Anesthesiologist Dr. Jerome Adams served as the 20th surgeon general of the United States from 2017 until 2021. His new book Crisis and Chaos: Lessons from the Front Lines of the War Against COVID-19 offers an insiders analysis of the successes and failures of the governments response to the pandemic. In it, Adams offers a vision of public health policy predicated on equity, community, and proven practices to stemming the spread of infectious disease. Ahead of the holiday season, Adams says the dismal numbers of those receiving updated vaccines this fall means the country is poised to once again stoke a spike in COVID cases. Speaking with WAMC, the former surgeon general also compared the effectiveness of the Trump and Biden administrations in managing the pandemic and underscored the ongoing importance of masking.
ADAMS: Well, as much of everyone wants to forget about COVID-19 and pretend it's gone, it remains a significant public health threat. But I want people to think about it the way we think about flu- We have bad flu seasons, every year around this time we remind people to get your flu vaccine, if you're older to get your Pneumovax vaccine, and this year is no exception. We know that hospitalizations are increasing CDC said 16,000 last week and in most places across the country, we're seeing those start to tick up. And we know that they're going to continue to go up as people travel, as people gather, as people come inside for the cold weather, and we want people to be able to protect themselves. A shocking stat where I'm from, New York- Only about 8% of people in the state have gotten their updated vaccine despite the fact that we know three quarters of adults have at least one risk factor for a negative outcome.
WAMC: I wanted to ask you about government messaging about the pandemic. Just this week, the White House put out an advisory to folks about getting vaccinated, tested, and treated for various ailments, including COVID-19- But there was no mention of masking, and it seems like the conversation around masking has really swung aggressively into almost nonexistent at this point in the American experience with COVID. Can you speak to that? What role does masking still play in this ongoing situation?
Well, I really, really appreciate that question. I have a new book out called Crisis and Chaos: Lessons from the Front Lines of the War Against COVID-19, and it's available on Amazon, Barnes and Noble. I talk about how we continue to make the same mistakes over and over again, no matter which administration is in charge. And one of those mistakes of not leveraging all the tools we have available or helping people understand their risks. We know that masking remains an important tool for people to utilize, particularly if you're at higher risk or in high-risk situations. So, when I travel, I still mask. I was in an airport yesterday, and I masked while I was in the airport traveling, when you're in crowded situations around people you don't know. But we also know that vaccines are a tool that allow you to be able to have a little bit more safety and security, particularly as you're gathering around loved ones. Testing and treatment, incredibly important. We want to make sure we're informing people about the tools and leveraging those tools so that they can stay as safe as possible this holiday season. One of the most important things folks can do is talk to their doctor, make sure they're up to date on flu, COVID, but also if you're older, Pneumovax and RSV vaccines.
Now, in your book Crisis and Chaos, you talk about the disparities between different communities and how they've experienced COVID-19. I want to know, from your perspective, what the COVID-19 pandemic has taught us about broadly held standards of health equity in America. What have we learned from this about how we treat different groups?
Well, health equity means making sure everyone has the opportunity to make healthy choices. That's all health equity means. If you're in a rural community, you don't have the same opportunities as someone perhaps in an urban community. You may not have Wi-Fi access, so you can't use telehealth, for instance. If you're someone who's African American, you may have distrust of the medical system based on well-founded experiences: Tuskegee, Henrietta Lacks, the continued issues that people face. If you're someone who's uninsured, you're going to have difficulty being healthy. And so, one of the lessons in my book Crisis and Chaos is that we can't have a healthy nation without health equity, and we need to make sure we're giving people the opportunity to make healthy choices. You can honor that individual choice while still making sure everyone has the opportunity to make healthy choices, including making sure we're fighting misinformation and disinformation so that people understand the benefits of vaccination, including making sure people have access to vaccines. And so that's why we now have them available at pharmacies in addition to doctors offices- We want to make it as easy as possible for people to make the right choice, because we far too often accuse people of being antivax when the truth is they may just simply have a question they can't get answered, because they don't have a doctor, or they may not have access to a place to get vaccinated.
Given your experience in the Trump administration, how would you compare and contrast how the Biden administration has comparatively handled COVID-19?
Well, that's a great question that I go over extensively in the book. And I try to be as objective as possible. I tell folks, I'm not a Republican or a Democrat, I'm a doctor, I'm an independent. And when you look at the numbers, we had about 250,000, 300,000 people who died in 2021 under the Trump administration from COVID-19. And that was a tragedy by any measure. You go to the next year under the Biden administration, you had 360,000. You had more people die despite vaccines, despite masks, despite testing, and even in 2022, you had 240,000 people die from COVID-19. So, what that tells you is that changing who's at the White House, changing who's at the CDC, changing who's the surgeon general is not changing our trajectory, and we need to make sure people are leveraging all the tools that they have available, that they're not being taken in by politics or by misinformation or disinformation. And I worry with increasing vaccine hesitancy that we're going to lose progress, even more so, on our populations health. And that's why I'm here today- I really want people to understand one of the most important things they can do to protect their holiday, to protect their loved ones, to protect themselves, to protect their plans, is to get an updated COVID or flu vaccine this holiday season, and then pick up my book Crisis and Chaos. Great tips on how you can stay safe, it really helps people understand all the mistakes we made during the pandemic, myself included. I'm very raw in there about the things that I wish I had done a better job of, and the things that we need to do better in the future.
When you look ahead to the next chapter of American public health, what do you think the major takeaways have been from this COVID-19 pandemic experience? And how can we leverage that into an equitable response to the next public health crisis?
Well, one of the big lessons is that we aren't going to be able to move forward if we don't pay attention to baseline health. So, I highlight this in the book- The idea that we did terribly under the Trump administration with COVID transmission is a myth. It is. When you look at the numbers, the transmission rates were about the same as most of Europe in 2020. Actually, transmission rates went through the roof in 2021 when we reopened under the new administration. But, what you did see a difference in in 2020 was hospitalization and death rates. And why is that? Well, it wasn't because of COVID policies, necessarily. So much of it was because of our poor baseline health. People with diabetes, people with obesity, people with high blood pressure are more prevalent in the United States, and they're much more likely to have negative outcomes. And interestingly enough, those are the people who I want to highlight should be particularly cognizant of getting a vaccination this season. If even if you're over 65, 90% of the deaths are occurring in that age group. But if you're taking an inhaler for lung disease, if you're taking insulin or diabetes medication, if you're someone who is taking medication for a heart problem, you need to especially make sure you're talking to your health provider and that you're getting up to date on your vaccinations. We need to pay attention to those higher-risk folks. That is a big lesson from the book. But again, as you've heard me say, we also need to understand that changing the captain of the Titanic but not changing the course isn't going to prevent us from hitting that iceberg. And so, we need to make sure we are trying to approach this in a nonpartisan way, and in a way that looks just at the facts. And the facts tell us we've got to do better. And one of the ways we do better is by increasing our vaccination rates, particularly during cold and flu season.
