Venture out when ‘nothing ventured, nothing gained’ has morphed into ‘nothing ventured, life gained’ and discretion has definitely become the better part of valor? No thanks, venturing out with some semblance of “normalcy” right now sounds as appetizing as acquiring some holes in the head. It’s not for me, at least not until more innocent, decidedly safer times return.
Gross COVID-19 mismanagement in the US. Rampant community transmission of the virus. No way of knowing who has it and who doesn’t when out. Similar situation may be at play in many other countries as well. Who knows. Access to non-manipulated data seems to be rarer than a flying pig.
Venturing out is now hopelessly tied to luck and trust. Who’s lucky enough to sit at home in the first place and who trusts that they can adequately protect themselves from getting infected when out.
Essential workers aren’t lucky enough to sit at home so they get exposed simply because the virus is now circulating widely in many parts of the US. Most of their employers appear to be doing neither enough nor providing enough to protect them from exposure meaning the chains of transmission aren’t getting broken. No clear, unified government strategy to contain the virus means we’ve been left to fend on our own.
It’s now clear that this virus spreads from close human contact indoors, from talking, coughing, sneezing, singing, and that face masks reduce the risk of transmission. Yet, many of the behaviors on display when out don’t inculcate trust in others; face mask under nose, under chin, hanging off of one ear or worse, no face mask at all.
Then there are the daily official pandemic case numbers. Tens of thousands are currently reported positive in the US every day but key actionable information remains missing.
Under what circumstances did they get exposed? Where? At work, to and from work, at home, from leisure activities?
When did they get exposed meaning how long from exposure to testing positive?
Were they symptomatic or asymptomatic when they got tested?
How many positives require hospitalization on a given day?
Who is even reporting daily COVID-19/pneumonia hospital admissions in a transparent, non-cooked, non-massaged manner?
What proportion of positive cases get isolated and how soon after diagnosis?
What proportion of positives any given day are linked to previous positives?
How many others are linked to someone who tests positive any given day? Are those contacts being tracked, tested and isolated so they’re taken out of circulation and so chains of transmission get broken?
How relevant are these positive test numbers anyway? Are they even real-time at all?
Were those reported positive today sampled yesterday or last week or two weeks ago or even earlier?
People wait hours in queues just to get tested. Reports say it often takes a week or even longer to get the results back. This means that much US COVID-19 testing data is weeks out of date by the time it’s reported to the public.
Greater the lag between sample taking and result reporting, less useful the results for others. People waiting for results may be spreading the virus in the meantime.
Most tests aren’t free, aren’t widely available. People have to drive for miles to get free tests. Many who should be tested aren’t meaning virus gets to spread even more.
No national testing strategy, no random testing plan, no systematic surveillance program in place to track COVID-19 across the US means asymptomatic and pre-symptomatic cases aren’t being tracked and isolated. Such people are also undoubtedly spreading the virus.
Which tests?
RT-PCR? Highly specific but less sensitive. Actual accuracy in the field may be ~70 to 90%.
Antigen test? Much less sensitive. Actual accuracy in the field may be as low as ~30%.
Antibody test? Measures past exposure, not present infection, so irrelevant for current infection rates. Likely generates fewer positives compared to the RT-PCR test.
Mixing results from these very different tests in public reporting helps manipulate test positive rates for headline management purposes but is extremely misleading and exposes the public to even more unnecessary risk.
One could try extrapolating from daily hospitalization and death data but that too suffers from outdatedness as well as creative and dangerously misleading data manipulation.
Are probable cases being counted as official COVID-19 deaths? If actual virus diagnosis lags by weeks, many dying today may test virus-positive weeks later or not get tested at all and not get counted in official COVID-19 death tolls.
Are pneumonia deaths that couldn’t get verified as COVID-19 in time being counted as official COVID-19 deaths? If not, then such death numbers may get omitted from official death tolls and keep fatality numbers artificially low.
So many opportunities for the virus to continue spreading plus testing backlogs only mean actual numbers of undetected cases continue to rise in the background, i.e., actual virus-positive cases in the community are much higher than their publicly reported counterparts.
Add to this already hair-raising recipe exponential growth of virus in the community and the result is a toxic mess of a risk. Under the circumstances, why would I venture out if it’s possible to stay in?
SARS-CoV-2 is obviously highly contagious, having spread the world over in a matter of months. Its infectiousness and symptoms don’t overlap exactly, and in fact infectiousness often seems to precede symptoms, with transmission capability highest near symptom onset and declining quickly thereafter (1, 2, 3, 4, 5, 6, 7).
While this peculiarity seems to have facilitated its quick spread, the tacit assumption in this question is that infection spread should have a different contour when spread by an infectious asymptomatic versus an infectious symptomatic. Sounds like a reasonable assumption at first blush.
Shouldn’t Infection spread by symptomatics and asymptomatics be clustered or dispersed, respectively, in theory?
A symptomatic is someone who manifests illness. Having some symptoms of illness is after all what it means to be symptomatic. They should obviously feel unwell. More severe their symptoms, more they should seek to restrict their movements, be less likely to be out and about, more likely to stay in to get over the illness and at best be around only their intimates, which would be a smaller circle of contacts compared to their symptom-free life.
By thus situating an infectious symptomatic within the very real physical constraints imposed by their infection and its inevitable illness consequences, it’s reasonable to speculate that the infection symptomatics spread would more likely be clustered rather than dispersed.
On the other hand, an infectious asymptomatic by the very definition should feel healthy, should be going about their daily business as usual, should have no suspicion that they are even infected and should unwittingly spread their infection hither and yon. This should result in dispersed infections.
Some so-called asymptomatics could actually be pre-symptomatics but that doesn’t make a difference in this regard since both should feel fine as they inadvertently spread infection.
Infection spread by symptomatics as well as by a/pre-symptomatics during the COVID-19 pandemic is clustered
Though this reasonable enough exercise in compare and contrast suggests that infections spread by a/pre-symptomatics should be more dispersed, reasonable exercises in contact tracing have repeatedly shown SARS-CoV-2 spread to be clustered regardless of who spreads it (3, 8, 9, 10, 11, 12) . How could that be?
The environment is the missing third party in infection spread
When reasonable conjectures postulate that things should pan out differently in two different circumstances but they don’t, it means something vital is missing from the equation and that is indeed the case here.
While a cursory glance would suggest that infection spread is a straightforward transaction between two bodies, a pas de deux between an infector and an infectee, a closer look shows that it’s actually a menage a trois, with the environment being the omnipresent yet largely under-appreciated third party involved in the unseen exchange.
Again and again during the COVID-19 pandemic, careful analyses of infection clusters and outbreaks have revealed a crucial, even decisive role for environment in general and closed indoor environments in particular in its spread (11, 13, 14, 15, 16, 17, 18, 19).
Close, prolonged contact in confined indoor spaces have figured so prominently in COVID-19 spread that in May 2020 (below from 8, emphasis mine),
“Japan’s Prime Minister’s Office and the Ministry of Health, Labour and Welfare announced 3 situations that could increase the risk for COVID-19 cases and advised the population to avoid the “Three Cs”: closed spaces with poor ventilation, crowded places, and close-contact settings”
Such findings imply that highly contagious though it undoubtedly is, SARS-CoV-2 could have a real constraint in spreading which specific closed indoor environments seem to have helped it overcome.
The conundrum of infectious a/pre-symptomatics and how closed indoor environments could facilitate their clustered spread of SARS-CoV-2
Infectious a/pre-symptomatics are advantageous for a pathogen by helping it spread stealthily across a population and thereby expeditiously gaining it access to many more bodies within which to proliferate.
However, to be simultaneously infectious and asymptomatic is a patent contradiction of terms.
In order for a host to be useful to a virus, its infection of their body needs to be productive so it can spread to another host. For that to happen, the virus needs to first invade and then proliferate within a host’s cells to expand its numbers to render that host infectious. Viral proliferation damages cells and this process of cellular damage within the host should evoke physiological responses, i.e., symptoms.
A virus that ‘seeks to capitalize’ on stealth spread via a/pre-symptomatics obviously requires it to navigate a tricky tightrope since that seeming advantage can be easily undercut by the tangible disadvantage of inevitable development of symptoms. Even though an infectious a/pre-symptomatic state implies that the virus could spread stealthily from an unwitting host, the process itself is thus a delicate dance on a knife’s edge.
The issue then turns on how much viral proliferation within a host is sufficient for infectiousness and whether that viral load would inevitably trigger symptoms or not. Reminiscent of Goldilocks and the three bears, SARS-CoV-2 in an a/pre-symptomatic needs to have proliferated just enough to be able to spread to another host because too much proliferation could induce symptoms in an infected host. The more severe such symptoms, the greater the likelihood that such a host would drastically curtail their movements and thereby limit the virus’ access to more hosts.
