Planning for Mpox on a College Campus: A Model-Based Decision-Support Tool.

BACKGROUND: In spring and summer 2022, an outbreak of mpox occurred worldwide, largely confined to men who have sex with men (MSM). There was concern that mpox could break swiftly into congregate settings and populations with high levels of regular frequent physical contact, like university campus communities. OBJECTIVE: To estimate the likelihood of an mpox outbreak and the potential effect of mitigation measures in a residential college setting. DESIGN: A stochastic dynamic SEIR (susceptible, exposed but not infectious, infectious, or recovered) model of mpox transmission in a study population was developed, composed of: a high-risk group representative of the population of MSM with a basic reproductive number (R 0) of 2.4 and a low-risk group with an R 0 of 0.8. Base input assumptions included an incubation time of 7.6 days and time to recovery of 21 days. SETTING: U.S. residential college campus. PARTICIPANTS: Hypothetical cohort of 6500 students. INTERVENTION: Isolation, quarantine, and vaccination of close contacts. MEASUREMENTS: Proportion of 1000 simulations producing sustained transmission; mean cases given sustained transmission; maximum students isolated, quarantined, and vaccinated. All projections are estimated over a planning horizon of 100 days. RESULTS: Without mitigation measures, the model estimated an 83% likelihood of sustained transmission, leading to an average of 183 cases. With detection and isolation of 20%, 50%, and 80% of cases, the average infections would fall to 117, 37, and 8, respectively. Reactive vaccination of contacts of detected cases (assuming 50% detection and isolation) reduced mean cases from 37 to 17, assuming 20 vaccinated contacts per detected case. Preemptive vaccination of 50% of the high-risk population before outbreak reduced cases from 37 to 14, assuming 50% detection and isolation. LIMITATION: A model is a stylized portrayal of behavior and transmission on a university campus. CONCLUSION: Based on our current understanding of mpox epidemiology among MSM in the United States, this model-based analysis suggests that future outbreaks of mpox on college campuses may be controlled with timely detection and isolation of symptomatic cases. PRIMARY FUNDING SOURCE: National Institutes of Health National Institute on Drug Abuse and National Institute of Allergy and Infectious Diseases.

Population-Level Strategies for Nirmatrelvir/Ritonavir Prescribing-A Cost-effectiveness Analysis.

Background: New coronavirus disease 2019 (COVID-19) medications force decision-makers to weigh limited evidence of efficacy and cost in determining which patient populations to target for treatment. A case in point is nirmatrelvir/ritonavir, a drug that has been recommended for elderly, high-risk individuals, regardless of vaccination status, even though clinical trials have only evaluated it in unvaccinated patients. A simple optimization framework might inform a more reasoned approach to the trade-offs implicit in the treatment allocation decision. Methods: We conducted a cost-effectiveness analysis using a decision-analytic model comparing 5 nirmatrelvir/ritonavir prescription policy strategies, stratified by vaccination status and risk for severe disease. We considered treatment effectiveness at preventing hospitalization ranging from 21% to 89%. Sensitivity analyses were performed on major parameters of interest. A web-based tool was developed to permit decision-makers to tailor the analysis to their settings and priorities. Results: Providing nirmatrelvir/ritonavir to unvaccinated patients at high risk for severe disease was cost-saving when effectiveness against hospitalization exceeded 33% and cost-effective under all other data scenarios we considered. The cost-effectiveness of other allocation strategies, including those for vaccinated adults and those at lower risk for severe disease, depended on willingness-to-pay thresholds, treatment cost and effectiveness, and the likelihood of severe disease. Conclusions: Priority for nirmatrelvir/ritonavir treatment should be given to unvaccinated persons at high risk of severe disease from COVID-19. Further priority may be assigned by weighing treatment effectiveness, disease severity, drug cost, and willingness to pay for deaths averted.

Estimating deaths averted and cost per life saved by scaling up mRNA COVID-19 vaccination in low-income and lower-middle-income countries in the COVID-19 Omicron variant era: a modelling study.

