Range.......1

Through the late 1920s and early 1930s, remote reaches of the Soviet Union were forced through social and economic changes that would normally take generations. Individual farmers in isolated areas of what is now Uzbekistan had long survived by cultivating small gardens for food, and cotton for everything else. Nearby in the mountain pasturelands of present-day Kyrgyzstan, herders kept animals. The population was entirely illiterate, and a hierarchical social structure was enforced by strict religious rules. The socialist revolution dismantled that way of life almost overnight. The Soviet government forced all that agricultural land to become large collective farms and began industrial development. The economy quickly became interconnected and complex. Farmers had to form collective work strategies, plan ahead for production, divvy up functions, and assess work along the way. Remote villages began communicating with distant cities. A network of schools opened in regions with 100 percent illiteracy, and adults began learning a system of matching symbols to sounds. Villagers had used numbers before, but only in practical transactions. Now they were taught the concept of a number as an abstraction that existed even without reference to counting animals or apportioning food. Some village women remained fully illiterate but took short courses on how to teach kindergartners. Other women were admitted for longer study at a teachers’ school. Classes in preschool education and the science and technology of agriculture were offered to students who had no formal education of any kind. Secondary schools and technical institutes soon followed. In 1931, amid that incredible transformation, a brilliant young Russian psychologist named Alexander Luria recognized a fleeting “natural experiment,” unique in the history of the world. He wondered if changing citizens’ work might also change their minds

When Luria arrived, the most remote villages had not yet been touched by the warp-speed restructuring of traditional society. Those villages gave him a control group. He learned the local language and brought fellow psychologists to engage villagers in relaxed social situations—teahouses or pastures—and discuss questions or tasks designed to discern their habits of mind. Some were very simple: present skeins of wool or silk in an array of hues and ask participants to describe them. The collective farmers and farm leaders, as well as the female students, easily picked out blue, red, and yellow, sometimes with variations, like dark blue or light yellow. The most remote villagers, who were still “premodern,” gave more diversified descriptions: cotton in bloom, decayed teeth, a lot of water, sky, pistachio. Then they were asked to sort the skeins into groups. The collective farmers, and young people with even a little formal education, did so easily, naturally forming color groups. Even when they did not know the name of a particular color, they had little trouble putting together darker and lighter shades of the same one. The remote villagers, on the other hand, refused, even those whose work was embroidery. “It can’t be done,” they said, or, “None of them are the same, you can’t put them together.” When prodded vigorously, and only if they were allowed to make many small groups, some relented and created sets that were apparently random. A few others appeared to sort the skeins according to color saturation, without regard to the color. Geometric shapes followed suit. The greater the dose of modernity, the more likely an individual grasped the abstract concept of “shapes” and made groups of triangles, rectangles, and circles, even if they had no formal education and did not know the shapes’ names.

The remote villagers, meanwhile, saw nothing alike in a square drawn with solid lines and the same exact square drawn with dotted lines. To Alieva, a twenty-six-year-old remote villager, the solid-line square was obviously a map, and the dotted-line square was a watch. “How can a map and a watch be put together?” she asked, incredulous. Khamid, a twenty-four-year-old remote villager, insisted that filled and unfilled circles could not go together because one was a coin and the other a moon. The pattern continued for every genre of question. Pressed to make conceptual groupings—akin to the similarities questions on IQ tests—remote villagers reverted to practical narratives based on their direct experience. When psychologists attempted to explain a “which one does not belong” grouping exercise to thirty-nine-year-old Rakmat, they gave him the example of three adults and one child, with the child obviously different from the others. Except Rakmat could not see it that way. “The boy must stay with the others!” he argued. The adults are working, “and if they have to keep running out to fetch things, they’ll never get the job done, but the boy can do the running for them.” Okay, then, how about a hammer, a saw, a hatchet, and a log—three of them are tools. They are not a group, Rakmat replied, because they are useless without the log, so why would they be together?

Other villagers removed either the hammer or the hatchet, which they saw as less versatile for use with the log, unless they considered pounding the hatchet into the log with the hammer, in which case it could stay. Perhaps, then, bird/ rifle/ dagger/ bullet? You can’t possibly remove one and have a group, a remote villager insisted. The bullet must be loaded in the rifle to kill the bird, and “then you have to cut the bird up with the dagger, since there’s no other way to do it.” These were just the introductions explaining the grouping task, not the actual questions. No amount of cajoling, explanation, or examples could get remote villagers to use reasoning based on any concept that was not a concrete part of their daily lives. The farmers and students who had begun to join the modern world were able to practice a kind of thinking called “eduction,” to work out guiding principles when given facts or materials, even in the absence of instructions, and even when they had never seen the material before. This, it turns out, is precisely what Raven’s Progressive Matrices tests. Imagine presenting the villagers living in premodern circumstances with abstract designs from the Raven’s test. Some of the changes wrought by modernity and collective culture seem almost magical. Luria found that most remote villagers were not subject to the same optical illusions as citizens of the industrialized world, like the Ebbinghaus illusion. Which middle circle below looks bigger?

If you said the one on the right, you’re probably a citizen of the industrialized world. The remote villagers saw, correctly, that they are the same, while the collective farmers and women in teachers’ school picked the one on the right. Those findings have been repeated in other traditional societies, and scientists have suggested it may reflect the fact that premodern people are not as drawn to the holistic context—the relationship of the various circles to one another—so their perception is not changed by the presence of extra circles. To use a common metaphor, premodern people miss the forest for the trees; modern people miss the trees for the forest. Since Luria’s voyage to the interior, scientists have replicated his work in other cultures. The Kpelle people in Liberia were subsistence rice farmers, but in the 1970s roads began snaking toward them, connecting the Kpelle to cities. Given similarities tests, teenagers who were engaged with modern institutions grouped items by abstract categories (“All of these things can keep us warm”), while the traditional teens generated groups that were comparatively arbitrary, and changed frequently even when they were asked to repeat the exact same task. Because the touched-by-modernity teens had constructed meaningful thematic groups, they also had far superior recall when asked later to recount the items. The more they had moved toward modernity, the more powerful their abstract thinking, and the less they had to rely on their concrete experience of the world as a reference point.

Time Inversion Tenet

An amazing recreation of the time inversion in tenet.

The Foreign Contribution (Regulation) Amendment Bill, 2020

The Foreign Contribution (Regulation) Amendment Bill, 2020 was introduced in Lok Sabha on September 20, 2020. The Bill amends the Foreign Contribution (Regulation) Act, 2010. The Act regulates the acceptance and utilisation of foreign contribution by individuals, associations and companies. Foreign contribution is the donation or transfer of any currency, security or article (of beyond a specified value) by a foreign source.

Prohibition to accept foreign contribution: Under the Act, certain persons are prohibited to accept any foreign contribution. These include: election candidates, editor or publisher of a newspaper, judges, government servants, members of any legislature, and political parties, among others. The Bill adds public servants (as defined under the Indian Penal Code) to this list. Public servant includes any person who is in service or pay of the government, or remunerated by the government for the performance of any public duty.

Transfer of foreign contribution: Under the Act, foreign contribution cannot be transferred to any other person unless such person is also registered to accept foreign contribution (or has obtained prior permission under the Act to obtain foreign contribution). The Bill amends this to prohibit the transfer of foreign contribution to any other person. The term ‘person’ under the Act includes an individual, an association, or a registered company.

Aadhaar for registration: The Act states that a person may accept foreign contribution if they have: (i) obtained a certificate of registration from central government, or (ii) not registered, but obtained prior permission from the government to accept foreign contribution. Any person seeking registration (or renewal of such registration) or prior permission for receiving foreign contribution must make an application to the central government in the prescribed manner. The Bill adds that any person seeking prior permission, registration or renewal of registration must provide the Aadhaar number of all its office bearers, directors or key functionaries, as an identification document. In case of a foreigner, they must provide a copy of the passport or the Overseas Citizen of India card for identification.

FCRA account: Under the Act, a registered person must accept foreign contribution only in a single branch of a scheduled bank specified by them. However, they may open more accounts in other banks for utilisation of the contribution. The Bill amends this to state that foreign contribution must be received only in an account designated by the bank as “FCRA account” in such branch of the State Bank of India, New Delhi, as notified by the central government. No funds other than the foreign contribution should be received or deposited in this account. The person may open another FCRA account in any scheduled bank of their choice for keeping or utilising the received contribution.

Restriction in utilisation of foreign contribution:. Under the Act, if a person accepting foreign contribution is found guilty of violating any provisions of the Act or the Foreign Contribution (Regulation) Act, 1976, the unutilised or unreceived foreign contribution may be utilised or received, only with the prior approval of the central government. The Bill adds that the government may also restrict usage of unutilised foreign contribution for persons who have been granted prior permission to receive such contribution. This may be done if, based on a summary inquiry, and pending any further inquiry, the government believes that such person has contravened provisions of the Act.

Renewal of license: Under the Act, every person who has been given a certificate of registration must renew the certificate within six months of expiration. The Bill provides that the government may conduct an inquiry before renewing the certificate to ensure that the person making the application: (i) is not fictitious or benami, (ii) has not been prosecuted or convicted for creating communal tension or indulging in activities aimed at religious conversion, and (iii) has not been found guilty of diversion or misutilisation of funds, among others conditions.

Reduction in use of foreign contribution for administrative purposes: Under the Act, a person who receives foreign contribution must use it only for the purpose for which the contribution is received. Further, they must not use more than 50% of the contribution for meeting administrative expenses. The Bill reduces this limit to 20%.