One implication of such reasoning is that infectious viral loads could be lower in a/pre-symptomatics compared to symptomatics but if that is indeed the case and several studies suggest otherwise (20, 21, 22, 23, 24, 25, 26), then a/pre-symptomatics should also be less efficient in spreading the virus, which is possible (27). However, that hasn’t precluded them from spreading this infection, often seemingly as easily as symptomatics (28). How could that be? That’s where closed indoor environments enter the picture.
SARS-CoV-2 spread’s noticeable dependence on closed indoor environments suggests that symptom-free proliferation in a host may often not be infectious enough, that virus build-up in poorly ventilated closed indoor spaces may in fact be necessary to accumulate dosage sufficient to initiate productive infections in new hosts. This may be one crucial reason why SARS-CoV-2 infections spread by either symptomatics or a/pre-symptomatics converge in both being clustered.
As have so many other human pathogens before it, SARS-CoV-2 also seems to have weaponized our own living and working conditions to facilitate its spread among us. In its case those conditions happen to be closed indoor settings with poor ventilation.
15. Leclerc, Quentin J., et al. “What settings have been linked to SARS-CoV-2 transmission clusters?.” Wellcome Open Research 5.83 (2020): 83. What settings have been linked to…
26. Kimball, Anne, et al. “Asymptomatic and presymptomatic SARS-CoV-2 infections in residents of a long-term care skilled nursing facility—King County, Washington, March 2020.” Morbidity and Mortality Weekly Report 69.13 (2020): 377. Asymptomatic and Presymptomatic SARS-CoV-2 Infections in Residents…
There just isn’t enough good quality representative data yet to conclude one way or another whether children spread the COVID-19 virus SARS-CoV-2 as easily as adults or less well. Let’s try to understand why that is.
In brief,
I. Why the role of children in COVID-19 spread remains unclear.
II. School re-openings are a global natural experiment that could reveal whether children can stoke and sustain community spread of SARS-CoV-2.
III. Some scientific papers, news reports and online resources on COVID-19 cases relevant for school re-openings.
I. Why the role of children in COVID-19 spread remains unclear.
a) Children were mostly stuck at home when SARS-CoV-2 was widespread within their communities.
b) Children were back in schools in person after SARS-CoV-2 dissipated within their communities.
c) Fewer opportunities for children-specific outbreaks means fewer studies on their ability to transmit SARS-CoV-2.
d) Children infected with SARS-CoV-2 tend to be and stay asymptomatic making it challenging to identify them during contact tracing.
e) Children aren’t a monolith. Ability to transmit SARS-CoV-2 appears to increase with age.
a) Children were stuck at home when SARS-CoV-2 was spreading like wildfire in their communities.
Schools the world over closed early on in response to this pandemic. Most children were thus largely isolated at home when this virus was circulating widely in their communities while adults did have to venture out for work and essentials, to keep the world running, so to say. As a result, adults and children initially diverged widely in their virus exposure levels.
School closures and stringent physical distancing measures have meant that children didn’t get exposed to the same extent as adults in the initial weeks and months of this pandemic. Children were more likely to get exposed only when adult(s) from their household bubbles got infected outside and brought it home to them while they themselves got much less opportunity the other way around, that is of catching this infection at school or elsewhere and spreading it at home, school and other places.
b) Children were back in schools in person after SARS-CoV-2 dissipated within their communities.
Since children have remained largely shielded from community exposure, how they and their normal daily school and life routines might spread SARS-CoV-2 when it’s circulating widely within their communities remains unclear.
c) Fewer opportunities for children-specific outbreaks means fewer studies on their ability to transmit SARS-CoV-2.
To understand the role of children in SARS-CoV-2 spread, interactions of children who turn up positive need to be carefully traced back for several weeks prior to their diagnosis in order to identify their contacts. Weeks because incubation periods can be much longer in children compared to adults. Those contacts need to be tested and traced in turn to uncover the chains of transmission.
Adults have had more opportunities to get infected so they’ve been tracked more often during this pandemic. Meanwhile, children haven’t been subjected to similarly intense contact tracing efforts simply because stuck at home, confined, they haven’t needed to be. Result is few studies on child-to-child or child-to-adult transmissions but plenty on adult-to-adult and adult-to-child transmissions. Children’s role in COVID-19 transmission thus remains uncertain and under-explored.
d) Children infected with SARS-CoV-2 tend to be and stay asymptomatic making it challenging to identify them during contact tracing.
Are asymptomatic children less infectious or silently spreading infection to others? Not clear yet.
Unless contact tracing is diligent and thorough enough to pick out an asymptomatic as reliably as it does symptomatic, silently spreading asymptomatic children could be easily overlooked when tracing out chains of infection transmission.
Sloppy contact tracing efforts that are symptom-based could easily miss asymptomatic children compared to all-encompassing ones that include everyone regardless of symptoms, disease severity or presence of co-morbidities.
Asymptomatics muddy the waters in terms of figuring out who infected whom. Even when a child got infected first in a household, if they remained asymptomatic, a symptomatic adult to whom they may have transmitted the infection might instead get erroneously labeled as the index case. Who such children contacted might not get investigated further.
If asymptomatic children harbored lower infectious viral loads and/or shed infectious virus for shorter durations, they could be easily missed by snapshot diagnostic tests and contact tracing efforts that would be too little, too late and not robust enough in terms of test sensitivity and timing.
e) Children aren’t a monolith. Ability to transmit SARS-CoV-2 appears to increase with age.
Children could potentially spread SARS-CoV-2, even if not like adults since
Children do get infected with this virus.
Infected symptomatic children can harbor similar infectious virus loads as their adult counterparts.
On the other hand,
Given apparently equivalent exposure to the virus, children have been found less likely to get infected in household settings than their cohabiting adults, leading some to speculate they could be poor at spreading SARS-CoV-2.
Children aren’t a monolith. Instead, data suggests children of different ages differ in their ability to transmit SARS-CoV-2. Middle and high schoolers appear similar to adults, and infants and primary schoolers less contagious.
Some infected children have been found to shed SARS-CoV-2 in their feces for prolonged periods. What are the infection-spreading implications of this feature? Not clear.
Such confusing contradictions prevail right now because existing COVID-19 transmission data on children aren’t representative of them as a group nor of their erstwhile daily routines and activities.
Some of this lack of data has to do with school shutdowns and stringent physical distancing measures that left children stuck at home and thus less likely to be index cases of outbreaks.
Another is that test, trace, isolate works ideally when case numbers are low. When they are high, as happens in outbreaks and hotspots, tracing each and every possible contact of each and every case quickly becomes impossible and gets tossed aside.
Unfortunately COVID-19 has generated a surfeit of data but sterling quality child-specific contact tracing data remains the proverbial needle in the haystack.
Infection is also a two- not one-way street between infector and infectee.
Questions that need to be considered about an infector.
How long is someone infectious?
How much infectious virus do they have?
How much infectious virus do they shed?
How much opportunity do they have to spread?
Questions that need to be considered about an infectee.
What is the minimum infectious virus dose needed for a productive infection? Age, gender and underlying health conditions ensure that this would be different from person to person. Someone with a chronic condition may get infected with a lower dose. Obviously risk of underlying, predisposing health conditions increases with age.
How much opportunity do they have of catching it? Obviously healthcare and other essential staff are at higher risk of catching it.
II. School re-openings are a global natural experiment that could reveal whether children can stoke and sustain community spread of SARS-CoV-2.
If children play a vital role in community spread of SARS-CoV-2, then school re-openings offer a global natural experiment in that different countries have different school infrastructures, cultures, classroom sizes, pupil-to-teacher ratios and infection prevention measures, to mention just a few relevant factors, each of which would play a role in school outbreaks. Different countries and indeed local areas therein also have different levels of community transmission that span the gamut from low to high.
Low community transmission. School re-openings in places where SARS-CoV-2 community spread is well-controlled should experience few or no outbreaks related to schools simply because there would be little virus to go around.
Questionable community transmission. School re-openings in places where SARS-CoV-2 community spread seems well-controlled but actually isn’t could experience outbreaks related to schools if children are important enough infection vectors that can surmount the public health control measures in place and thereby expose their weakness.
High community transmission. School re-openings in places where SARS-CoV-2 community spread isn’t well-controlled would experience outbreaks related to schools simply because so much virus would be going around that just about everyone including children would be involved in catch and spread.
Even though children account for few confirmed COVID-19 cases and fewer still become very ill, increasing numbers would inevitably catch it to spread it when the virus is circulating widely in tandem with the rise in total infected.
Places where SARS-CoV-2 spread isn’t fully in control are more likely to reveal the actual role of children in sustaining SARS-CoV-2 community spread. This is why school re-openings represent a global natural experiment that could uncover what role, if any, children play in SARS-CoV-2 community spread.
