Our ability to collect data gets far ahead of our ability to fully use it, yet data may hold the key

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Our ability to collect data gets far ahead of our ability to fully use it, yet data may hold the key to solving some of the biggest global challenges facing us today.
Take, for instance, the frequent outbreaks of waterborne diseases as a consequence of war or natural disasters. The most recent example can be found in the country, where roughly 10,000 new suspected cases of cholera(霍乱) are reported each week — and history is filled with similar stories. What if we could better understand the environmental factors that contributed to the disease, predict which communities are at higher risk, and put in place protective measures to stop the spread? Answers to this question and others like it could potentially help us prevent a catastrophe.
As a big data scientist, I studied data from wide-ranging, public sources to identify patterns, hoping to predict trends that could be a threat to global security. Various data streams are important because the ground truth data (such as surveys) is often delayed, limited, incorrect or, sometimes, nonexistent.
For example, knowing the incidence(发生率) of mosquito-borne disease in communities would help us predict the risk of mosquito-spread disease such as dengue, the leading cause of illness and death in the tropics. However, mosquito data at a global (and even national) level is not accessible.
To address this gap, we’re using other sources such as satellite pictures, climate data and population information to forecast the risk of dengue. Specifically, we had success in predicting the spread of dengue in Brazil at the regional, state and city level using these data streams as well as clinical observation data and online searchers that used terms related to the disease. While our predictions aren’t perfect, they show promise.
Similarly, to forecast the flu season, we have found that online searches can complement(补充) clinical data. Because the rate of people searching the internet for flu symptoms often increases during their beginning, we can predict a sharp increase in cases where clinical data delays. All of this shows the potential of big data. The information is there; now it’s time to use it.
32. What do the examples in paragraphs 2 and 4 show?
A. Big data is still hard to get and use.
B. People aren’t skilled at dealing with big data.
C. Big data is not always an imagined method.
D. Catastrophes might be prevented with big data.
33. According to the text, survey data        .
A. is a main form of multiple data streams
B. is an effective way to collect information
C. is sometimes unreliable and unavailable
D. is a timely alternative to multiple data streams
34. What does the underlined part “this gap” in paragraph 5 refer to?
A. The lack of big data on mosquitoes.
B. The lack of different data streams.
C. The risk of an outbreak of a disease.
D. The ignorance of how a disease spreads.
35. What’s the best title of the text?
A. How do we collect and use data?
B. What are the challenges facing us now?
C. How can big data help save the world?
D. What is the answer to preventing catastrophes?
【答案】32. D    33. C    34. A    35. C
【解析】