Surrender of certificate: The Bill adds a provision allowing the central government to permit a person to surrender their registration certificate. The government may do so if, post an inquiry, it is satisfied that such person has not contravened any provisions of the Act, and the management of its foreign contribution (and related assets) has been vested in an authority prescribed by the government.

Suspension of registration: Under the Act, the government may suspend the registration of a person for a period not exceeding 180 days. The Bill adds that such suspension may be extended up to an additional 180 days.

Jhum cultivation

What is Jhum cultivation?

Farmers slash and burn a patch of land, start growing food crops.
When soil fertility declines they shift to another place, burning the jungle again.
For various names for Jhum
Favor of Jhum cultivation

Uses forest’s natural cycle of regeneration.
Organic farming, doesn’t use pesticides or chemical fertilizers. Trees burned to provide potash to the soil
Cooperation: after jhuming, the land distributed among farmers.
Jhum causes only temporary loss of jungle. Because once monsoon over, the farmers abandon the land. Jungle regenerates quickly.

The Jhum cycle normally runs for around 6-10 years. i.e. when farmers return to the same patch of land and burn forest again.
During those 6-10 years, same jungle provide forest produce to the tribals.
Contrary to that, monoculture plantation causes permanent loss of forest, due to chemical inputs. so once, you cut down a forest to raise monoculture plantation, you cannot reconvert the same land into natural forest again.

Jhuming done in steep hill slopes where sedentary cultivation not possible. So it’s a reflex to physiographical characters of the North east.

overall, Jhum economically productive + ecologically sustainable

Against Jhum cultivation

If you leave the jungle for ten years, it’ll regenerate. But nowadays farmers come back in jut ~5 years. Not enough time for the forest to regenerate.
North eastern forest are major carbon sinks, home to biodiversity. Must be protected.
Jhum farming families always suffer food, fuel and fodder problems, leading to poverty and malnutrition.
tons of biomass gets loss due to burning of tress.

Tree burning leads to:
higher CO2, NO2 and other Greenhouse gases (GHGs). This wasn’t an issue in ancient times (when there was no industrialization). But we cannot afford more GHG in modern era.
higher run off of rainwater. hence draught, drinking water shortage.
we cannot find oaks, bamboo and teak forests in many regions of North East- only deciduous scrubs left. this erodes biodiversity of the region.

soil erosion, siltation in dams.

Too much Democracy impacting Economic Freedom

Research paper on Partial vaccination

Abstract

This review article outlines the key concepts in vaccine epidemiology, such as basic reproductive numbers, force of infection, vaccine efficacy and effectiveness, vaccine failure, herd immunity, herd effect, epidemiological shift, disease modeling, and describes the application of this knowledge both at program levels and in the practice by family physicians, epidemiologists, and pediatricians. A case has been made for increased knowledge and understanding of vaccine epidemiology among key stakeholders including policy makers, immunization program managers, public health experts, pediatricians, family physicians, and other experts/individuals involved in immunization service delivery. It has been argued that knowledge of vaccine epidemiology which is likely to benefit the society through contributions to the informed decision-making and improving vaccination coverage in the low and middle income countries (LMICs). The article ends with suggestions for the provision of systematic training and learning platforms in vaccine epidemiology to save millions of preventable deaths and improve health outcomes through life-course.

Introduction

The benefits of vaccination, one of the most cost-effective public health interventions, have not fully reached target beneficiaries in many low- and middle-income countries (LMICs).[1] Though the field of vaccine research and vaccinology has received a lot of attention since the discovery of the smallpox vaccine by Edward Jenner (1749-1823) in 1798, more than two centuries later, an estimated 20% of deaths among children aged less than 5 years occur due to diseases preventable by currently licensed vaccines.[2,3] Since the discovery of smallpox vaccine, a number of vaccines have become available. “Vaccine research and vaccinology” had witnessed a sort of ‘renaissances in vaccine research and uses’ in the early 1970s and 1980s, and now in the 21st century there are licensed vaccines against nearly 27 agents and ongoing research on candidate vaccines against nearly 130 agents.[1]

There is increasing recognition of the role of vaccines as proven lifesaving interventions and that of the epidemiological principles in maximizing the benefits of vaccines and vaccination. While vaccinology delves into understanding how vaccines work, epidemiology helps to ascertain whether a particular vaccine is needed in targeted population (or age group) or not? For physicians and vaccine users alike, epidemiology and immunology are two important fields in medical science and public health, which helps in the better appreciation of the promise and potential of vaccines. While immunology is essential for understanding vaccine-host interactions, epidemiology is essential for understanding the implications of a vaccination program on the community and individuals. “Vaccine epidemiology” could be described as an interface between public health, basic medical sciences, and clinical medicine aimed at maximizing the benefit of existing knowledge in these areas.

The learning and study of vaccine epidemiology could help in the following: To make decisions on how to choose vaccines for inclusion in a public health program; to assess the disease burden; to identify target pathogens for vaccine research; to identify sources and transmission pathways of disease-causing agents; to determine vaccination strategies; to design disease-specific control, elimination, and eradication strategies; to monitor performance indicators; to take steps to improve surveillance; and to measure the progress and impact of vaccination strategies.

This review article aims to outline the basic concepts and key principles of vaccine epidemiology, and to briefly describe how vaccination program managers and vaccinologists could use this knowledge and understanding in their respective fields of work.

Go to: Historical Background The terms “vaccine” and “vaccinology” came into use soon after Edward Jenner discovered the smallpox vaccine. Jenner called the smallpox vaccine “variola vaccinae.” For his contribution, Jenner is often referred to as the “Father of Vaccinology” (though this epithet is sometimes also used for Louis Pasteur). The word “vaccine” originated from vacca, a Latin term for the cow.[4] The credit for the first use of the term “vaccine” goes to Swiss physician Louis Odier (1748-1817), and the terms “vaccination” and “to vaccinate” were first used by Richard Dunning (1710- 1797).[5]

Epidemiology, which literally means “the study of what is upon the people,” is derived from the Greek epi meaning “upon, among,” demos meaning “people,” and logos meaning “study or discourse.” Physicians from the times of Hippocrates (460-370 BC) tried to understand the pattern of diseases in the community, though the term “epidemiology” was first used to describe the study of epidemics in 1802 by the Spanish physician Villalba in the Epidemiología Española.[6] In modern times, John Snow (1813-1858) and William Farr (1807-1883) pioneered the work on epidemiology and are often referred as one of the “fathers of modern epidemiology.”[7,8] Epidemiology, though practiced from earlier times than vaccinology, gained attention and prominence in the 19th century. Now, the practice of vaccinology has become closely linked with that of epidemiology.

Go to: Key Concepts in Vaccinology

A vaccine is “an inactivated or attenuated pathogen or a component of a pathogen (nucleic acid, protein) that when administered to the host, stimulates a protective response of the cells in the immune system,” or it is “an immune-biological substance designed to produce specific protection against a given disease.”[9] The process of administering the vaccine is called vaccination. In other words, vaccination is the process of protecting susceptible individuals from diseases by the administration of a living or modified agent (e.g., oral polio vaccine), a suspension of killed organisms (as in pertussis), or an inactivated toxin (as in tetanus). Immunization is “the artificial induction of active immunity by introducing into a susceptible host the specific antigen of a pathogenic organism.”[9] However, immunization and vaccination are often used interchangeably. Vaccinology combines the principles of microbiology, immunology, epidemiology, public health, and pharmacy, amongst other.

The aim of vaccination is to protect individuals who are at risk of a disease. The children, the elderly, immune-compromised individuals, people living with chronic diseases, and people living in disease-endemic areas are those most commonly at risk. Vaccination is a common strategy to control, eliminate, eradicate, or contain disease (i.e., mass immunization strategy). If one wishes to learn about and understand vaccines, vaccination, and immunization programs, one needs to start with the understanding of key terms such as “antigen,” “antibody,” “immunoglobulins,” and “antisera,” among others. These are often described in the textbooks on this topic and therefore not covered in this article.

A vaccine is different from immunoglobulin in that the vaccines help in developing protective antibodies in the body of the individual to whom these are administered, and protection is available after a lag period of a few weeks to several months. However, immunoglobulin provides immediate protection. The vaccine administration is followed by two types of immune responses: Primary and secondary [Figure 1].[9,10]

There are different types of vaccines: Live, killed, conjugate, component, and recombinant vaccines. While live vaccines provide protection after the administration of a single dose (though not always), the nonlive (or killed) vaccines usually require multiple doses for a satisfactory primary response. A minimum of 4 weeks’ interval is required between successive doses, though a longer interval (often, 8 weeks is considered optimal) results in higher antibody levels. The booster doses are generally given 6 or more months after the completion of the primary series. The booster doses have rapid and higher antibody response, a higher affinity for antibody production, and provide longer duration of protection (this is linked to secondary immune response).[11]

The antibody responses to vaccines are usually identified by “the correlates of protection,” an immune response that is responsible for and statistically interrelated with protection and usually linked to B-cell dependent response. Though, for a number of new vaccines, it is assumed that T-cells also play a role in correlates of protection. The correlates of protection are identified by animal challenge models and efficacy trials.[12]

Go to: Key Concepts in Epidemiology

Epidemiology pinpoints the weak links in the chains, sources, and transmission pathways of the pathogen so that the interventions can be directed. The understanding of epidemiology is required from the very early stage of priority-setting for disease burden, understanding the basis of correlates of protection, development of vaccines, evaluating different vaccination strategies including epidemiological and economic modeling, deciding national vaccination strategies, developing surveillance mechanisms, impact assessment, and designing vaccine introduction strategies.