Person 20 to 44 years old among N.B.'s latest COVID deaths, child under 4 hospitalized
Bobbi-Jean MacKinnon | CBC News | Posted: Wednesday, November 15th, 2023 5:02 PM | Last Updated: November 15th
Virus activity Oct. 29 toNov. 4 remains 'moderate,' Respiratory Watch report says
Image | nurse, patient, Dr. Georges-L.-Dumont University Hospital Centre, ER
Caption: A total of 557 New Brunswickers have now been hospitalized for or with COVID-19 since the start of the respiratory season on Aug. 27. (Patrick Lacelle/Radio-Canada)
At least two more New Brunswickers have died from COVID-19, including one aged 20 to 44, while a child under four is among the 52 people hospitalized for or with the virus, the latest weekly figures released by the province Wednesday show.
"All indicators remained stable throughout the current reporting period."
Both people died in hospital during the reporting week. The other person was aged 65 or older.
The pandemic death toll is no longer provided, but the virus has now killed at least 958 New Brunswickers.
The province's "excess mortality" or "untimely mortality," will likely increase by an estimated 20 per cent per week in the coming weeks, as people die from either acute COVID or the downstream consequences of past infections, according to Tara Moriarty, an infectious diseases researcher.
That means New Brunswick will see about 20 per cent more deaths per week than predicted based on demographic factors, such as the growth and aging of the population.
"It's still going to take at least six months before we see all the numbers come in, but right now [that's what] the forecast is suggesting," said Moriarty, co-founder of COVID-19 Resources Canada, a grassroots group of experts and community members that aims to provide logistic and scientific support to public health response efforts.
Other than the child, the hospitalized patients include nine people 45 to 64 years old, and 42 people 65 or older.
COVID hospitalizations are costing the province an estimated $1 million a day, according to Moriarty, who is an associate professor at the University of Toronto.
Image | Tara Moriarty
Caption: Tara Moriarty, an associate professor and infectious diseases researcher at the University of Toronto, said last fall and winter saw some of the highest excess or untimely mortality of the COVID-19 pandemic across the country, but she hopes it won't be 'quite as high' this year. (Submitted by Tara Moriarty)
Hospitalizations and ICU admissions in New Brunswick and across the country are about 12 times higher than the lowest point of the COVID epidemic, and roughly the same as they were at this time last year when they were "quite high," said Moriarty.
"We're quite concerned right now because of the effect of these COVID surges on the health-care system and access to health care for people who need it for all of the non-COVID reasons as well," she said.
Eleven lab-confirmed COVID outbreaks have been declared, according to the province four of them in nursing homes and seven in "other facilities," which could include adult residential homes and correctional centres.
There have been 114 new cases of COVID confirmed through PCR (polymerase chain reaction) lab tests, down from 128, the Respiratory Watch report shows.
The positivity rate the percentage of the total PCR lab tests performed that produced a positive result is 12 per cent, down from 14 per cent.
Moriarty estimates about one in 30 New Brunswickers are currently infected with COVID.
That's down from the previous estimate of one in 17, as of Nov. 4 to Nov. 17.
"We think that's improving, based on what we can see from the test positivity rates and wastewater," said Moriarty.
But New Brunswick is still "not great right now," she stressed.
The national average for Nov. 4 to Nov. 17 was about one in 23 Canadians infected.
The province's hazard index for that period was tied with Ontario for third highest in the country at 20.9. Only Saskatchewan and Newfoundland were worse, at 21.0 and 21.6 respectively, her group's website shows.
"A lot" of infections are being contracted in hospitals or health-care settings, based on data from across the country, said Moriarty.
"It's very important to have as much protection as possible in hospitals because people who are in them are, by definition, sick or having surgery and more vulnerable to, you know, the outcomes of an infection like COVID-19," she said.
Studies conducted in other countries suggest consistent masking is one way to reduce in-hospital transmission, said Moriarty.
Another is testing everyone upon admission, which most jurisdictions have dropped.
Image | COVID-Booster 20230303
Caption: The updated COVID-19 vaccines are 'a pretty good match' to the strains that are circulating right now, said Moriarty. (Lars Hagberg/The Canadian Press)
"We really do need to be protecting people in these settings and recognizing that, you know, a COVID infection for someone who's hospitalized can mean death, or can mean that they take a long time to recover and they may never regain the abilities and, you know, the skills and daily life function that they had before getting sick."
Moriarty urges people who are eligible to get an updated COVID-19 vaccine.
"Don't delay, go and get it now because there are a lot of infections out there right now and you don't want to end up in the hospital or needing hospital when we have so many other people needing hospital at the same time," she said.
A total of 77,545 COVID-19 vaccines have been administered since Oct. 4, according to figures from the Department of Health.
Moriarty also recommends wearing well-fitted masks indoors, avoiding non-essential indoor gatherings or moving them outdoors, and staying home when sick.
"They're not perfect, but if we layer them up you really can reduce the risk of having an infection," she said, pointing to the estimated 30 per cent of Canadians aged 65 or older who may not have been infected yet, based on seroprevalence data from the COVID Immunity Task Force, or the percentage of people who have antibodies in their blood to the virus that causes COVID-19.
This age group is considered at greater risk and "generally have been taking more precautions," she said.
Three of the patients require intensive care, down from six.
Fewer Horizon health-care workers are off the job after testing positive for the virus 20, compared to 34 absent infected employees the previous week.
Four new cases of the flu have been confirmed through lab tests.
Eighteen cases have now been reported since the respiratory season began on Aug. 27.
A total of 112,695 people have been vaccinated for influenza since Oct. 4, the Department of Health says.
Feline infectious peritonitis (FIP), also known as felinecoronavirus, started spreading through Cyprus in January.
This deadly virus is arecombination of a feline coronavirus and a canine coronavirus and is called F-CoV-23.
Professor Danielle Gunn-Moore from Edinburgh University has spent more than two decades researching the illness and, speaking to Sky News, described the virus as"particularly nasty".
The deadly mutation of the virus can be fatal to cats if left untreated and can cause symptoms including seizures and breathing issues.
There are major concerns the deadly virus could spread in the UK. (Image: Getty Images)
The first case of the deadly FIP has been reported in the UK, according to Sky News.
The news outlet reported: "The cat was brought to the UK from Cyprus and was taken to the vet after developing symptoms.
"The cat is in quarantine so it can't go outside and is now being treated with a high dose of anti-virals."
