ODI Covid-19 Project

“Agent-based models are particularly good in situations where you need to explicitly model the interactions and the behaviour of the individual components of a system,” says Nick Malleson, a professor of spatial science at the University of Leeds, who has worked with Improbable to study crime patterns. “I think the reason that they've become popular for [studying] disease spread is that very often in a disease spread, you might need to look at how people are interacting – when they come into contact in shops, how the social networks affect how people move, how they behave, how they interact, all these kinds of things.”

The model, based on 13-year-old code for a long-feared influenza pandemic, assumed that the demand for intensive care units would be the same for both infections. Data from China soon showed this to be dangerously wrong, but the model was only updated when more data poured out of Italy, where intensive care was swiftly overwhelmed and deaths shot up.

It did not consider the impact of widespread rapid testing, contact tracing and isolation, which can be used in the early stages of an epidemic or in lockdown conditions to keep infections down to such an extent that when restrictions are lifted the virus should not rebound.

One moment the prime minister, Boris Johnson, was asking people with symptoms to stay home for seven days; a few days later, he had ordered a lockdown. What changed was data from Italy’s experience of the pandemic, in which more people were critically ill than anticipated, and from the NHS about its inability to cope if the same should happen in the UK.

ISB1523: Anonymisation Standard for Publishing Health and Social Care Data This process standard for publishing health and social care data provides an agreed and standardised approach to anonymisation.

Although the law makes a clear distinction between identifying and non-identifying data, where that line should be drawn may be far from clear in practice. That is why this anonymisation standard for publishing health and social care data is needed. This process standard provides an agreed and standardised approach, grounded in the law, enabling organisations to: distinguish between identifying and non-identifying information deploy a standard approach and a set of standard tools to anonymise information to ensure that, as far as it is reasonably practicable to do so, information published does not identify individuals.

Potential Coronavirus (COVID-19) symptoms reported through NHS Pathways and 111 online

Summary Data published on potential COVID-19 symptoms reported through NHS Pathways and 111 online Dashboard shows the total number of NHS Pathways triages through 111 and 999, and online assessments in 111 online which have received a potential COVID-19 final disposition. This data is based on potential COVID-19 symptoms reported by members of the public to NHS Pathways through NHS 111 or 999 and 111 online,  and is not based on the outcomes of tests for coronavirus. This is not a count of people.

A new map from the developer Meli Harvey shows exactly how narrow the sidewalks in New York are, with colors overlaid onto the city’s grid. (Harvey used data from New York City’s sidewalk dataset to construct their map.) The result is a stark validation of what most New Yorkers felt to be true: there’s just not enough space for most people to stay the recommended six feet apart.

The red colour of the QR code on Ms Sun’s “Hangzhou Health Code” app indicated that she was supposed to be undergoing 14 days of self-quarantine. Had the code been yellow, it would have meant she was a lower risk and had to isolate herself for seven days. For free passage around the city, people must produce their phones at checkpoints and show they have a green QR code. Pictured is another method of keeping tabs on people: drivers have to scan the code held up by a drone to register for entry into the city, in this case Shenzhen.

But those efforts involve only a single province. Creating such systems is far harder when it entails data-sharing between provinces, or between provincial and central authorities. Co-operation is undermined by competition for favour in Beijing. The boss of a foreign artificial-intelligence developer in China says that fusing datasets within a single firm is often quick, but not if it involves co-operation between different institutions. “The person in charge is unwilling to take the risk,” he says, and usually reckons that doing nothing is safer than sharing.

One way the virus hurts the economy is by disrupting the supply of labour, goods and services. People fall ill. Schools close, forcing parents to stay at home. Quarantines might force workplaces to shut entirely. This is accompanied by sizeable demand effects. Some are unavoidable: sick people go out less and buy fewer goods. Public-health measures, too, restrict economic activity. Putting more money into consumers’ hands will do little to offset this drag, unlike your garden-variety downturn. Activity will resume only once the outbreak runs its course.

