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.

COVID-19 Testing and Case Rates and Social Contact Among Residential College Students in Connecticut During the 2020-2021 Academic Year.

Importance: During the 2020-2021 academic year, many institutions of higher education reopened to residential students while pursuing strategies to mitigate the risk of SARS-CoV-2 transmission on campus. Reopening guidance emphasized polymerase chain reaction or antigen testing for residential students and social distancing measures to reduce the frequency of close interpersonal contact, and Connecticut colleges and universities used a variety of approaches to reopen campuses to residential students. Objective: To characterize institutional reopening strategies and COVID-19 outcomes in 18 residential college and university campuses across Connecticut. Design, Setting, and Participants: This retrospective cohort study used data on COVID-19 testing and cases and social contact from 18 college and university campuses in Connecticut that had residential students during the 2020-2021 academic year. Exposures: Tests for COVID-19 performed per week per residential student. Main Outcomes and Measures: Cases per week per residential student and mean (95% CI) social contact per week per residential student. Results: Between 235 and 4603 residential students attended the fall semester across each of 18 institutions of higher education in Connecticut, with fewer residential students at most institutions during the spring semester. In census block groups containing residence halls, the fall student move-in resulted in a 475% (95% CI, 373%-606%) increase in mean contact, and the spring move-in resulted in a 561% (95% CI, 441%-713%) increase in mean contact compared with the 7 weeks prior to move-in. The association between test frequency and case rate per residential student was complex; institutions that tested students infrequently detected few cases but failed to blunt transmission, whereas institutions that tested students more frequently detected more cases and prevented further spread. In fall 2020, each additional test per student per week was associated with a decrease of 0.0014 cases per student per week (95% CI, -0.0028 to -0.00001). Conclusions and Relevance: The findings of this cohort study suggest that, in the era of available vaccinations and highly transmissible SARS-CoV-2 variants, colleges and universities should continue to test residential students and use mitigation strategies to control on-campus COVID-19 cases.

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.

COVID-19 screening strategies that permit the safe re-opening of college campuses

IMPORTANCE: The COVID-19 pandemic poses an existential threat to many US residential colleges: either they open their doors to students in September or they risk serious financial consequences. OBJECTIVE: To define SARS-CoV-2 screening performance standards that would permit the safe return of students to campus for the Fall 2020 semester. DESIGN: Decision and cost-effectiveness analysis linked to a compartmental epidemic model to evaluate campus screening using tests of varying frequency (daily-weekly), sensitivity (70%-99%), specificity (98%-99.7%), and cost ($10-$50/test). Reproductive numbers Rt = {1.5, 2.5, 3.5} defined three epidemic scenarios, with additional infections imported via exogenous shocks. We generally adhered to US government guidance for parameterization data. PARTICIPANTS: A hypothetical cohort of 5000 college-age, uninfected students. Main Outcome(s) and Measure(s): Cumulative tests, infections, and costs;daily isolation dormitory census;incremental cost-effectiveness;and budget impact. All measured over an 80-day, abbreviated semester. RESULTS: With Rt = 2.5, daily screening with a 70% sensitive, 98% specific test produces 85 cumulative student infections and isolation dormitory daily census averaging 108 (88% false positives). Screening every 2 (7) days nets 135 (3662) cumulative infections and daily isolation census 66 (252) with 73% (4%) false positives. Across all scenarios, test frequency exerts more influence on outcomes than test sensitivity. Cost-effectiveness analysis selects screening every {2, 1, 7} days with a 70% sensitive test as the preferred strategy for Rt = {2.5, 3.5, 1.5}, implying a screening cost of {$470, $920, $120} per student per semester. Conclusions & Relevance: Rapid, inexpensive and frequently conducted screening (even if only 70% sensitive) would be cost-effective and produce a modest number of COVID-19 infections. While the optimal screening frequency hinges on the success of behavioral interventions to reduce the base severity of transmission (Rt), this could permit the safe return of student to campus.

Clinical and Economic Impact of Widespread Rapid Testing to Decrease SARS-CoV-2 Transmission.