Professor Gunn-Moore, speaking to Sky News, addedit's likely this isn't the first case to reach the country andthere were "major concerns" the virus could spread in the UK.
There are a number of symptoms to look out for in yourcat that my be signs of FIP, according to Sky News.
Symptoms of FIP include:
See the symptoms to look out for feline coronavirus. (Image: Getty Images)
Professor Gunn-Moore warned cat owners in the UKto be extra-vigilant following the recent report of the deadly virus.
She added:"If they see the cat becoming depressed or swollen belly or wobbly back end, seizures, anything like that - just a cat that's not feeling well - go to your vet quickly and say you're worried about F-CoV-23."
If the vet does diagnose FIP, they should contactthe RoyalSchool of Veterinary Studies at the University of Edinburgh which will testthe virus to see if it is the standard strain or F-CoV-23.
The virus is mostly spread via an infected cat's faeces.
If your cat may come into contact with a cat that's got a connection to Cyprus - a neighbour's cat, for example, or other animals at a cattery - you should be extra vigilant.
There are two anti-viral COVID-19 drugs that are used to treat FIP and are licensed for use in the UK.
Professor Gunn-Moore said: "While they are expensive, they are effective at treating the illness - and seem to work well on the new strain."
Infected cats in Cyprus started receiving a human COVID-19 treatment in August, reported Sky News,which Professor Gunn-Moore saidhad been "effective" in treating the illness.
In this analysis of 135,759 pediatric patients diagnosed with acute conjunctivitis between March 2017 to February 2022, we examined pediatric conjunctivitis infection rates before, during, and after the COVID-19 pandemic restriction and assessed the impact of the pandemic and lockdowns on acute infectious conjunctivitis incidence among the pediatric population in southern Israel CHS. To the best of our knowledge, this is the first study to examine the trends of acute infectious conjunctivitis between three pre-COVID-19 pandemic years and the two pandemic years.
Our study demonstrated a stable trend in the incidence of acute conjunctivitis episodes before the COVID-19 pandemic. During the three COVID-19 lockdowns, we observed a shift in normalcy. The number of diagnosed patients decreased among all ages. This decline was statistically significant in a time series approach, even after adjusting for seasonality. When compared to a control group consisting of non-infectious skin diseases, the decline was notably greater in cases of acute conjunctivitis, suggesting the evident declines represents an actual reduction in the incidence of infection, rather than a change in health seeking behaviors or access to healthcare during this period. The pattern of reduced infection rates among the pediatric population during the COVID-19 pandemic lockdowns was described in other communicable diseases2,4,17,18,19,20.
The decline in acute conjunctivitis rates can be attributed to the social and physical distancing mandated by governmental restrictions and lockdowns. The public was required to wear a face mask and stay within a limited home range, 100m limit range on the first lockdown, 500m on the second, and 1000m on the third. All regular educational frameworks were closed, and classes were conducted online during the full lockdowns. In the intervals between full lockdowns, attending school grounds and classrooms was permitted for specific school grades, primarily for children in early school grades, depending on the number of positive COVID-19 cases in the resident district. When school attendance met the requirements, classes were performed in small and invariable teaching capsules and carried out on different timeframes. Youth groups and other afterschool events were canceled during and between the lockdowns21,22,23.
The highly contagious syndrome of infectious conjunctivitis has accounted for many outbreaks in various leisure, healthcare, and educational frameworks. Among the pediatric population, the most common transmission is thought to be through direct contact24. Poor hand hygiene and sharing contaminated objects can also be attributed to the transmission of infectious conjunctivitis8. Hand and personal hygiene campaigns issued by the government, raising awareness for COVID-19 virus transmission, were spread vastly across the country, contributing to lower rates of infectious conjunctivitis. Meticulous hand hygiene practices ability to reduce transmission of other common pathogens among the pediatric population was described before25.
Lockdown end had different outcomes depending on the age group. For the 611 years and 1218 years age groups, the restriction termination resulted in lower conjunctivitis rates than before the pandemic. The decline in conjunctivitis rates among these age groups was statistically significant in a time series analysis, even after adjusting for seasonal variations. The establishment of better hand hygiene practices mentioned earlier might explain the lower rates of conjunctivitis among these groups, even after lifting COVID-19 restrictions. Despite the positive effect hand hygiene might have, another plausible explanation for reduced post-lockdown conjunctivitis rates is rooted in the mental and behavioral effects of the COVID-19 pandemic. Children and teens suffer higher levels of depression and anxiety, influencing their integration capabilities and social skills, resulting in their gravitation toward online social interactions over real-life gatherings26.
Among the younger age groups, 01 and 15, the rates in the post-lockdowns months had begun to return to their pre-pandemic rates. With a slightly non statistically significant increased incidence among the 15 years age group and a subtle decrease among the 01 age group. Since conjunctivitis rates before the COVID-19 pandemic reflect regular everyday activity, the pattern seen among these age groups correlates with the return to the usual routine. This might also be clarified by the immaturity of children at these ages, affecting their abilities to perform hygiene practices and to maintain social awareness of pathogen transmission.
Adenovirus, responsible for most viral conjunctivitis, has been known to facilitate outbreaks in a seasonal pattern, typically contagious in the winter and spring5,7. Interestingly, we found that during post-lockdown months, there was an additional, non-typical peak, which occurred during the end of the spring, lasting through the summer, similar in form to the winter peak, followed by a usual winter seasonal peak. The pattern of seasonal switch was described in a similar study conducted in Israel14. Respiratory syncytial virus (RSV) demonstrated a similar non-typical seasonal pattern in Australia and Israel with unparalleled virus outbreaks during the summer occurring during the pandemic restrictions gradually lifting27,28. The added peak lasting through the summer supports human behavior as the etiology of the seasonality pattern of acute viral conjunctivitis. The fact that individuals spend more time indoors in proximity to others might play a more significant role in the higher incidence of acute conjunctivitis during the winter than the low temperatures considered favorable to some pathogens.
The additional summer peak could also be a non-infectious form of acute conjunctivitis. The lack of exposure to common allergens among children at a young age due to COVID-19 pandemic restrictions, along with over-cleanliness (i.e., Hygiene hypothesis), resulted in higher rates of allergic manifestations of conjunctivitis29. A previous study examined the differential diagnosis and treatment for patients presenting with pink eye to general practitioners and ophthalmologists in nine countries; demonstrated a higher tendency for diagnoses of allergic conjunctivitis among general medical practitioners compared to an ophthalmologist during the spring and summer seasons30. In our study, a large portion of the diagnoses was made by pediatricians and general practitioners, therefore strengthening our results regarding the lower rates of infectious conjunctivitis. Pediatricians tend to diagnose patients with pink eye as allergic conjunctivitis during the spring and summer; the fact that those doctors, who made the majority of the diagnoses gave conjunctivitis diagnoses strengthens our results.