The term “disease burden” or burden of disease (BoD) occupies a key place in epidemiology. The BoD could be measured by incidence or prevalence of a disease (prevaccine and postvaccine); severity/mortality (measured as case fatality ratio, hospitalization, and disease sequelae); disability [measured by disability-adjusted life years (DALYs)] and quality-adjusted life years (QALYs)]; economics (measured by cost-effectiveness, cost benefit, and cost utility); and social aspects (measured by societal disruption, economic disruption, and household impact).[13] The key concepts and study designs (i.e., cross-sectional, case-control, nested case-control, cohort studies) to understand epidemiology (disease occurrence and trends) are well, documented and thus not described in this article.[14,15,16]

However, vaccine probe studies requires special mention here, a vaccine probe study is a randomized cluster trial of a vaccine in which, usually, vaccine effectiveness (in other trials, usually efficacy is assessed) endpoints are used. The difference in the incidence of disease between vaccinated and unvaccinated children represents the vaccine-preventable disease burden. These are technically vaccine-effectiveness trials and have been used to measure the vaccine-preventable proportion/incidence of clinically (not microbiologically) defined outcomes. This approach has been used successfully in several countries for studies on Haemophilus influenzae type b (Hib) conjugate and pneumococcal conjugate vaccines.[17,18]

Go to: Vaccine Epidemiology

Vaccine epidemiology is the study of the interactions and effects of vaccines (and vaccination programs) on epidemiology of vaccine preventable diseases. Understanding the pattern of disease by geographical, rural-urban, and gender variations, linkage between disease burden and immunization coverage is based on principles of epidemiology. Which time of the year the polio mass immunization campaign should be conducted? For conducting mass campaigns, which age group should be targeted? Where should immunization efforts be concerted? Why do outbreaks occur? Why is it that some children do not suffer disease even though they have not received any vaccination? These are some of the questions answered through the application.

Vaccine efficacy and effectiveness

Vaccines have effect at both individual and population levels. The “biological or individual level effect” of vaccines includes effects on susceptibility (VEs), on infectiousness (VEi), and on disease progression (VEp). The “population level effects” of vaccination depend on the coverage and distribution of the vaccines, as well as on how well different groups mix with each other.[29,30,31] These effects could result from the biologic as well as behavioral effects of the vaccination. Overall, the public health effect of vaccination programs depends on the effect in both vaccination and the unvaccinated population. This gives at least three types of population level effects of vaccination:

Indirect effect: The population level effect of widespread vaccination on people not receiving vaccine

Total effect: Combination of population level effect and effect of vaccination on individuals receiving vaccine

Overall public health effect: The effect of vaccination program based upon weighted average of indirect effect on the individual not receiving vaccine and total effect on individual receiving vaccination.

In this context, the terms “vaccine efficacy,” “vaccine effectiveness,” and “program effectiveness” are commonly used. Vaccine efficacy is the percentage reduction in disease incidence attributable to vaccination (usually) calculated by means of the following equation:

VE (%) = (RU - RV)/RU × 100

where RU = the incidence risk or attack rate in unvaccinated people and RV = the incidence or attack rate in vaccinated people.[29,30]

The equation for vaccine efficacy can be reformulated as:

VE = 1 -RV/RU × 100

where RV/RU is the relative risk or rate ratio in vaccinated and unvaccinated people.

The vaccine efficacy is measured by observational studies under field conditions within a vaccination program or measured by trials conducted under normal program conditions. The vaccine efficacy for a number of vaccines is known, such as Measles 90-95%; mumps: 72-88%; and rubella 95-98%.[32,33] In vaccine trials, the vaccine's efficacy (among other things, including safety) is assessed. This is an important criterion for licensing of the vaccines and for making decisions on programmatic use. Vaccine efficacy is dependent on internal or individual factors, for example the efficacy of the measles vaccine depends on the presence of inhibitory maternal antibodies, the immunologic maturity of the vaccine recipient, and the dose and strain of the vaccine virus.[34]

Vaccine effectiveness is the sum of the reduction in the clinical events that might be expected to be associated with the disease.[28,29] Under program-based conditions, the effectiveness of the measles vaccine depends on the coverage, cold chain maintenance, correct injection techniques and safety, inaccurate recordkeeping/recall resulting in misclassification errors, and population-specific factors [human immunodeficiency virus (HIV) infection, malnutrition, etc.]. The most commonly used study design to assess a vaccine's effectiveness is a retrospective case-control analysis, and the odds ratio thus obtained can be used to calculate vaccine effectiveness, as follows:

Effectiveness = (1-OR) × 100

Vaccine effectiveness could be assessed by observational studies: Cohort studies, household contact study, case-control study and screening. How the information from screening could be used for estimating of vaccine efficacy is shown in Figure 2.[35,36]

Vaccine efficacy and effectiveness have often been used interchangeably in scientific literature. Vaccine effectiveness is often referred to as vaccine efficacy in field conditions. In other words, vaccine effectiveness is a combination of vaccine efficacy and field conditions such as coverage, immune status of population, and conditions under which the vaccine was administered (cold chain). In general, efficacy is higher than effectiveness. However, vaccines that show herd effect could have higher effectiveness than vaccine efficacy. For example, under program conditions, vaccine effectiveness is lower than vaccine efficacy, while herd effect improves effectiveness and can take it above efficacy. If analyzed from an outbreak, the formula for estimation of vaccine effectiveness is: Attack rate among vaccinated (ARV) vs attack rate among unvaccinated (URU). The formula used for assessing vaccine efficacy with this information is: Vaccine Efficacy (VE) = (ARU-ARV)/ARU*00.[35,36]

The “program effectiveness” refers to “the effectiveness of all antigens in an immunization program at implementation level at district, state and national levels.” The program effectiveness is also assessed by analyzing the trends in the occurrence of vaccine-preventable diseases (or VPDs) in identified settings and situation, before and after vaccine introductions. Overall mortality reduction is often considered as an indicator of vaccine program effectiveness/impact. Program effectiveness is the combination of more than one vaccine's effectiveness. Impact is the population level effect of a vaccination program, which depends on many factors, including vaccine efficacy, herd immunity, and effectiveness.

Go to:

Study Designs to Assess Vaccine Efficacy and Program Effectiveness Serological and epidemiological studies can be used to determine vaccine efficacy and program effectiveness with minor methodological adoptions.[9,15,16,18,33,34,35,36] Among serological studies, two sub types of studies are utilized for vaccine efficacy: Seroconversion studies and seroprevalence studies. Seroconversion studies are useful in measuring the induction of an immune response in the host. In the absence of disease, it indicates the persistence of antibodies and immunity. These studies are particularly useful in choosing the appropriate age for vaccination. Seroprevalence studies monitor the prevalence of antibodies due to disease in the population and indicate the pattern of occurrence of diseases.

The epidemiological approaches measure the ARV and ARU in various settings. Thereafter, the formula suggested above could be used for estimating vaccine efficacy. The epidemiological study designs[9,15,16,18,33,34,35,36] include:

Double-blind, randomized, placebo-control trials: The ideal vaccine efficacy study is a clinical trial starting with persons susceptible to disease. However, such studies are not possible after the vaccine is licensed, as it becomes unethical to use placebo when the vaccine is of proven benefit

Observational cohort studies: These are conducted when the randomized-controlled trials or secondary attack rate trials are not ethically justified, or are not feasible due to low incidence of the disease, or there is a requirement for long-term follow-up for the calculation of efficacy (e.g., hepatitis B vaccination in neonates, or where the number of individuals is too large to follow up)

Case-control studies: These studies are most useful when personal immunization records are not generally available but some other sources such as records from clinics can be obtained. Case-control studies may be useful when prospective controlled trials are not feasible due to low incidence of disease

Stepped wedge design studies: These are used when previous studies have indicated that the intervention is likely to be beneficial and the public health needs to introduce the intervention precludes withholding it from a population. The intervention is introduced in phases, group by group, until the entire target population is covered. The groups form the unit of randomization

Outbreak investigations (Community-wide, total population, or population clusters): Such studies are best done when the outbreak is in a defined population, such as a village, town, city, or school

Secondary attack rates in families and/or clusters: The assessment of secondary attack rate in family members of the “index case” provides a good opportunity to assess vaccine efficacy

Screening of population: This method provides an estimate of vaccine efficacy if some other information is available. The formula used for assessing vaccine efficacy is given below and is used for assessing vaccine efficacy:

PCV = [PPV- (PPV*VE)]/[1-(PPV*VE)]

where PCV = proportion of cases occurring among vaccinated individuals; PPV = proportion of population vaccinated; and VE = vaccine efficacy. If any of the two values in this formula is known, the third value can be derived [Figure 2].

Cluster Survey Method: In some of the endemic areas, vaccine efficacy can be assessed, even in the absence of an outbreak, by using coverage survey methods.

Go to: Other Important Concepts in Epidemiology Vaccine failure When a person who has been fully vaccinated develops the disease against which she/he has been vaccinated, it is referred to as vaccine failure. This could be of two types-

Primary vaccine failure occurs when the recipient does not produce enough antibodies when first vaccinated. Infection can therefore occur at any time post vaccination. For example, this occurs in about 10% of those who receive the measles, mumps, and rubella (MMR) vaccine[37]

Secondary vaccine failure occurs when adequate protective levels of antibodies are produced immediately after the vaccination, but the levels fall over time. The incidence of secondary vaccine failure therefore increases with time after the initial vaccination and hence booster doses are required. This is a characteristic of a number of the inactivated vaccines.[37]

Herd immunity and herd effect

Herd immunity may be defined as the resistance of a group or a community in total, against the invasion and spread of an infectious agent as a result of a large proportion of individuals in the group being immunized. Herd immunity or contact immunity develops in the case of certain live vaccines (e.g., OPV), wherein the nonvaccinated individuals also develop immunity to the pathogen just by coming in contact with the vaccinated individual.[38]

The level of herd immunity can be assessed through cross-sectional and longitudinal serological surveys. The serological surveys are usually based on serum or saliva in viral infections and activated T-cells for bacterial and protozoal infections. There are a number of quantitative assays, too.[39]

Additionally, immunological and disease surveillance methods provide the empirical base for the analysis and interpretation of herd immunity. Mathematical and statistical methods play an important role in the analysis of infectious disease transmission and control. They help to define both what needs to be measured, and how best to measure and define epidemiological quantities. The level of herd immunity can be measured by reference to the magnitude of reduction in the value of Ro.[22]

Herd immunity threshold (H) is defined as the minimum proportion to be immunized in a population for elimination of infection.

