It is really difficult to determine which policies effectively control COVID

Nobody wants to go back under severe social restrictions. But the growing number of cases is causing many countries to put in place targeted blockades and other limits to try to bring the pandemic back under control, a move that has sparked backlash in many places. Hence, it seems worth asking what the optimal combination of restrictions might be. How is greater control over the pandemic achieved for the less restrictive social environment?

This is exactly what an international team of researchers has attempted to discover, as described in a paper published today. And, as the researchers come up with some potential answers, their article ends with an additional message: This is a really difficult question to answer. So, to some extent, many countries will have to act with imperfect information and hope for the best.

how do you respond to this?

In an ideal world, we would have an idea of ​​the impact of every possible social restriction: closing restaurants, initiating contact tracing, closing schools, and so on. In light of this information, we could examine the infection rate and its trajectory, then identify the smallest possible set of restrictions that could cause a decrease in the infection rate. But the real world is currently a long way from this idealized situation, which is what motivated researchers to try and provide a little more certainty about the effectiveness of the different restrictions.

But the real world also makes this a difficult question to answer. After all, no one has adopted a single restriction; in general, a number of limits have been introduced in most countries, with further restrictions being gradually added. Additionally, different cultures may find it harder to comply with limits on bars and restaurants than they would to have a mandate to wear the mask – different limits will see different levels of compliance.

To try to get around this problem, the researchers collected large collections of public health policies put in place during the initial wave of infections in March and April. They then analyzed all of them using four different methods and looked for those that were consistently associated with better outcomes in terms of total number of infections.

It turned out a lot of information. The researchers’ initial analysis was a list of more than 6,000 different policy changes implemented in 79 different countries, states or provinces. But they then went on to use two additional sources of restriction data to validate the initial analysis, adding a total of 42,000 additional interventions.

The correlations between these interventions and infection rates were then tested using four different mathematical approaches. (This was a case-control matching algorithm, two different types of regression analysis, and transformer modeling). The approaches that yielded significant results in three or four of these tests were considered likely to be effective.

Not so fast

Unfortunately, one of the limitations of this analysis became evident quite early on. During the first wave of the pandemic, testing capacity was generally limited, particularly in the United States. As such, the established number of cases was often more related to testing capacity than the presence of the virus. Unsurprisingly, algorithms have typically identified policies such as “testing capacity enhancement” and “increased surveillance” as having a negative effect on pandemic control. The reality is that, in most countries, these policies have likely led to increased case identification.

Aside from this kind of limitation, there was a high degree of correlation between policies marked as effective by different algorithms, which should provide some reason for confidence in other results. Some of these are exactly the things people don’t like: national lockdowns and stay-at-home orders. The ban on small gatherings turned out to be the maximum intervention. Another major intervention, the increased supply of personal protective equipment, is not really a surprise. Same with the fact that educating the public about the virus and the pandemic seemed to have a positive effect in three of the four analyzes.

Other things were quite surprising. Border restrictions have proven to be quite effective, although they could be distorted by a number of island nations, such as Taiwan and New Zealand, which have been very successful in controlling virus entry. Another consensus choice was the closure of the school. Some previous studies had suggested that uptake in schools was minimal, while others had supported the measure, so the evidence here has been ambiguous.

When the research team turned to the larger database of policy interventions, however, there was no complete agreement with the initial analysis. Of the seven analyzes that had seen all four analyzes marked them as effective, only three found the same level of agreement when the same four analyzes were repeated on the largest database. But once again, social restrictions such as the ending of small or large gatherings and the closure of offices were good, as well as the closure of borders. So even though a specific policy may not have been replicated, closely related policies often did, which could suggest that we would have gotten clearer results by grouping similar restrictions into one group.

A further indication of this could be the fact that national blocs have only been assessed as having inconsistent effectiveness. The researchers suspect this was caused by lockouts that occurred only in the wake of a series of previous restrictions, so the locks didn’t add as much as they could if implemented in isolation.

One of the policies that scored specifically negative across all data sources was widespread disinfection of the environment. It is not clear why this was the case. Another policy that didn’t seem to do much was social distancing measures specifically targeting public transport.

General messages

Another thing the research team verified is what they call the “entropy” of the results. That is, if a policy is universally effective wherever it is tried, it will have low entropy. From this, we can deduce that cultural differences were unlikely to have had a huge impact on the success of the policy. It turns out that some of the most effective measures, such as social distancing and travel restrictions, had high entropy, meaning they worked in some countries but not as well in others. One need only look at the different levels of compliance with restrictions within the United States to understand how this can happen.

However, the local culture seemed to influence the effectiveness of the policy, not if it was effective at all. Various forms of social distancing and restrictions on meetings, businesses and restaurants scored well in most of the analyzes.

It was also significant that better communication on policy motivations often had as strong an effect as policy implementation. If you explain to people why you may have to close restaurants, then the chances are that many people will stop going to restaurants regardless of whether the rules are in place or not.

However, the information is too complicated for the ideal goal we would like to see: identifying the minimum level of restrictions that can keep the pandemic in check. As hospitals fill with capacity, we will likely have to put in place severe social restrictions just to make sure we don’t allow the healthcare system to become overloaded. One thing the study provides, however, is another proof that these restrictions will do the job.

Nature Human Behavior, 2020. DOI: 10.1038 / s41562-020-01009-0 (About DOIs).