Coming Of Age In Unknown Country Analysis
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Coming of Age: Idealized Youth (video essay)
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Even in a world where voters would like to have good, data-driven policy that leads to better outcomes, you may not get that because in a noisy information environment it's hard to identify the connections between policy and outcomes, and data-driven governance may not be rewarded. The government handled it well back then. Ebola is the epidemic that didn't happen, as far as the United States is concerned.
Research shows the Democrats were still punished for it, however, even though it was arguably staved off by good, data-driven policy. They lost votes in those midterms as a result of their reaction to the Ebola epidemic, because other politicians had an incentive to strategically exploit the situation. Many politicians tried to connect Ebola to President Obama and immigration, with some success with the immigration argument. The bottom line is that politicians are providing information in this environment, they have incentives to create noise, and in this case they were rewarded for it. Data-driven governance wasn't rewarded. That is still the case with COVID, where politicians are not necessarily being rewarded for data-driven decisions like encouraging vaccinations or mask mandates.
What changes to the information environment have enabled low-quality data to reign? Social media plays a big role here, but it's not only that. The generally fragmented media environment allows cheaper access by politicians to media channels. Back when there were only three major TV networks, politicians were not content creators, but now they are. That is a big change that makes the information environment less conducive to a data-driven approach. It's not just the public accessing information; the politicians are players in the information environment. Politicians used to have to own a TV channel or control the state TV to flood the information environment with noise.
It's easier now because access is easier. What has been the impact of increased access to data and information on social capital? Social capital boils down to the ability and the things that build the ability to engage in collective action. Trust is central to social capital because trust is what enables that collective action. In a low trust environment, the problem of the noisy information environment is magnified.
You can't trust news sources if you can't trust people in general. It was a new disease that we knew nothing about, so people had to rely on external information. It is hard to figure out who actually knows what they're talking about because we are in a low-trust environment. You can contrast the response that happened here, where we're living through a period of depreciating social capital, versus a place like Germany.
How do we cut through the noise to support high-quality data and governance? One approach from early in the pandemic was fact-checking. But it's not always the case that good information will drive out bad information, it can also be the reverse. Fact-checking has often been ineffective because it just adds more information to this already overcrowded information environment. People are going to choose their data sources through trust. Therefore, we need to look for trust relationships to share high-quality information.
For instance, if we have a community that is reluctant to get vaccinated, we need to find a messenger that the community trusts. Trust is of the essence here because we're in a situation where it's hard to assess the quality of information. We need to find trustworthy messengers, knowing that there are incentives on the other side to fight back with noise. Explore Vaccination Progress by U. Explore Vaccination Progress by Country. New Explore how U. New By Chris Beyrer. By Larry Corey. For example, some of those doses are going to be targeted to India, which is facing a tragic surge in cases.
The idea is to get vaccines to where they are needed most. But it has to be done early. And then the third strategy is based on foreign policy decisions. This is my least favorite. For example, the Biden Administration has donated doses to Canada and Mexico, obviously because they are countries on our borders where we have a self-interest in preventing virus importations. Is there enough time to deliver the vaccines to make a difference? Well, with India, I must say, it's late. Then you need to give them two doses. You can look at where outbreaks are really emerging like in Nepal or Thailand to target vaccine doses. What more can the United States do to address the global situation?
The United States needs to do a lot more than donating vaccines. The United States needs to support the expansion of vaccine manufacturing both domestically and abroad. It needs to help build that capacity, provide training for personnel, and relax export restrictions on supplies, reagents, and chemicals that go into manufacturing vaccines. This needs to be a global partnership among the wealthy nations to coordinate all of the responses needed. We need to substantially reduce the burden of disease for humanitarian purposes and reduce transmission of the virus for self-interest to prevent the emergence of variants that could cause outbreaks in the United States. We are already at a point in the United States where we have a surplus of vaccines.