BACKGROUND: The value of frequent, rapid testing to reduce community transmission of SARS-CoV-2 is poorly understood. OBJECTIVE: To define performance standards and predict the clinical, epidemiological, and economic outcomes of nationwide, home-based, antigen testing. DESIGN: A simple compartmental epidemic model estimated viral transmission, clinical history, and resource use, with and without testing. DATA SOURCES: Parameter values and ranges informed by Centers for Disease Control guidance and published literature. TARGET POPULATION: United States population. TIME HORIZON: 60 days. PERSPECTIVE: Societal. Costs include: testing, inpatient care, and lost workdays. INTERVENTION: Home-based SARS-CoV-2 antigen testing. OUTCOME MEASURES: Cumulative infections and deaths, numbers isolated and/or hospitalized, and total costs. RESULTS OF BASE-CASE ANALYSIS: Without a testing intervention, the model anticipates 15 million infections, 125,000 deaths, and $10.4 billion in costs ($6.5 billion inpatient; $3.9 billion lost productivity) over a 60-day horizon. Weekly availability of testing may avert 4 million infections and 19,000 deaths, raising costs by $21.5 billion. Lower inpatient outlays ($5.9 billion) would partially offset additional testing expenditures ($12.0 billion) and workdays lost ($13.9 billion), yielding incremental costs per infection (death) averted of $5,400 ($1,100,000). RESULTS OF SENSITIVITY ANALYSIS: Outcome estimates vary widely under different behavioral assumptions and testing frequencies. However, key findings persist across all scenarios: large reductions in infections, mortality, and hospitalizations; and costs per death averted roughly an order of magnitude lower than commonly accepted willingness-to-pay values per statistical life saved ($5-17 million). LIMITATIONS: Analysis restricted to at-home testing and limited by uncertainties about test performance. CONCLUSION: High-frequency home testing for SARS-CoV-2 using an inexpensive, imperfect test could contribute to pandemic control at justifiable cost and warrants consideration as part of a national containment strategy.

Assessing COVID-19 Prevention Strategies to Permit the Safe Opening of Residential Colleges in Fall 2021.

BACKGROUND: Effective vaccines, improved testing technologies, and decreases in COVID-19 incidence prompt an examination of the choices available to residential college administrators seeking to safely resume in-person campus activities in fall 2021. OBJECTIVE: To help college administrators design and evaluate customized COVID-19 safety plans. DESIGN: Decision analysis using a compartmental epidemic model to optimize vaccination, testing, and other nonpharmaceutical interventions depending on decision makers' preferences, choices, and assumptions about epidemic severity and vaccine effectiveness against infection, transmission, and disease progression. SETTING: U.S. residential colleges. PARTICIPANTS: Hypothetical cohort of 5000 persons (students, faculty, and staff) living and working in close proximity on campus. MEASUREMENTS: Cumulative infections over a 120-day semester. RESULTS: Under base-case assumptions, if 90% coverage can be attained with a vaccine that is 85% protective against infection and 25% protective against asymptomatic transmission, the model finds that campus activities can be resumed while holding cumulative cases below 5% of the population without the need for routine, asymptomatic testing. With 50% population coverage using such a vaccine, a similar cap on cumulative cases would require either daily asymptomatic testing of unvaccinated persons or a combination of less frequent testing and resumption of aggressive distancing and other nonpharmaceutical prevention policies. Colleges returning to pre-COVID-19 campus activities without either broad vaccination coverage or high-frequency testing put their campus population at risk for widespread viral transmission. LIMITATION: Uncertainty in data, particularly vaccine effectiveness (preventive and transmission); no distinguishing between students and employees; and assumes limited community intermixing. CONCLUSION: Vaccination coverage is the most powerful tool available to residential college administrators seeking to achieve a safe return to prepandemic operations this fall. Given the breadth of potential outcomes in the face of uncontrollable and uncertain factors, even colleges with high vaccination rates should be prepared to reinstitute or expand testing and distancing policies on short notice. PRIMARY FUNDING SOURCE: National Institute on Drug Abuse.