III. Some scientific papers, news reports and online resources on COVID-19 cases relevant for school re-openings.
Renz-Polster, Herbert, Joachim Fischer, and Freia De Bock. “Dyke wardens or Drivers? Why children may play an attenuating role in the spread of SARS-CoV-2.” (2020). https://osf.io/5n8da/download?format=pdf
≈ Comments Off on Why is every positive COVID-19 test reported as ‘a case’ despite the asymptomatic and benign nature of much of the virus? Aren’t there more refined reporting methods that could spare the wholesale panic?
Short summary: A few scant months of treating COVID-19 patients couldn’t possibly enable someone to identify with certainty for each and every case at the time of their diagnosis itself one outcome among an increasing menu of options:
asymptomatic, i.e., ‘true’ asymptomatic
symptomatic and eventually cured
symptomatic and chronic
symptomatic and severe, maybe cured, maybe not
symptomatic and sure to die from the disease
The present reality is that risk for one is risk for all simply because anyone could catch and pass on the novel highly contagious SARS-CoV-2 virus that no existing therapies can quickly control or kill within an infected person. Doctors are simply learning to treat COVID-19 on the fly.
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Questions about COVID-19 risks and odds boil down to,
What are the risks for a case?
What are the odds of severe acute disease/death?
An increasingly tangible third possibility is of lingering chronic malaise lasting days, weeks and even months in many cases around the world.
Risk isn’t one and done when it comes to a novel highly contagious virus like SARS-CoV-2
Asymptomatics don’t live all by themselves on an island without contact with anyone else, which is why they caught the virus in the first place. Risk isn’t theirs alone because one who catches the virus could also spread it to others.
A COVID-19 case may indeed stay asymptomatic and overcome the infection without suffering short- or long-term harm but could they guarantee
That no one else catches the virus from them?
That if anyone does catch it from them, they too likewise remain asymptomatic and free of harm?
Rhetorical questions because the obvious answer is no.
Some who get infected with this virus are observed to remain asymptomatic, especially if they’re <20 years of age. Lucky they but they could spread it to someone not so lucky who could end up on a ventilator or worse, dead. After all, SARS-CoV-2 viral loads can be similar in both clinical and subclinical (‘asymptomatic’) cases, and both seem equally contagious.
Symptom-free survival or death aren’t the only two options from COVID-19
Long-term debility is another possibility. A clinical spectrum is emerging among symptomatics who don’t die.
ARDS. An atypical one mind you, not the one most clinicians are used to seeing day in, day out in emergency rooms.
Blood clots anywhere in the body that could cause anything from stroke to other types of potentially lasting organ damage. For example, someone with blood clots leading to kidney damage might need dialysis for life.
Symptoms either due to the virus itself or from strong inflammatory responses to it could masquerade as potential heart attack. Either way, high likelihood of chronic heart disease.
A suite of chronic symptoms ranging from sporadic diarrhea to brain fog to breathlessness, lasting >60 days at this point in several.
Novel virus means doctors are learning to treat COVID-19 on the fly
Refined reporting methods are an unreasonable ask at this early stage when
Doctors treating COVID-19 are dealing with a new disease and the unexpected complications in its wake.
No specific therapies nor vaccines exist at present to help quickly reduce viral loads.
Asymptomatic is an inherently problematic designation since only some are ‘true’ asymptomatics who never show symptoms at all while others are pre-symptomatics diagnosed before they showed overt symptoms.
Doctors couldn’t possibly know the clinical consequences of a symptomatic COVID-19 case 1, 2 or 10 years later.
Such unpredictability is quite simply the current reality.
High contagiousness and novelty of SARS-CoV-2 virus make estimating individual odds of severe outcomes a red herring
What are the odds a generic person who caught SARS-CoV-2 would suffer severe outcomes? That’s a red herring of a question replete with pitfalls because sizing up such odds is cold comfort. Too little is presently known about SARS-COV-2 the virus and COVID-19 the disease for speculations about risks and odds to be anything but a case of playing Russian roulette.
A sizable proportion in each decade of life over the age of 20 seems likely to suffer severe outcomes.
Risks of severe outcomes increase with age and with one or more conditions associated with metabolic syndrome: diabetes, obesity, chronic heart/lung/kidney disease, as well as in those with compromised immune function such as cancer patients.
Problem is right now, a good quarter to a third or even more of the world’s population has 1 or more underlying conditions that predispose them to higher risk of severe COVID-19 disease. That’s a huge number, too huge for any healthcare system to handle at one time if the virus is allowed to run through a population like wildfire, as parts of Brazil and the US are painfully discovering to their own cost.
Older age itself seems to be an independent risk factor for severe disease in that even healthy-looking elderly are at heightened risk of severe outcomes. Plenty of elderly all over the world so another huge number of people that need to stay away from this virus but good.
Old age and aspects of metabolic syndrome explain only some, not all the risk for severe COVID-19 disease. That’s just more of the Russian roulette theme in that present knowledge of COVID-19 doesn’t offer an easy guarantee of benign outcome for anyone who catches this virus. That the vast majority of those infected and <20 years of age seem to have a benign outcome is a silver lining at best.
Coming full circle, a happy-go-lucky teenager who catches this virus might well stay asymptomatic and get rid of it with neither short- nor long-term consequences. Could the same be assured about their much older teachers, parents, grandparents, other relatives, acquaintances and employers, any one of whom might catch it from them and any one of whom might have one or more of those chronic conditions that predispose to severe COVID-19 disease and even death? Could such a teenager even assure one of their peers that they wouldn’t develop severe disease? No, they can’t simply because no one, not even doctors who are treating COVID-19, could do so.
Letting unintended consequences run rampant across the COVID-19 pandemic has made it incalculably worse. Hand in glove is a deadly collective amnesia about infection and contagion that expresses itself as either complacence or contempt towards microbes. Well, this virus obviously capitalizes on such human foibles by instead showing us who’s in charge, it not us. No wonder there are 18+ million global cases 7+months into this pandemic.
Present collective actionable knowledge of COVID-19 being little, it’s not only safer but also prudent to assume that the risk for severe disease/death from it is there for any one among us. Only by each of us taking all possible precautions against getting infected could its chains of transmission break and virus spread become low enough that public health measures to test, contact trace, isolate and follow up each and every case become realistic. Only then could societies get back to something of a semblance of normalcy. Like it or not, this means a collective effort because if one fails, one alone doesn’t pay the price but instead someone else or even many others may suffer and/or die.
Both and not much in that order; super-spreading events stem from a combination of highly contagious individuals, i.e, super-spreaders, and risky actions/places, and analyses of infectious disease outbreaks show that to differentiate between the two is an impractical version of a distinction without a difference.
Super-spreader isn’t an identity etched in stone. Rather, it’s often situational and contingent on factors beyond an individual’s physiology. Risk depends on what it takes for a given pathogen to spread.
The anatomy of super-spreading events also reveals that infectious agents spread among us not of their own relentless volition alone. That’s but one part of it. The other consists of our own witting or unwitting participation. Not just a careless cough or witless touch of a dirty hand to the mouth, how we live, travel and work as well as our customs, traditions and rituals are part of an extensive human-made labyrinth that can help pathogens survive, spread and even thrive.
Super-spreaders of a particular infectious disease don’t walk around with that identity tattooed to their foreheads. Indeed they couldn’t because this capacity of theirs is only identified retrospectively in the wake of a super-spreading event that brings them to attention in the first place. Between a super-spreader and a super-spreading event lies hidden opportunity exploited by the pathogen, opportunity that the pathogen reveals in the process of exploiting it.
Super-spreading is thus a case study in opportunism where pathogens exploit readily available loopholes offered by a combination of individual physiology and behavior as well as by built environments and how we live and move about within them. More than anything, novel pathogens exploit and thereby expose those pre-existing fault lines in societies that happen to be conducive to their own spread. Illustrative examples from the COVID-19 pandemic and a historical example of Vibrio cholerae, a bacterium, reveal this in no uncertain terms.
Indoor density & ventilation matter for SARS-CoV-2 super-spreading
I. Super-spreading associated with a bus
January 19, 2020. 126 people took a 100-minute round-trip bus trip (50 minutes each way) to attend a 150-minute temple event in Ningbo, Zhejiang province, China.
A total of 293 attended the event. Bus #1 carried 59 and bus #2 67 people while 172 others also attended the event. Both buses had air conditioning systems operating on re-circulating mode with vents below the windows, two openable windows on each side and no attached toilet. Passengers occupied the same seats to and fro. The event began at 10 AM, ended at 12:30 PM and included a lunch with 10 attendees at each round table.
Bus #2 had a 64 year old infected person (index patient or infector), the only one exposed to residents from Wuhan and the first to develop symptoms. Reportedly asymptomatic during the bus trip. Developed cough, chills and myalgias the following day. Her husband and daughter developed fever and cough on January 22. All 3 were SARS-CoV-2 confirmed on January 28.
Bus #1: 0/59 infected.
Bus #2: 24/67 infected (includes index patient). 23 secondary cases from bus #2 (34.3% attack rate).
Other attendees: 7/172 (4.1% attack rate), all in close contact with the index patient, i.e., 7 secondary cases among those at the event but not on the buses.