OBJECTIVES: While almost 60% of the world has received at least one dose of COVID-19 vaccine, the global distribution of vaccination has not been equitable. Only 4% of the population of low-income countries (LICs) has received a full primary vaccine series, compared with over 70% of the population of high-income nations. DESIGN: We used economic and epidemiological models, parameterised with public data on global vaccination and COVID-19 deaths, to estimate the potential benefits of scaling up vaccination programmes in LICs and lower-middle-income countries (LMICs) in 2022 in the context of global spread of the Omicron variant of SARS-CoV2. SETTING: Low-income and lower-middle-income nations. MAIN OUTCOME MEASURES: Outcomes were expressed as number of avertable deaths through vaccination, costs of scale-up and cost per death averted. We conducted sensitivity analyses over a wide range of parameter estimates to account for uncertainty around key inputs. FINDINGS: Globally, universal vaccination in LIC/LMIC with three doses of an mRNA vaccine would result in an estimated 1.5 million COVID-19 deaths averted with a total estimated cost of US$61 billion and an estimated cost-per-COVID-19 death averted of US$40 800 (sensitivity analysis range: US$7400-US$81 500). Lower estimated infection fatality ratios, higher cost-per-dose and lower vaccine effectiveness or uptake lead to higher cost-per-death averted estimates in the analysis. CONCLUSIONS: Scaling up COVID-19 global vaccination would avert millions of COVID-19 deaths and represents a reasonable investment in the context of the value of a statistical life. Given the magnitude of expected mortality facing LIC/LMIC without vaccination, this effort should be an urgent priority.

Monkeypox emergency: Urgent questions and perspectives.

Amidst the COVID-19 pandemic, we now face another public health emergency in the form of monkeypox virus. As of August 1, the Centers for Disease Control and Prevention report over 23,000 cases in 80 countries. An inclusive and global collaborative effort to understand the biology, evolution, and spread of the virus as well as commitment to vaccine equity will be critical toward containing this outbreak. We share the voices of leading experts in this space on what they see as the most pressing questions and directions for the community.

Adventures in COVID-19 Policy Modeling: Education Edition.

PURPOSE OF REVIEW: To introduce readers to policy modeling, a multidisciplinary field of quantitative analysis, primarily used to help guide decision-making. This review focuses on the choices facing educational administrators, from K-12 to universities in the USA, as they confronted the COVID-19 pandemic. We survey three key model-based approaches to mitigation of SARS-CoV-2 spread in schools and on university campuses. RECENT FINDINGS: Frequent testing, coupled with strict attention to behavioral interventions to prevent further transmission can avoid large outbreaks on college campuses. K-12 administrators can greatly reduce the risks of severe outbreaks of COVID-19 in schools through various mitigation measures including classroom infection control, scheduling and cohorting strategies, staff and teacher vaccination, and asymptomatic screening. Safer re-opening of college and university campuses as well as in-person instruction for K-12 students is possible, under many though not all epidemic scenarios if rigorous disease control and screening programs are in place.

Understanding the Barriers to Pooled SARS-CoV-2 Testing in the United States.

Pooled testing for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) detection is instrumental for increasing test capacity while decreasing test cost. Pooled testing programs permit sustainable, long-term surveillance measures, which are essential for the early detection of virus resurgence in communities or the emergence of variants of concern. While numerous pooled approaches have been proposed to increase test capacity, uptake by laboratories has been limited. On 9 December 2020, we invited 362 U.S. laboratories that inquired about the Yale School of Public Health SalivaDirect test to participate in a survey to evaluate testing constraints and pooling strategies for SARS-CoV-2 testing. The survey was distributed using Qualtrics, and three reminders were sent. The survey closed on 21 January 2021. Of 93 responses received (25.7% response rate), 90 were from Clinical Laboratory Improvement Amendments (CLIA)-certified laboratories conducting SARS-CoV-2 testing. The remaining three were excluded from the analyses. Responses indicated that the major barriers to the uptake of pooled testing in the United States may not simply be the number of tests a laboratory can process per day, but rather the lack of clear protocols and adequate resources; laboratories are working with fixed physical and human capital constraints. Importantly, laboratories across the country are heterogeneous in infrastructure and workflow. The need for SARS-CoV-2 testing will remain for years to come. Testing programs can be maintained through pooled PCR testing strategies, and while statisticians, operations researchers, and others with expertise in sampling design have important value to add, laboratories require support on how to transition from traditional diagnostic testing to pooled surveillance. IMPORTANCE While numerous pooled SARS-CoV-2 testing approaches have been described in an effort to increase testing capacity and decrease test prices, uptake by laboratories has been limited. Responses to our survey of United States-based laboratories highlight the importance of consulting end-users-those that solutions are being designed for-so challenges can be addressed in a manner tailored to meet the specific needs out in the field. It may be surprising to those designing pooled testing strategies to learn that laboratories view pooling as more time-consuming than testing samples individually, and therefore that it is thought to create delays in test reporting.

Maximizing the Efficiency of Active Case Finding for SARS-CoV-2 Using Bandit Algorithms.