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.

Clinical and Economic Effects of Widespread Rapid Testing to Decrease SARS-CoV-2 Transmission.

BACKGROUND: The value of frequent, rapid testing to reduce community transmission of SARS-CoV-2 is poorly understood. OBJECTIVE: To define performance standards and predict the clinical, epidemiologic, and economic outcomes of nationwide, home-based antigen testing. DESIGN: A simple compartmental epidemic model that estimated viral transmission, portrayed disease progression, and forecast resource use, with and without testing. DATA SOURCES: Parameter values and ranges as informed by Centers for Disease Control and Prevention guidance and published literature. TARGET POPULATION: U.S. population. TIME HORIZON: 60 days. PERSPECTIVE: Societal; costs included testing, inpatient care, and lost workdays. INTERVENTION: Home-based SARS-CoV-2 antigen testing. OUTCOME MEASURES: Cumulative infections and deaths, number of persons isolated and hospitalized, and total costs. RESULTS OF BASE-CASE ANALYSIS: Without a testing intervention, the model anticipates 11.6 million infections, 119 000 deaths, and $10.1 billion in costs ($6.5 billion in inpatient care and $3.5 billion in lost productivity) over a 60-day horizon. Weekly availability of testing would avert 2.8 million infections and 15 700 deaths, increasing costs by $22.3 billion. Lower inpatient outlays ($5.9 billion) would partially offset additional testing expenditures ($12.5 billion) and workdays lost ($14.0 billion), yielding incremental cost-effectiveness ratios of $7890 per infection averted and $1 430 000 per death averted. RESULTS OF SENSITIVITY ANALYSIS: Outcome estimates vary widely under different behavioral assumptions and testing frequencies. However, key findings persist across all scenarios, with large reductions in infections, mortality, and hospitalizations. Costs per death averted are roughly an order of magnitude lower than commonly accepted willingness-to-pay values per statistical life saved ($5 to $17 million). LIMITATIONS: Analysis was restricted to at-home testing. There are uncertainties concerning test performance. CONCLUSION: High-frequency home testing for SARS-CoV-2 with an inexpensive, imperfect test could contribute to pandemic control at justifiable cost and warrants consideration as part of a national containment strategy. PRIMARY FUNDING SOURCE: National Institutes of Health.

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.

Clinical Outcomes Of A COVID-19 Vaccine: Implementation Over Efficacy.

The global effort to develop a coronavirus disease 2019 (COVID-19) vaccine is on track to produce one or more authorized vaccines. We examine how different definitions and thresholds of vaccine efficacy, coupled with different levels of implementation effectiveness and background epidemic severity, translate into outcomes including cumulative infections, hospitalizations, and deaths. Using a mathematical simulation of vaccination, we find that factors related to implementation will contribute more to the success of vaccination programs than a vaccine's efficacy as determined in clinical trials. The benefits of a vaccine will decline substantially in the event of manufacturing or deployment delays, significant vaccine hesitancy, or greater epidemic severity. Our findings demonstrate the urgent need for health officials to invest greater financial resources and attention to vaccine production and distribution programs, to redouble efforts to promote public confidence in COVID-19 vaccines, and to encourage continued adherence to other mitigation approaches, even after a vaccine becomes available.

Assessment of SARS-CoV-2 Screening Strategies to Permit the Safe Reopening of College Campuses in the United States.

