Some professions love their amateurs. Astronomers, for instance, are dependent on the efforts of amateurs to pick up on new heavenly bodies, for example, and to process images.<\/p>\n
Not so much bio-statisticians, though. Here’s a gracious, but I think quite irritated, Jacques Raubenheimer of the University of Sydney, writing<\/a> at The Conversation<\/strong><\/em> about analysis of Covid-19<\/strong> in real-time:<\/p>\n Some social media users\u00a0get angry<\/a>\u00a0when the\u00a0statistics are adjusted<\/a>,\u00a0fuelling<\/a>\u00a0conspiracy theories<\/a>.<\/p>\n But few realise how\u00a0mammoth, chaotic<\/a>\u00a0and\u00a0complex<\/a>\u00a0the task is of tracking statistics on a disease like this.<\/p>\n Countries and even states may count cases and deaths differently. It also takes time to gather the data, meaning retrospective adjustments are made.<\/p>\n We\u2019ll only know the true figures for this pandemic in retrospect. Equally so, early models were not necessarily wrong because the modellers were deceitful, but because they had insufficient data to work from.<\/p>\n Welcome to the world of data management, data cleaning and data modelling, which many armchair statisticians don\u2019t always appreciate. Until now.<\/p><\/blockquote>\n It’s short and to the point. I appreciated point #1 a lot, as I’d deduced it but wondered if I had it right:<\/p>\n Which is not to say the death rate’s not scary, but it’s not a measurement of potential disaster. There are a lot of diseases that have high fatality rates, but because the infection rate is low, even without social mitigation measures, they don’t mean that much. If you catch it, we’re sorry you’re dying, but the rest of society is not at risk.<\/p>\n But epidemiologists worry about those diseases picking up a mutation or three that increases the infection rate<\/em>.<\/p>\n Add to that the lack of medical resources when it comes to epidemics, and that’s why the medics have permanent scrunches in their brows – putting those two together means the death rate goes up. And, while I knew this in the back of my mind, I appreciate the reminder:<\/p>\n Flu\u2019s R\u2080\u00a0is about 1.3<\/a>. Although COVID-19 estimates vary, its R\u2080 sits around a\u00a0median of 2.8<\/a>. Because of the way infections grow exponentially (see below), the jump from 1.3 to 2.8 means COVID-19 is vastly more infectious than flu.<\/p><\/blockquote>\n It’s a bit like that old brain-teaser about algae doubling every day, or the one about doubling the number of grains of rice on each successive square of a chessboard – it does up much faster than human intuition would guess.<\/p>\n I liked this article – well-organized, succinct, and politely pissed off at the people who only think they know what they’re doing.<\/p>\n","protected":false},"excerpt":{"rendered":" Some professions love their amateurs. Astronomers, for instance, are dependent on the efforts of amateurs to pick up on new heavenly bodies, for example, and to process images. Not so much bio-statisticians, though. Here’s a gracious, but I think quite irritated, Jacques Raubenheimer of the University of Sydney, writing at \u2026 Continue reading \n
5. Yes, the data are messy, incomplete and may change<\/h2>\n
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1. It\u2019s the infection rate that\u2019s scary, not the death rate<\/h2>\n<\/blockquote>\n