IF YOU WANTED TO “FLATTEN THE CURVE” IN 2019, you might have been changing students' grades or stamping down a rug ripple. Today, that phrase refers only to the vital task of reducing the peak number of people concurrently infected with the COVID-19 virus. Beginning in early 2020, graphs depicting the expected number of infections spread through social networks, much like the virus itself. We've all become consumers of epidemiological models, the mathematical entities that spit out these ominous trend lines. • Such models have existed for decades but have never received such widespread attention. They're informing public policy, financial planning, health care allocation, doomsday speculation, and Twitter hot takes. In the first quarter of 2020, government leaders were publicly parsing these computational speculations, making huge decisions about whether to shut down schools, businesses, and travel. Would an unchecked outbreak kill millions, or fizzle out? Which interventions would help the most? How sure could we be of any forecast? Models disagreed, and some people pointed to whichever curve best supported their predilections. It didn't help that the researchers building the models were still figuring out what the heck they were doing.

中文翻译：

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模型背后的混乱：太多的 COVID-19 模型导致政策制定者误入歧途。以下是明天的模型将如何正确处理

如果你想在 2019 年“拉平曲线”，你可能一直在改变学生的成绩或压下地毯的涟漪。今天，该短语仅指减少同时感染 COVID-19 病毒的人数峰值这一重要任务。从 2020 年初开始，图表描绘了通过社交网络传播的预期感染数量，就像病毒本身一样。我们都已经成为流行病学模型的消费者，这些模型是产生这些不祥趋势线的数学实体。• 此类模型已存在数十年，但从未受到如此广泛的关注。他们为公共政策、财务规划、医疗保健分配、世界末日猜测和 Twitter 热门话题提供信息。2020年第一季度，政府领导公开解析这些计算推测，做出关于是否关闭学校、企业和旅行​​的重大决定。未经控制的爆发会杀死数百万人，还是会失败？哪些干预措施最有帮助？我们对任何预测有多大把握？模型不同意，有些人指出哪条曲线最能支持他们的偏好。构建模型的研究人员仍在弄清楚他们在做什么，这无济于事。