The concept of immunity debt may also underpin the atypical surge in acute conjunctivitis cases post-lockdown. The prolonged isolation during lockdown likely diminished exposure to various pathogens, leading to an accumulation of immunity debt. Once social interactions resumed post-lockdown, heightened susceptibility due to this debt could have contributed to the uncharacteristic summer peak in conjunctivitis cases, as suggested31,32. While this concept may hold true for infectious agents transmitted via the respiratory tract (e.g. meningococcal illness or influenza), its applicability to pathogens transmitted via direct contact such as many causative organisms of conjunctivitis remains questionable and deserves further study.
The U.S. Department of Agricultures (USDA) Animal and Plant Health Inspection Service (APHIS) has awarded the University of Wisconsin School of Veterinary Medicine (SVM) $3.7 million in cooperative agreements to develop vaccines designed to prevent or limit the impact of future disease outbreaks that can spread between animals and humans.
These cooperative agreements, which are part of a larger $56 million initiative by the USDA, will support two separate but related projects at SVM aimed at understanding how SARS-CoV-2 (the virus responsible for COVID-19) behaves in animals, how it moves between animals and people, and what can be done to interrupt the chain of transmission.
The ability of SARS-CoV-2 to mutate, adapt, and spread among domestic, wild and captive animals, and then jump from these animals to infect and cause severe disease in humans, is a major concern among public health officials.
The rapid development of vaccines and anti-viral medications have proven to be effective in preventing COVID-19-associated severe disease and hospitalizations, but the continuous emergence of highly transmissible SARS-CoV-2 viral variants that evade vaccine- or infection-elicited antibodies can lead to breakthrough infections, and sometimes severe disease even in vaccinated people.
The following two projects funded by these cooperative agreements help prevent and/or limit the impact of future pandemics:
The first project, Universal Mucosally Administered SARS-CoV-2 Vaccine for Animals, is designed to identify effective interventions and other measures to prevent transmission of SARS-CoV-2 at the human-animal interface and help mitigate potential impacts on the food supply. The research team, co-led by Marulasiddappa Suresh and Jorge Osorio, both professors in the SVM Department of Pathobiological Sciences, will seek to develop pan-coronaviral vaccines that can be used to protect domestic and wild animals against several coronavirus infections, including COVID-19, Severe Acute Respiratory Syndrome (SARS), and Middle Eastern Respiratory Syndrome (MERS). Their team will also explore formulating vaccines that could be administered to many animals via oral baits or nasal spray.
The end users of this research will include pets and their owners, production animals, susceptible wildlife species that serve as reservoirs for SARS-CoV-2 and other beta-coronaviruses, and human communities.
In order to limit the emergence of SARS-CoV-2 variants in nature, it is critical that we develop effective tools that block the spread of the virus between humans and other animal species, said Suresh. Our goal is to create vaccines that can break that transmission cycle of SARS-CoV-2 and other coronaviruses at the animal-human interface, which, if were successful, would have significant implications for public health and ultimately play an important role in preventing future pandemics.
This second project, titled Multivalent Vaccines Against SARS-CoV-2 in Mustelids
aims to expand knowledge of species susceptibility to SARS-CoV-2 and to better understand the potential roles or routes of transmission. One key feature of this project will be the development of mosaic vaccines that can protect against multiple variants as well as reduce both animal-to-animal and animal-to-human transmissions. Researchers will also evaluate the performance of novel vaccines against SARS-CoV-2 in ferrets. The project will be co-led by Adel Talaat, professor of microbiology, Peter Halfmann, research scientist, and Marulasiddappa Suresh, all in the SVM Department of Pathobiological Sciences.
The end-users of this research will include veterinarians who treat ferrets raised as pets and who manage farmed minks, herd owners of mink farms, vaccine companies and researchers of zoonotic diseases of wildlife animals that belong to the Mustelidae family, which include wild ferrets, mink, badgers and otters.
By developing a protective COVID-19 vaccine for ferrets, a common pet in many households in the United States, we would not only be diminishing the chances of transmitting the virus to minks and other wildlife animals in our environment, but it would go a long way toward protecting human health as well, said Talaat.
According to Dr. Michael Watson, APHIS acting administrator at the USDA, both of these research projects will help USDA/APHIS accomplish its goal of building an early warning system to potentially prevent or limit the next zoonotic disease outbreak.
APHIS has long relied on collaboration with our state, Tribal, federal, and private partners to help protect our nations agricultural and natural resources, Watson said. We are excited about the opportunity these new partnerships give us to build critical One Health coordination and capacity while furthering the science on SARS-CoV-2. This important work will strengthen our foundation to protect humans and animals for years to come.
One Health is a philosophical and scientific framework that recognizes humans, animals, and the world we live in are inextricably linked and that a collaborative effort of multiple disciplines working locally, nationally, and globally is necessary to attain optimal health for all species and the environments they share.
Founded in 1983, the UW School of Veterinary Medicine provides outstanding programs in veterinary medical education, research, clinical practice, and service that enhance the health and welfare of both animals and people and contribute to the economic and environmental well-being of the state of Wisconsin, the nation and the world.
-Gian Galassi
Suffolk County reported the following information related to COVID-19 on November 15, 2023
According to CDC, hospital admission rates and the percentage of COVID-19 deaths among all deaths are now the primary surveillance metrics.
COVID-19 Hospitalizations for the week ending November 4, 2023
Daily Hospitalization Summary for Suffolk County From November 15, 2023
NOTE: HOSPITALS ARE NO LONGER REPORTING DATA TO NYSDOH ON WEEKENDS OR HOLIDAYS.
Fatalities 11/15/23
COVID-19 Case Tracker November 13, 2023
Note: As of May 11, 2023, COVID-19 Community Levels (CCLs) and COVID-19 Community Transmission Levels are no longer calculatable, according to the Centers for Disease Control and Prevention.
* As of 4/4/22, HHS no longer requires entities conducting COVID testing to report negative or indeterminate antigen test results. This may impact the number and interpretation of total test results reported to the state and also impacts calculation of test percent positivity. Because of this, as of 4/5/22, test percent positivity is calculated using PCR tests only. Reporting of total new daily cases (positive results) and cases per 100k will continue to include PCR and antigen tests.
COVID-19 Vaccination Information
Last updated 5/12/23
Vaccination Clinics
As of September 12, 2023, the Suffolk County Department of Health Services is not authorized to offer COVID-19 vaccines to ALL Suffolk County residents.