Obviously we expanded eligibility for children 12 to 15 years old, and this will increase our need. To date 1. This needs to be an urgent and collaborative global effort. Can surplus vaccines be used as booster shots later? I believe that is one of the reasons why there has been some reluctance to donate a large number of doses. Many experts believe we will need them. The question is when. The Biden Administration justifiably has been focused on bringing the pandemic under control in the United States. It does not want to put the United States into a shortage, which I think is reasonable.
But the United States still need to be a global leader in addressing global vaccine inequities. The United States will need to maintain a supply of vaccine for potential booster doses. The last of these, a Japanese male in his 70s dies nearly 3monhs after the ship outbreak. By May 1st over 40 cruise ships have had confirmed positive cases of coronavirus. As of the 30th April, the CFR prediction interval is 0. Evaluating CFR during a pandemic is, however, a very hazardous exercise, and high-end estimates should be treated with caution as the H1N1 pandemic highlights that original estimates were out by a factor greater than We now want to draw your attention to the flaws in CFR estimation due to the changing nature of the testing regimes.
CFRs across countries are, therefore, highly variable, depending on who is tested for what reasons. There is no consistency. See CFR figures by countries over time:. It is increasingly clear that current testing strategies are not capturing everybody. In South Korea, considerable numbers who tested positive were also asymptomatics- l ikely driving the rapid worldwide spread. CFR rates are subject to selection bias as more severe cases are tested — generally those in the hospital settings or those with more severe symptoms. The number of currently infected asymptomatics is uncertain: estimates put it at least a half are asymptomatic; the proportion not coming forward for testing is also highly doubtful i.
Emerging evidence suggests many more people are infected. We could make a simple estimation of the IFR as 0. However, the considerable uncertainty over how many people have the disease, the proportion asymptomatic and the demographics of those affected means this IFR is likely an overestimate. In Swine flu, the IFR ended up as 0. In Iceland, where the most testing per capita has occurred, the IFR lies somewhere between 0. Taking account of historical experience, trends in the data, increased number of infections in the population at largest, and potential impact of misclassification of deaths give a presumed estimate for the COVID IFR somewhere between 0. If younger populations are infected more the IFR will be lower.
A single estimate of IFR across all age groups is unhelpful particularly given the significant changes in risk of death with age. The IFR will also vary substantially based on country demographics such as age. Modelling the data on the prevalence of comorbidities is also essential to understand the CFR and IFR by age the prevalence of comorbidities is highly age-dependent and is higher in socially deprived populations. It is also not clear if the presence of other circulating influenza illnesses acts to increase the IFR testing for co-pathogens is not occurring.
And whether certain populations e. It is now essential to understand whether individuals are dying with or from the disease. Understanding this issue is critical. Cause of death information from death certificates is often inaccurate and incomplete , particularly for conditions such as pneumonia. These factors would act to lower the IFR. Antibody testing will provide an accurate understanding of how many people have been infected so far, and permit a more accurate estimate of the IFR.
We do not currently have a good understanding of What proportion are asymptomatic? We are tracking excess mortality Assessment of Mortality in the Covid outbreak to understand this phenomenon to determine how many excess deaths occur during the pandemic. Accurate data on deaths and cause of death which is not forthcoming is vital to determine the effect of the COVID pandemic. See Lancet report: CFRs on mortality rate estimates can be misleading if the CFR is based on the number of deaths per number of confirmed cases at the same time. Full bio and disclosure statement here. Disclaimer: the article has not been peer-reviewed; it should not replace individual clinical judgement, and the sources cited should be checked.
The views are not a substitute for professional medical advice. Estimating Case fatality rates in the early stage of outbreaks is subject to considerable uncertainties, the estimates are likely to change as more data emerges. Navigate this website. The number of cases detected by testing will vary considerably by country; Selection bias can mean those with severe disease are preferentially tested; There may be delays between symptoms onset and deaths which can lead to underestimation of the CFR; There may be factors that account for increased death rates such as coinfection, more inadequate healthcare, patient demographics i. Differences in how deaths are attributed to Coronavirus: dying with the disease association is not the same as dying from the disease causation.
Update 21st March: The epidemic curve of onset of symptoms peaked around January 23rd to 26th, then began to decline up to February 11th. The CFR was 2.