H = 1 - 1/Ro = (Ro -1)/Ro As the immunization coverage increases, the incidence and prevalence rates may decrease not only due to the direct effect of immunization per se but also because of indirect effects, such as the development of herd immunity and herd effect.[38,40]

“Herd effect” or “herd protection” is “the reduction of infection or disease in the unimmunised segment as a result of immunising a proportion of the population” or is “the change induced in epidemiology (incidence reduction) among unvaccinated members when a good proportion is vaccinated.” Herd effect is seen only for infections where humans are the source, and it extends beyond the age the vaccine is given, i.e., Haemophilus influenzae type B (Hib) vaccine is given to infants and protected other under-5 children, flu vaccine to children and beneficial effect among other family members.

Epidemiologic shift or transition

Epidemiological shift or transition denotes the change in the pattern of disease in a specified population. The impact on the person characteristics of a disease is the shift in the age of occurrence and severity of the diseases as observed consistently in communities with partial immunization coverage or immunization coverage for specific age groups only. A number of factors including the age at the time of vaccination, target population for vaccination, serotypes covered by the vaccines (where the disease in question is caused by multiple serotypes), and overall vaccination coverage may affect the epidemiological shift or transition.[41,42]

The phenomenon has importance in diseases such as hepatitis A, rubella, and varicella, wherein the severity of disease worsens with advancing age. It also has significance in diseases where multiple serotypes are associated with the diseases such as pneumococcal diseases and when targeting specific serotype by vaccine may lead to the emergence of other types of serotypes. The epidemiological shift or transition sometimes may offshoots the benefits accrued by the vaccination program. This showcases the need for tracking the epidemiological changes in the vaccination programs and initiating appropriate corrective measures.

One of the well-documented example of epidemiological shifts has been documented from Greece, following the introduction of MMR vaccine in public health program of the country. When the MMR vaccine was introduced in 1975 in Greece, the coverage with the vaccine was around 50-60% of the cohort, which reduced the incidence of diseases in the targeted population; however, shifted the average age of infection to older population. However, the susceptible cohort of un-vaccinated continued to increase over period of time with epidemiological shift to older age groups. By the early 1990s, specially those unvaccinated girls reached in the reproductive age group, still susceptible to rubella virus disease. In such cases, if the infections happened during the time of pregnancy, it led to development of congenital rubella syndrome (CRS) in fetus/infants. In 1993, it was noted that Greece had the highest incidence of congenital rubella syndrome (CRS).[42] This example highlights the need and importance for high coverage at the time of vaccine introduction and sustenance of the coverage in the subsequent cohorts. This situation is sometimes referred to as “perverse outcome,” where disease severity increases with age at infection: Vaccination can increase the burden of severe diseases, by raising the average age of infections. The total number of infections falls but the total number of severe disease increases, e.g., CRS, measles, encephalitis, and orchitis due to mumps.

Go to: Vaccine-preventable Disease Surveillance Disease surveillance is another public health and epidemiology tool. A functioning disease surveillance system helps in understanding disease epidemiology before vaccines are introduced. Thereafter, it guides how well the vaccination program is doing in reducing the BoD. It helps in decisionmaking on the introduction of vaccines and also in assessing the impact of interventions. Unfortunately, the disease surveillance system in the majority of the LMICs requires a major boost.

Go to: Disease Modeling The models are often referred to as “tools for thinking and simplification of systems,” suitable for analysis.[43]

Epidemiology aims to measure the disease burden; however, where measurement is not practical, estimates must be developed. The modern epidemiological methods and disease modeling have reached the level where accurate projection can be made based on existing knowledge and information. The estimates derived from various sources are often used in vaccination programs. The estimates are used for decisionmaking at local levels (i.e., state and national levels), for deriving estimates for neighboring countries (with similar settings) and for global (or international) levels. The estimates, if done with similar methods can provide useful information for interstate, intercountry, and interdisease comparisons, to observe the disease trend over a period of time, and for comparison of choices between intervention versus none versus others

In vaccination programs, a number of models are used: A static or decision analysis model is used on the assumption of a constant force of infection (or fixed risk). These models are more commonly used for noninfectious diseases. The static models are usually applied to a single cohort[45]

Markov models[46]

Dynamic model used for infectious diseases. Suspected, infected, and recovered (SIR) approach is an example of a dynamic model. These models are applied to multiple cohorts.[47]

Economic evaluation Economic evaluation in healthcare addresses the question whether an intervention or procedure is worth doing when compared with other possible uses of the same resources.[44] This is based on the premise that resources are finite and there are opportunity costs. In such analysis, both costs (resources used) and outcomes (benefits) are considered. There are number of analyses including cost-effective analysis, cost-benefit analysis, cost analysis, and cost utility analysis.

Go to: Immunization Program Assessments and Evaluations It is imperative to ensure the quality of immunization services is evaluated and assessed on a regular basis. The epidemiological methods provide useful tools for such evaluations.

Thirty cluster survey: This is standard World Health Organization (WHO) methodology to determine immunization coverage based on a survey of small number of individuals (for example, 210 in 30 clusters of seven children each). The home visits are made and a immunization record or history is taken for children aged 12-23 months. The survey provides fairly correct information about immunization coverage in the area. However, it is important that these clusters are selected based on standardized methodology and statistical tools[48]

Seventy-five-household survey: In this approach, 75 households near the health facility are surveyed. This methodology follows the notion that the households closest to the facilities can provide the best estimates of immunization coverage[49]

Missed-opportunity survey, Lot quality assurance survey (LQAS), the multiple indicator cluster survey (MICS), and coverage evaluation surveys (CES) are the other methods.[49]

Go to: Application of Vaccine Epidemiology in Vaccination Programs Vaccine epidemiology, as described in the earlier sections, is a multidisciplinary science. It has a role to play from vaccine research (proof-of-concept stage and then in clinical trials), in decisionmaking on new vaccine introduction, and once vaccines are introduced in the post-marketing surveillance and other aspects. The practice of vaccinology is gathering momentum since the first immunization schedule was published by the WHO in 1961.[50] Now in the 21st century, there are more licensed vaccines, more in the pipeline, more number of people than ever receive vaccines. There is an increasing amount of research in laboratories, deliberations in academic institutions, and policy discourses in ministries of health about vaccines and vaccination schedules. There is an increasing awareness within the general public about vaccines and vaccination schedules.

One of the important development in the last 2 decades has been that the electronic media and the Internet have empowered people with information. The information received from various sources on the Internet is mostly useful for parents and the general public but is not always correct. At times, it reflects one sided view, and people with vested interests may misuse the information and media. The risk of such incomplete information has been reflected in some of the recent outbreaks of measles in European countries where the Internet has been a major source of information, and people used this source for decisionmaking. Such misinformation has affected the adoption of human papillomavirus (HPV) vaccination in a few countries.[51,52] These examples reflect the two sides of technology, which can help in increasing coverage of vaccines but could also spread misinformation which can lead to disease outbreaks.

The incidences of “vaccine refusal” or “vaccine hesitancy” are increasing.[53] This is an area in which the knowledge and understanding of vaccine epidemiology could help in improving immunization coverage (or at least prevent undesired fall in immunization coverage). The vaccine epidemiology can help in responding to the misinformation and addressing the challenge. Vaccine epidemiology can provide guidance in understanding which diseases are common in which parts of the world and therefore help in decisionmaking about which vaccine should be received by the people traveling to particular endemic countries. It guides in the selection of vaccines for special target groups, i.e., pregnant women, the elderly, and in the changing context.

The disease surveillance system is often used to measure the impact of vaccination programs on disease burden. The vaccine preventable diseases surveillances system could provide useful insight on the benefits of vaccination and is an important tool for programmatic modifications and advocacy. The National Immunization Technical Advisory Groups (NITAGs) use vaccine epidemiology for decision making. The national vaccination policies and immunization guidelines need to be informed by the vaccine epidemiology.

There are important roles of vaccine epidemiology in reducing morbidity and mortality from vaccine-preventable diseases. This knowledge could be best utilized by policy makers for immunization program decisionmaking and by family physicians and public health specialists for advising individuals on the benefits of vaccination.

In LMICs there is limited capacity for training in vaccinology and epidemiology. There are very few training opportunities and courses that teach vaccine epidemiology. It is a paradox that countries requiring maximum capacity have very limited opportunity. This affects both vaccine research and decisionmaking.

In the absence of sufficient capacity, the country program managers in LMICs often have to rely on international experts for decisionmaking. This adversely affects the reputation and credibility of the country's program managers and raises questions regarding the decisionmaking process, contributing to the delay in the benefits of proven interventions reaching those who are most susceptible to vaccine-preventable diseases.