Total of 30 secondary cases.
Further contact tracing identified 22 tertiary cases who caught the infection from the secondary cases (below from 1).
Note the stark difference in attack rates between the bus (23/67; 34.3%) and event (7/172; 4.1.%). Same super-spreader yet many more people got infected from exposure on bus #2 than from attending the event.
II. Super-spreading associated with a meatpacking plant. Distance from index case found to matter.
Meatpacking plants. Need to be kept cold. Huge fans re-circulate air so they’re noisy workplaces. Need to talk loudly to communicate with co-workers. Physically demanding production lines so heavy breathing is part of work life. Most workers also share living quarters and commute together to and from workplaces in vans provided by employers. In short, ready-made conditions for spread of a novel highly contagious, respiratory virus.
In a COVID-19 outbreak in Germany’s largest meat processing plant (below from 2, a pre-print),
“ The shift comprises 147 individuals, most of whom work at fixed positions in a conveyor-belt processing line. The processing line occupies an elongated area approximately 32 meters (m) long and 8.5 m wide (see floor plan in supplementary Fig. S1A). Quarters of beef enter at one end of the line (referred to as proximal in the following) and are processed as they move in longitudinal direction across the room, until cuts are finally packaged near the far end of the line (referred to as distal in the following). Eight air conditioning units placed near the ceiling in the proximal half of the room constantly cool the air. Fans project the air in a lateral direction, either directly from frontal openings in the unit or via perforated hoses mounted underneath the ceiling (see schematics in Figs. S1A-C), effectively sectioning the room into zones in which air is perpetually recirculated.”
The authors conclude (below from 2, emphasis mine),
“ Our findings indicate that a physical distance of 2 meters does not suffice to prevent transmission in environmental conditions such as those studied here; additional measures such as improved ventilation and airflow, installation of filtering devices or use of high-quality face masks are required to reduce the infection risk in these environments.”
III. Super-spreading associated with an indoor wedding.
March 13, 2020. 2-hour indoor wedding in Jordan with ~360 attendees. Total of 85 cases by 4 weeks after the wedding (3).
Presumed index case: The bride’s 58 year old father. Arrived in Jordan 4 days before the wedding from Spain, after the latter’s community transmission was well under way. Developed fever, cough and runny nose 2 days before the wedding and tested SARS-CoV-2 positive on March 15.
Tertiary cases: 9, all household contacts of wedding attendees.
“In Jordan, close physical contact, such as same-sex hugging, cheek-kissing, and hand shaking, are traditional wedding practices that convey congratulations to the host families. Also, immediate family members, especially parents of the bride and the groom, usually stand at the entrance of the wedding hall to receive congratulations from all guests. These factors, in addition to crowded dancing and close face-to-face communication, likely contributed to the large number of infections from this wedding.”
Even with a super-spreader or two in their midst, would so many have become infected in each of these 3 super-spreading events if not for built environments and/or social conditions that were conducive to spread of a novel highly contagious, respiratory virus? Clearly not and this is what large-scale analyses of COVID-19 disease clusters and outbreaks in Japan and China also show.
IV. 11 clusters with 110 total cases in Japan as of February 26, 2020, all entailed (below from 4, a preprint, emphasis mine),
“close contact in indoor environments, including fitness gyms, a restaurant boat on a river, hospitals, and a snow festival where there were eating spaces in tents with minimal ventilation rate”
V. Similarly, an analysis of COVID-19 clusters in China involving >/=3 cases between January 4 and February 11, 2020 found (5, a preprint, emphasis mine),
“Three hundred and eighteen outbreaks with three or more cases were identified, involving 1245 confirmed cases in 120 prefectural cities. We divided the venues in which the outbreaks occurred into six categories: homes, transport, food, entertainment, shopping, and miscellaneous. Among the identified outbreaks, 53·8% involved three cases, 26·4% involved four cases, and only 1·6% involved ten or more cases. Home outbreaks were the dominant category (254 of 318 outbreaks; 79·9%), followed by transport (108; 34·0%; note that many outbreaks involved more than one venue category). Most home outbreaks involved three to five cases. We identified only a single outbreak in an outdoor environment, which involved two cases.”
Cholera? Make sure drinking water and sewage never, ever mix.
mapped cholera cases using a then-revolutionary dot map to implicate a hand pump on Broad Street as the likely source of infection. Today Snow’s investigation is rightly considered a landmark in medical research and indeed even denotes the birth of modern epidemiology (below from 6).
How did this hand pump come to be contaminated with cholera in the first place?
The most likely source appears to have been soiled diapers (nappies) of a sickly five month old baby, a child of the Lewis family who lived in the back parlor of 40 Broad Street which had 11 rooms occupied by renters across its multiple floors.
The baby had diarrhea in June and recovered but by August 1854 the foul-smelling, watery, greenish stools were back. The baby’s mother, Sarah Lewis, would soak the baby’s soiled nappies in buckets of cold water and dump this soiled water out into the cesspool in front of the house (7, 8, 9).
Until this time the Broad Street pump had been considered a reliable source of clean well water. That changed with Snow’s graph implicating this particular pump as the likely source of the local cholera outbreak. An excavation carried out on behalf of the Cholera Inquiry Committee in the parish indeed found decayed brickwork between pump well and drain, the same drain into which the water from the soiled diapers had been emptied (below from 9).
Back then, microbes hadn’t yet been discovered, V. cholerae hadn’t yet been identified as the causative agent for cholera and all sorts of theories about miasmas and humors were used to explain causes of all sorts of diseases, not just cholera. No surprise then that the Committee vacillated instead of wholeheartedly accepting Snow’s assertion that contaminated drinking water was the source of infection.
Scientific research has subsequently shown us better. We now understand fecal-oral transmission of infectious diseases such as cholera and the importance of carefully segregating drinking water from sewage. Such understanding didn’t yet exist back in 1854 (below from 9, emphasis mine).
“Why was the cesspool at 40 Broad Street not maintained? Such neglect was increasingly common in London, due in part to economic circumstances. At the time of the Broad Street pump outbreak, London had about two hundred thousand cesspools. For many years, the contents of the cesspools were sold as agricultural manure to be used as fertilizer in the many farms that surrounded London. The money earned from manure sales would then be used to maintain the cesspools. Yet during the nineteenth century as London’s population grew ever more rapidly, farms were forced to move further from the central city.
Transportation costs increased, adding to the expense of acquiring cesspool-based manure. Starting in 1847, another change took place that undercut the sale of cesspool manure. Solidified bird droppings (or guano) were brought in as fertilizer from South America at a price far below cesspool manure.
With no economic incentive to sell their feces, poor people would empty human wastes into the streets, or directly into the London waterways. Most lacked public health understanding of how disease was spread, as did many medical and health officials of the times. In the absence of manure sales, cesspools became expensive to clean. As a result, they were poorly maintained and infrequently emptied. Over time this neglect lead to cracks and crevices, which offered opportunities for the spread of enteric pathogens. Such spread of Vibrio cholerae probably occurred at 40 Broad Street.”
Note how economic considerations and even globalization (South American guano as fertilizer) played decisive roles in basic matters of public health and hygiene already back in the Victorian era.
The COVID-19 pandemic reveals similar considerations at play in outbreaks linked to essential services and workers in different industrial workplaces such as meatpacking plants and warehouses, to mention just a couple. As the French saying goes, ‘plus ca change, plus c’est la meme chose’. Newer ways of living and doing offer pathogens newer ways of super-spreading, as we’re speedily finding out all too well to our own dismay and detriment.
6. Gilbert, Edmund William. “Pioneer maps of health and disease in England.” The Geographical Journal 124.2 (1958): 172-183.
7. Vinten-Johansen, Peter, et al. Cholera, chloroform, and the science of medicine: a life of John Snow. Medicine, 2003.
8. Johnson, Steven. The ghost map: The story of London’s most terrifying epidemic–and how it changed science, cities, and the modern world. Penguin, 2006.
Viral loads over the course of infection and dose(s) at time of exposure are entirely different matters. Reported viral loads in asymptomatic, mild, severe and critical COVID-19 cases are the outcome of complex interactions between the virus and an individual’s unique immune responses after it has replicated in their bodies. Infectious dose as in infectious virus exposure dose is what the body initially encounters.
However, connecting the beginning (infectious virus exposure dose) directly to the end (COVID-19 disease severity) is far from straightforward because the host versus pathogen is an intricate, multi-step dance. At best, this pandemic is a natural experiment that suggests exposure dose could play a role since now-known predispositions explain only some and not all the risk for COVID-19 disease severity/death.
In brief,
How infectious virus exposure dose could explain some of COVID-19’s disease severity/death in theory.
Though readily inferable, why linking infectious virus exposure dose to disease severity is difficult.
How cumulative exposures may play a role in COVID-19’s disease severity/death.
