Data science versus the COVID-19 pandemic: flattening the curve – but how?


While things change from day to day, we may currently have enough data, models and opinions to make some data-driven observations of how COVID-19 pandemic is spreading. Perhaps, more importantly, we can venture what it takes to stop it.

the COVID-19 virus was first found in late 2019 in China. Since then, it seems that it has stopped spreading in China, while unfortunately it is in various stages of development around the rest of the world. It is doubtful whether the available data at this time are sufficient to draw conclusions.

machine Learning prediction experts from Carnegie Mellon University working on COVID-19 forecasts, e.g. recognizes that there is far more uncertainty than usual. They still believe that their work will be worth informing the CDC and improving agency preparation. Let’s look at how different people use data for their analysis around the world and try to draw from their insights.

Analysis # 1: It’s exponential or why you should act now

40 million views and 28 translations in a week is a lot, even for an article on a life and death issue. The author of this medium post, Tomas Pueyo, is not an epidemiologist. However, this does not necessarily mean that his analysis of epidemiology data is incomplete. If nothing else, it is pretty close, looks convincing and has been praised by some health experts and researchers.

Pueyo’s analysis exemplifies what has become known as the “flattening of the curve” approach. The bottom line of the analysis is that COVID-19 is a pandemic now, so it cannot be removed. But what can be done is to reduce its effect. Virtually everyone will be infected, so the goal should be to have as few people infected at the same time as possible.

The analysis draws from data in places like taiwan and South Korea, where this approach was adopted early and adhered to. The way to do this is at social distance, and the faster this happens, the more effective it will be according to this analysis.

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The central idea behind the concept of flattening the COVID-19 spread curve is to ensure that not all are infected at the same time. In this way, the health infrastructure may have better opportunities to cope.

The basis of the analysis is data from a number of scientific publications or reprints. The difference is important here. It is known that the scientific publishing process suffers from a number of problems where what we would call time to market is prominent among them.

Peer review and publishing can take anywhere from a few months to a few years to complete. This means that in cases such as this where data availability is important, the process is favored for either unproven but readily available data or scientific paper printouts.

You can access data through dashboards and data hubs created by various organizations, ranging from governments to private companies and volunteers. Preprints for scientific paper can be found in hubs such as arXiv or Zenodothat allows researchers to share their findings immediately.

These sources differ from the official ones in some important ways. The data and findings shared through them do not come with official approval unless otherwise stated, and have not undergone a peer review process. This does not necessarily make them unreliable, but it does need to be critically evaluated.

Analysis # 2: It is not exponential or herd immunity

An important assumption underlying the “act now” analysis is that the infection rate COVID-19 follows what is called a exponential curve. However, we have seen this assumption challenged. Let’s be clear – nowhere have we seen any serious analyzes challenging the fact that social distance is a necessary measure at times like these. This is about something more nuanced.

What people like Richard Baldwin and Thomas’s house points out that the COVID-19 infection rate curve is technically not exponential. Rather, they point out that it follows the epidemiology curve. While exponential curves continue to rise, epidemiology curves rise to a peak, then decline, and may then have another peak.

This has to do with whether social distancing and other related measures continue to be enforced. If not, another infection wave may occur. At this point, however, the analyzes seem to share paths and reach different conclusions.

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Simulation of COVID-19’s new case development in 2020. Source: Anderson et al. (2020)

Baldwin, professor of international economics at The Graduate Institute in Geneva, notes that curve smoothing policies have immediate economic consequences. Therefore, he initiates analyzes of how governments could respondas well as rising inequality in the United States, to end the pandemic, it can result in social upheaval.

House, a reader in mathematics at Manchester University, tested his model of the impact of three-week social distance measures that reported by Sky News. His conclusions suggest that if measures were started 40 days after the outbreak, the total number of cases in subsequent weeks would be significantly lower than if they were started later.

But the model suggests that cases would increase rapidly when measures were eased, which in fact merely delayed the peak of cases. In contrast, the same measures later in the outbreak resulted in a different wave of cases, but the peak for each was lower. It cut half the maximum number of people who were ill at any one time and dramatically cut down the total infected, which early interventions did not.

Parliament concludes that delaying the action can give the population immunity to build up, reducing the number of people vulnerable to infection. Sky News notes that similar models are likely to support the UK government’s strategy, which may explain why it has simply encouraged those with symptoms to stay home, while other countries have been more aggressive in their approach, closure of bars or bans against public collections.

However, it has been noted that the term herd immunity has been misinterpreted. Graham Medley of the London School of Hygiene and Tropical Medicine, chair of a group of researchers modeling the spread of infectious diseases and advising the government on pandemic reactions, says the real goal is the same as in other countries: the flat curve at to delay the onset of infections. As a consequence, the nation may obtain herd immunity; It is a side effect, not a goal.

Smarter COVID-19 decision making and resources

If there is a lesson to be drawn from reading these analyzes side by side, it would be complicated. Decision making, not even aided by data science and analysis, is not straightforward, especially in an area that is foreign to most of us as epidemiology. However, some rules for data-driven decision making still apply.

Cassie Kozyrkov is Head of Decision Intelligence at Google. She recently wrote to Medium article on smarter decisions on COVID-19. Kozyrkov does not pretend to be an epidemiologist and does not call for action. Instead, her goal is to share with the world a healthy, generic decision-making process driven by specific steps, criteria and data.

Coronavirus disease COVID-19 infection medical. New official name for Coronavirus disease named COVID-19, pandemic risk on world map background

Epidemiology is a domain most of us know very little about. But data-driven decision-making principles can still help cut noise through at times like these

Getty Images / iStockphoto

Kozyrkov’s method consists of six steps:

  • Face your irrationality
  • Understand yourself and set goals
  • Given possible actions
  • Action choice triggers
  • Choosing minimum sources for sources,
  • Collection of information and action – or not

In a way, that’s how it is DataOps for the people. Each step in this method corresponds to a step in data-driven decision making applied at the organizational level. From changing organizational culture, to setting goals, to practicing data management and evaluating data sources. Being aware of the process and acting accordingly can also be advantageous at the individual level.

Pueyo also followed this recipe in a way: “What I did was gather experts’ opinions,” he said. “Everything I have is from raw data or analysis from other people.”

Wrapping, here are some resources that you may find useful if you want to stay up-to-date with the latest data, or get more involved in using technology to help with the COVID-19 pandemic.





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