Contact Numbers
A Multi-Risk SIR Model with Optimally Targeted Lockdown
Daron Acemoglu et al.
NBER Working Paper, May 2020
Abstract:
We develop a multi-risk SIR model (MR-SIR) where infection, hospitalization and fatality rates vary between groups — in particular between the “young”, “the middle-aged” and the “old”. Our MR-SIR model enables a tractable quantitative analysis of optimal policy similar to those already developed in the context of the homogeneous-agent SIR models. For baseline parameter values for the COVID-19 pandemic applied to the US, we find that optimal policies differentially targeting risk/age groups significantly outperform optimal uniform policies and most of the gains can be realized by having stricter lockdown policies on the oldest group. For example, for the same economic cost (24.3% decline in GDP), optimal semi-targeted or fully-targeted policies reduce mortality from 1.83% to 0.71% (thus, saving 2.7 million lives) relative to optimal uniform policies. Intuitively, a strict and long lockdown for the most vulnerable group both reduces infections and enables less strict lockdowns for the lower-risk groups. We also study the impacts of social distancing, the matching technology, the expected arrival time of a vaccine, and testing with or without tracing on optimal policies. Overall, targeted policies that are combined with measures that reduce interactions between groups and increase testing and isolation of the infected can minimize both economic losses and deaths in our model.
Preliminary Investigation of the Association Between COVID-19 and Suicidal Thoughts and Behaviors in the U.S.
Brooke Ammerman et al.
University of Notre Dame Working Paper, April 2020
Abstract:
Evidence suggests that the negative consequences of COVID-19 may extend far beyond its considerable death toll, having a significant impact on psychological well-being. Prior work has highlighted that previous epidemics are linked to elevated suicide rates, however, there is no research to date on the relationship between the COVID-19 pandemic and suicidal thoughts and behaviors. Utilizing an online survey, the current study aimed to better understand the presence, and extent, of the association between COVID-19-related experiences and past-month suicidal thoughts and behaviors among adults in the United States. Results support an association between several COVID-19-related experiences (i.e., general distress, fear of physical harm, effects of social distancing policies) and past-month suicidal ideation and attempts. Further, we found that a significant proportion of those with recent suicidal ideation explicitly link their suicidal thoughts to COVID-19. Exploratory analyses highlight a potential additional link between COVID-19 and suicidal behavior, suggesting that a portion of individuals may be intentionally exposing themselves to the virus with intent to kill themselves. These findings underscore the need for increased suicide risk screening and access to mental health services. Particular attention should be paid to employing public health campaigns to disseminate information on such services in order to reduce the enormity of distress and emotional impairment associated with COVID-19 in the United States.
Risk Perception Through the Lens of Politics in the Time of the COVID-19 Pandemic
John Barrios & Yael Hochberg
NBER Working Paper, April 2020
Abstract:
Even when, objectively speaking, death is on the line, partisan bias still colors beliefs about facts. We show that a higher share of Trump voters in a county is associated with lower perceptions of risk during the COVID-19 pandemic. As Trump voter share rises, individuals search less for information on the virus, and engage in less social distancing behavior, as measured by smartphone location patterns. These patterns persist in the face of state-level mandates to close schools and businesses or to “stay home,” and reverse only when conservative politicians are exposed and the White House releases federal social distancing guidelines.
Misinformation During a Pandemic
Leonardo Bursztyn et al.
University of Chicago Working Paper, April 2020
Abstract:
We study the effects of news coverage of the novel coronavirus by the two most widely-viewed cable news shows in the United States — Hannity and Tucker Carlson Tonight, both on Fox News — on viewers’ behavior and downstream health outcomes. Carlson warned viewers about the threat posed by the coronavirus from early February, while Hannity originally dismissed the risks associated with the virus before gradually adjusting his position starting late February. We first validate these differences in content with independent coding of show transcripts. In line with the differences in content, we present novel survey evidence that Hannity’s viewers changed behavior in response to the virus later than other Fox News viewers, while Carlson’s viewers changed behavior earlier. We then turn to the effects on the pandemic itself, examining health outcomes across counties. First, we document that greater viewership of Hannity relative to Tucker Carlson Tonight is strongly associated with a greater number of COVID-19 cases and deaths in the early stages of the pandemic. The relationship is stable across an expansive set of robustness tests. To better identify the effect of differential viewership of the two shows, we employ a novel instrumental variable strategy exploiting variation in when shows are broadcast in relation to local sunset times. These estimates also show that greater exposure to Hannity relative to Tucker Carlson Tonight is associated with a greater number of county-level cases and deaths. Furthermore, the results suggest that in mid-March, after Hannity’s shift in tone, the diverging trajectories on COVID-19 cases begin to revert. We provide additional evidence consistent with misinformation being an important mechanism driving the effects in the data. While our findings cannot yet speak to long-term effects, they indicate that provision of misinformation in the early stages of a pandemic can have important consequences for how a disease ultimately affects the population.