Go to: Conclusion The understanding of vaccine epidemiology has potential to save additional lives from vaccine preventable diseases and improve health outcomes through life course. The vaccine epidemiology has definitive role in extending the benefits of vaccines to additional populations and in the selection of target groups for vaccination, amongst other. However, systematic efforts would be needed to translate this knowledge into actions. The mechanisms and institutional capacity has to be built into low and middle income countries (LMICs) on vaccine epidemiology. The national governments and international development partners need to support and promote courses and training programs for vaccine epidemiology, and the academic communities need to work together. Vaccine epidemiology should be part of key modules in the teaching of undergraduate and postgraduate medical students. Public-health program managers and policy makers should be trained in vaccine epidemiology through continued medical education and on-the-job training programs.

Tenet movie review

Having more than proved his worth,CIA superspy The Protagonist (John David Washington) is inducted into secret organisation Tenet, on the trail of bullets that go backwards in time. From there he finds himself facing off against arms dealer Andrei Sator (Kenneth Branagh) in a bid to avert World War III.

By Alex Godfrey | Posted 21 Aug 2020 Release Date: 16 Jul 2020

The blams come thick and fast. Tenet, in fact, might be Christopher Nolan’s blammiest film yet. BLAM! A terrifying thing just happened. BLAM! A shocking moment of revelation. BLAM! Here’s a speedboat. (There really is a massive blam accompanying an otherwise ordinary shot of two people on a speedboat.) It’s not even Hans Zimmer this time — here the great Ludwig Göransson (Black Panther, The Mandalorian) is on scoring duties, making it all his own (you will nod your head intensely) but without ever scrimping on the blams. Because if a Christopher Nolan film doesn’t sound like the end of the world, then something’s wrong. And this one really is about the end of the world.

We’re told early on — defiantly and resolutely — that this is not a film about time-travel. There are a handful of instances in Tenet where one character lays things out to another, each time telling them it’s okay if they don’t quite get it. “Don’t try to understand it,” says Clémence Poésy’s Laura, Tenet’s Q to John David Washington’s James Bond, as she introduces him to backward bullets (they go back in time… don’t try to understand it) and gives him a brief primer. It’s not time-travel, she tells him, it’s “technology that can reverse an object’s entropy”. In other words, Christopher Nolan wants you to know that this is not Back To The Future. This is serious business. This is about the prevention of World War III. “Nuclear holocaust?” asks Washington’s protagonist. No, she says — this is worse.

This scene, Nolan setting out his stall, is scored sumptuously, romantically — it’s one big swoon, and it speaks volumes. Despite a complex relationship serving as the film’s broken heart (courtesy of Kenneth Branagh’s arms-dealing oligarch Andrei and his estranged and abused wife Kat, played by Elizabeth Debicki), Nolan’s great love affair, of course, is with time itself. From Memento’s muddied, memory-straining recollections to Dunkirk’s triple-pronged timeline and Interstellar’s generational rifts, he can’t get enough of the stuff, and Tenet is awash in it. It’s not a plot device — it’s the thing itself, something to be explored, investigated, played with, twisted, bent.

Nolan has made his own Bond film here, borrowing everything he likes about it, binning everything he doesn’t, then Nolaning it all up.

And yet: this is an action film. It opens with a brutal, prolonged siege at the Kiev Opera House, in which people fight for their lives and lose, in which all hell breaks loose, and in which Göransson and Nolan’s sound designers intend to deafen you. You have Washington and Robert Pattinson bungee-jumping up and into a building (and that’s without any of the time-bending). You have a lean and mean kitchen fight in which a cheese grater is deployed (and not for cheese). You have a 747 being blown up, you have a thrilling car chase (which does feature some time-bending), and extended set-pieces in which your eyes will see things they haven’t quite seen before. For the most part, there are no Hollywood hysterics; it is big — often very big — but not bombastic.

Tenet is Bond without the baggage. Filmed in Italy, Estonia, India, Norway, the UK and the US, it’s a globetrotting espionage extravaganza that does everything 007 does but without having to lean into the heritage, or indeed the clichés. Just as with Indiana Jones, for which George Lucas persuaded Bond fan Steven Spielberg they could create their own hero instead of piggybacking on someone else’s, Nolan has made his own Bond film here, borrowing everything he likes about it, binning everything he doesn’t, then Nolaning it all up (ie: mucking about with the fabric of time). And while Washington — never not magnetic, every second of this film – isn’t a suave playboy in the slightest, he has the swagger — and the odd wisecrack. “Easy,” he says in response to some light manhandling from one of Andrei’s security goons. “Where I’m from, you buy me dinner first.” In the same sequence, Andrei — a big bad if ever there was one — asks him: “How would you like to die?” Elsewhere we meet an arms dealer who casually swigs his whiskey while he has a gun to his head. This is absolutely the same playground that 007 runs around in, with the same toys. It just feeds it all through a physics machine.

For the most part, that’s welcome. “Try to keep up,” one character says in regards to the mechanics of it all. “Does your head hurt?” another asks later. Somebody is told they need to stop thinking in linear terms. No doubt some big brains will be fine with all of this — and will be able to follow the plot — but for the rest of us, Tenet is often a baffling, bewildering ride. Does it matter? Kind of. It’s hard to completely invest in things that go completely over your head. The broad strokes are there, and it’s consistently compelling, but it’s a little taxing. No doubt it all makes sense on Nolan’s hard drive, but it’s difficult to emotionally engage with it all.

If that’s even what the film wants us to do. These are great actors — Washington, Pattinson, Branagh and Debicki are all immensely watchable — but only towards the end, as things begin to pay off, do you really get the chills here and there. For the most part, everybody’s on a mission, doing their job, the film barely stopping to breathe, certainly not to take any sentimental detours. And nobody involved looms larger than Nolan himself. This is a film engineered for dissection and deconstruction. Just as Inception was, this is an M.C. Escher painting, but folded, origami-like, and with holes poked into it for its own denizens to fall through. It may not be Back To The Future, but regardless, it has its cake, eats it, then goes back in time and eats it again. It may not be a hokey time-travel film, but that doesn’t mean Nolan can’t get his rocks off playing around with paradoxes.

And ultimately, for all of that, Tenet once again proves Nolan’s undying commitment to big-screen thrills and spills. There’s a lot riding on this film, to resurrect cinema, to wrench people away from their televisions, facemasks and all. It may well do the trick: if you’re after a big old explosive Nolan braingasm, that is exactly what you’re going to get, shot on old-fashioned film too (as the end credits proudly state). By the time it’s done, you might not know what the hell’s gone on, but it is exciting nevertheless. It is ferociously entertaining.

Once again seizing control of the medium, Nolan attempts to alter the fabric of reality, or at least blow the roof off the multiplexes. Big, bold, baffling and bonkers

Why buffet invested in Japanese stocks

Chetpet eco park sunrise

nice place to walk during covid times

Ant financial ipo suspension

Clarity matters, especially during turbulent times. Almost buried in the main news this week is the world’s largest stock listing getting shut down right before its launch. Ant Group, the financial technology giant, was set to raise $34.5 billion in the Shanghai and Hong Kong stock exchange, which would have translated into a market valuation of $313 billion.

If the initial public offering (IPO) were to happen, Ant Group would have exceeded the size of JPMorgan Chase, Goldman Sachs, and Wells Fargo. But then this IPO veers off course.

Chinese authorities have cited “major issues” for halting the IPO, but major issues do not show up this late in the game. A record 19.05 trillion yuan ($2.85 trillion) worth of bids was received from retail investors for Ant’s shares on Shanghai’s Star Market, exceeding the supply of shares 870 times.

All these point to Jack Ma, the founder of Alibaba who personally controls 8.8% of Ant. He gave a talk last month in Shanghai criticizing regulators in China. “We shouldn’t use the way to manage a train station to regulate an airport,” Ma said. “We cannot regulate the future with yesterday’s means.”

Now we know who the real boss is.

Why You Need Clear Thinking Now

If you are like me, this is too much drama. There is too much information. What you and I want to know is really the future of finance. We want to know that the sort of fintech made by Ant will continue to gain traction.

If you work in the financial sector, you want to know if these innovations will still spread around the world. If you are retail investors, you want to know if the creditability of the Chinese stock market has been annihilated. Should you still diversify your pension plan with Chinese holdings or not?

In short, we have too many data points. We need clarity instead, to make decisions on things within our control.

How Future-ready Are Your Banks?

At the Center for Future Readiness at the IMD business school, we have been tracking companies’ readiness. In the financial sector, this is evaluated based on firms’ ability to leverage robo-advisory, artificial intelligence, mobile services, and blockchain. These are capabilities that CEOs have long recognized.

Here is what we have found. Those who ranked high in our study turned out to have followed a set of distinct logic. They acted less like a bank. They orchestrated ecosystems. They scaled fast.

Ranking Company Name
1 ANT GROUP
2 MASTERCARD
3 SQUARE, INC.
4 VISA INC.
5 PAYPAL HOLDINGS, INC.
6 JPMORGAN CHASE & CO.
7 PING AN INSURANCE (GROUP) CO. OF CHINA LTD
8 CREDIT SUISSE AG
9 BANK OF AMERICA CORPORATION
10 ALLIANZ SE
11 AMERICAN EXPRESS COMPANY
12 UBS AG
13 WELLS FARGO & COMPANY
14 AXA SA
15 HSBC HOLDINGS PLC
Rankings of leading financial services companies based on a “leap readiness index.” To arrive at these rankings, we relied on hard market data. This included 7 categories with 23 measurements.

Note that every financial institute—regardless of ranking—has its own digital strategy. But the top players scale digital innovation faster than others, and Ant does it to an extreme extent.

I remember visiting Ant’s headquarters in Hangzhou 18 months ago. Sitting down with a manager, I asked about staff growth. Despite the runaway growth in revenue, Ant was not on a hiring binge.