The department will offer the updated vaccine to only uninsured and underinsured patients through New York State's Vaccines for Children program and Vaccines for Adults program, also known as the Bridge Access Program.
Those with insurance that covers the COVID-19 vaccine are encouraged to receive their vaccines at their local pharmacies, health care providers offices, or local federally qualified health centers.
The department has ordered the updated COVID-19 vaccine and will announce when the vaccine becomes available.
FOR HEALTHCARE PROVIDERS
New York State Links
CDC COVID Data Tracker Rates of laboratory-confirmed COVID-19 hospitalizations by vaccination status
For additional information or explanation of data, click on the links provided in throughout this page.
Student enters Habif Health and Wellness Center. (Lydia Nicholson | Student Life)
Washington University has reverted its flu vaccine policy to the pre-pandemic standard. The flu vaccine, along with the COVID-19 booster, is now highly recommended instead of required.
The reversal also means that students will no longer need to have the flu or COVID-19 vaccines to enroll in classes, effective this year. Select employeesof WashU, such as those in the WashU School of Medicine, are still mandated to get the vaccine.
Executive Director of the Washington University Health and Wellness Committee, Dr. Cheri LeBlanc, said that the University had originally mandated flu shots in order to prevent a twin pandemic of the flu and coronavirus, following the guidelines of the COVID-19 Medical Advisory Group.
The University still held flu shot clinics at the Habif Health and Wellness Center, with no additional cost to students. Habif is also hosting COVID-19 vaccination clinics, with the next one being on Dec. 1.
The recent policy shift by the University has sparked a range of opinions among the student body.
Sophomore MJ Jones said she is pessimistic about the effect of a shift in vaccine requirements.
The fact that even fewer people are going to be vaccinated seems like its going to be very negative on the student body, Jones said.
Senior Benjamin Yi also said that, while it is nice that WashU is encouraging the flu vaccine, he has also seen an uptick in the number of people getting ill.
My friend got sick three times this year, Yi said.
The COVID-19 vaccine is also no longer required to enroll at WashU, making the shift to highly recommended as of July 1, 2023.
MJ Jones said she is in favor of a vaccine mandate for the University.
I totally understand autonomy, but I do think were one of the more permissive universities when it comes to COVID vaccinations, Jones said. I think some stuff, like flu vaccinations, you just have to do to make the student body safer.
I would enforce a bit more on the COVID vaccine than the flu vaccine, first-year Yahir Dominguez said. In my case, I havent had the flu vaccine in a while and I have never gotten the influenza virus since then.
The impact on these policy changes will be observed as the school year progresses. It is yet to be seen if the lack of a requirement will result in more students becoming sick.
I still feel [safe], mainly because WashU still takes the steps of precaution for when somebody actually does get the COVID infection, Dominguez said.
Anna Calvo, a first-year, said that not requiring the flu shot will not make a huge difference because most students would choose to get the vaccine anyways.
First-year Cameron Gratz said hes not particularly worried about the new vaccine policy.
I do trust people to make the right decisions, even if its not required, Gratz said. Maybe not as comfortable as I would be if there were a requirement, but I dont feel unsafe.
In the end, the lack of University-mandated flu and COVID vaccines places the health of students in their own hands. Jones said she hopes students still get vaccinated.
I just think its like seatbelts on cars, Jones said, Like some stuff you just got to do so everyones a little bit safer.
This article was updated on November 16, 2023 to clarify the mandates effect on students, rather than all faculty.
The unprecedented coronavirus disease 2019 (COVID-19) pandemic facilitated the rapid development and subsequent deployment of several novel and highly effective vaccines. However, the role of antiviral treatments remained pivotal, especially for certain patient populations incapable of mounting an effective vaccine response or those reluctant to be vaccinated.
In a recent review published in the journalCellular & Molecular Immunology, researchers examine the therapeutic benefits of nirmatrelvir and its possible effects on severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-specific adaptive immune responses.Herein, the researchers also discuss the immunomodulatory properties of bisphosphonates, a class of drugs traditionally used to treat bone disorders, and their potential role in mitigating severe SARS-CoV-2 infection.
Nirmatrelvir is an orally available drug that inhibitors the main proteinase (Mpro) of SARS-CoV-2. More specifically, nirmatrelvir targets the 3-chymotrypsin-like cysteine protease (3CLPro), a protease that cleaves two polyproteins, pp1a and pp1ab, which are encoded by the SARS-CoV-2 ribonucleic acid (RNA) genome to exert its antiviral effects.
Paxlovid, which is a combination treatment of both nirmatrelvir and ritonavir, has been shown to reduce hospitalization and death due to COVID-19 by up to 86%. Despite the reported efficacy of nirmatrelvir in treating COVID-19, the extent to which this agent impacts SARS-CoV-2-specific adaptive immunity remains unclear.
In murine models, nirmatrelvir treatment reduced SARS-CoV-2-specific antibodies and T-cell titers, thereby preventing severe infection. Secondary viral exposure further reduced the recruitment of memory B- and T-cells.
These findings indicate that while nirmatrelvir effectively prevents severe disease by reducing viral titers, it may impact the ability of the host immune system to recognize and combat SARS-CoV-2 in the future. Several mechanisms have been proposed to be responsible for this effect. For example, by limiting the exposure of SARS-CoV-2 antigen(s) to the immune system, nirmatrelvir compromises the processes of nave T- and B-cells.
Prior to the development of COVID-19 vaccines, the international scientific community pursued the repurposing of existing drugs as potential therapeutics for treating this viral disease. Many existing drugs were evaluated for their potential to treat COVID-19 through bothin silicoandin vitroanalyses.
Beyond their exceptional potential to cease osteoclast-mediated bone resorption, bisphosphonates can exert multifaceted immunomodulatory effects. Current indications for bisphonates include osteoporosis, Pagets disease, and malignancy-induced hypercalcemia, as well as an adjuvant to certain breast cancer treatments.
Bisphosphonates can be classified as amino-bisphosphonates or non-amino-bisphosphonates based on their nitrogenous and non-nitrogenous chemical composition, respectively. Amino-biphosphonates are capable of modulating various types of immune cells, including neutrophils, monocytes, T cells, and macrophages. Animal studies have shown that amino and non-amino bisphosphonates can also enhance antibody and T-cell responses to viral antigens.
In an effort to better understand the potential clinical utility of bisphosphonates in the treatment of COVID-19, one retrospective study assessed whether prior bisphosphonate treatment impacted COVID-19 outcomes. Notably, individuals treated with amino-bisphosphonates had a three- to five-fold reduced risk of being diagnosed with COVID-19 or hospitalized for this condition.
Nevertheless, additional studies are needed to fully assess the potential use of bisphosphonates as prophylactic agents for patients who are at a greater risk of severe COVID-19.