State Preventive Medicine: Public Health, Indian Removal, and the Growth of State Capacity, 1800–1840
Ruth Bloch Rubin
Studies in American Political Development, forthcoming
Abstract:
Despite growing awareness of the American state's active role in the early nineteenth century, scholars have tended to ignore the early republic's public health apparatus. The few studies that do chronicle antebellum health initiatives confine themselves to programs intended to directly reward citizens — and particularly those who contributed politically or economically to the nation's founding and expansion. As this detailed study of the Indian Vaccination Act of 1832 makes clear, however, antebellum policymakers saw value in providing medical care to those outside their settler citizenry. Blending liberal, republican, and ascriptive ideas, the vaccination program joined two competing political logics: one emphasizing the humanity of indigenous people and the importance of providing for their welfare, and the other prioritizing the state's interest in an efficient “removal” process. Evidencing far more autonomy and administrative capacity than the average nineteenth-century bureaucracy, the War Department played a pivotal role in petitioning Congress for, and ultimately administering, the vaccination program. Unwilling to cede regulatory power over indigenous health to more proximate local governments or private parties, the War Department preferred its own military manpower — a decision that would profoundly shape the design and reception of subsequent Native health programs.
Warning Against Recurring Risks: An Information Design Approach
Saed Alizamir, Francis de Véricourt & Shouqiang Wang
Management Science, forthcoming
Abstract:
The World Health Organization seeks effective ways to alert its member states about global pandemics. Motivated by this challenge, we study a public agency’s problem of designing warning policies to mitigate potential disasters that occur with advance notice. The agency privately receives early information about recurring harmful events and issues warnings to induce an uninformed stakeholder to take preemptive actions. The agency’s decision to issue a warning critically depends on its reputation, which we define as the stakeholder’s belief regarding the accuracy of the agency’s information. The agency faces then a trade-off between eliciting a proper response today and maintaining its reputation to elicit responses to future events. We formulate this problem as a dynamic Bayesian persuasion game, which we solve in closed form. We find that the agency sometimes strategically misrepresents its advance information about a current threat to cultivate its future reputation. When its reputation is sufficiently low, the agency downplays the risk and actually downplays more as its reputation improves. By contrast, when its reputation is high, the agency sometimes exaggerates the threat and exaggerates more as its reputation deteriorates. Only when its reputation is moderate does the agency send warning messages that fully disclose its private information. Our study suggests a plausible and novel rationale for some of the false alarms or omissions observed in practice. We further test the robustness of our findings to imperfect advance information, disasters without advance notice, and heterogeneous receivers.
Lock-downs, Loneliness and Life Satisfaction
Daniel Hamermesh
NBER Working Paper, April 2020
Abstract:
Using the 2012-13 American Time Use Survey, I find that both who people spend time with and how they spend it affect their happiness, adjusted for numerous demographic and economic variables. Satisfaction among married individuals increases most with additional time spent with spouse. Among singles, satisfaction decreases most as more time is spent alone. Assuming that lock-downs constrain married people to spend time solely with their spouses, simulations show that their happiness may have been increased compared to before the lock-downs; but sufficiently large losses of work time and income reverse this inference. Simulations demonstrate clearly that, assuming lock-downs impose solitude on singles, their happiness was reduced, reductions that are made more severe by income and work losses.
Vaccine skepticism reflects basic cognitive differences in mortality-related event frequency estimation
Mark LaCour & Tyler Davis
Vaccine, 6 May 2020, Pages 3790-3799
Abstract:
Vaccines have prevented and nearly eliminated several deadly diseases, yet they face skepticism from the public. One potential driver of vaccine skepticism is how people process event frequencies such as rare adverse reactions to vaccines. Misestimations may distort the perceived risks of vaccinating. The current study examined how vaccine skepticism is related to accuracy in event frequency processing. In Experiment 1, participants (n = 158) estimated the frequencies of several vital statistics (e.g., ‘How many people die per year in the U.S. from emphysema?’). Higher levels of vaccine skepticism were associated with lower accuracy in frequency estimation and over-estimation of rare events. In Experiment 2 (n = 109), we again found that vaccine skepticism was negatively associated with vital statistic estimation accuracy but not for emotionally neutral or positive events. These results suggest that vaccine skepticism may arise from basic individual differences in processing events associated with mortality or negative affect.
