In the spring, many countries that have controlled COVID-19 cases now have increased infections, increasing the likelihood of facing a second case, as many epidemiological models predicted. But in the United States, the number of cases has not fallen to a low level. Instead, it varied between high levels of infection and very high peaks of the case. Why is everything so different in the state?
There are many possible reasons, but a series of new studies essentially blame all obvious causes. The United States has ended social distancing rules too quickly, has not built up enough testing and contact tracking, and has not adopted habits such as using masks. It can help replace failure elsewhere. The fact that some of these studies have used very different methods to reach similar conclusions suggests that such conclusions are likely to be maintained as more studies come in.
One of the studies conducted by the US-South African team was Relaxation of social distancing rules in America. The authors compiled a list of restrictions for each state and the District of Columbia and tracked the number of COVID-19 deaths in each state over the eight weeks before the rule ended. The number of deaths was used as a surrogate for the total number of cases, as it was difficult to determine the actual infection rate due to the irregular availability of tests.
Most states have begun to relax these rules from the end of April. However, as the authors pointed out, they did so without the ability to control the infection through other means. “The mitigation of these measures is intended to be accompanied by appropriate behavioral practices (e.g. wearing a mask and physical distancing) and control measures (e.g. contact tracking and increased test availability) so that epidemic control can be maintained.” . This was not possible because of the limited testing ability and widespread behavioral practices.
So, after lifting restrictions, the authors collected data on COVID-19 deaths in the state and compared the two trajectories. A linear regression model was used to take the number of COVID-19 deaths and estimate the likely number of viral reproductions in each state and DC.
Of the 51 cases, 44 have witnessed slow reproduction of the virus while social limitations exist. Overall, the authors estimate that the United States saw an average decrease of 0.004 per day in the number of reproductions of the virus during this period. While not dramatic, this meant that the number of breeds was less than 1 when 46 people began to relax the rules of social distancing. This is a situation that ultimately means an end to the epidemic.
Unfortunately, the decline ended with relaxation of the rules. After the restriction disappeared, the estimated number of reproductions increased from a decrease of 0.004 per day to an increase of 0.013. After the rules were relaxed, only eight states and DCs were able to keep their breeding numbers below 1.0, which means that the epidemic has returned to its growth path.
There are many differences from state to state between the limits enacted and how high the infection was when those limits were first enacted. So, it’s not surprising that there is no simple pattern of “before” or “after” restrictions when researchers separate each state. However, both the overall results and the national average clearly suggest that the pandemic-focused restrictions ended too soon.
And not enough
If that’s not enough Dynamic modeling paper Focusing on a slightly different question leads to the same conclusion. The work done by a group of researchers at Texas A&M focuses on what is needed to control the epidemic without returning to strict restrictions on social interactions. However, in the process of finding what would be needed to control the pandemic, the A&M team figured out what these restrictions could currently achieve.
The researchers built a standard dynamics model and used mobility data from companies such as Google and Open Table to adjust the social restraint period and post-resume properties. They also added data on state-level cases and deaths, and then used historical data to validate the model.
When I actually analyzed the model, I reproduced the above results to some extent. In all but five states, the effective reproductive value of the virus was less than 1 at the beginning of the pandemic, “mainly achieved by states that protect in place.” When these restrictions were lifted, the model began to see an increase in infections, and by mid-July, it was more likely to have viral fertility rates that could expand the pandemic in 42 states and DCs.
By July 22, the last date used in the analysis, the opportunity to control the epidemic is almost over. Only three states in the northeast can take control without adding social restrictions again. Absolutely no one can do that by relaxing the existing limitations. Even if the state doubled the existing tests and contrast tracking, only eight were able to reduce the number of viral propagation to the point where the pandemic could be controlled. The other 30 should do that and Increases social restrictions. The rest will have to go back to serious closure.
The authors concluded that “in most states, the control strategies implemented during the shelter period have been shown to be sufficient to contain the outbreak.” “However, in most states, our modeling suggests that the resumption proceeded too quickly or without adequate testing and contact tracking to prevent recurrence of the epidemic.”
Already wearing a mask
The authors admit that their models have noticeable weaknesses. It assumes that personal protection measures such as the use of face masks and physical distancing are adopted roughly in proportion to the number of people who follow the social restrictions required by the state. It’s not an irrational assumption, but it does make it impossible for the model to analyze the effectiveness of these personal measures apart from the official policy on limiting social contact.
It makes us draft Although not yet peer-reviewed, we use data from Ontario, Canada to address the issue ourselves. The authors compared infection rates before and after adopting the duty to wear a mask in 34 different public health districts in Ontario. Like the A&M group, authors use Google mobility data to control the frequency of personal interactions. Overall, they estimate that the use of masks has reduced Ontario’s infection rate by 20 to 40 percent.
This is not surprising at all. From the outset, public health officials say that social restrictions are needed to control infection rates, so testing and contact tracking could be effective in controlling the epidemic. The epidemic’s data serves to indicate that this initial advice was exactly right. However, the U.S. response was to lift restrictions before infection rates were controlled and to limit testing sufficiently to make contact tracking nearly impossible. As an added bonus, the state has created a possible alternative way to limit infectious diseases, such as the use of protective masks, which are political issues.
So, while the newspaper provides some indicators of what the United States needs to keep from seeing the pandemic continue to spread out of control, it also serves to highlight how we did almost everything wrong.