A downturn stemming from an epidemic is an unusual one. And it might prove unusually difficult for policy makers to fight, in the United States and abroad.For one, the coronavirus epidemic has come with extraordinary, intense uncertainty. Officials are not sure how many cases there are and how deadly the virus is. Businesses and households are uncertain of how long the danger will last and what measures governments might take to counter it. People are afraid, as the market panic demonstrates, and it may take months for that fear to abate.

judiciously targeted bailouts are really the only way I can think of to keep businesses and people from going bankrupt given the absence of pandemic insurance.”

Harvard’s Jason Furman has suggested that Congress send every adult American $1,000 and every child $500

Everyone is cooking the data, one way or another. And yet, even though these inconsistencies are public and plain, people continue to rely on charts showing different numbers, with no indication that they are not all produced with the same rigor or vigor. This is bad.

The other problem is, now that the U.S. appears to be ramping up testing, the number of cases will grow quickly. Public-health officials are currently cautioning people not to worry as that happens, but it will be hard to disambiguate what proportion of the ballooning number of cases is the result of more testing and what proportion is from the actual spread of the virus.

People trust data. Numbers seem real. Charts have charismatic power. People believe what can be quantified. But data do not always accurately reflect the state of the world. Or as one scholar put it in a book title: “Raw Data” Is an Oxymoron.

Yet if we are not careful, the epidemic might nevertheless mark an important watershed in the history of surveillance. Not only because it might normalise the deployment of mass surveillance tools in countries that have so far rejected them, but even more so because it signifies a dramatic transition from “over the skin” to “under the skin” surveillance. 

A self-motivated and well-informed population is usually far more powerful and effective than a policed, ignorant population. 

The government has already boasted that millions of citizens have shared this information with them at a time when most Iranians are completely in the dark about the threat from coronavirus. The government is being accused of covering up the real infection and death rates with experts claiming the real figures are exponentially higher. With confusion and fear gripping many parts of Iran, this app is looking to take advantage of that to boost Tehran's surveillance capabilities.

It is impossible to say how many of Iran’s citizens have downloaded the app, but according to this tweet from the ICT minister MJ Azari Jahromi, at least 3.5 million people have now shared their precise location and intimate details with the government.

“The regime’s attitude is evident during the coronavirus crisis,” Gobadi said. “Instead of being transparent and alerting the public regarding the real scope of the crisis, the regime has resorted to a massive campaign of deception and concealment particularly as it pertains to the number of victims and fatalities.”

According to the article, the mayor wrote on his website: “Compliance with the regime is constantly monitored, including with the help of facial recognition systems and other technical measures.”

As South Korean media pored over their movements, citizens looked on with a mixture of horror and fascination as their private lives were laid bare, leading to speculation that they were having an affair and that the secretary had undergone plastic surgery.

He said he was taking this decision not just as a prime minister, but as our family elder. Who else can decide, without consulting the state governments that would have to deal with the fallout of this decision, that a nation of 1.38bn people should be locked down with zero preparation and with four hours’ notice? His methods definitely give the impression that India’s prime minister thinks of citizens as a hostile force that needs to be ambushed, taken by surprise, but never trusted.

The scene was biblical. Or perhaps not. The Bible could not have known numbers such as these. The lockdown to enforce physical distancing had resulted in the opposite — physical compression on an unthinkable scale. This is true even within India’s towns and cities. The main roads might be empty, but the poor are sealed into cramped quarters in slums and shanties.

Whatever it is, coronavirus has made the mighty kneel and brought the world to a halt like nothing else could. Our minds are still racing back and forth, longing for a return to “normality”, trying to stitch our future to our past and refusing to acknowledge the rupture. But the rupture exists. And in the midst of this terrible despair, it offers us a chance to rethink the doomsday machine we have built for ourselves. Nothing could be worse than a return to normality.

Now, as the covid-19 pandemic rages, several independent sources say that Palantir has started work with a new client: Britain’s National Health Service.