“You don’t need more people?” I asked.
“No. We automate everything once a new business stabilizes.”

“So where do those people go?” “They go to develop another new business,” the manager said.

A business is built by humans and then run by machines. Then the humans are redeployed to other ventures. It is the logical thing to do. However, only a few companies besides Ant could do it with such ferocity. You may ask, is Ant totally unique? Is this a new strategy unseen by the world?

Not really. If you were to look at Visa, Mastercard, PayPal, Square, or Ping An, they are all on the same trajectory. What that means is this: The kind of fintech revolution wrought by Ant will not stop. Ant or not, the inevitable remains the same.

Should I Invest in Ant When It’s Ready Again? There is no single authority responsible for regulating fintech products and services. The main regulatory bodies include the People’s Bank of China (PBOC), the China Banking and Insurance Regulatory Commission (CBIRC), and the China Securities Regulatory Commission (CSRC). In other words, it is a diffused political system.

But since they can pause the world’s largest IPO, this also meant they share a similar viewpoint. They all want to send chills down Jack Ma’s back. His success is a story tied to the national narrative, both in substance and in form. Any deviation from it will have consequences. No one is too big to attack.

And so Ant is likely, and already is, regulated like any other banks. That means meeting the same capital requirement, auditing criteria, and compliance standard. It will be a challenge for a data-driven, AI-first enterprise like Ant. It will mean that, for the first time, software programmers at Ant need to still move fast but not break things.

Is it still a good investment opportunity? Well, depending on the revised price level. Here’s one way to look at it.

Ant has made a $3.5 billion profit in six months. Let us assume it stops growing for the next six. It will still end the year with $7 billion. Compare that with a truly mature tech company like Netflix, whose P/E ratio is around 80. Ant would still have an estimated valuation of $560 billion.

But maybe you want to use a Chinese firm as a benchmark. Let’s take Tencent, another Chinese technology giant with a fast-growing payments business. It is trading at about 40x earnings. Applying the same multiple to Ant, that would imply a $280 billion valuation. Again, it’s assuming no profit growth in the second half of the year. Such a scenario is virtually impossible. Ant’s profit for the first half exceeded the full-year total for 2019.

A $280 billion valuation is still huge. Obviously, all eyes will still be watching the IPO of the decade, delayed.

Howard Yu is the LEGO professor of management and innovation at Switzerland's IMD and is director of the advanced management program (AMP). His book Leap: How to Thrive in a World Where Everything Can Be Copied was published by PublicAffairs in 2018.
Jialu Shan is a Research Fellow at The Global Center for Digital Business Transformation–An IMD and Cisco Initiative.

Why are Bank stocks undervalued

By JAY WEI
Updated Aug 7, 2020
Bank stocks are notorious for trading at prices below book value per share, even when a bank's revenue and earnings are on the rise. As banks grow larger and expand into nontraditional financial activities, especially trading, their risk profiles become multidimensional and more difficult to construct, increasing business and investment uncertainties.

This is presumably the main reason why bank stocks tend to be conservatively valued by investors who must be concerned about a bank's hidden risk exposures. Trading for their own accounts as dealers in various financial derivatives markets exposes banks to potentially large-scale losses, something investors have decided to take into full consideration when valuing bank stocks.

Book Value per Share
Book value per share is a good measure to value bank stocks. The price-to-book (P/B) ratio is applied with a bank's stock price compared to equity book value per share, meaning that the ratio looks at a company's market cap in comparison to its book value.

The alternative of comparing a stock's price to earnings, or price-to-earnings (P/E) ratio, may produce unreliable valuation results, as bank earnings can easily swing back and forth in large variations from one quarter to the next due to unpredictable, complex banking operations.

Using book value per share, the valuation is referenced to equity that has less ongoing volatility than quarterly earnings in terms of percentage changes because equity has a much larger base, providing a more stable valuation measurement.

Banks With Discount P/B Ratio
The P/B ratio can be above or below one, depending on whether a stock is trading at a price more than or less than equity book value per share. An above-one P/B ratio means the stock is being valued at a premium in the market to equity book value, whereas a below-one P/B ratio means the stock is being valued at a discount to equity book value. For instance, Capital One Financial (COF) and Citigroup (C) had P/B ratios of 0.92 and 0.91,

Many banks rely on trading operations to boost core financial performance, with their annual dealer trading account profits all in the billions. However, trading activities present inherent risk exposures and could quickly turn to the downside.

Wells Fargo & Co. (WFC) in 2018 saw its stock trading at a premium due to its equity book value per share, with a P/B ratio of 1.42 in Q3 2018.3 One reason for this was that Wells Fargo was relatively less focused on trading activities than its peers, potentially reducing its risk exposures.

Valuation Risks
While trading mostly derivatives can generate some of the biggest profits for banks, it also exposes them to potentially catastrophic risks. A bank's investments in trading account assets can reach hundreds of billions of dollars, taking a large chunk out of its total assets.

For the fiscal quarter ending Sept. 30, 2018, Bank of America (BAC) saw its equity trading revenue up 2.5% to $1 billion, while its fixed-income trading fell by 5% to $2.06 billion over the same period.4 Moreover, trading investments are only part of a bank's total risk exposures when banks can leverage their derivatives trading to almost unimaginable amounts and keep them off the balance sheets.

For example, at the end of 2017, Bank of America had total derivatives risk exposure of more than $30 trillion, and Citigroup had more than $47 trillion.5 These stratospheric numbers in potential trading losses dwarf their total market caps at the time of $282 billion and $173 billion for the two banks, respectively.67

Faced with such a magnitude of risk uncertainty, investors are best served to discount any earnings coming out of a bank's derivatives trading. Despite being partly responsible for the extent of the 2008 market crash, banking regulation has been minimized over the past few years, leading banks to take on increasing risks, expand their trading books, and leverage their derivatives positions.

The Bottom Line
Banks and other financial companies may have attractive price-to-book ratios, putting them on the radar for some value investors. However, upon closer inspection, one should pay attention to the enormous amount of derivatives exposure that these banks carry. Of course, many of these derivatives positions offset each other, but a careful analysis should be undertaken nonetheless

This Overlooked Variable is the key to the Pandemic

An article in "The Atlantic" by Zeynep Tufecki

There’s something strange about this coronavirus pandemic. Even after months of extensive research by the global scientific community, many questions remain open.

Why, for instance, was there such an enormous death toll in northern Italy, but not the rest of the country? Just three contiguous regions in northern Italy have 25,000 of the country’s nearly 36,000 total deaths; just one region, Lombardy, has about 17,000 deaths. Almost all of these were concentrated in the first few months of the outbreak. What happened in Guayaquil, Ecuador, in April, when so many died so quickly that bodies were abandoned in the sidewalks and streets?* Why, in the spring of 2020, did so few cities account for a substantial portion of global deaths, while many others with similar density, weather, age distribution, and travel patterns were spared? What can we really learn from Sweden, hailed as a great success by some because of its low case counts and deaths as the rest of Europe experiences a second wave, and as a big failure by others because it did not lock down and suffered excessive death rates earlier in the pandemic? Why did widespread predictions of catastrophe in Japan not bear out? The baffling examples go on.

I’ve heard many explanations for these widely differing trajectories over the past nine months—weather, elderly populations, vitamin D, prior immunity, herd immunity—but none of them explains the timing or the scale of these drastic variations. But there is a potential, overlooked way of understanding this pandemic that would help answer these questions, reshuffle many of the current heated arguments, and, crucially, help us get the spread of COVID-19 under control.

By now many people have heard about R0—the basic reproductive number of a pathogen, a measure of its contagiousness on average. But unless you’ve been reading scientific journals, you’re less likely to have encountered k, the measure of its dispersion. The definition of k is a mouthful, but it’s simply a way of asking whether a virus spreads in a steady manner or in big bursts, whereby one person infects many, all at once. After nine months of collecting epidemiological data, we know that this is an overdispersed pathogen, meaning that it tends to spread in clusters, but this knowledge has not yet fully entered our way of thinking about the pandemic—or our preventive practices.

The now-famed R0 (pronounced as “r-naught”) is an average measure of a pathogen’s contagiousness, or the mean number of susceptible people expected to become infected after being exposed to a person with the disease. If one ill person infects three others on average, the R0 is three. This parameter has been widely touted as a key factor in understanding how the pandemic operates. News media have produced multiple explainers and visualizations for it. Movies praised for their scientific accuracy on pandemics are lauded for having characters explain the “all-important” R0. Dashboards track its real-time evolution, often referred to as R or Rt, in response to our interventions. (If people are masking and isolating or immunity is rising, a disease can’t spread the same way anymore, hence the difference between R0 and R.)

Unfortunately, averages aren’t always useful for understanding the distribution of a phenomenon, especially if it has widely varying behavior. If Amazon’s CEO, Jeff Bezos, walks into a bar with 100 regular people in it, the average wealth in that bar suddenly exceeds $1 billion. If I also walk into that bar, not much will change. Clearly, the average is not that useful a number to understand the distribution of wealth in that bar, or how to change it. Sometimes, the mean is not the message. Meanwhile, if the bar has a person infected with COVID-19, and if it is also poorly ventilated and loud, causing people to speak loudly at close range, almost everyone in the room could potentially be infected—a pattern that’s been observed many times since the pandemic begin, and that is similarly not captured by R. That’s where the dispersion comes in.

