grow into an outbreak, therefore predicting or anticipating the event.

然而，真正的潛力将來自能夠準确地識别出潛在病毒性疾病高發區域的能力，該區域很容易成長為暴發，是以可以預測或預測該事件。

By taking into account inherent or created conditions of the specific area, travel patterns, food habits, micro and macro environmental parameters and generally using global datasets on diseases to detect weak signals and then mapping risk-prone areas we could then use AI to predict the danger.

通過考慮特定區域的固有或創造條件，出行方式，飲食習慣，微觀和宏觀環境參數，并且通常使用疾病的全球資料集來檢測弱信号，然後繪制易患風險區域的圖，然後我們可以使用AI來預測危險。

The crucial element here is the broadness, quality and detail of the data since the model needs to make predictions at a global scale. Therefore, to accurately forecast diseases across the world and not just in a few locations the data needs to be expansive.

這裡的關鍵因素是資料的廣度，品質和細節，因為該模型需要在全球範圍内進行預測。 是以，要準确地預測世界範圍内的疾病，而不僅僅是在少數幾個地方，就需要擴充資料。

As opposed to a few years ago, the ability of the latest software to listen to a much wider range of sources and signals is significantly higher and should only increase in the coming years.

與幾年前相比，最新軟體收聽更大範圍的信号源和信号的能力明顯更高，并且隻會在未來幾年内增加。

預測模型的一些挑戰 (Some of the challenges of the predictive models)

Although exciting, this technological advance is mostly fuelled by very variable and frequently inaccurate data, which is currently generating doubts on the veracity of the data sources but also, if the model does offer predicative scenarios, it could become less accurate as arising cases are prevented.

盡管令人興奮，但這種技術進步主要是由變化不定且經常不準确的資料所推動的，目前，這些資料使人們對資料源的準确性産生懷疑，而且，如果模型确實提供了預測性方案，則由于避免了發生的情況，它的準确性可能會降低。 。

What’s more, while trying to predict potential outcomes in other industries may be somewhat simpler, accurately looking into future epidemics or outbreaks could be intrinsically linked to our current biological and medical knowledge. Even now, after the contagion occurred there has been confusion over symptoms and the way the virus passes between people. Therefore, trying to predict where a disease may spread from hundreds of sites is a far more complex task then one would imagine.

而且，盡管試圖預測其他行業的潛在結果可能會更簡單，但準确地調查未來的流行病或暴發可能與我們目前的生物學和醫學知識具有内在聯系。 即使到了現在，傳染病發生後，人們對病毒的症狀和傳播方式仍感到困惑。 是以，試圖預測一種疾病可能從數百個部位傳播的位置比人們想象的要複雜得多。

One of the main concerns of feeding the AI with inconsistent data is the criticality of the decisions to be made, specifically when applied to health. It will therefore be essential for the data to triangulate between quality, reliability and agility and find a balance between the trade-offs between agility and reliability depending on the context at hand. As for the confidence or reliability of the data itself, risk can be minimised if more data is made open to the public for analysis, which then calls for a hard conversation on privacy.

向AI提供不一緻的資料的主要問題之一是要做出的決策的重要性，特别是在應用于健康時。 是以，至關重要的是，資料必須在品質，可靠性和靈活性之間進行三角劃分，并根據目前的環境在靈活性和可靠性之間進行權衡。 至于資料本身的置信度或可靠性，如果向公衆開放更多資料進行分析，則可以将風險降到最低，這需要就隐私問題進行艱苦的對話。

Although it wouldn’t necessarily solve privacy issues, some distributed ledgers (e.g. Blockchain) could solve some of the other issues by bringing more decentralization, transparency and guarantee of data integrity.

盡管不一定解決隐私問題，但某些分布式分類帳(例如，區塊鍊)可以通過帶來更多的去中心化，透明性和資料完整性保證來解決其他一些問題。

未來機會 (Future opportunities)

AI has shown much promise in augmenting human capabilities. Certainly, the combination of the current and future available data with our intelligence and the power of AI will create more innovation and get us more prepared for future risks. As with every crisis, we have become stronger, more innovative and creative, hopefully leaving societies more prepared to face such threats.

人工智能在增強人類能力方面顯示出巨大的希望。 當然，将目前和将來的可用資料與我們的智能和AI的強大功能相結合，将帶來更多創新，并使我們為未來的風險做好更多的準備。 與每一次危機一樣，我們變得更加強大，更具創新和創造力，希望社會更加準備面對這樣的威脅。

COVID-19 attracted a lot of attention to the financial impact, but even the cost of common infectious diseases can be enormous.

COVID-19對于财務影響引起了很多關注，但即使是普通傳染病的代價也可能是巨大的。

Certainly now, investors and nation leaders are more aware than before of the importance of having not only robust health systems and solutions to threats but also predictive insights to prevent those threats where possible, or at least see them coming. And since fighting pandemics like COVID-19 could cost 500 times as much as pandemic prevention measures, it is not unreasonable to think that the era of predictive analytics is just starting. More so, considering the accelerating number of catastrophes occurring and which are still expected to happen (particularly given the current global warming crisis we are facing), the necessity to move fast is becoming pressing.

當然，現在，投資者和國家上司人比以往任何時候都更加意識到，不僅擁有強大的衛生系統和對威脅的解決方案，而且具有預測性的見識以盡可能防止或至少防止威脅的重要性。 而且，由于與COVID-19等大流行病作鬥争的成本可能是大流行預防措施的500倍，是以可以認為預測分析時代才剛剛開始并不無道理。 更重要的是，考慮到正在發生的災難仍在加速，并且仍然有望發生(特别是考慮到目前我們正面臨的全球變暖危機)，是以必須Swift采取行動。

Leveraging Big Data & AI to deliver predictive analytics will definitely improve decision making not only in healthcare but across all sectors and industries.

利用大資料和人工智能提供預測分析，不僅可以改善醫療保健領域的決策，還可以改善所有部門和行業的決策。

翻譯自: https://medium.com/carre4/could-predictive-analytics-prevent-future-pandemics-a2d1830d075b

資料分析如何進行預測分析