Increasing Prevalence of Antinuclear Antibodies in the United States
Gregg Dinse et al.
Arthritis & Rheumatology, forthcoming
Objective: Growing evidence suggests increasing frequencies of autoimmunity and certain autoimmune diseases, but findings are limited by the lack of systematic data and evolving approaches and definitions. We investigated whether the prevalence of antinuclear antibodies (ANA), the most common biomarker of autoimmunity, changed over a recent 25‐year span in the U.S.
Methods: Serum ANA were measured by standard indirect immunofluorescence assays on HEp‐2 cells in 14,211 participants ≥12 years old from the U.S. National Health and Nutrition Examination Survey, with approximately one‐third from each of three time periods: 1988‐1991, 1999‐2004, and 2011‐2012. We used logistic regression adjusted for sex, age, race/ethnicity, and survey‐design variables to estimate changes in ANA prevalence across the periods.
Results: The prevalence of ANA was 11.0% (CI=9.7‐12.6%) in 1988‐1991, 11.5% (CI=10.3‐12.8%) in 1999‐2004, and 15.9% (CI=14.3‐17.6%) in 2011‐2012 (trend P<0.0001), which corresponds to 22, 27, and 41 million affected individuals, respectively. Among adolescents (ages 12‐19 years), ANA prevalence rose steeply, with odds ratios of 2.02 (CI=1.16‐3.53) and 2.88 (CI=1.64‐5.04) in the second and third time periods relative to the first (trend P<0.0001). ANA prevalence increased in both sexes (especially males), older adults (ages ≥50 years), and non‐Hispanic whites. These increases were not explained by concurrent trends in obesity/overweight, smoking, or drinking.
Action bias in the public’s clinically inappropriate expectations for antibiotics
Alistair Thorpe et al.
Journal of Experimental Psychology: Applied, forthcoming
Abstract:
Clinical guidelines recommend that physicians educate patients about illnesses and antibiotics to eliminate inappropriate preferences for antibiotics. We expected that information provision about illnesses and antibiotics would reduce but not eliminate inappropriate preferences for antibiotics and that cognitive biases could explain why some people resist the effect of information provision. In 2 experiments, participants (n₁ = 424; n₂ = 434) either received incomplete information (about the viral etiology of their infection) or complete information (about viral etiology and the ineffectiveness and harms of taking antibiotics), before deciding to rest or take antibiotics. Those in the complete information conditions responded to items on 4 biases: action bias, social norm, source discrediting, and information neglect. In 2 follow-up experiments (n₁ = 150; n₂ = 732), we aimed to counteract the action bias by reframing the perception of the resting option as an action. Complete information provision reduced but did not eliminate inappropriate preferences for antibiotics. Around 10% of people wanted antibiotics even when informed they are harmful and offer no benefit and even when the alternative option (i.e., rest) was framed as an active treatment option. Results suggest an action bias underpins this preference but appears challenging to counteract.
Predicting the short-term success of human influenza virus variants with machine learning
Maryam Hayati, Priscila Biller & Caroline Colijn
Proceedings of the Royal Society: Biological Sciences, 8 April 2020
Abstract:
Seasonal influenza viruses are constantly changing and produce a different set of circulating strains each season. Small genetic changes can accumulate over time and result in antigenically different viruses; this may prevent the body’s immune system from recognizing those viruses. Due to rapid mutations, in particular, in the haemagglutinin (HA) gene, seasonal influenza vaccines must be updated frequently. This requires choosing strains to include in the updates to maximize the vaccines’ benefits, according to estimates of which strains will be circulating in upcoming seasons. This is a challenging prediction task. In this paper, we use longitudinally sampled phylogenetic trees based on HA sequences from human influenza viruses, together with counts of epitope site polymorphisms in HA, to predict which influenza virus strains are likely to be successful. We extract small groups of taxa (subtrees) and use a suite of features of these subtrees as key inputs to the machine learning tools. Using a range of training and testing strategies, including training on H3N2 and testing on H1N1, we find that successful prediction of future expansion of small subtrees is possible from these data, with accuracies of 0.71–0.85 and a classifier ‘area under the curve’ 0.75–0.9.