How infectious virus exposure dose could explain some of COVID-19’s disease severity/death in theory
SARS-CoV-2 is a novel virus so higher its infectious exposure dose, greater the likelihood of severe disease for the following reasons,
Novel virus means no pre-existing immunity, which In turn means an infected person wouldn’t have ready-made antibodies to immediately and specifically neutralize virus particles. Initial antibodies (IgM) would take at least a week to emerge and the really effective ones (IgG) at least 3 weeks. Virus-specific T cells would also take 2 to 3 weeks to really get going.
Novel virus also means no vaccines nor targeted therapies so no recall immunity nor other ways to quickly reduce viral loads.
Higher the exposure dose(s),
Greater the burden on a nascent virus-specific immune response.
More likely that virus-specific immune responses could fail to catch up to a presumably rapidly replicating virus, let alone outpace it.
Thus, higher exposure dose(s) could in theory overwhelm and outrun the inherent capacity of virus-specific T and B lymphocytes to replicate to keep up.
Though readily inferable, why linking infectious virus exposure dose to disease severity is difficult
Can’t deliberately infect human volunteers with different doses of a potentially lethal virus – obviously a strict ethical no-no.
Infecting laboratory animals with different doses of pathogens is a highly flawed approach as well since the data from such experiments have little or no relevance to human infections with any pathogen, SARS-CoV-2 being case in point.
On the one hand, >65 years of age and certain comorbidities such as diabetes, obesity, hypertension, chronic heart or lung disease denote increased risk of COVID-19 disease severity or death. Thus, complex genetic and epigenetic predispositions link who caught SARS-CoV-2 to COVID-19 outcome.
On the other hand, plenty of younger people without such conditions have also experienced COVID-19 severity/death.
Differences in infectious virus exposure dose could explain some of COVID-19’s severe outcomes that age and/or comorbidities can’t.
Moreover, experimental animal models explain little about human infections because infectious dose itself isn’t one and done in the natural course of events. Rather, the COVID-19 pandemic handily reveals that cumulative exposure is likely quite common.
How cumulative exposures may play a role in COVID-19’s disease severity/death
Poorly ventilated built environments and density indoors seem to present the highest risk of catching SARS-CoV-2 which could in and of itself explain some of the observed gradation in infection risk,
At highest risk are those who treat and tend to the infected: doctors, nurses and other healthcare staff.
Next at risk are those who come across a lot of people indoors in their daily line of work or housing: home health, assisted living and other such types of care staff and a roster of different kinds of essential workers: in essential services along the length and breadth of the food supply chain including warehouses and groceries, those in other essential supply chains, essential government workers and those living in dorm housing, prisons, group homes, care homes, etc.
Least at risk are those who can work from home: only need venture out of their household cocoons on occasional forays for food and other essentials.
Poorly ventilated built environments also create the opportunity for multiple, sequential exposures in quick succession – cumulative exposures. Examples of greater likelihood of cumulative exposures, especially in the community transmission setting include
Hospitals where healthcare staff physically treat multiple COVID-19 patients every single day for days on end and where access to PPE is inadequate.
Hospitals where cleaning staff have to clean up all sorts of hazardous waste including bodily fluids of COVID-19 patients every single day for days and even weeks on end and where access to PPE is inadequate, especially for these workers on the lowest rungs of the totem pole.
Exposure doses of respiratory therapists who get sprayed with huge boluses of virus every time they intubate an extremely sick COVID-19 patient, especially so when access to PPE is inadequate.
HVAC specialists who set up hospital negative pressure rooms used to isolate COVID-19 patients. They could get exposed to prodigious amounts of virus when those systems have to be repaired, which may be more frequent when such rooms are used constantly.
Mass transit staff who spend their working hours in poorly ventilated, enclosed, built environments such as subways, airports, trains, buses, and train and bus stations that could easily concentrate and spread the infectious output of any number of infected commuters. If reliable access to PPE is largely optional or non-existent, it would increase the risk.
People in poorly ventilated commercial spaces such as office buildings, airports, malls, hotels, restaurants, stores, warehouses, factories and conference centers where again the built environment could facilitate the accumulation of infectious output of many infected individuals.
Attendees and staff at social events in poorly ventilated, enclosed, built environments such as gyms, bars, restaurants, chapels, conference centers and homes where prolonged social contact in close confines would be the norm and where one or more of the attendees might be infectious and unwittingly shedding abundant loads of virus. Reliable PPE access could be minimal to non-existent.
The likelihood that someone working or living in these environments would get just one infectious dose of the virus from infected people around them is slim to none. Instead, aren’t they more likely to get cumulative doses of infectious virus, i.e., cumulative exposures?
The immune system can cope when this happens with a pathogen the body’s seen before because memory T and B cells are inherently wired differently so they expand in a short enough time to keep up with the virus. However, even immunological memory wanes over time. Much would depend on how recently memory cells were created or re-stimulated; more recent the previous encounter, higher the current numbers of memory lymphocytes ready to expand again.
Even if pre-existing immunity to other, related viruses (cross-reactivity) plays a role in SARS-CoV-2 control, which some data now suggests it indeed could, it too could likely falter when faced with cumulative exposures because numbers of cross-reactive memory T and B cell clones would depend on when a person was last exposed to the cross-reaction-inducing entity. Waning cross-reactive memory may also explain some of the increased risk of COVID-19 severity with age.
Most exposed children are reported as largely asymptomatic or with mild infection. Two mutually exclusive elements could explain this.
More recent cross-reactive memory because of more recent coronavirus or other infections so higher numbers of memory lymphocytes that could quickly engage and subdue SARS-CoV-2.
Less likelihood of unavoidable cumulative exposures since children aren’t locked in a Dickension world of dingy, dank and cramped workhouses getting worked to the bone, at least not in much of today’s world.
Cumulative exposures could thus plausibly overwhelm an immune system responding to a novel virus, even among the healthy, as we can surmise from the record numbers of healthcare staff falling sick and even dying, the tragic death of Li Wenliang being case in point.
Virus dose per exposure, frequency of exposure and mode of transmission matter most in estimating infection risk.
Prevailing evidence suggests that SARS-CoV-2 largely spreads through air.
Plenty of evidence now that indoor crowding and poor ventilation facilitate its spread.
Different individuals have different degrees of risk due to different exposure dose and frequency.
Minimizing virus exposure dose and frequency is a realistic goal. Eliminating exposure altogether isn’t.
A few case reports and anecdotal data suggest a variety of face masks may be protective against SARS-CoV-2. This implies they could minimize exposure risk.
These features and assumptions together suggest that even face masks other than N95 could be potentially life-saving.
Protection measures should flow from what’s known about how SARS-CoV-2 spreads. Virus dose per exposure, frequency of exposure and mode of transmission matter most in figuring out how best to protect against any virus.
Prevailing evidence suggests that SARS-CoV-2 largely spreads through air. Since it’s a respiratory virus, it largely spreads from the nose/throat of an infected person. Respiratory projectiles come out as either larger droplets or smaller aerosols. Key distinction between the two is size and length of time they can stay airborne (below from 1, 2, 3).
Plenty of evidence now that indoor crowding and poor ventilation facilitate SARS-CoV-2 spread. Whether SARS-CoV-2-laden respiratory projectiles stay afloat for hours as aerosols is still being hotly debated. Regardless, examples such as the Skagit county choir (4) or the Guangzhou restaurant (5) suggest iteasily spreads indoors in poorly ventilated spaces,
From coughing, sneezing or panting (that violently emit respiratory projectiles) to talking or even just breathing (that non-violently emit respiratory projectiles) indoors at close quarters,
From singing indoors at close quarters in choirs (4).
From talking loudly, shouting or yelling indoors at close quarters in noisy workplaces such as meatpacking plants (26, 27, 28, 29, 30) or in vacation spots such as cruise liners (31) or at social events in bars and night clubs (32, 33).
It seems to spread less from contaminated surfaces though even there face masks protect from hand-to-face transmission.
These features specific to COVID-19 outbreaks suggest that a face mask that fully and tightly covers the mouth and nostrils could greatly minimize exposure if an infected person in one’s vicinity is spewing virus. Universal masking would minimize exposure risk across the board by reducing viral output into the environment from the infected as well as reducing exposure in the uninfected.
Different individuals have different degrees of risk due to different exposure dose and frequency.
Risk of exposure to the SARS-CoV-2 virus obviously runs the gamut if we consider just two extremes,
On the one hand, an ER doctor in a COVID-19 hotspot who sees a steady stream of seriously ill patients every day for weeks on end.
On the other hand, someone working from home who only occasionally ventures out for groceries and other essentials.
Obviously risk of virus exposure is going to be wildly different between these two extremes. What an ER doctor needs to do to protect themselves from SARS-CoV-2 doesn’t apply to someone working from home.
Minimizing virus exposure dose and frequency is a realistic goal. Eliminating exposure altogether isn’t.
Goal isn’t and shouldn’t be to stop each and every virus particle. That’s practically impossible even with an N95 mask.
