COVID-19 does not harm all people equally. Age is the single most important risk factor in predicting hospitalization or death from SARS-CoV-2 infection, with more than a thousand-fold higher risk of poor outcomes for older people relative to young children, a fact known from the beginning of the pandemic. Others with chronic conditions such as obesity, and some immunocompromised populations, also face elevated mortality and morbidity risk. Early on, particularly prevaccination, institutionalized populations, including those in nursing homes and jails, also faced specific challenges, as did high-risk indigenous populations.

Given these epidemiological facts, it was a critically important public health priority to properly protect these high-risk populations in order to reduce their risk of infection. It is therefore vital to conduct an honest evaluation of the successes and failures of state, local, and national public health agencies to protect the most vulnerable Americans.

It has been known since the Athenian plague of 430 BC that recovered individuals are protected when re-exposed to an infectious disease, at least for some amount of time. This is called infection-acquired immunity or natural immunity, as opposed to vaccine-acquired immunity. Protection may be absolute or partial, resulting in sterilizing immunity that prevents reinfection or in non-sterilizing immunity that decreases severity of disease if reinfected. With few individuals becoming reinfected early in the pandemic, it was obvious that most recovered individuals mounted robust and protective immune responses. Although sterilizing immunity may wane over time, protection from severe disease post-COVID-19 infection is, so far, long-lasting, similar to other coronaviruses that cause common colds.

The issue of infection-acquired immunity was and is at the core of many disputed pandemic policies. Without durable infection-acquired immunity, herd immunity cannot be  reached,  there would be no effective vaccines, and  high-risk individuals would have to be sheltered forever unless the virus was eradicated. However, evidence existed early on that prior infection conferred durable protective immunity in the case of SARS-CoV2, meaning that efforts should have been aimed at protecting high-risk individuals until sufficient immunity could be reached in the population through a combination of infection-acquired and vaccine-acquired immunity.

Another reason that denial of natural immunity led to misguided COVID-19 policies is that vaccines were assumed to have superior immunity compared to natural infection, an assumption that led to widespread vaccine mandates even in previously infected people. Prior infection and vaccines both provide a form of immunity. Acknowledgement of infection-acquired immunity is not an argument against vaccines. For example, the purpose of the measles vaccine is to prevent measles, but those who have already had measles do not need the vaccine. 

Schools closed in March 2020 across the USA, initially for 2 weeks, but then extended for the vast majority until the end of the school year, with in-person teaching replaced by online instruction. Some schools opened in the fall of 2020 while other schools remained virtual throughout the 2020/21 academic year. Some schools were even virtual or experienced brief closures during the 2021/22 academic year, while many others went remote during surges. In some school districts, a hybrid approach was used, with in-person schools on some days and online schools on other days. In other districts school was entirely remote with little in person synchronous instruction for most of the 2020/2021 year. In contrast, most European children returned to school after a short shut down while Sweden never closed schools for children under the age of 15. 

The collateral damage associated with pandemic lockdown policies is enormous, cutting across multiple areas of physical and mental health, education, culture, religion, the economy, and the social fabric of society. In this document, we use the term ‘lockdowns’ broadly to refer to a suite of policies ranging from school and university closures, mandatory online education, health-care and travel restrictions, business closures, stay-at-home and work-from-home orders, COVID-19-related firings, and the canceling and prevention of cultural, social and religious events. Collateral public health damage has affected all segments of society, but children, low income people, manual laborers, the elderly, and people with chronic health problems have been hardest hit, resulting in increased wealth and health inequalities. 

Some of the consequences of lockdowns were immediate, such as the deterioration of cardiovascular disease outcomes and mental health, while other negative consequences, due to, for example, delayed cancer screenings and school closures, may not be realized or fully felt for decades. States, counties, and the federal government will continue to collect data and compare outcomes in states with prolonged deep lockdowns (OR, CA, MD, e.g.) versus in states that had fewer COVID-19 restrictions (SD, NE, IA, FL e.g.). Early reports indicate profound differences, with estimates suggesting that 75-80% of the excess deaths during the pandemic were not attributable to COVID-19 but to pandemic policies that led people to miss addiction treatment, to stay home when they were experiencing symptoms of a heart attack, and others. 

Disease surveillance is a primary duty of public health agencies, to monitor the spread, prevalence, and seriousness of diseases in different geographical regions and population groups. This task includes gathering and disseminating basic information about incidence, hospitalizations, mortality, infection-fatality rates, sero-prevalence/antibodies, T-cell immunity, vaccinations, vaccine efficacy, vaccine adverse events, variants, and other parameters. Such knowledge lays the foundation for public health recommendations. Without reliable disease surveillance data, public health agencies, politicians, scientists and the public are operating blindly. For influenza, salmonella, e.coli and dozens of other infectious diseases, the CDC has reliable disease surveillance systems in place. For COVID-19, there was a profound lack of reliable and unbiased data, even after the first few confusing months of the pandemic. The lack of accurate data persists to this day.