A comprehensive assessment of the long-term implications of all COVID-19 prevention and treatment interventions on antiviral immunity is crucial to understanding the full spectrum of effects that these agents might have on the host immune response.
Many COVID-19 therapeutics have yielded promising outcomes in randomized controlled trials, which subsequently led to their approval for use in treating SARS-CoV-2 infection. However, more comprehensive data could help establish definitive conclusions on their long-term impact.
Managing the spread of SARS-CoV-2 and preventing severe COVID-19 outcomes appears to be reliant on a delicate balance between immediate treatment while also ensuring long-term immunity. Thus, the emphasis remains on coalescing all therapeutic solutions for COVID-19 with resolute scientific inquiry.
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We build a data-driven, granular ABM of the New YorkNewarkJersey City, NYNJPA metro area. The main agents of the model are the 416,442 individuals of a synthetic population that is representative of the real population across multiple socioeconomic characteristics, including household composition, age, income, occupation and possibility to work from home (WFH) (for a schematic representation, see Fig. 1, and for a detailed description of the model, see Methods and Supplementary Information).
The economic module depicts the flow of goods and services between industries and from industries to final consumers (inputoutput network), while the epidemic module tracks pathogen exposure at workplaces, community/consumption venues, schools and households (contact network). Agents display high heterogeneity across various socioeconomic characteristics (see box). The economic and epidemic modules are closely linked: the epidemic module impacts economic outcomes by reducing consumption due to infection fear, while the economic model influences epidemic spread by altering workplace and community contacts through employment changes in different industries.
The epidemic module of the ABM is built on the contact network that connects synthetic individuals. This network has multiple layers, where each layer captures interactions occurring (1) in the household, (2) in school, (3) in the workplace and (4) in the community (during on-site consumption, such as in shops, restaurants or movie theatres). Epidemic propagation occurs on these networks, built via anonymized, privacy-enhanced mobility data from opted-in users, which inform workplace and community interactions. These data, collected through a General Data Protection Regulation (GDPR)-compliant framework by Cuebiq, provide daily workplace visitation patterns and estimates for colocation probabilities in community spaces, based on a Foursquare dataset. The ABM employs a stochastic, discrete-time disease transmission model on the contact network and synthetic population, with individuals transitioning between epidemic states based on key time-to-event intervals (for example, incubation period, generation time and so on) derived from SARS-CoV-2 transmission data.
From an economic point of view, individuals play a role both as workers and consumers. They work in one of multiple industries, producing goods and services that are either sold to other industries as intermediate products or sold to final consumers as consumption products. The economic module specifically emphasizes employment and consumption. In particular, hiring and firing decisions are driven by industry workforce requirements, closures of economic activities and possibility of remote work. Consumption patterns vary among agents based on age and income, and dynamically adjust in response to the evolving state of the pandemic. Specifically, households tend to curtail their demand for services from customer-facing industries because of the fear of the disease (customer-facing industries are entertainment, accommodationfood, other services, retail, transportation, health and education; Supplementary Section 3.1.3). The model also considers the inputoutput network of intermediates that industries use to produce final goods and services18, leading to the propagation of COVID-19 shocks to the entire economy.
We calibrate the models key parameters, including the parameter regulating the behavioural changes, dubbed fear of infection, to fit crucial epidemic and economic statistics from the first wave in the NY metro area (Supplementary Section 4). Epidemiological parameters are adjusted to fit ancestral SARS-CoV-2 lineages (Supplementary Table 5). Simulations start on 12 February 2020, protective measures are imposed on 16 March and relaxed on 15 May 15, and simulations end on 30 June. As protective measures, we close schools, mandate WFH and shut down all non-essential economic activities, such as entertainment and most of the accommodationfood industry, but also large parts of manufacturing and construction. We use the official NY regulations to estimate the degree to which a given industry is essential (Supplementary Section 3.2.1) and assume that workers who can WFH are not directly affected by these closures2. We name this set of assumptions the empirical scenario.
Our model accurately matches the six official economic statistics we calibrated it on (Fig. 2a). It correctly reproduces the fact that employment declined more strongly than gross domestic product (GDP) (this is because industries most affected by shutdown orders produce less output per worker). It also correctly reproduces the fact that consumption of goods and services produced by customer-facing industries declined more strongly than consumption of goods and services produced by industries that are not customer facing, ranging from manufacturing products to utilities and financial services (Supplementary Fig. 15).
a,b, Statistics that were directly targeted in the parameter calibration. a, The percentage change from OctoberDecember 2019 (2019Q4) to AprilMay 2020 (2020Q2) across six official economic statistics, in the model and in the data (Supplementary Section 4). Here, and throughout the paper, we report the mean and error bars (2.597.5 percentile range) across simulation runs that differ by stochastic factors (Methods). b, A comparison between model and data for the number of COVID-19 deaths, which is the key epidemic statistic that we targeted. ce, Validation results for statistics that were not directly targeted. c, Employment in April 2020 as a percentage of employment in February 2020, across the main two-digit NAICS industries, in the model and in the data. The circle size is proportional to employment in February 2020. d, Employment and consumption, in the model and in the data3, among low-income and high-income households (low income <$27,000 and high income >$60,000; these bands are chosen for comparison to real data; Supplementary Section 5.1.1). e, The ratio between community contacts with infectious individuals (Supplementary Section 5.2) before and after the imposition of protective measures, in the model and in the data, for the seven customer-facing industries (Supplementary Section 3.1.3). The circle size is proportional to the share of pre-pandemic contacts.
Our model can also be validated against empirical properties that were not directly targeted in the parameter calibration procedure (Fig. 2c,d). First, thanks to our estimate of pandemic shocks2 and shock propagation model17, we are able to recover industry-specific changes in employment induced by the pandemic (Fig. 2c), with a Pearson correlation coefficient of 0.82 (P value 2104) between model and data. This accuracy is in large part due to our models ability to take industry-specific estimates as inputs, but also to the propagation mechanisms embedded in the model. Indeed, the estimates by supply shocks alone have only a Pearson correlation of 0.69 (P value 4103, Supplementary Fig. 14). Second, thanks to our granular and data-driven characterization of employment and consumption patterns (Supplementary Figs. 8 and 12), we reproduce a key fact: low-income individuals were more likely to become unemployed but reduced consumption less than high-income individuals3,19 (Fig. 2d). This happens because low-income individuals are more likely to work in the occupations most affected by closures, such as food preparation and serving, building and grounds cleaning and personal care and service (Supplementary Fig. 16), but they spend a larger share of their income on essential goods and services such as housing and utilities (we do not consider here the effect of the stimulus programme, which would further increase the spending of low-income individuals).