Instead goal should be to minimize virus exposure dose and frequency each and every time out of the house, especially in a place with rampant community transmission which implies anyone could be infected with no way to know for sure hence all the more reason to assume anyone outside one’s own household bubble could be infected. Such self-preserving caution becomes all the more necessary because evidence suggests
COVID-19 asymptomatics and presymptomatics are likely just as infectious as the symptomatic.
SARS-CoV-2 viral loads peak in the days before symptoms begin and some evidence suggests even merely talking might suffice to spread infectious droplets.
While certain comorbidities such as diabetes, obesity, chronic heart or lung disease represent greater risk of COVID-19 severity or death, they don’t explain all severity or death. Indeed there are reports of those without presently known pre-existing conditions who have had severe disease or died.
This in turn implies that face masks other than N95 might not just be better than nothing, they could even be life-saving.
All sorts of face masks have been found to be pretty good at blocking droplets and that’s nothing to sneeze at since blocking droplets could help block the bulk of viral load someone might be spewing at close quarters. In fact, studies show that even not particularly well-fitting homemade masks are quite effective in blocking droplets (larger size) and even some aerosols (smaller size) (34).
A few case reports and anecdotal data suggest a variety of face masks may be protective against SARS-CoV-2.
Universal surgical mask wearing was associated with zero nosocomial (hospital) transmission of SARS-CoV-2 in a Hong Kong hospital (35).
In February 2020, all 41 healthcare workers in Singapore who were exposed to a patient with severe pneumonia before COVID-19 diagnosis were protected against infection. 85% wore a surgical mask while the rest wore N95 masks (below from 36),
“On the basis of contact tracing, 41 health care workers were identified as having exposure to aerosol-generating procedures for at least 10 minutes at a distance of less than 2 meters from the patient. The aerosol-generating procedures included endotracheal intubation, extubation, noninvasive ventilation, and exposure to aerosols in an open circuit (4). All 41 health care workers were placed under home isolation for 2 weeks, with daily monitoring for cough, dyspnea, and myalgia and twice-daily temperature measurements. In addition, they had nasopharyngeal swabs scheduled on the first day of home isolation, which could have been day 1, 2, 4, or 5 after last exposure to patient, and a second swab scheduled on day 14 after their last exposure. The swabs were tested for SARS–CoV-2 by using a PCR assay. None of the exposed health care workers developed symptoms, and all PCR tests were negative (Table).”
A man flying from China to Toronto was later diagnosed COVID-19+. He wore a face mask throughout the flight and the 25 seated closest to him or who attended to him all tested negative and no one from that flight was reported COVID-19+ (37).
A county-level analysis of US COVID-19 cases between March 31 and May 22, 2020, found greater declines in daily case growth rates in states that mandated face mask use in public compared to those that didn’t (38).
After Jena became the only city in Germany to make face masks compulsory in public in April, 2020, it became the only one to report no new infections for several days in a row (39, 40, 41).
Two Missouri hair stylists worked with 139 clients while symptomatic, one for 8 days (May 12 – 20, 2020) and the other for 5 days (May 15 – 20, 2020). That entire period one wore a double-layered cotton face covering and the other the same or a surgical mask. The clients wore face masks as well (104 were interviewed; cloth face coverings, n=49; surgical masks, n=48; N95, n=5; don’t know, n=2; not all clients were surveyed) and 67 of them were tested. All tested negative and no symptomatic secondary cases have been reported from this exposure thus far (42). These results emphasize the protective importance of face coverings considering close contacts of one of the stylists later developed symptoms and tested SARS-CoV-2+ (below from 42),
“Six close contacts of stylists A and B outside of salon A were identified: four of stylist A and two of stylist B. All four of stylist A’s contacts later developed symptoms and had positive PCR test results for SARS-CoV-2. These contacts were stylist A’s cohabitating husband and her daughter, son-in-law, and their roommate, all of whom lived together in another household. None of stylist B’s contacts became symptomatic.”
Bibliography
1. Pasnick, Susan, et al. “SARS-CoV-2 Transmission and the Risk of Aerosol Generating Procedures.” American Journal of Respiratory and Critical Care Medicine ja (2020). https://www.atsjournals.org/doi/pdf/10.1164/rccm.2020C11
38. Lyu, Wei, and George L. Wehby. “Community Use Of Face Masks And COVID-19: Evidence From A Natural Experiment Of State Mandates In The US: Study examines impact on COVID-19 growth rates associated with state government mandates requiring face mask use in public.” Health Affairs (2020): 10-1377. https://www.wichita.gov/COVID19Docs/Community%20Use%20of%20Face%20Masks%206.16.2020.pdf
39. Mitze, Timo, et al. “Face Masks Considerably Reduce COVID-19 Cases in Germany: A Synthetic Control Method Approach.” (2020). http://ftp.iza.org/dp13319.pdf
We’re getting a crash course on exposure, prevalence, incidence, herd immunity. In the context of a raging viral pandemic, these terms are conceptually tied together around the notion of surveillance.
Survey the population at large to assess numbers of those infected or recovered by looking for viral RNA using RT-PCR or for antibodies against viral antigens, respectively, to understand where SARS-CoV-2 is spreading/has spread at a given point in time.
While epidemiologists, public health officials and governments debate how best to go about surveying individuals in order to estimate these numbers, surveying the environment is easier and far cheaper to boot.
One readily applicable approach is to monitor sewer systems and untreated/raw wastewater. Key to its success depends on viral loads in feces, appropriate positive and negative controls and on detection limits of methods used.
Such an approach is possible because SARS-CoV-2 is a respiratory virus that’s nevertheless shed in feces. Not just by the symptomatic (1, 2, 3, 4, 5) but also by the asymptomatic/pre-symptomatic (6, 7) and even the convalescent (8, 9, 10, 11). Such qualities make wastewater SARS-CoV-2 surveillance not only useful but also more advantageous compared to individual clinical sampling. Whether virus in feces is infectious, which some papers have indeed shown it to be (12, 13), is a separate matter not relevant to wastewater surveillance.
Advantages of wastewater SARS-CoV-2 surveillance
Cheaper, requires less effort compared to clinical surveillance.
Where one wastewater sample could provide a snapshot for an entire population represented within a given catchment area, clinical surveillance of the same population requires sample collection and testing of multiples more, consuming much more time, effort, labor and resources.
Virus or viral material in feces make sewage monitoring a cheap, handy and sensitive method to assess local environmental viral loads across both time and space.
Clinical surveillance is plagued with selection bias and test kit shortages.
Who to test has become an especially fraught issue in COVID-19 where asymptomatics and pre-symptomatics are now known to play a major role in infection spread.
Wastewater sampling on the other hand represents viral output from everyone within a given catchment area regardless of presence/absence of overt clinical symptoms.
Where clinical surveillance is scattershot and often snags only the tip of the iceberg, untreated wastewater surveillance is better at snagging estimates of the total viral output from everyone within the community it serves, the asymptomatic, the presymptomatic as well as the fully symptomatic.
The COVID-19 pandemic is a dreadful coming together of too many susceptible people with no herd immunity and an extremely contagious novel virus.
Once community spread gets established, too many test kits are needed to sample everyone repeatedly. That makes it too expensive and labor intensive to conduct blanket door-to-door tracing to actively monitor virus circulation across entire populations into the foreseeable future.
However, until vaccine/specific antivirals are available to stop SARS-CoV-2 in its tracks, active surveillance of some sort needs to go hand in glove with non-pharmaceutical interventions (NPI) such as physical distancing, masking in public places and shutting down of group activities when a local hotspot emerges somewhere.
This is where wastewater SARS-CoV-2 detection promises to prove its mettle by allowing more efficient surveillance even as it acts as an accurate early warning system of future case increases (14). Ability to predict where cases are likely to rise could allow local governments to more efficiently predict where future lockdowns/shelter-in-place policies may be necessary and to better manage them.
Wastewater sampling circumvents the stigma and social isolation that could be associated with individual diagnosis (15).
Could wastewater SARS-CoV-2 surveillance be sensitive enough to generate meaningful data about community spread?
SARS-CoV-2 fecal viral load can range from 10^4 to 10^6 copies per ml (13, 14) and can be shed for days or even weeks (3, 5, 7, 8, 9, 10, 11). One analysis concludes (14),
“Detection limits of the analysis method for SARS-CoV-2 in wastewater using the RT-qPCR assay is roughly estimated as 2 copies/mL in wastewater, assuming 100-times of a concentration factor through pretreatment and RNA extraction. It is equivalent to 6.0X10^10 copies per 100 000 population in the sewer catchment area when the daily wastewater quantity is 300 L/person/day. This means that SARS-CoV-2 is detectable if one in 100 000 persons sheds 10^9 copies/g-feces in 200 g of feces. The presence of a patient with such a high load may be possible because individual viral shedding is highly variable.”