There are COVID-19 incidents in which a single person likely infected 80 percent or more of the people in the room in just a few hours. But, at other times, COVID-19 can be surprisingly much less contagious. Overdispersion and super-spreading of this virus are found in research across the globe. A growing number of studies estimate that a majority of infected people may not infect a single other person. A recent paper found that in Hong Kong, which had extensive testing and contact tracing, about 19 percent of cases were responsible for 80 percent of transmission, while 69 percent of cases did not infect another person. This finding is not rare: Multiple studies from the beginning have suggested that as few as 10 to 20 percent of infected people may be responsible for as much as 80 to 90 percent of transmission, and that many people barely transmit it.

This highly skewed, imbalanced distribution means that an early run of bad luck with a few super-spreading events, or clusters, can produce dramatically different outcomes even for otherwise similar countries. Scientists looked globally at known early-introduction events, in which an infected person comes into a country, and found that in some places, such imported cases led to no deaths or known infections, while in others, they sparked sizable outbreaks. Using genomic analysis, researchers in New Zealand looked at more than half the confirmed cases in the country and found a staggering 277 separate introductions in the early months, but also that only 19 percent of introductions led to more than one additional case. A recent review shows that this may even be true in congregate living spaces, such as nursing homes, and that multiple introductions may be necessary before an outbreak takes off. Meanwhile, in Daegu, South Korea, just one woman, dubbed Patient 31, generated more than 5,000 known cases in a megachurch cluster.

Unsurprisingly, SARS-CoV, the previous incarnation of SARS-CoV-2 that caused the 2003 SARS outbreak, was also overdispersed in this way: The majority of infected people did not transmit it, but a few super-spreading events caused most of the outbreaks. MERS, another coronavirus cousin of SARS, also appears overdispersed, but luckily, it does not—yet—transmit well among humans.

This kind of behavior, alternating between being super infectious and fairly noninfectious, is exactly what k captures, and what focusing solely on R hides. Samuel Scarpino, an assistant professor of epidemiology and complex systems at Northeastern, told me that this has been a huge challenge, especially for health authorities in Western societies, where the pandemic playbook was geared toward the flu—and not without reason, because pandemic flu is a genuine threat. However, influenza does not have the same level of clustering behavior.

We can think of disease patterns as leaning deterministic or stochastic: In the former, an outbreak’s distribution is more linear and predictable; in the latter, randomness plays a much larger role and predictions are hard, if not impossible, to make. In deterministic trajectories, we expect what happened yesterday to give us a good sense of what to expect tomorrow. Stochastic phenomena, however, don’t operate like that—the same inputs don’t always produce the same outputs, and things can tip over quickly from one state to the other. As Scarpino told me, “Diseases like the flu are pretty nearly deterministic and R0 (while flawed) paints about the right picture (nearly impossible to stop until there’s a vaccine).” That’s not necessarily the case with super-spreading diseases.

Nature and society are replete with such imbalanced phenomena, some of which are said to work according to the Pareto principle, named after the sociologist Vilfredo Pareto. Pareto’s insight is sometimes called the 80/20 principle—80 percent of outcomes of interest are caused by 20 percent of inputs—though the numbers don’t have to be that strict. Rather, the Pareto principle means that a small number of events or people are responsible for the majority of consequences. This will come as no surprise to anyone who has worked in the service sector, for example, where a small group of problem customers can create almost all the extra work. In cases like those, booting just those customers from the business or giving them a hefty discount may solve the problem, but if the complaints are evenly distributed, different strategies will be necessary. Similarly, focusing on the R alone, or using a flu-pandemic playbook, won’t necessarily work well for an overdispersed pandemic.

Hitoshi Oshitani, a member of the National COVID-19 Cluster Taskforce at Japan’s Ministry of Health, Labour and Welfare and a professor at Tohoku University who told me that Japan focused on the overdispersion impact from early on, likens his country’s approach to looking at a forest and trying to find the clusters, not the trees. Meanwhile, he believes, the Western world was getting distracted by the trees, and got lost among them. To fight a super-spreading disease effectively, policy makers need to figure out why super-spreading happens, and they need to understand how it affects everything, including our contact-tracing methods and our testing regimes.

There may be many different reasons a pathogen super-spreads. Yellow fever spreads mainly via the mosquito Aedes aegypti, but until the insect’s role was discovered, its transmission pattern bedeviled many scientists. Tuberculosis was thought to be spread by close-range droplets until an ingenious set of experiments proved that it was airborne. Much is still unknown about the super-spreading of SARS-CoV-2. It might be that some people are super-emitters of the virus, in that they spread it a lot more than other people. Like other diseases, contact patterns surely play a part: A politician on the campaign trail or a student in a college dorm is very different in how many people they could potentially expose compared with, say, an elderly person living in a small household. However, looking at nine months of epidemiological data, we have important clues to some of the factors.

In study after study, we see that super-spreading clusters of COVID-19 almost overwhelmingly occur in poorly ventilated, indoor environments where many people congregate over time—weddings, churches, choirs, gyms, funerals, restaurants, and such—especially when there is loud talking or singing without masks. For super-spreading events to occur, multiple things have to be happening at the same time, and the risk is not equal in every setting and activity, Muge Cevik, a clinical lecturer in infectious diseases and medical virology at the University of St. Andrews and a co-author of a recent extensive review of transmission conditions for COVID-19, told me.

Cevik identifies “prolonged contact, poor ventilation, [a] highly infectious person, [and] crowding” as the key elements for a super-spreader event. Super-spreading can also occur indoors beyond the six-feet guideline, because SARS-CoV-2, the pathogen causing COVID-19, can travel through the air and accumulate, especially if ventilation is poor. Given that some people infect others before they show symptoms, or when they have very mild or even no symptoms, it’s not always possible to know if we are highly infectious ourselves. We don’t even know if there are more factors yet to be discovered that influence super-spreading. But we don’t need to know all the sufficient factors that go into a super-spreading event to avoid what seems to be a necessary condition most of the time: many people, especially in a poorly ventilated indoor setting, and especially not wearing masks. As Natalie Dean, a biostatistician at the University of Florida, told me, given the huge numbers associated with these clusters, targeting them would be very effective in getting our transmission numbers down.

Overdispersion should also inform our contact-tracing efforts. In fact, we may need to turn them upside down. Right now, many states and nations engage in what is called forward or prospective contact tracing. Once an infected person is identified, we try to find out with whom they interacted afterward so that we can warn, test, isolate, and quarantine these potential exposures. But that’s not the only way to trace contacts. And, because of overdispersion, it’s not necessarily where the most bang for the buck lies. Instead, in many cases, we should try to work backwards to see who first infected the subject.

Because of overdispersion, most people will have been infected by someone who also infected other people, because only a small percentage of people infect many at a time, whereas most infect zero or maybe one person. As Adam Kucharski, an epidemiologist and the author of the book The Rules of Contagion, explained to me, if we can use retrospective contact tracing to find the person who infected our patient, and then trace the forward contacts of the infecting person, we are generally going to find a lot more cases compared with forward-tracing contacts of the infected patient, which will merely identify potential exposures, many of which will not happen anyway, because most transmission chains die out on their own.

The reason for backward tracing’s importance is similar to what the sociologist Scott L. Feld called the friendship paradox: Your friends are, on average, going to have more friends than you. (Sorry!) It’s straightforward once you take the network-level view. Friendships are not distributed equally; some people have a lot of friends, and your friend circle is more likely to include those social butterflies, because how could it not? They friended you and others. And those social butterflies will drive up the average number of friends that your friends have compared with you, a regular person. (Of course, this will not hold for the social butterflies themselves, but overdispersion means that there are much fewer of them.) Similarly, the infectious person who is transmitting the disease is like the pandemic social butterfly: The average number of people they infect will be much higher than most of the population, who will transmit the disease much less frequently. Indeed, as Kucharski and his co-authors show mathematically, overdispersion means that “forward tracing alone can, on average, identify at most the mean number of secondary infections (i.e. R)”; in contrast, “backward tracing increases this maximum number of traceable individuals by a factor of 2-3, as index cases are more likely to come from clusters than a case is to generate a cluster.”

Even in an overdispersed pandemic, it’s not pointless to do forward tracing to be able to warn and test people, if there are extra resources and testing capacity. But it doesn’t make sense to do forward tracing while not devoting enough resources to backward tracing and finding clusters, which cause so much damage.

Another significant consequence of overdispersion is that it highlights the importance of certain kinds of rapid, cheap tests. Consider the current dominant model of test and trace. In many places, health authorities try to trace and find forward contacts of an infected person: everyone they were in touch with since getting infected. They then try to test all of them with expensive, slow, but highly accurate PCR (polymerase chain reaction) tests. But that’s not necessarily the best way when clusters are so important in spreading the disease.

PCR tests identify RNA segments of the coronavirus in samples from nasal swabs—like looking for its signature. Such diagnostic tests are measured on two different dimensions: Are they good at identifying people who are not infected (specificity), and are they good at identifying people who are infected (sensitivity)? PCR tests are highly accurate for both dimensions. However, PCR tests are also slow and expensive, and they require a long, uncomfortable swab up the nose at a medical facility. The slow processing times means that people don’t get timely information when they need it. Worse, PCR tests are so responsive that they can find tiny remnants of coronavirus signatures long after someone has stopped being contagious, which can cause unnecessary quarantines.

Meanwhile, researchers have shown that rapid tests that are very accurate for identifying people who do not have the disease, but not as good at identifying infected individuals, can help us contain this pandemic. As Dylan Morris, a doctoral candidate in ecology and evolutionary biology at Princeton, told me, cheap, low-sensitivity tests can help mitigate a pandemic even if it is not overdispersed, but they are particularly valuable for cluster identification during an overdispersed one. This is especially helpful because some of these tests can be administered via saliva and other less-invasive methods, and be distributed outside medical facilities.

