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Archive for May 8th, 2020

Two types of epidemiology: Models v. Evidence

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Jonathan Fuller writes in the Boston Review:

The lasting icon of the COVID-19 pandemic will likely be the graphic associated with “flattening the curve.” The image is now familiar: a skewed bell curve measuring coronavirus cases that towers above a horizontal line—the health system’s capacity—only to be flattened by an invisible force representing “non-pharmaceutical interventions” such as school closures, social distancing, and full-on lockdowns.

How do the coronavirus models generating these hypothetical curves square with the evidence? What roles do models and evidence play in a pandemic? Answering these questions requires reconciling two competing philosophies in the science of COVID-19.

In one camp are infectious disease epidemiologists, who work very closely with institutions of public health. They have used a multitude of models to create virtual worlds in which sim viruses wash over sim populations—sometimes unabated, sometimes held back by a virtual dam of social interventions. This deluge of simulated outcomes played a significant role in leading government actors to shut borders as well as doors to schools and businesses. But the hypothetical curves are smooth, while real-world data are rough. Some detractors have questioned whether we have good evidence for the assumptions the models rely on, and even the necessity of the dramatic steps taken to curb the pandemic. Among this camp are several clinical epidemiologists, who typically provide guidance for clinical practice—regarding, for example, the effectiveness of medical interventions—rather than public health.

The latter camp has won significant media attention in recent weeks. Bill Gates—whose foundation funds the research behind the most visible outbreak model in the United States, developed by the Institute for Health Metrics and Evaluation (IHME) at the University of Washington—worries that COVID-19 might be a “once-in-a-century pandemic.” A notable detractor from this view is Stanford’s John Ioannidis, a clinical epidemiologist, meta-researcher, and reliable skeptic who has openly wondered whether the coronavirus pandemic might rather be a “once-in-a-century evidence fiasco.” He argues that better data are needed to justify the drastic measures undertaken to contain the pandemic in the United States and elsewhere.

Ioannidis claims, in particular, that our data about the pandemic are unreliable, leading to exaggerated estimates of risk. He also points to a systematic review published in 2011 of the evidence regarding physical interventions that aim to reduce the spread of respiratory viruses, worrying that the available evidence is nonrandomized and prone to bias. (A systematic review specific to COVID-19 has now been published; it concurs that the quality of evidence is “low” to “very low” but nonetheless supports the use of quarantine and other public health measures.) According to Ioannidis, the current steps we are taking are “non-evidence-based.”

This talk of “biased evidence” and “evidence-based interventions” is characteristic of the evidence-based medicine (EBM) community, a close relative of clinical epidemiology. In a series of blog posts, for example, Tom Jefferson and Carl Heneghan of the Oxford Centre for Evidence-Based Medicine similarly lament the poor-quality data and evidence guiding action in the pandemic and even suggest that lockdown is the wrong call.

In the other corner, Harvard’s Marc Lipsitch, an infectious disease epidemiologist, agrees that we lack good data in many respects. Countering Ioannidis’s hesitation, however, Lipsitch responds: “We know enough to act; indeed, there is an imperative to act strongly and swiftly.” According to this argument, we could not afford to wait for better data when the consequences of delaying action are disastrous, and did have reason enough to act decisively.

Public health epidemiologists and clinical epidemiologists have overlapping methods and expertise; they all seek to improve health by studying populations. Yet to some extent, public health epidemiology and clinical epidemiology are distinct traditions in health care, competing philosophies of scientific knowledge. Public health epidemiology, including infectious disease epidemiology, tends to embrace theory and diversity of data; it is methodologically liberal and pragmatic. Clinical epidemiology, by contrast, tends to champion evidence and quality of data; it is comparatively more methodologically conservative and skeptical. (There is currently a movement in public health epidemiology that is in some ways closer to the clinical epidemiology philosophy, but I won’t discuss it here.)

To be clear, these comparisons are fair only writ large; they describe disciplinary orthodoxy as a whole rather than the work of any given epidemiologist. Still, it is possible to discern two distinct philosophies in epidemiology, and both have something to offer in the coronavirus crisis over models and evidence. A deeper understanding of modeling and evidence is the key not only to reconciling these divergent scientific mindsets but also to resolving the crisis.

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Public health epidemiology uses theory, especially theory from other health sciences like microbiology, to model infection and understand patterns and causes of disease. Many of the epidemic models that the public and public health researchers alike have been voraciously consuming—including models produced by Imperial College London that informed the U.K. and U.S. coronavirus response—are SIR-type models. The theory underlying these models is old, originating in the Kermack–McKendrick theory in the 1920s and ’30s, and even earlier in the germ theory in the second half of the nineteenth century. The SIR framework partitions a population into at least three groups: those who are susceptible to future infection (S), those who are currently infectious (I), and those who have been removed from the infectious group through recovery or death (R). An SIR model uses a system of differential equations to model the dynamics of the outbreak, the movement of individuals among the various groups over time.

Other models in the SIR family add additional groups to these three basic ones, such as a group for those who are infected with the virus but not yet infectious to others. Agent-based models also represent infection dynamics (how the number of cases changes over time), but they do so by modeling behaviors for each member of the simulated population individually. Curve-fitting models like the one used by the IHME are less theoretical; they extrapolate from previous infection curves to make predictions about the future. All these different models have been used in the COVID-19 pandemic. The diversity of approaches, along with divergent estimates for model parameters, partly explains the range of predictions we have seen.

Public health epidemiology also relies on  . . .

Continue reading. There’s much more.

Written by Leisureguy

8 May 2020 at 2:18 pm

Traveler’s guide to the city of Ninevah (for travelers around 650 BCE)

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The British Museum offers a helpful guide for a time-traveling tourist in Ninevah in the 7th century BCE. It begins:


The city of Nineveh has recently undergone extensive development to become the new capital of the mighty Assyrian empire. It is now a vast metropolis surrounded by massive walls some 12 kilometres in length that encompass an area of 750 hectares (7.5km2) in size. While official statistics on the population of Nineveh are not available, it reportedly takes three days to cross the city.

This cosmopolitan city is located on the eastern bank of the River Tigris at the intersection of the road which connects the highlands of the north with the prosperous lands of Babylonia and Chaldea in the south.

veritable paradise on earth, the fertile lands surrounding Nineveh are perfect for growing the huge volumes of staple crops such as wheat and barley needed to feed the population of this colossal city. Benefitting from plentiful rainfall, the city is also situated where the River Khosr meets the River Tigris, which guarantees an abundant supply of water. A monumental aqueduct brings water over a vast distance to feed the city’s network of canals. Upstream from the city you will find orchards planted with vines, fruit trees and olive groves.

When to visit

The summer months in Assyria are ferociously hot and are best avoided. Winters are often very wet and the city is transformed into a quagmire. The best times to visit are autumn and spring, when the city has warm days with cool mornings and evenings. . .

Continue reading.

Written by Leisureguy

8 May 2020 at 2:11 pm

Posted in Daily life

Depth of oil wells

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Written by Leisureguy

8 May 2020 at 10:44 am

Posted in Business, Technology

Floris No. 89 and the Fine Marvel

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Just another fine shave: the Wet Shaving Products Prince is a fine little fellow, and the lather from this almost-vintage Floris No. 89 shaving soap was excellent in fragrance and consistency. Fine’s Marvel razor head is quite good (see here on a UFO bronze handle). Three passes, a splash of the aftershave, and I’m off to the supermarket for some groceries — including buttermilk and milk to try DIY buttermilk.

Written by Leisureguy

8 May 2020 at 6:39 am

Posted in Shaving

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