Importance: The coronavirus disease 2019 (COVID-19) pandemic poses an existential threat to many US residential colleges; either they open their doors to students in September or they risk serious financial consequences. Objective: To define severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) screening performance standards that would permit the safe return of students to US residential college campuses for the fall 2020 semester. Design, Setting, and Participants: This analytic modeling study included a hypothetical cohort of 4990 students without SARS-CoV-2 infection and 10 with undetected, asymptomatic SARS-CoV-2 infection at the start of the semester. The decision and cost-effectiveness analyses were linked to a compartmental epidemic model to evaluate symptom-based screening and tests of varying frequency (ie, every 1, 2, 3, and 7 days), sensitivity (ie, 70%-99%), specificity (ie, 98%-99.7%), and cost (ie, $10/test-$50/test). Reproductive numbers (Rt) were 1.5, 2.5, and 3.5, defining 3 epidemic scenarios, with additional infections imported via exogenous shocks. The model assumed a symptomatic case fatality risk of 0.05% and a 30% probability that infection would eventually lead to observable COVID-19-defining symptoms in the cohort. Model projections were for an 80-day, abbreviated fall 2020 semester. This study adhered to US government guidance for parameterization data. Main Outcomes and Measures: Cumulative tests, infections, and costs; daily isolation dormitory census; incremental cost-effectiveness; and budget impact. Results: At the start of the semester, the hypothetical cohort of 5000 students included 4990 (99.8%) with no SARS-CoV-2 infection and 10 (0.2%) with SARS-CoV-2 infection. Assuming an Rt of 2.5 and daily screening with 70% sensitivity, a test with 98% specificity yielded 162 cumulative student infections and a mean isolation dormitory daily census of 116, with 21 students (18%) with true-positive results. Screening every 2 days resulted in 243 cumulative infections and a mean daily isolation census of 76, with 28 students (37%) with true-positive results. Screening every 7 days resulted in 1840 cumulative infections and a mean daily isolation census of 121 students, with 108 students (90%) with true-positive results. Across all scenarios, test frequency was more strongly associated with cumulative infection than test sensitivity. This model did not identify symptom-based screening alone as sufficient to contain an outbreak under any of the scenarios we considered. Cost-effectiveness analysis selected screening with a test with 70% sensitivity every 2, 1, or 7 days as the preferred strategy for an Rt of 2.5, 3.5, or 1.5, respectively, implying screening costs of $470, $910, or $120, respectively, per student per semester. Conclusions and Relevance: In this analytic modeling study, screening every 2 days using a rapid, inexpensive, and even poorly sensitive (>70%) test, coupled with strict behavioral interventions to keep Rt less than 2.5, is estimated to maintain a controllable number of COVID-19 infections and permit the safe return of students to campus.

COVID-19 screening strategies that permit the safe re-opening of college campuses.

IMPORTANCE: The COVID-19 pandemic poses an existential threat to many US residential colleges: either they open their doors to students in September or they risk serious financial consequences. OBJECTIVE: To define SARS-CoV-2 screening performance standards that would permit the safe return of students to campus for the Fall 2020 semester. DESIGN: Decision and cost-effectiveness analysis linked to a compartmental epidemic model to evaluate campus screening using tests of varying frequency (daily-weekly), sensitivity (70%-99%), specificity (98%-99.7%), and cost ($10-$50/test). Reproductive numbers Rt = {1.5, 2.5, 3.5} defined three epidemic scenarios, with additional infections imported via exogenous shocks. We generally adhered to US government guidance for parameterization data. PARTICIPANTS: A hypothetical cohort of 5000 college-age, uninfected students. Main Outcome(s) and Measure(s): Cumulative tests, infections, and costs; daily isolation dormitory census; incremental cost-effectiveness; and budget impact. All measured over an 80-day, abbreviated semester. RESULTS: With Rt = 2.5, daily screening with a 70% sensitive, 98% specific test produces 85 cumulative student infections and isolation dormitory daily census averaging 108 (88% false positives). Screening every 2 (7) days nets 135 (3662) cumulative infections and daily isolation census 66 (252) with 73% (4%) false positives. Across all scenarios, test frequency exerts more influence on outcomes than test sensitivity. Cost-effectiveness analysis selects screening every {2, 1, 7} days with a 70% sensitive test as the preferred strategy for Rt = {2.5, 3.5, 1.5}, implying a screening cost of {$470, $920, $120} per student per semester. Conclusions & Relevance: Rapid, inexpensive and frequently conducted screening (even if only 70% sensitive) would be cost-effective and produce a modest number of COVID-19 infections. While the optimal screening frequency hinges on the success of behavioral interventions to reduce the base severity of transmission (Rt), this could permit the safe return of student to campus.