Could widespread use of antiviral treatment curb the COVID-19 pandemic? A modeling study.

BACKGROUND: Despite the development of safe and effective vaccines, effective treatments for COVID-19 disease are still urgently needed. Several antiviral drugs have shown to be effective in reducing progression of COVID-19 disease. METHODS: In the present work, we use an agent-based mathematical model to assess the potential population impact of the use of antiviral treatments in four countries with different demographic structure and current levels of vaccination coverage: Kenya, Mexico, United States (US) and Belgium. We analyzed antiviral effects on reducing hospitalization and death, and potential antiviral effects on reducing transmission. For each country, we varied daily treatment initiation rate (DTIR) and antiviral effect in reducing transmission (AVT). RESULTS: Irrespective of location and AVT, widespread antiviral treatment of symptomatic adult infections (20% DTIR) prevented the majority of COVID-19 deaths, and recruiting 6% of all adult symptomatic infections daily reduced mortality by over 20% in all countries. Furthermore, our model projected that targeting antiviral treatment to the oldest age group (65 years old and older, DTIR of 20%) can prevent over 30% of deaths. Our results suggest that early antiviral treatment (as soon as possible after inception of infection) is needed to mitigate transmission, preventing 50% more infections compared to late treatment (started 3 to 5 days after symptoms onset). Our results highlight the synergistic effect of vaccination and antiviral treatment: as the vaccination rate increases, antivirals have a larger relative impact on population transmission. Finally, our model projects that even in highly vaccinated populations, adding antiviral treatment can be extremely helpful to mitigate COVID-19 deaths. CONCLUSIONS: These results suggest that antiviral treatments can become a strategic tool that, in combination with vaccination, can significantly reduce COVID-19 hospitalizations and deaths and can help control SARS-CoV-2 transmission.

Successes and Failures of the Live-attenuated Influenza Vaccine: Can We Do Better?

BACKGROUND: The effectiveness of the live-attenuated influenza vaccine (LAIV) can vary widely, ranging from 0% to 50%. The reasons for these discrepancies remain largely unclear. METHODS: We use mathematical models to explore how the efficacy of LAIV is affected by the degree of mismatch with the currently circulating influenza strain and interference with pre-existing immunity. The models incorporate 3 key antigenic distances: the distances between the vaccine strain, pre-existing immunity, and the challenge strain. RESULTS: Our models show that an LAIV that is matched with the currently circulating strain is likely to have only modest efficacy. Our results suggest that the efficacy of the vaccine would be increased (optimized) if, rather than being matched to the circulating strain, it is antigenically slightly further from pre-existing immunity than the circulating strain. The models also suggest 2 regimes in which LAIV that is matched to circulating strains may be protective: in children before they have built immunity to circulating strains and in response to novel strains (such as antigenic shifts) which are at substantial antigenic distance from previously circulating strains. We provide an explanation for the variation in vaccine effectiveness between studies and countries of vaccine effectiveness observed during the 2014-2015 influenza season. CONCLUSIONS: LAIV is offered to children across the world; however, its effectiveness significantly varies between studies. Here, we propose a mechanistic explanation to understand these differences. We further propose a way to select the LAIV strain that would have a higher chance of being protective.

Evaluating the Effectiveness of Social Distancing Interventions to Delay or Flatten the Epidemic Curve of Coronavirus Disease.

By April 2, 2020, >1 million persons worldwide were infected with severe acute respiratory syndrome coronavirus 2. We used a mathematical model to investigate the effectiveness of social distancing interventions in a mid-sized city. Interventions reduced contacts of adults >60 years of age, adults 20-59 years of age, and children <19 years of age for 6 weeks. Our results suggest interventions started earlier in the epidemic delay the epidemic curve and interventions started later flatten the epidemic curve. We noted that, while social distancing interventions were in place, most new cases, hospitalizations, and deaths were averted, even with modest reductions in contact among adults. However, when interventions ended, the epidemic rebounded. Our models suggest that social distancing can provide crucial time to increase healthcare capacity but must occur in conjunction with testing and contact tracing of all suspected cases to mitigate virus transmission.

Dynamics of Persistent Oral Cytomegalovirus Shedding During Primary Infection in Ugandan Infants.

