Covid-19 cases are dropping fast. Why?

‘One month ago, the CDC published the results of more than 20 pandemic forecasting models. Most projected that COVID-19 cases would continue to grow through February, or at least plateau. Instead, COVID-19 is in retreat in America. New daily cases have plunged, and hospitalizations are down almost 50 percent in the past month. This is not an artifact of infrequent testing, since the share of regional daily tests that are coming back positive has declined even more than the number of cases. Some pandemic statistics are foggy, but the current decline of COVID-19 is crystal clear.

**Four reasons: social distancing, seasonality, seroprevalence, and shots.’

Read here (The Atlantic, Feb 17, 2021) 

Let’s remember that the coronavirus is still a mystery

‘I find a gulf in perceptions between experts and nonexperts. Many Americans believe that we are now emerging from the pandemic and that, as President Trump says, we can see light at the end of the tunnel. Yet many epidemiologists, while acknowledging how little they know, are deeply apprehensive about a big second wave this fall, more brutal than anything we’ve endured so far. That mix of humility and apprehensiveness seems the best guide as we devise policy to survive a plague. Hope for the best while preparing for the worst.’

Read here (New York Times, May 20, 2020)

Sympathy for the epidemiologists: Paul Krugman

‘...the White House probably likes IHME [University of Washington’s Institute for Health Metrics and Evaluation] less today than it did yesterday: the institute just drastically revised its projected death total upward, from 72,000 to 134,000. Documents obtained by The New York Times suggest that modelers within the U.S. government have also revised death projections sharply upward...

‘So let me give a shout-out to the hard-working, much-criticized epidemiologists trying to get this pandemic right. You may take a lot of abuse when you get it wrong, which you unavoidably will on occasion. But you’re doing what must be done. Also, welcome to my world.’

Read here (New York Times, May 5, 2020)

Shocking draft FEMA report sees 200,000 Covid-19 cases, 3,000 deaths daily by June 1

‘The shocking numbers come just as dozens of states begin to drop strict social distancing requirements and open businesses to workers and customers at President Donald Trump’s urging. A rate of 3,000 deaths a day would be about 90,000 deaths a month. That death toll rate would be a 70% increase from the current average of 1,750 a day. The number of current cases of COVID-19 in the nation is about 25,000 daily.’

Read here (Huffington Post, May 5, 2020). Download here

Three potential futures for Covid-19: recurring small outbreaks, a monster wave, or a persistent crisis

‘What all three scenarios agree on is this: There is virtually no chance Covid-19 will end when the world bids good riddance to a calamitous 2020. The reason is the same as why the disease has taken such a toll its first time through: No one had immunity to the new coronavirus.

“This pandemic is not going to settle down until there is sufficient population immunity,” slightly above 50%, epidemiologist Gabriel Leung of the University of Hong Kong told a New York Academy of Sciences briefing.

‘Since the world “is far from that level of immunity,” said Osterholm (he estimates that no more than 5% of the world population is immune to the new coronavirus as a result of surviving their infection), “this virus is going to keep finding people. It’s going to keep spreading through the population.” And that, he said, “means we’re in for a long haul”.’

Read here (STAT News, May 1, 2020)

When will COVID-19 end? Data-driven estimation of end dates (as of April 25, 2020, daily updated)

‘This site provides continuous predictive monitoring of COVID-19 developments as a complement to monitoring confirmed cases. SIR (susceptible-infected-recovered) model is regressed with data from different countries to estimate the pandemic life cycle curves and predict when the pandemic might end in respective countries and the world, with codes from Milan Batista and data from Our World in Data. Given the rapidly changing situations, the predictive monitors are updated daily with the latest data.’

Read here (Singapore University of Technology and Design, April 25, 2020)

Health D-G: Malaysia now in recovery phase

‘Based on the various modelling that we have done, we realise that when we look into the data, April 3, when we had 217 new cases, was the peak then.

‘ “Then, we were expecting a peak on April 14, so much so that the prediction was 6,300 total cases. But we did not see that peak. We thought perhaps Phases 1 and 2 of the movement control order (MCO) had flattened the curve. Now, we have come to realise that we are in the recovery phase,” Noor Hisham said during his daily press briefing on the Covid-19 situation in the country today.

‘Nevertheless, he did not rule out the possibility that a surge in new cases may occur if the precautions undertaken during Phases 1 and 2 of the MCO are not continued.’

Read here (The Edge, April 24, 2020)

Improving epidemic surveillance and response: Big data is dead, long live big data. The Lancet

Urgent investment in surveillance systems and global partnerships are needed to prepare for the pandemics that will continue to emerge in the coming decades. The following are three key challenges that pertain to creating useful epidemic forecasts during an outbreak.

The first challenge: Misaligned incentives. Academics are largely incentivised to write scientific articles and to fund their work through individually led grants... Companies are incentivised by profit, and are rightly beholden to national regulatory frameworks and the public with respect to the data they collect.

The second challenge: Gap between (1) technological or methodological innovation, which often occurs in academic settings in high-income countries, and (2) implementation in field settings, frequently done by NGOs or governments in low-income and middle-income countries.

The third challenge: Epidemic forecasting is inherently uncertain... [With] emerging outbreaks—with COVID-19 highlighting this point—we often lack accurate data about case counts and biological processes driving an epidemic, let alone the behavioural responses of people affected, making it challenging to swiftly adapt or interpret very complex models on the spatiotemporal scales relevant for decision making.

‘These innovations will remain dislocated and impractical until the challenges above are addressed. Encouragingly, all three issues could be improved by moving much of the focus of funding and expertise to the populations most vulnerable to epidemics.’

Read here (The Lancet, March 17, 2020)