In an overdispersed regime, identifying transmission events (someone infected someone else) is more important than identifying infected individuals. Consider an infected person and their 20 forward contacts—people they met since they got infected. Let’s say we test 10 of them with a cheap, rapid test and get our results back in an hour or two. This isn’t a great way to determine exactly who is sick out of that 10, because our test will miss some positives, but that’s fine for our purposes. If everyone is negative, we can act as if nobody is infected, because the test is pretty good at finding negatives. However, the moment we find a few transmissions, we know we may have a super-spreader event, and we can tell all 20 people to assume they are positive and to self-isolate—if there are one or two transmissions, there are likely more, exactly because of the clustering behavior. Depending on age and other factors, we can test those people individually using PCR tests, which can pinpoint who is infected, or ask them all to wait it out.

Scarpino told me that overdispersion also enhances the utility of other aggregate methods, such as wastewater testing, especially in congregate settings like dorms or nursing homes, allowing us to detect clusters without testing everyone. Wastewater testing also has low sensitivity; it may miss positives if too few people are infected, but that’s fine for population-screening purposes. If the wastewater testing is signaling that there are likely no infections, we do not need to test everyone to find every last potential case. However, the moment we see signs of a cluster, we can rapidly isolate everyone, again while awaiting further individualized testing via PCR tests, depending on the situation.

Unfortunately, until recently, many such cheap tests had been held up by regulatory agencies in the United States, partly because they were concerned with their relative lack of accuracy in identifying positive cases compared with PCR tests—a worry that missed their population-level usefulness for this particular overdispersed pathogen.

To return to the mysteries of this pandemic, what did happen early on to cause such drastically different trajectories in otherwise similar places? Why haven’t our usual analytic tools—case studies, multi-country comparisons—given us better answers? It’s not intellectually satisfying, but because of the overdispersion and its stochasticity, there may not be an explanation beyond that the worst-hit regions, at least initially, simply had a few unlucky early super-spreading events. It wasn’t just pure luck: Dense populations, older citizens, and congregate living, for example, made cities around the world more susceptible to outbreaks compared with rural, less dense places and those with younger populations, less mass transit, or healthier citizenry. But why Daegu in February and not Seoul, despite the two cities being in the same country, under the same government, people, weather, and more? As frustrating at it may be, sometimes, the answer is merely where Patient 31 and the megachurch she attended happened to be.

Overdispersion makes it harder for us to absorb lessons from the world, because it interferes with how we ordinarily think about cause and effect. For example, it means that events that result in spreading and non-spreading of the virus are asymmetric in their ability to inform us. Take the highly publicized case in Springfield, Missouri, in which two infected hairstylists, both of whom wore masks, continued to work with clients while symptomatic. It turns out that no apparent infections were found among the 139 exposed clients (67 were directly tested; the rest did not report getting sick). While there is a lot of evidence that masks are crucial in dampening transmission, that event alone wouldn’t tell us if masks work. In contrast, studying transmission, the rarer event, can be quite informative. Had those two hairstylists transmitted the virus to large numbers of people despite everyone wearing masks, it would be important evidence that, perhaps, masks aren’t useful in preventing super-spreading.

Comparisons, too, give us less information compared with phenomena for which input and output are more tightly coupled. When that’s the case, we can check for the presence of a factor (say, sunshine or Vitamin D) and see if it correlates with a consequence (infection rate). But that’s much harder when the consequence can vary widely depending on a few strokes of luck, the way that the wrong person was in the wrong place sometime in mid-February in South Korea. That’s one reason multi-country comparisons have struggled to identify dynamics that sufficiently explain the trajectories of different places.

Once we recognize super-spreading as a key lever, countries that look as if they were too relaxed in some aspects appear very different, and our usual polarized debates about the pandemic are scrambled, too. Take Sweden, an alleged example of the great success or the terrible failure of herd immunity without lockdowns, depending on whom you ask. In reality, although Sweden joins many other countries in failing to protect elderly populations in congregate-living facilities, its measures that target super-spreading have been stricter than many other European countries. Although it did not have a complete lockdown, as Kucharski pointed out to me, Sweden imposed a 50-person limit on indoor gatherings in March, and did not remove the cap even as many other European countries eased such restrictions after beating back the first wave. (Many are once again restricting gathering sizes after seeing a resurgence.) Plus, the country has a small household size and fewer multigenerational households compared with most of Europe, which further limits transmission and cluster possibilities. It kept schools fully open without distancing or masks, but only for children under 16, who are unlikely to be super-spreaders of this disease. Both transmission and illness risks go up with age, and Sweden went all online for higher-risk high-school and university students—the opposite of what we did in the United States. It also encouraged social-distancing, and closed down indoor places that failed to observe the rules. From an overdispersion and super-spreading point of view, Sweden would not necessarily be classified as among the most lax countries, but nor is it the most strict. It simply doesn’t deserve this oversize place in our debates assessing different strategies.

Although overdispersion makes some usual methods of studying causal connections harder, we can study failures to understand which conditions turn bad luck into catastrophes. We can also study sustained success, because bad luck will eventually hit everyone, and the response matters.

The most informative case studies may well be those who had terrible luck initially, like South Korea, and yet managed to bring about significant suppression. In contrast, Europe was widely praised for its opening early on, but that was premature; many countries there are now experiencing widespread rises in cases and look similar to the United States in some measures. In fact, Europe’s achieving a measure of success this summer and relaxing, including opening up indoor events with larger numbers, is instructive in another important aspect of managing an overdispersed pathogen: Compared with a steadier regime, success in a stochastic scenario can be more fragile than it looks.

Once a country has too many outbreaks, it’s almost as if the pandemic switches into “flu mode,” as Scarpino put it, meaning high, sustained levels of community spread even though a majority of infected people may not be transmitting onward. Scarpino explained that barring truly drastic measures, once in that widespread and elevated mode, COVID-19 can keep spreading because of the sheer number of chains already out there. Plus, the overwhelming numbers may eventually spark more clusters, further worsening the situation.

As Kucharski put it, a relatively quiet period can hide how quickly things can tip over into large outbreaks and how a few chained amplification events can rapidly turn a seemingly under-control situation into a disaster. We’re often told that if Rt, the real-time measure of the average spread, is above one, the pandemic is growing, and that below one, it’s dying out. That may be true for an epidemic that is not overdispersed, and while an Rt below one is certainly good, it’s misleading to take too much comfort from a low Rt when just a few events can reignite massive numbers. No country should forget South Korea’s Patient 31.

That said, overdispersion is also a cause for hope, as South Korea’s aggressive and successful response to that outbreak—with a massive testing, tracing, and isolating regime—shows. Since then, South Korea has also been practicing sustained vigilance, and has demonstrated the importance of backward tracing. When a series of clusters linked to nightclubs broke out in Seoul recently, health authorities aggressively traced and tested tens of thousands of people linked to the venues, regardless of their interactions with the index case, six feet apart or not—a sensible response, given that we know the pathogen is airborne.

Perhaps one of the most interesting cases has been Japan, a country with middling luck that got hit early on and followed what appeared to be an unconventional model, not deploying mass testing and never fully shutting down. By the end of March, influential economists were publishing reports with dire warnings, predicting overloads in the hospital system and huge spikes in deaths. The predicted catastrophe never came to be, however, and although the country faced some future waves, there was never a large spike in deaths despite its aging population, uninterrupted use of mass transportation, dense cities, and lack of a formal lockdown.

It’s not that Japan was better situated than the United States in the beginning. Similar to the U.S. and Europe, Oshitani told me, Japan did not initially have the PCR capacity to do widespread testing. Nor could it impose a full lockdown or strict stay-at-home orders; even if that had been desirable, it would not have been legally possible in Japan.

Oshitani told me that in Japan, they had noticed the overdispersion characteristics of COVID-19 as early as February, and thus created a strategy focusing mostly on cluster-busting, which tries to prevent one cluster from igniting another. Oshitani said he believes that “the chain of transmission cannot be sustained without a chain of clusters or a megacluster.” Japan thus carried out a cluster-busting approach, including undertaking aggressive backward tracing to uncover clusters. Japan also focused on ventilation, counseling its population to avoid places where the three C’s come together—crowds in closed spaces in close contact, especially if there’s talking or singing—bringing together the science of overdispersion with the recognition of airborne aerosol transmission, as well as presymptomatic and asymptomatic transmission.

Oshitani contrasts the Japanese strategy, nailing almost every important feature of the pandemic early on, with the Western response, trying to eliminate the disease “one by one” when that’s not necessarily the main way it spreads. Indeed, Japan got its cases down, but kept up its vigilance: When the government started noticing an uptick in community cases, it initiated a state of emergency in April and tried hard to incentivize the kinds of businesses that could lead to super-spreading events, such as theaters, music venues, and sports stadiums, to close down temporarily. Now schools are back in session in person, and even stadiums are open—but without chanting.

It’s not always the restrictiveness of the rules, but whether they target the right dangers. As Morris put it, “Japan’s commitment to ‘cluster-busting’ allowed it to achieve impressive mitigation with judiciously chosen restrictions. Countries that have ignored super-spreading have risked getting the worst of both worlds: burdensome restrictions that fail to achieve substantial mitigation. The U.K.’s recent decision to limit outdoor gatherings to six people while allowing pubs and bars to remain open is just one of many such examples.”

Could we get back to a much more normal life by focusing on limiting the conditions for super-spreading events, aggressively engaging in cluster-busting, and deploying cheap, rapid mass tests—that is, once we get our case numbers down to low enough numbers to carry out such a strategy? (Many places with low community transmission could start immediately.) Once we look for and see the forest, it becomes easier to find our way out.