In the past 24 hours, the number of cases of the new coronavirus originating in Wuhan, China, nearly doubled to more than 4,400. Since the outbreak was announced on December 31, the virus has taken the lives of 107 people.
Less than four weeks into the outbreak, fear about how bad this could get is spreading faster than the virus. And with good reason.
While the vast majority of cases and deaths are occurring on mainland China, 2019-nCoV has already made its way to at least a dozen other countries, including the US, Germany, and Canada. People are buying face masks. Markets are on edge. Cities and countries are responding with mass quarantines and travel bans. The whole thing feels a lot like the 2011 pandemic film, Contagion.
Answering this requires knowing the answers to two other questions: How easily does the 2019-nCoV spread from person to person, and how deadly is the virus? At the moment, scientists only have informed guesses, which are likely to solidify in the coming weeks and months. But what we know so far is instructive.
With every disease outbreak, epidemiologists try to figure out how far — and how fast — a virus is likely to spread through a population. To do that, they use the basic reproduction number, called the “R naught,” or R0.
Most simply, the figure refers to how many other people one sick person is likely to infect on average in a group that’s susceptible to the disease (meaning they don’t already have immunity from a vaccine or fighting off the disease before).
The R0 is super important in the context of public health because it foretells how big an outbreak will be. The higher the number, the greater likelihood a lot of people will fall sick.
Measles, the most contagious virus researchers know about, can linger in the air of a room and sicken people up to two hours after an infected person who coughed or sneezed there has left. If people exposed to the virus aren’t vaccinated, measles’ R0 can be as high as 18.
Ebola is much less efficient: Its R0 is typically just 2, since most infected individuals die before they can pass the virus to someone else.
Now, here’s a big caveat: The R0 is not “something that is fixed,” said Marion Koopmans, who studies emerging infectious diseases and heads the department of virology at Erasmus Medical Center in Rotterdam, Netherlands.
Diseases behave differently in different environments, depending on factors like population density and susceptibility to a disease in a population. For example, in the case of norovirus — that nasty and highly contagious bug infamous for causing outbreaks of stomach flu on cruise ships — the R0 estimates vary depending on whether the outbreak is contained in one place (like a hospital) or spread more widely.
Some individuals are also more contagious — and have a higher R0 — than others, because of their viral load, for example, or the airflow in the building where they’re sick. (The folks who are especially contagious are known as “super-spreaders.”)
With these caveats in mind, here’s what we know about the R0 for the new coronavirus. According to a preliminary estimate from WHO, at the moment, each individual infected with 2019-nCoV has transmitted the virus to an average of 1.4 to 2.5 others. That would make 2019-nCoV less contagious than SARS, which had an R0 of 3, but more contagious than seasonal flu.
That’s just the WHO’s word. There are literally dozens of estimates about 2019-nCoV’s R0 floating around, from research groups around the world. And different research groups use different statistical models, assumptions, and data to plug into their models.
In all, I found a broad range of R0 estimates — from 1.4 to 5.47 — being put forward.
If one narrows the estimates to some of the world’s top epidemiological modeling labs — like Maia Majumder’s at Boston Children’s Hospital or Christian Althaus’s at the University of Bern or Jon Read’s at Lancaster — the range looks a lot smaller: 2 to 3.8. That would make the new coronavirus at least as contagious as seasonal flu and potentially more contagious than SARS.
“Given the recent emergence of this disease, the very limited data available, and the very different methods employed for estimation, the consistency of these estimates is remarkable,” Toronto epidemiologist David Fisman told Vox over email.
Still, it’s early days. “It’s difficult or impossible to get an accurate R0 at the beginning of an epidemic,” said Daniel Lucey, an infectious diseases physician and adjunct professor of infectious diseases at Georgetown University Medical Center. We don’t yet know exactly when or how the outbreak began, where it’s spread, or how many people are sick. Only in the coming weeks — as researchers gather more data on how the virus is moving — will they be able to refine the R0.
For now, though, there are a couple of things they can say. “Because it’s above 1, that means we know it can cause sustained transmission in humans,” said Maia Majumder, faculty at Boston Children’s Hospital’s Computational Health Informatics Program. An R0 below one means an outbreak is likely to burn out. But, “Just because the number is high [like SARS’s R0 or the upper end of the current 2019-nCoV estimate] doesn’t mean it’s going to cause a massive pandemic.”
“We do have good examples of high reproductive number diseases like SARS,” Majumder added. “It had no vaccine, no specific care approach, and we still managed to get the situation under control.” That’s because the R0 can’t account for all the interventions public health officials put in place, like infection control measures in hospitals or antivirals.
So even as the R0 evolves in the coming days, and even if it gets higher, that doesn’t necessarily mean the outbreak will grow into a pandemic.
Next to the R0, the other most important way to understand how bad an outbreak could get is the case fatality rate, or CFR. In simple terms, it’s the proportion of deaths a disease causes within a group of people who have the disease.
Here, too, there are problems with arriving at a solid estimate at the moment. To have a firm understanding of the CFR, you need to know how many people in a population have the virus, and among those, how many die.And early on in outbreaks, we don’t often know.