On the epidemic side, our model correctly matches the death count data on which it has been calibrated, correctly replicating the spike in the number of reported deaths in April 2020 and the strong reduction in June (Fig. 2d and Supplementary Fig. 18). It also correctly estimates the changes in contact patterns that occurred after protective measures were implemented, although these data were not used for parameter calibration (Fig. 2e). Both in the model and in the data, community contacts substantially reduced (Pearson 0.75 and P value 0.05), more in mostly non-essential industries such as entertainment and restaurants than in mostly essential industries such as retail and health. We also accurately estimate the reduction in workplace contacts across industries (Supplementary Fig. 20; Pearson 0.88 and P value 5106), the temporal profile of reduction in contacts (Supplementary Fig. 19) and the increase in prevalence over time (Supplementary Fig. 18). Finally, the model makes a number of estimates about how many infections happen across each layer and industry over time, as well as which occupation, income and age groups are most affected (Supplementary Figs. 2123). While we are not able to find data to quantitatively evaluate these estimates, our literature review provides some support to these findings (Supplementary Section 5.2.1).
In our analysis, we quantitatively explore the effects of three key factors that shaped the behavioural and policy response to the first COVID-19 wave. In the following, we use baseline to refer to estimated parameters calibrated with empirical data.
As a first set of counterfactual scenarios, we explore the effect of adjusting the magnitude of the fear of infection parameter that regulates behaviour change. Generally, we treat this parameter as uniform across individuals, per survey evidence20. However, we also consider an age-specific fear counterfactual. Our baseline calibration yields a fear of infection parameter distribution (Supplementary Fig. 13), implying a 14% consumption demand reduction in customer-facing industries due to infection fear at the epidemic peak. This calibrated value, which merges NPI effects with behaviour change, cannot be causally interpreted. Absence of NPIs would necessitate a steeper consumption drop for the model to explain observed behavioural changes, thus estimating higher infection fear. But the lack of real-world data without NPIs hampers this estimate. Instead, we explore two counterfactuals: low (0.1 times baseline) and high (10 times baseline), translating to a 1% and 77% consumption reduction due to fear at the peak, respectively. These scenarios facilitate comparing stronger fear of infection effects with stricter NPIs.
Next, we vary two policy-related factors. First, we experiment with different economic activity closures. Besides the baseline scenario with all non-essential industries closed, we consider two milder closure scenarios: (1) only non-essential customer-facing industries are closed and (2) no closures, with all economic activities open. Second, we simulate protective measures starting either 4weeks earlier (17 February 2020) or 2weeks later (30 March 2020). Additional counterfactuals, including partial closure of customer-facing industries and no WFH or school closures, are explored in Supplementary Information (Supplementary Figs. 24 and 25).
Aggregate economic and epidemic results are shown in Fig. 3, while results disaggregated by income, geography and industry are shown in Fig. 4 (Supplementary Figs. 2733). Figure 3a conveys our first main result: stricter closure of economic activities and higher fear of infection both lead to increased unemployment and fewer COVID-19 deaths. To illustrate this, consider a scenario with baseline fear of infection and all economic activities open, represented by the light-coloured circle. If we maintain the fear of infection at the baseline level, but close all non-essential economic activities (as in the baseline scenario), unemployment surges by 64%, while the number of deaths drops by 35%. Likewise, if we instead keep the closure level at the empirical baseline but increase fear of infection (represented by the dark triangle), we see a 40% rise in unemployment and a 50% decrease in deaths relative to the empirical scenario. Similar trends are observed in other scenarios. Although the total death count and average unemployment can vary substantially across simulation runs, the relative impacts of different policies remain robust (Supplementary Fig. 26).
a,b, Deaths and unemployment across scenarios. For each scenario, we show the aggregate unemployment rate and the cumulative number of deaths, as averaged throughout the simulation period and the simulation runs (Supplementary Fig. 26 shows the variability across simulation runs and discusses its interpretation). The empirical scenario is highlighted to serve as a benchmark. Scenarios are distinguished by the strength of behaviour change, as exemplified by the fear of infection parameter (square: low; circle: baseline; triangle: high). a, Scenarios are further distinguished by the specific closure of economic activities (all non-essential industries, as occurred empirically, only customer-facing industries and no closures), keeping the start of protective measures fixed at the baseline, empirically observed date. b, Scenarios are further distinguished by the start of protective measures (baseline: 16 March 2020, as empirically; early: 17 February 2020 and late: 30 March 2020), keeping closures fixed at all non-essential industries. c, For the specific combination of high fear of infection and three different starts of protective measures, a time series of unemployment and deaths corresponding to the three scenarios is shown.
a, Workplace infections and unemployment across income classes. Top: we vary the level of closures, keeping fear of infection and the start of protective measures to their baseline values (so the case with all non-essential activities closed is the empirical scenario). Bottom: we vary fear of infection keeping all economic activities open and starting protective measures on the baseline date. b, Maps of unemployment across census tracts in New York City, corresponding to two scenarios in a, including the empirical scenario (asterisk) and the counterfactual with no closures and low fear (hash). c, Infections and reduction in consumption across five selected industries and three levels of closures, for baseline fear and start of protective measures.
Both higher fear of infection and stricter closures lead to saving lives at the expense of jobs, for low and high income workers alike (Fig. 4a). However, for low income workers, higher fear of infection or stricter closures have a larger effect, leading to more lives saved and more jobs lost, compared with high-income workers. As we will show later, outside the household setting, most infections occur in customer-facing industries, where most low-income workers are concentrated. Thus, mandated closure or spontaneous avoidance of these industries leads to both more unemployment and fewer workplace infections among low-income workers.
The unequal economic outcomes of the empirical scenario also lead to geographical disparities. Figure 4b shows two maps of unemployment in Manhattan in the empirical scenario (asterisk) and in a counterfactual with low fear and no closures (hash). We see that in the counterfactual, the unemployment rate is very evenly spatially distributed, while in the empirical scenario, low-income areas such as the Queens and the Bronx have a high unemployment rate of more than 20%, compared with high-income areas such as Manhattan, with unemployment rates around 15%.
Overall, these results contribute to the ongoing debate on the relative effectiveness of behavioural change versus NPIs in preserving both public health and the economy. While it is intuitive to expect stricter mandated NPIs to increase unemployment and decrease COVID-19 deaths, it is less apparent that heightened behavioural adaptation would yield similar results (Discussion). Our findings highlight a qualitative parallel between substantial behavioural change and stringent economic activity closures. Spontaneous avoidance of services offered by customer-facing industries, akin to their mandated closure, results in increased unemployment but fewer fatalities. This trend is particularly pronounced among low-income individuals.