Even as vaccination for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) expands in the United States, cases will linger among unvaccinated individuals for at least the next year, allowing the spread of the coronavirus to continue in communities across the country. Detecting these infections, particularly asymptomatic ones, is critical to stemming further transmission of the virus in the months ahead. This will require active surveillance efforts in which these undetected cases are proactively sought out rather than waiting for individuals to present to testing sites for diagnosis. However, finding these pockets of asymptomatic cases (i.e., hotspots) is akin to searching for needles in a haystack as choosing where and when to test within communities is hampered by a lack of epidemiological information to guide decision makers' allocation of these resources. Making sequential decisions with partial information is a classic problem in decision science, the explore v. exploit dilemma. Using methods-bandit algorithms-similar to those used to search for other kinds of lost or hidden objects, from downed aircraft or underground oil deposits, we can address the explore v. exploit tradeoff facing active surveillance efforts and optimize the deployment of mobile testing resources to maximize the yield of new SARS-CoV-2 diagnoses. These bandit algorithms can be implemented easily as a guide to active case finding for SARS-CoV-2. A simple Thompson sampling algorithm and an extension of it to integrate spatial correlation in the data are now embedded in a fully functional prototype of a web app to allow policymakers to use either of these algorithms to target SARS-CoV-2 testing. In this instance, potential testing locations were identified by using mobility data from UberMedia to target high-frequency venues in Columbus, Ohio, as part of a planned feasibility study of the algorithms in the field. However, it is easily adaptable to other jurisdictions, requiring only a set of candidate test locations with point-to-point distances between all locations, whether or not mobility data are integrated into decision making in choosing places to test.

Preventing the Spread of COVID-19 in Immigration Detention Centers Requires the Release of Detainees.

Immigration detention centers are densely populated facilities in which restrictive conditions limit detainees' abilities to engage in social distancing or hygiene practices designed to prevent the spread of COVID-19. With tens of thousands of adults and children in more than 200 immigration detention centers across the United States, immigration detention centers are likely to experience COVID-19 outbreaks and add substantially to the population of those infected.Despite compelling evidence indicating a heightened risk of infection among detainees, state and federal governments have done little to protect the health of detained im-migrants. An evidence-based public health framework must guide the COVID-19 response in immigration detention centers.We draw on the hierarchy of controls framework to demonstrate how immigration detention centers are failing to implement even the least effective control strategies. Drawing on this framework and recent legal and medical advocacy efforts, we argue that safely releasing detainees from immigration detention centers into their communities is the most effective way to prevent COVID-19 outbreaks in immigration detention settings. Failure to do so will result in infection and death among those detained and deepen existing health and social inequities.

Adaptive Policies to Balance Health Benefits and Economic Costs of Physical Distancing Interventions during the COVID-19 Pandemic.

Policy makers need decision tools to determine when to use physical distancing interventions to maximize the control of COVID-19 while minimizing the economic and social costs of these interventions. We describe a pragmatic decision tool to characterize adaptive policies that combine real-time surveillance data with clear decision rules to guide when to trigger, continue, or stop physical distancing interventions during the current pandemic. In model-based experiments, we find that adaptive policies characterized by our proposed approach prevent more deaths and require a shorter overall duration of physical distancing than alternative physical distancing policies. Our proposed approach can readily be extended to more complex models and interventions.

Eviction, Health Inequity, and the Spread of COVID-19: Housing Policy as a Primary Pandemic Mitigation Strategy.

The COVID-19 pandemic precipitated catastrophic job loss, unprecedented unemployment rates, and severe economic hardship in renter households. As a result, housing precarity and the risk of eviction increased and worsened during the pandemic, especially among people of color and low-income populations. This paper considers the implications of this eviction crisis for health and health inequity, and the need for eviction prevention policies during the pandemic. Eviction and housing displacement are particularly threatening to individual and public health during a pandemic. Eviction is likely to increase COVID-19 infection rates because it results in overcrowded living environments, doubling up, transiency, limited access to healthcare, and a decreased ability to comply with pandemic mitigation strategies (e.g., social distancing, self-quarantine, and hygiene practices). Indeed, recent studies suggest that eviction may increase the spread of COVID-19 and that the absence or lifting of eviction moratoria may be associated with an increased rate of COVID-19 infection and death. Eviction is also a driver of health inequity as historic trends, and recent data demonstrate that people of color are more likely to face eviction and associated comorbidities. Black people have had less confidence in their ability to pay rent and are dying at 2.1 times the rate of non-Hispanic Whites. Indigenous Americans and Hispanic/Latinx people face an infection rate almost 3 times the rate of non-Hispanic whites. Disproportionate rates of both COVID-19 and eviction in communities of color compound negative health effects make eviction prevention a critical intervention to address racial health inequity. In light of the undisputed connection between eviction and health outcomes, eviction prevention, through moratoria and other supportive measures, is a key component of pandemic control strategies to mitigate COVID-19 spread and death.

Stop the Death March

Let's face it. We're on a Covid-19 Republican death march heading into the rest of the summer and fall. We cannot wait for January 2021 to shift course. Our lives depend on disruption now. Indeed, our survival hinges on making it impossible for our leaders to ignore us: We have to shift the political