That’s because the sickest are usually the ones who show up at doctor’s offices and in hospitals. But there may be hundreds or thousands of others with the virus who never show symptoms, or never bother going to see a doctor because they’re not very sick. (That’s why the CFR can often look much worse in the early days of an outbreak.)
Getting an accurate CFR would require a survey of the Chinese population, to find out who has antibodies for the virus, said Majumder, including the folks who didn’t even know they had it. That’ll give experts the denominator — the real case toll — in the CFR equation. “Until we’ve done [that] — and I’m sure it’ll happen sometime in the future — there are going to be some people that have mild infections or are asymptomatic infections that we’re not picking up.” Plus, there are many people with the infection in limbo in hospitals, who may or may not survive the pneumonia that comes with it.
So while there’s a great hunger for clarity about how bad the outbreak will become, frustratingly at this stage, researchers need time to work that out.
In the meantime, there’s a tendency for speculation to fill the vacuum. For example, there’s a lot of guesswork about the case fatality rate for the new virus. A bunch of people are taking the number of deaths this disease has caused, and dividing that by the number of cases diagnosed, Majumder said. (As of this writing, that’d be 106 divided by 4,629 — for a CFR of 2 percent, making this virus less deadly than SARS or whooping cough and more deadly than the seasonal flu.)
But again, it’ll be a while before we know the true number of cases and have a clear picture of the deaths.
Here is what we know for sure: While more than 100 people have died in this outbreak so far, and seasonal flu kills between 250,000 and 650,000 people annually. For most people, “you’re probably more likely to be catching flu than you are to be getting coronavirus,” said Devi Sridhar, chair in global public health at the University of Edinburgh.
In a recent study posted to the medRxiv* preprint server, researchers evaluated individuals who had severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections and were diagnosed with diabetes mellitus within six months of the onset of coronavirus disease 2019 (COVID-19) to understand the temporal relationship between SARS-CoV-2 infections and diabetes mellitus.
Recent research indicates a potential increase in the new-onset diabetes mellitus diagnoses after SARS-CoV-2 infections. While the causative mechanisms are not clearly understood, various hypotheses suggest the roles of stress-induced hyperglycemia during SARS-CoV-2 infections, changes in the innate immune system, virus-induced damage or changes to the beta cells or vasculature of the pancreas, as well as the side effects of the treatment in the increased incidence of diabetes mellitus diagnoses.
Furthermore, the drastic lifestyle changes brought about by the COVID-19 pandemic have decreased physical activity and increased obesity. The stress induced by the pandemic has also increased endogenous cortisol levels, a known risk factor for diabetes mellitus. Examining the temporal relationship between SARS-CoV-2 infections and new-onset cases of diabetes mellitus will help develop effective screening and therapeutic strategies.
About the study
In the present study, the team conducted a nationwide analysis using electronic health records aggregated in the National COVID Cohort Collaborative (N3C) database in the United States (U.S.). They analyzed all individuals with SARS-CoV-2 infections and type 2 diabetes mellitus between March 2020 and February 2022. Data from the health records for the six months preceding and following the SARS-CoV-2 infections were included to avoid selection and ascertainment bias.
SARS-CoV-2 infections were confirmed based on the International Classification of Diseases, Tenth Revision (ICD-10) code, or laboratory test results. New-onset diabetes mellitus cases were defined as those that did not have an ICD code for diabetes mellitus in their electronic health records before September 2019. The incidence of diabetes mellitus was then analyzed concerning SARS-CoV-2 infections.
The results reported a sharp increase in new-onset diabetes mellitus diagnoses in the 30 days following SARS-CoV-2 infections, with the incidence of new diagnoses decreasing in the post-acute stage up to approximately a year after the infection. Surprisingly, the number of new-onset diabetes mellitus cases in the months following SARS-CoV-2 infections is lower than in the months preceding the infection.
The authors believe that the increase in healthcare interactions brought about due to the COVID-19 pandemic might explain the notable increase in diabetes mellitus diagnoses in the time surrounding SARS-CoV-2 infections. New patients might have been tested for hemoglobin A1C or glucose levels during their first interaction with the healthcare system, the results of which might have then been used to diagnose diabetes mellitus.
Additionally, SARS-CoV-2 infection-induced physiological stress could have triggered diabetes mellitus in high-risk individuals who might have developed the disease later in life without COVID-19.
According to the authors, the overall risk of developing diabetes mellitus has increased, irrespective of SARS-CoV-2 infections, due to the drastic decrease in physical activity, weight gain, and the stress induced by the COVID-19 pandemic. Furthermore, a longer follow-up period might report an increased incidence in new-onset diabetes mellitus cases, with the SARS-CoV-2 infection precipitating disease development in individuals who might not have otherwise developed diabetes.
To summarize, the researchers conducted a cross-sectional, nationwide analysis of individuals in the U.S. to understand the temporal relationship between diagnoses of new-onset diabetes mellitus and SARS-CoV-2 infections. The results reported a spike in diabetes mellitus diagnoses in the one month following SARS-CoV-2 infections, followed by a marked decrease in the number of diagnoses for up to a year after the infection.