Note though that this analysis doesn’t account for cumulative wastewater viral loads from numerous infected individuals within a given population, which wouldn’t depend on one outlier individual excreting enormous viral loads in their feces. Given the sensitivity of the viral RT-PCR test, wastewater surveillance is thus more and not less likely capable of snagging broad outlines of community outbreaks.
Wastewater SARS-CoV-2 surveillance could thus become a mainstay and serve as a harbinger of future community outbreaks.
Studies on untreated wastewater surveillance of SARS-CoV-2, most still preprints, have found it predicted broader community spread in Japan (16), Spain (17), Australia (18), France (19), Italy (20), the Netherlands (21), and the US (22). Wastewater SARS-CoV-2 preceding community outbreaks implies the following,
That it need not supplant individual clinical diagnosis but rather complement it by forecasting upcoming outbreaks.
That in such instances, local clinical testing likely lags community spread at that point in time. This means
Testing should be rapidly ramped up in those instances to first catch up to and then outpace the virus as it spreads through that community.
Bringing the outbreak under control would require a combination of testing and NPI to break transmission chains within that community.
In any case, absent specific treatments/vaccines, SARS-CoV-2 doesn’t seem to be going away on its own so wastewater surveillance may need to become a cornerstone for managing and mitigating local outbreaks.
Illustrative examples of wastewater SARS-CoV-2 surveillance that accurately predicted community outbreaks
One example each from Japan (16) and Spain (17), both still preprints, show that wastewater SARS-CoV-2 surveillance can be a highly sensitive early warning signal that COVID-19 cases are about to explode in the sampled area in the near future.
While pooling samples might make COVID-19 testing more efficient (1), a one size fits all approach wouldn’t work. To generate useful information, each pooling effort first requires empirically working out critical kinks, some general and others unique. Test here refers to RT-PCR for detecting SARS-CoV-2 viral RNA.
Advantages of pooled or group testing are obvious
Save time, resources and equipment.
Do fewer tests and yet cover a larger proportion of the population.
When a pooled sample is negative, all the individual samples in it are likewise deemed negative. No more need to test them separately. Thus need to run fewer tests to estimate infection prevalence within a given population at a given point in time.
Disadvantages of pooled or group testing
According to statisticians, pooled or group testing isn’t more efficient than individual testing when prevalence is high, say >30% (1, 2). In practical terms, this means lower the prevalence, greater the value of pooled or group testing.
Accounting for reduced test sensitivity could be problematic. Samples from individuals with low viral loads would get diluted even further when pooled with those who are virus negative, increasing the likelihood that the former could be erroneously reported as negative, i.e., false negatives.
For example, if ‘true’ asymptomatics (as opposed to pre-symptomatics) harbored lower viral loads, happenstance pools of asymptomatics and true negatives would reduce the chance of accurately detecting the former.
I. General kinks
Optimal group size: Has to be worked out individually for each available RT-PCR test and diagnostic system designed to detect SARS-CoV-2 virus to ensure pooling/grouping doesn’t compromise test sensitivity.
Sample material choice: Pool what material? Clinical samples or the purified RNA extracted from them?
Most published or preprint SARS-CoV-2 papers have compared pooling of either clinical sample (material from naso or oropharyngeal swabs or their lysates) or the RNA extracted from them to the respective individual sample materials.
An Ethiopian preprint compared both types of sample materials side by side and found it could detect positive SARS-CoV-2 from clinical samples pooled 8:1 (8 negatives pooled with 1 positive) but could detect it at a lower dilution of 10:1 when looking at RNA pools (3), suggesting RNA extract pooling may retain greater test sensitivity, an unsurprising result since clinical samples often contain biological substances such as enzymes that are natural inhibitors of the PCR reaction. However, choosing between clinical samples or their lysates versus RNA extracted from them isn’t easy since both approaches have their advantages and disadvantages.
Given human genetic diversity, some individuals would likely have higher baseline concentrations of such inhibitory substances in their mucosal secretions compared to others. Pooling in such cases could inadvertently expand their inhibitory potential leading to higher risk of false negatives especially when pooling among samples with low viral loads.
On the other hand, pooling purified RNA extracts is more expensive because extracting and purifying RNA from each individual clinical sample is more resource intensive compared to doing the same from one pooled one.
Bottom line, choosing which sample material to pool comes with different price tags and could have outsize consequences on test results. Budgets would play a role. So would the dynamics of the outbreak within a population being considered for pooled/group testing.
For example, while clinical sample pooling might be a safe choice for testing older residents in a skilled nursing facility, assuming both greater prevalence as well as higher individual viral loads, purified RNA extract pooling might be a better choice for testing among younger college students, many of whom might be more likely to be asymptomatic with lower viral loads.
II. Specific kinks
Optimal group size: How many samples to pool? That would vary from place to place as well as from time to time within a given place, being unique to each place based on prevailing local virus prevalence at a given time.
Sample source choice: Pool which samples? Just a bunch collected from random people or should there be some method to the madness? A few different types of pooling or grouping find value in this regard,
Cohort pooling: choosing to pool samples from people in a way that maximizes the ability to detect maximal number of infections using minimal number of tests, that should be the goal of any cohort pooling effort.
A simple rule of thumb would be close physical proximity to one another for protracted periods of time on a regular, e.g., daily, basis. Those living in the same household or working in the same physical space are obvious candidates. It could also be those living in the same apartment block, same floor, etc. The inherent logic to the choice of cohort that lends meaning to pooling or grouping choice in each and every instance should be maximizing infection detection with minimal tests.
Sub-pooling: Consider a hypothetical. Let’s say 15 potentially exposed individuals need to be tested. Instead of pooling samples from all 15 together, how about first pooling 3 each to generate 5 sub-pools and then making one main pool from those 5 sub-pools? This way if the main pool turns up positive, instead of immediately resorting to testing all 15 individually, the next step could be to instead run the 5 sub-pools. If only one of those sub-pools turned up positive next, 12 of the 15 individuals would now automatically be deemed negative with no need to test them further. Sub-pooling in this instance would require a total of 9 tests to identify one positive among 15 instead of a total of 16 if only one master pool were used.
Combinatorial or matrix pooling: Pool samples into groups such that each sample is part of multiple pools (4). Helps mitigate the risk of a false negative result from a low viral load sample when pooled with virus negative samples.
Learn from history: how does the blood supply industry use pooled samples to routinely screen blood and blood products for Hepatitis, HIV, etc.?
Test samples individually or pool them to detect an infectious agent isn’t a new question thrown up by the COVID-19 pandemic. Rather, it’s one that the blood supply system has had to struggle with ever since blood transfusions were found to transmit infectious diseases such as Hepatitis and HIV.
Blood donations and transfusions are now such a routine staple of life that the elaborate screening systems that exist to ensure that blood and blood products remain largely free of transmissible infectious agents remains quite invisible to the public.
Advocacy by famous examples such as Ryan White, a hemophiliac who contracted HIV through a contaminated blood donation, helped usher in the reactionary but preventive system that tries to minimize such risk (5).
However, prohibitive cost means that some of this screening is done on minipools, i.e., samples ranging from a few to many dozens pooled together prior to running the screening test to detect a given infectious agent (6). Sample pooling is also routine in the livestock industry and wildlife surveillance programs for detecting foot-and-mouth and other infections.
These fields have already worked out many of the general kinks such as sample size relationship to test sensitivity that could be directly applied to SARS-CoV-2 surveillance testing.
Illustrative evidence that pooled/group testing could be useful in SARS-CoV-2 detection
A retrospective analysis using presumably clinical sample pooling found 2 positives among 2740 samples in the San Francisco Bay area using pools of 10 (7).
A retrospective analysis using clinical sample pooling of either 5 or 10 detected positives as long as the unpooled sample had a viral load of at least a million RNA copies per swab (8). The study also suggested that targeting two rather than just one SARS-CoV-2 gene in RT-PCR tests might increase the efficiency of detection when pooling samples with low viral loads.
A retrospective analysis using RNA extracts in Homburg, Germany, could detect 23 positives among 1191 using only 267 tests based on pool sizes ranging from 4 to 30 (9).
Ultimately, sample pooling for SARS-CoV-2 needs to prove its utility without compromising test sensitivity and specificity to provide results within the limits of detection of the test in order for its cost-effectiveness to be worth it. It will likely prove its worth in some places but not in others. Infection prevalence at the time of pooled sample testing might turn out to be the wild card in all of this; higher the prevalence, lower its utility.
6. Perkins, Herbert A., and Michael P. Busch. “Transfusion‐associated infections: 50 years of relentless challenges and remarkable progress.” Transfusion 50.10 (2010): 2080-2099.
≈ Comments Off on What do you think of Purina’s new cat food, which the company claims will “neutralize” Fel d1 and essentially render cats hypoallergenic?
Fel d1-neutralizing cat food may be less useful than a cursory glance at the idea would suggest.