Throughout the pandemic, policy makers from local levels (county and state health officials, school boards, and governors) to national and federal levels such as CDC directors and White House officials, relied on modeling to guide decisions. Public health has a long history of using epidemiologic models for a variety of purposes: (i) To gain understanding of infectious disease dynamics, (ii) to predict future health care needs to ensure sufficient capacity, and (iii) to fill in for missing real world data. When using models to make public-health policy decisions, it is crucial that politicians, policy makers, and public health officials clearly understand data weaknesses, underlying assumptions used to generate models and forecasts, the nature of input parameters, and uncertainties inherent in any model. 

At the outset, models from the Institute of Health Metrics and Evaluation at the University of Washington (IHME) and Imperial College in London, as well as models generated by the CDC, were influential both locally and nationally. These models tried to forecast COVID-19 cases, hospitalizations, and deaths under different pandemic lockdown strategies, by modeling the effects on COVID-19 from school closures, public gathering restrictions, suspension of health care services, business closures, limiting restaurant capacity, quarantining people, travel restrictions, and mass asymptomatic testing. Mask models were used as support for mask mandates and models assuming that vaccination halted transmission were used when approving, recommending and mandating vaccines. 

Since it quickly became evident that SARS-CoV2 spread rapidly and could not be eradicated, it was critically important to promptly find treatments to minimize mortality and reduce hospitalizations. Because developing new pharmaceutical drugs from scratch is a lengthy and expensive process, it was important to quickly evaluate existing drugs to see if they could be repurposed as COVID-19 treatments. In addition, the clinical medicine community urgently needed data and guidance concerning costs and benefits of proposed and widely used treatments.

The NIH rapidly initiated preclinical and clinical trials to evaluate hundreds of new and repurposed drugs for potential antiviral effects. The difficulty of this task may explain why there are few drugs to treat COVID-19. Even to treat influenza, which is not a novel virus, there are few effective approved antiviral drugs. 

Below we discuss the most notable drugs and interventions, and those that were most widely used. We also address issues surrounding data collection timeliness, information dissemination, drug accessibility, and politicization of certain therapeutics.

COVID-19 vaccines were developed and given emergency use authorization (EUA) in record time. In late 2020, the Food and Drug Administration (FDA) granted EUA to three COVID-19 vaccines for adults: Pfizer (2 doses), Moderna (2 doses) and Johnson & Johnson (1 dose). Subsequently, the Pfizer and Moderna vaccines also received EUA approval for use in children as young as 6 months of age. Pfizer, Moderna and Johnson & Johnson boosters were also approved. Federal, state, and local governments, as well as many companies, hospitals, restaurants, universities, and a few K-12 school systems, imposed vaccine mandates for work, business, education, travel and cultural events. As of  December 2022, only vaccinated visitors can enter the USA. 

Vaccination policies were some of the most divisive elements of the pandemic, engendering protests at various times and termination of employment for some professions or government employees over their refusal to get vaccinated. Because mandates were initially based on the assumption that vaccines were capable of halting transmission, it is important to delve into the trials in detail.

Testing for SARS-CoV-2 is important for multiple reasons. At the clinical level, when someone has COVID-like symptoms, it is important to find out whether they have COVID-19 or something else, in order to provide effective treatment. To prevent COVID-19 spread, it is important to test hospital and nursing home staff and visitors so they do not infect frail elderly high-risk individuals. It is also important for disease surveillance and sero-prevalence estimation. This latter topic is covered in Chapter 5 on Public Health Data. 

As early as February 2020, public health agencies emphasized testing in combination with contact tracing as interventions to suppress COVID-19 spread. To the extent that this was a policy position, large-scale rapid testing was needed. When it became clear COVID-19 could not be eradicated, testing was still important to guide treatment and to protect those who were at high risk of severe disease. However, testing continued to be used and recommended for the general population, including in very low risk children, without evidence of individual or community-wide benefit from doing so. Positive tests forced children to miss school and adults to miss work without evidence of these strategies effectively decreasing community transmission or benefiting the health of the community.

COVID-19 testing in the U.S. was marked by periods of significant under-testing, over-testing, and socioeconomic inequities in access to testing due to hoarding of tests by wealthy institutions such as elite universities.

Public mask use was rare in the United States before the COVID-19 pandemic. On April 3 2020, the CDC began recommending face coverings, including both cloth and surgical masks, for everyone two years old and up. The CDC cited no evidence for the efficacy of masks and the previous lack of evidence on efficacy of mask wearing for other respiratory viruses was ignored or distorted. During the pandemic, universal and school-masking became increasingly controversial and polarized. 

In supporting mask mandates for people ages 2 and up, the CDC and other government officials: 1) Exaggerated the benefits of masks based on pre-pandemic studies, 2) Promoted studies that supported masking recommendations/mandates, while ignoring or censoring those that did not, 3) Did not fund randomized controlled trials to determine the efficacy of masking, 4) Failed to explain why masking recommendations were not aligned with many European countries, especially for children, and 5) Failed to acknowledge potential harms of masking, especially for children.