Credit CDC
#18,085
While there is currently no evidence that H5N1 is spreading from human-to-human in the community, that isn't quite as reassuring as it sounds. We've seen anecdotal reports of symptomatic dairy workers who have refused to be tested, and the number of farm workers tested for H5N1 in the United States - according to the CDC - remains quite small (n=39).
Recent changes to reporting rules for hospitals - which took effect May 1st (see below) - have - at least temporarily - removed an important surveillance tool. The CDC is asking that hospitals continue to submit reports, although it isn't clear how many are doing so.
Testing of suspected cases, contract tracing, and epidemiological investigations are primarily being handled on the local level. And we get very little information on those operations.
It is fair to say that surveillance and reporting for influenza in the United States is not as robust, or as coordinated, as we'd like with a novel virus like H5N1 knocking on our door.
Of course, even if novel flu surveillance were cranking on all cylinders, there are no guarantees that early community spread would be detected. Previous studies have strongly suggested quite the opposite.
- A study published in 2013 (see CID Journal: Estimates Of Human Infection From H3N2v (Jul 2011-Apr 2012) - estimated that during a time when only 13 cases of novel flu were reported by the CDC - that the actual number of infections was likely 200 times (or more) higher.
- Although the 2009 Swine flu pandemic was first reported in Southern California in late April of 2009, we now know the virus had been circulating - unnoticed - for at least 2 months in Mexico (see Early Outbreak of 2009 Influenza A (H1N1) in Mexico Prior to Identification of pH1N1 Virus).
- During the opening weeks of China's H7N9 outbreak in 2013, in Lancet: Clinical Severity Of Human H7N9 Infection, we saw estimates that the number of `symptomatic' cases was likely anywhere between 10 and 200 times higher than reported.
- A little over a year ago, a study from the UK HSA (see UK Novel Flu Surveillance: Quantifying TTD) estimated the TTD (Time To Detect) a novel H5N1 virus in the community via passive surveillance could take weeks, and the virus might only be picked up after hundreds or possibly even thousands of infections.
None of this means that H5N1 is quietly spreading under the radar, only that our ability to detect it in its early stages is limited.
All of which brings us to a new study - authored by researchers at the CDC (but with the disclaimer that `The findings and conclusions in this report are those of the authors and do not necessarily represent the views of the Centers for Disease Control and Prevention') - that attempts to quantify - using a variety of scenarios - how long it would likely take to detect a novel virus spreading in the United States.
Essentially they are saying if the virus is capable of X, and we do Y, then we can reasonably expect to detect after Z.
But if we do something less than Y, then all bets are off. As the authors state, their estimates assume sustained community transmission is already occurring, with > 100 cases in the community.
. . . Our primary aim was to estimate detection capabilities once sustained human-to-human transmission is occurring within the United States, we did not consider surveillance for earlier events that might spark such transmission, such as spillover from infected animals or introductions from outside the United States.
Sporadic infections, or even limited household clusters, would presumably fall below this threshold. The authors also state:
Although the probability of detecting one case was generally high, the percent of total cases detected was low, especially in the lower severity scenarios.
This study is heavy on statistical calculations (all above my pay grade), so I'll let others weigh in on that. Due to its length, I've only posted some excerpts. I'll have a bit more after the break.
First published: 26 May 2024The findings and conclusions in this report are those of the authors and do not necessarily represent the views of the Centers for Disease Control and Prevention
ABSTRACT
Background
Novel influenza viruses pose a potential pandemic risk, and rapid detection of infections in humans is critical to characterizing the virus and facilitating the implementation of public health response measures.
Methods
We use a probabilistic framework to estimate the likelihood that novel influenza virus cases would be detected through testing in different community and healthcare settings (urgent care, emergency department, hospital, and intensive care unit [ICU]) while at low frequencies in the United States. Parameters were informed by data on seasonal influenza virus activity and existing testing practices.
Results
In a baseline scenario reflecting the presence of 100 novel virus infections with similar severity to seasonal influenza viruses, the median probability of detecting at least one infection per month was highest in urgent care settings (72%) and when community testing was conducted at random among the general population (77%).
However, urgent care testing was over 15 times more efficient (estimated as the number of cases detected per 100,000 tests) due to the larger number of tests required for community testing. In scenarios that assumed increased clinical severity of novel virus infection, median detection probabilities increased across all healthcare settings, particularly in hospitals and ICUs (up to 100%) where testing also became more efficient.
Conclusions
Our results suggest that novel influenza virus circulation is likely to be detected through existing healthcare surveillance, with the most efficient testing setting impacted by the disease severity profile. These analyses can help inform future testing strategies to maximize the likelihood of novel influenza detection.
(SNIP)
Novel influenza viruses pose a potential pandemic risk, and prompt detection is critical to characterizing the virus causing the infection and facilitating a rapid public health response. Here we demonstrate how a simple probabilistic framework can be used to estimate novel influenza virus detection probabilities through testing in different community and healthcare settings, and can help inform the targeting of future testing efforts. Our work was motivated by the 2022–2024 H5N1 situation in the United States but could be applied more broadly to other locations and/or other potential novel influenza viruses.
It is pretty much axiomatic that when dealing with emerging infectious diseases, more testing is preferable to less. But for the past few years, in an attempt to `move on' from the economic, societal, and political pain of the COVID pandemic, governments and agencies have quietly dismantled surveillance and reporting requirements (see No News Is . . . Now Commonplace).
Today, 90% of the world's countries no longer report COVID deaths or ICU admissions. While that leaves us in the dark as to what the virus is doing, it does make for reassuring Epi line charts.
Some countries only belatedly report novel flu infections (assuming they report at all), while many others have failed to meet the basic surveillance and reporting goals from the 2005 IHR agreement (see Lancet Preprint: National Surveillance for Novel Diseases - A Systematic Analysis of 195 Countries).
Even the testing of cattle and dairy workers for H5N1 in the United States has met stiff resistance, with some stakeholders apparently content to follow a `don't test, don't tell' policy.
While we might not like what we find, our ability to gear up for - and deal - with the next pandemic virus is very much dependent on our willingness to look for it before it begins its world tour.
And right now, our heart doesn't seem to be in it.