The idea: Cat food that tries to neutralize the cat allergen Fel d1 at its source in the cat’s saliva itself
Fel d1-specific IgE antibodies in allergic humans need to bind free Fel d1 to trigger their allergy. Feed cats food that contains chicken antibodies against Fel d1 to bind and neutralize it. Such cats would then deposit not free but rather antibody-bound Fel d1 on their fur and surroundings while grooming themselves. Lower free Fel d1 levels, lower the chance of triggering allergy or at least that’s the idea.
No harm to cats and no provocation of allergy in cat parents. Cats themselves would suffer no deleterious effects from eating such food while Fel d1-allergic humans would be protected from exposure to free Fel d1. Win-win idea in theory.
Cute idea on paper but the reality’s a lot harder to digest. Plenty of crucial unanswered questions for starters,
Does this method of reducing free Fel d1 levels on cats reduce frequency and severity of allergy symptoms in those around them? No evidence for that yet.
What is the minimum amount of free Fel d1 necessary and sufficient to trigger allergy in someone already allergic to it? Not known.
Does this antibody-laden food consistently reduce the amount of free Fel d1 below such levels on and around cats fed this food? Not possible to know at this point.
How much Fel d1-specific antibody should be present in food to consistently reduce its amount below such levels? Not clear.
What’s the point of this dubious intervention if free Fel d1 levels remain above such levels even after cats eat this food, especially considering the fact that Fel d1 levels tend to vary greatly between cats? Some cats are shown to secrete <10µg while others >500µg of Fel d1 per gram of hair. How could one set dose of antibody in cat food possibly reduce Fel d1 levels sufficiently across such a broad concentration range to effectively reduce allergy in those exposed to cats fed this food?
Antibodies are highly complex biological molecules that are also highly sensitive to environmental conditions. How stable could they be inside cat food designed for extended shelf-life?
Would daily long-term intake of such antibody levels be safe for the cat itself? Not known.
How much Fel d1 secreted by a cat is likely to get bound by antibody in cat food? Not much. Definitely not all. And maybe not enough to prevent allergy in those already allergic to Fel d1.
And therein lies another proverbial fly in the ointment.
Fel d1-specific antibody present in cat food would bind what’s locally available in the cat’s saliva as it chews the food.
Simply from the mechanics of food intake, the process of chewing and swallowing would lead to some Fel d1+antibody presumably ending up in the cat’s GI tract in and getting digested.
Some unknown remaining amount of Fel d1+antibody would then presumably end up as Purina hopes on the cat’s coat and surroundings as it grooms itself.
What about the Fel d1 that a cat secretes when it’s not eating food? Wouldn’t it remain free to trigger allergy in those already Fel d1-allergic?
One of the main practical drawbacks of this cute sounding approach is thus that it’s likely to neutralize only some and not all Fel d1 present in a cat’s saliva. It’s also unclear at this point whether such reductions are sufficient to prevent allergy trigger.
Brief what and how of common cat allergy; allergy to Fel d1 found in cat hair and dander
Cats synthesize a variety of molecules that are potential allergens to humans, the most common being the protein Fel d1 secreted in their saliva and then deposited on their coats when they groom themselves. It also makes its way into their surroundings as fomites (environmental contamination), found everywhere that cat hair and dander get deposited. It can also remain air-borne for some time, meaning it’s also easy to inhale.
Antibodies are Y-shaped molecules that do business at both ends. The top Y end or Fab binds antigens while the stem portion or Fc binds Fc receptors expressed by a variety of cell types. Antibodies in humans are either IgM, IgG, IgA or IgE and each of these antibody classes binds different Fc receptors expressed by different cell types. Different Fc receptors on different antibody classes is why antibody binding can have very different outcomes for one class versus another.
Individuals are either allergic or not to cat Fel d1, the crucial difference being that when individuals allergic to Fel d1 come in contact with it, they make IgE antibodies against it. The problem in cat allergy comes not from an IgE antibody binding Fel d1 but from what happens when the Fel d1-bound IgE binds FcE receptors expressed by mast cells. Such binding of an IgE’s Fc to the FcE receptor on the mast cell causes the latter to degranulate.
Mast cells are essentially a bag filled with a variety of poison pills that spew their contents out when triggered to do so by an antigen-bound IgE antibody binding the FcE receptor they express abundantly on their cell surface. Mast cell contents include highly potent molecules such as histamine and prostaglandins, molecules useful when their targets are microbial pathogens or parasites in their immediate vicinity but problematic when their targets are otherwise innocuous and commonly found molecules such as Fel d1, so-called allergens that trigger allergy only in those who are allergic.
Such inappropriate release of mast cell degranulation products produces itching, skin reactions, redness, local swelling or edema and other symptoms typical of an allergic reaction.
Unfortunately, data for how effectively and reproducibly antibody-laden cat food reduces Fel d1 in cat hair and saliva is neither rigorous nor convincing enough
Proof of the pudding is in the eating. Published data suggests that the ability of chicken anti-Fel d1 antibodies in cat food to neutralize Fel d1 is highly variable.
In this study 105 cats were first fed a control diet for 2 weeks and then fed a test diet containing chicken antibody to Fel d1 for 10 weeks. The test diet was formulated to contain 8ppm of polyclonal chicken anti-Fel d1 IgY antibody (IgY is an antibody class found in chickens that is considered to be equivalent to mammalian IgG; polyclonal means different antibodies from different B cells, each binding a different piece of Fel d1).
The authors collected cat hair twice weekly during the 2 week control period and weekly during the 10 week test period. They measured active (that is unbound by antibody) Fel d1 amounts in the hair. They also assessed the cats’ body weights as a surrogate of safety of test diet intake.
Problematic data and premise. The data in this paper reveal a variety of problems, both with the study itself as well as the way the data are presented.
Standard error – Wikipedia is a statistical tool intended to account for natural and expected variations between iterations of the same experiment. To wit,
“the relationship between the standard error of the mean and the standard deviation is such that, for a given sample size, the standard error of the mean equals the standard deviation divided by the square root of the sample size. In other words, the standard error of the mean is a measure of the dispersion of sample means around the population mean.”
Unfortunately abuse of this otherwise quite sensible statistical tool is at epidemic levels in biological sciences and especially in basic biomedical research where it’s now routinely used to artificially minimize within-group variations in order to artificially accentuatebetween-group differences.
This paper is a case in point.
Table 1 lists the mean weekly body weight, food consumption and active Fel d1 over the 10 weeks the cats were on the test diet (below from 1).
Just one experiment was performed so why are they reporting the data as mean +/- SE (standard error of the mean) if not to artificially suppress the data spread within each week over the 10 weeks of the test diet phase, especially for the active Fel d1 levels (column 3 in the table)?
Notice that the SE numbers for active Fel d1 levels (column 3 in Table 1; Figure 1 shows the same data in the form of a bar graph) are in the teens, which implies that the standard deviations (SDs) from which these SEs are derived must themselves be as high as the means themselves. In practical terms this means that rather than steadily declining over the 10 week course of test diet as the authors would like to suggest, active Fel d1 levels actually varied all over the map.
This data spread would have been clearer to see if the authors had chosen to present the data in the form of a scatter plot but then the cat would have been out of the bag, wouldn’t it?
The reason the authors used SE in Table 1 becomes clear from Table 2 where they show the same data but this time as mean +/- SD (below from 1).
The difference is that in Table 2, they have grouped the data into quartiles based on their mean baseline Fel d1 levels. Turns out Fel d1 levels vary enormously between cats. In fact, the data in Table 2 shows an almost 8-fold difference in baseline Fel d1 levels between cats with the lowest (column 1) versus those with the highest (column 4) levels.
Such tremendous variation in baseline Fel d1 levels between cats introduces another important practical obstacle for this approach. How could a single antibody amount, namely 8ppm, be justified as adequate for neutralizing Fel d1 across such an enormous range? How did the authors even hit upon this number, 8ppm, in the first place? That’s not clear.
It’s also difficult if not impossible to reconcile the implied steady drop in active Fel d1 levels with the fact that the authors also steadily decreased the amount of food offered over the 10 week course of the test diet. How could active Fel d1 levels drop in that manner when steadily lower amounts of antibody were available to bind them over that period?
Study 2
So much for study 1. Let’s look now at study 2, also published in 2019 (2).
Two trials were performed. In one, 6 adult domestic shorthair cats were first fed a control diet for 2 weeks and then the antibody-containing test diet for 6 weeks. Saliva was collected throughout to measure levels of free Fel d1. In the other trial, adult domestic shorthair cats were first fed a control diet for 1 week and then 20 continued to get this diet for another 4 weeks while a test group of 11 got the antibody-containing test diet.
Salivary Fel d1 levels in these groups of cats ranged from 2 to 20µg/ml. While the data is shown as means +/- SD, the variations are high enough to make it difficult to interpret what they even mean (below from 2). Further, actual Fel d1 changes between cats fed control or test diets seem minimal or non-existent (Figure 2) even though the authors cook the data to suggest otherwise (Figure 1).
TK Talk 