Our model also evaluates the efficacy of closing all non-essential economic activities, including large segments of manufacturing and construction, compared with exclusively closing customer-facing industries. We find that the mandated closure of all non-essential activities only marginally decreases deaths compared with solely closing customer-facing industries, but it drastically increases unemployment. In comparison with the baseline scenario, a counterfactual that only closes customer-facing industries results in a slightly higher death rate (4% higher), but substantially mitigates unemployment, reducing it by 36%. To explain these results, consider Fig. 4c. Most infections occur in customer-facing sectors such as entertainment and accommodationfood. Their closure curbs infections considerably but also consumption and employment. Conversely, closing manufacturing or construction marginally impacts infections but drastically reduces consumption. Professional services remains largely unaffected also because of WFH adaptability.
Methodologically, these industry-specific results were obtained because we associated each consumption venue from mobility data to an economic activity, allowing the quantification of industry-specific contacts. This granular, data-driven approach provides insights that more aggregate, qualitative models might overlook.
Another counterfactual exploration concerns the effectiveness of starting epidemic mitigation and control earlier (4weeks before) or later (2weeks later) than in the empirical scenario. As we show in Fig. 3b, delaying these measures marginally reduces unemployment by 2% but causes a notable 50% rise in deaths. In a high fear-of-infection scenario, late measures result in both a 46% increase in deaths and a 12% rise in unemployment. The mechanism for these results is suggested in Fig. 3c, where we show time series across the three counterfactuals with high fear of infection. We see that an early start of mitigation measures prevents an epidemic wave, leading to no further increase in unemployment due to fear of infection. Conversely, with a baseline or late start, substantial behaviour change leads to reduced consumption, and this, in turn, leads industries to fire their employees, increasing unemployment. Thus, starting mitigation measures early is crucial to improve epidemic outcomes, and possibly economic outcomes too. Our preliminary investigation (Supplementary Section 6.4) also shows that with an early start of protective measures, it is possible to avoid an excessive burden on the healthcare system, as measured by a usage of more than 50% of the nominal capacity of intensive care units.
In the empirical scenario, the parameterization of the fear of infection is uniformly applied across individuals, as suggested by survey evidence20. However, we also examined a counterfactual in which young individuals adopt less behavioural changes (low fear of the disease) than older individuals (high fear of the disease), considering this might be a more optimal situation for pandemic control through behavioural change. Here, at-risk older individuals would internalize the infection risk more, while younger individuals, less likely to suffer severe consequences, could maintain higher consumption and contribute to herd immunity. We explore how this scenario plays out quantitatively in the data-driven, granular agent-based model.
To explore the effects of heterogeneous behavioural changes, we group all households into three classes based on the age of their household head (034, 3564 and 65+years). We assume that fear of infection in each class is proportional to the risk of death in that class (Supplementary Section 6.5). We also normalize the fear of infection parameter across age bracket so that the mean takes the same baseline value that we considered in the empirical scenario. This normalization ensures that results are driven by a different distribution of fear across age groups, rather than by changes in overall fear. At the end of this procedure, the fear level among households aged 034years is 0.02 times the baseline, households aged 3564years have a fear level of 0.48 times the baseline and households aged 65+years have a considerably higher fear level of 4.91 times the baseline. We compare the age-specific scenario with the scenario in which all agents have uniform baseline fear (as in the empirical scenario).
We report aggregate results in Fig. 5a, which reproduces the same scenarios as Fig. 3 for uniform fear, next to the new results for age-specific fear. Adjusting for age-specific fear, while keeping other factors constant, marginally reduces both unemployment and deaths compared with uniform fear. In the all open scenario, where the effect is most pronounced, age-specific fear reduces deaths by 6% and unemployment by 5%. For comparison, closing customer-facing industries cuts deaths by 28% but increases unemployment by 22%.
a, Aggregate deaths and unemployment across scenarios. The general interpretation is the same as Fig. 3; here, uniform fear is represented by circles and age-specific fear is represented by plus symbols. b, For the scenario all openearly start, time series of the level of workplace and community contacts and consumption demand of customer-facing industries, disaggregated by type of fear (solid lines, age-specific fear and dashed lines, uniform fear) and by age groups with heterogeneous fear. These time series show how fear of infection reduces contacts and consumption demand. c, For the same scenario as b, the time series of infections per 1,000 individuals disaggregated by age groups and by whether they occurred outside or within households, and actual consumption demand (relative to the pre-pandemic level) disaggregated by industry and by whether industries are customer facing or not.
In Fig. 5b,c, we examine industry- and age-specific effects, focusing on ages 034years and 65+years. First, in Fig. 5b, we show how fear of infection reduces contacts and consumption demand. As expected, uniform fear leads to equal reductions across all ages by construction, while age-specific fear instead leads to the least reduction in young agents and the most in older agents. Total consumption decreases less than contacts as it may not require direct contact, such as ordering takeaway food (Methods).
In Fig. 5c, we first consider infections (left plots). We distinguish infections occurring inside households from those happening outside (community or workplace contacts). Outside the household, infections among older agents decrease with age-specific fear compared with uniform fear, especially around the epidemic peak where they are 30% lower. However, in the waning phase of the epidemic, infections are comparable in both scenarios. In contrast, we see an opposite trend among young households, where a large number of infections happen later due to their very low fear of infection. Within households, the differences between age-specific and uniform scenarios are less pronounced.
The relatively small decrease in deaths with age-specific fear can be explained in two ways. On the one hand, the time series of reductions in contacts and infections show that older individuals drastically reduce their contacts only after the epidemic peak. This delay results from the lag between infection and death reporting; behaviour change intensifies when individuals become aware of the number of COVID-19 deaths. On the other hand, older individuals cannot avoid infections within their own households.
As we can also see in Fig. 5c (right plots), age-dependent fear of infection alters consumption demand across industries. Consumption demand decreases more in health, a customer-facing industry on which old agents spend a disproportionate amount of income, and less in accommodationfood, on which young agents spend a higher share of their income. At the same time, consumption demand increases towards industries that are not customer facing because households reallocate part of their consumption budget to those industries. As individuals in older age groups decrease consumption more and thus have more budget to reallocate because of higher fear of infection, this results in higher consumption demand towards industries that have high consumption share among old individuals, such as finance. By contrast, because younger individuals spend a large fraction of their income on real estate and, with low fear, they do not reallocate much, the increase in demand for real estate services is lower than if fear of infection was uniform across ages.
In summary, these findings show that even when individuals adjust their behaviours in response to their personal risk levels during a pandemic, it only modestly affects health and economic outcomes. Moreover, our results quantify the complex ripple effects across various sociodemographic groups.