The authors believe that the sudden increase in diabetes diagnoses could be due to increased healthcare interactions brought about by the COVID-19 pandemic. The new-onset diabetes mellitus cases could also be a reaction to the physiological stress induced by SARS-CoV-2 infections.
Furthermore, the drastic lifestyle changes brought about by the COVID-19 pandemic might be responsible for the high incidence of diabetes mellitus, irrespective of SARS-CoV-2 infections. However, extensive research is required to understand the epidemiology and mechanisms connecting SARS-CoV-2 infections with new-onset diabetes mellitus.
medRxiv publishes preliminary scientific reports that are not peer-reviewed and, therefore, should not be regarded as conclusive, guide clinical practice/health-related behavior, or treated as established information.
An infectious diseases physician in Toronto is reporting an increase in the number of older patients he is seeing with seasonal influenza.
Dr. Isaac Bogoch at Toronto General Hospital noted this year’s flu season started early and escalated quickly.
According to the Public Health Agency of Canada, children under five are still making up the largest age bracket of flu patients in hospital. However, rates among seniors (aged 65 and up) are on the rise.
Bogoch expects the number of flu cases to keep increasing. The season usually peaks in January.
To track the number of flu cases in Durham Region this season, click here.
Influenza-related hospitalizations in 🇨🇦:
👉Highest rates are in the 0-4 & 65+ age groups.
👉Vaccines reduce illness & are available for those 6 months & up.
Patients with inflammatory bowel disease (IBD) treated with infliximab who were vaccinated against SARS-CoV-2 were more likely to have a breakthrough infection than patients treated with vedolizumab, but the benefits of the vaccine are still superior.
A team, led by Zhigang Liu, PhD, Department of Metabolism, Digestion and Reproduction, Imperial College London, determined how infliximab and vedolizumab affect vaccine-induced neutralizing antibodies against highly transmissible omicron (B.1.1.529) BA.1, and BA.4 and BA.5 (hereafter BA.4/5) SARS-CoV-2 variants.
Anti-TNF drugs, including infliximab, are linked to attenuated antibody responses following SARS-CoV-2 vaccination. The variants included in the analysis have the ability to evade host immunity and with emerging sublineages are currently the dominating variants causing the current waves of infection.
In the prospective, multicenter, observation, CLARITY IBD cohort study, the investigators looked at the effect of infliximab and vedolizumab on SARS-CoV-2 infections and vaccinations in patients with IBD.
The study included patients aged 5 years or older with an IBD diagnosis that were treated with infliximab or vedolizumab for 6 weeks or longer in infusion units at 92 hospitals in the UK. Each participant had uninterrupted biological therapy since recruitment and were not previously diagnosed with a SARS-CoV-2 infection.
The investigators sought primary outcomes of neutralizing antibody responses against SARS-CoV-2 wild-type and omicron subvariants BA.1 and BA.4/5 following 3 doses of a SARS-CoV-2 vaccine.
The team also investigated the risk of breakthrough infections in relation to neutralizing antibody titers using Cox proportional hazard models.
There were 7224 patients with IBD recruited to the study between September 22 and December 23, 2020. Of this group, 1288 had no previous SARS-CoV-2 infections after 3 doses of the vaccine that were established on either infliximab (n = 871) or vedolizumab (n = 417). The median age of the patient population was 46.1 years.
Following 3 doses of SARS-CoV-2 vaccine, 50% neutralizing titers were significantly lower in the infliximab group compared to patients treated with vedolizumab against wild-type (geometric mean, 2062; 95% CI, 1720–2473 vs geometric mean, 3440; 95% CI, 2939–4026; P <0.0001), BA.1 (geographic mean, 107.3; 95% CI, 86.40–133.2 vs geographic mean, 648.9; 95% CI, 523.5–804.5; P <0.0001), and BA.4/5 (geographic mean, 40.63; 95% CI, 31.99–51.60] vs geographic mean, 223.0; 95% CI, 183.1–271.4; P <0.0001) variants.
Breakthrough infections more frequently occurred in patients treated with infliximab (n = 119; 13.7%; 95% CI, 11.5–16.2) than in those treated with vedolizumab (n = 29; 7.0%; 95% CI, 4.8–10.0; P = 0.00040).
The Cox proportional hazard models show time to breakthrough infection after the third vaccine dose in the infliximab group was associated with a higher hazard risk than treatment with vedolizumab (HR, 1.71; 95% CI, 1.08-2.71; P = 0.022).
There was also higher neutralizing antibody titers against BA.4/5 with a lower hazard risk in the group with a breakthrough infection and a longer time to breakthrough infection (HR, 0.87; 95% CI, 0.79-0.95; P = 0.0028).
“Our findings underline the importance of continued SARS-CoV-2 vaccination programs, including second-generation bivalent vaccines, especially in patient subgroups where vaccine immunogenicity and efficacy might be reduced, such as those on anti-TNF therapies,” the authors wrote.
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