Cytomegalovirus (CMV) infection occurs frequently in young children, who, when infected, are then a major source of transmission. Oral CMV shedding by 14 infants with primary infection was comprehensively characterized using quantitative polymerase chain reaction weekly for ≥9 months. Three phases of oral shedding were identified: expansion, transition, and clearance. Viral expansion occurred over a median of 7 weeks, with a median doubling time of 3 days. During the transition phase, expansion slowed over a median of 6 weeks before peak viral load was reached. Clearance was slow (22-day median half-life), and shedding did not resolve during observation for any infant. Mathematical modeling demonstrated that prolonged oral CMV expansion is explained by a low within-host reproduction number (median, 1.63) and a delayed immune response that only decreases the infected cell half-life by 44%. Thus, the prolonged oral CMV shedding observed during primary infection can be explained by slow viral expansion and inefficient immunologic control.

The majority of CD4+ T-cell depletion during acute simian-human immunodeficiency virus SHIV89.6P infection occurs in uninfected cells.

UNLABELLED: Untreated human immunodeficiency virus (HIV) infection is characterized by depletion of CD4(+) T cells, ultimately leading to the impairment of host immune defenses and death. HIV-infected CD4(+) T cells die from direct virus-induced apoptosis and CD8 T-cell-mediated elimination, but a broader and more profound depletion occurs in uninfected CD4(+) T cells via multiple indirect effects of infection. We fit mathematical models to data from experiments that tested an HIV eradication strategy in which five macaques with a proportion of CD4(+) T cells resistant to simian-human immunodeficiency virus (SHIV) entry were challenged with SHIV89.6P, a highly pathogenic dual-tropic chimeric SIV-HIV viral strain that results in rapid loss of both SHIV-susceptible and SHIV-resistant CD4(+) T cells. Our results suggest that uninfected (bystander) cell death accounts for the majority of CD4(+) T-lymphocyte loss, with at least 60% and 99% of CD4(+) T cell death occurring in uninfected cells during acute and established infection, respectively. Mechanisms to limit the profound indirect killing effects associated with HIV infection may be associated with immune preservation and improved long-term survival. IMPORTANCE: HIV infection induces a massive depletion of CD4(+) T cells, leading to profound immunodeficiency, opportunistic infections, and eventually death. While HIV induces apoptosis (programmed cell death) by directly entering and replicating in CD4(+) T cells, uninfected CD4(+) T cells also undergo apoptosis due to ongoing toxic inflammation in the region of infection. In this paper, we use mathematical models in conjunction with data from simian-human immunodeficiency virus SHIV89.6P infection in macaques (a model of HIV infection in humans) to estimate the percentage of cell death that occurs in uninfected cells during the initial period of infection. We reveal that the vast majority of cell death occurs in these cells, which are not infected. The "bystander effects" that lead to enormous reductions in the number of uninfected CD4(+) T cells may be a target for future interventions that aim to limit the extent of damage caused by HIV.

Optimal vaccine allocation for the early mitigation of pandemic influenza.

With new cases of avian influenza H5N1 (H5N1AV) arising frequently, the threat of a new influenza pandemic remains a challenge for public health. Several vaccines have been developed specifically targeting H5N1AV, but their production is limited and only a few million doses are readily available. Because there is an important time lag between the emergence of new pandemic strain and the development and distribution of a vaccine, shortage of vaccine is very likely at the beginning of a pandemic. We coupled a mathematical model with a genetic algorithm to optimally and dynamically distribute vaccine in a network of cities, connected by the airline transportation network. By minimizing the illness attack rate (i.e., the percentage of people in the population who become infected and ill), we focus on optimizing vaccine allocation in a network of 16 cities in Southeast Asia when only a few million doses are available. In our base case, we assume the vaccine is well-matched and vaccination occurs 5 to 10 days after the beginning of the epidemic. The effectiveness of all the vaccination strategies drops off as the timing is delayed or the vaccine is less well-matched. Under the best assumptions, optimal vaccination strategies substantially reduced the illness attack rate, with a maximal reduction in the attack rate of 85%. Furthermore, our results suggest that cooperative strategies where the resources are optimally distributed among the cities perform much better than the strategies where the vaccine is equally distributed among the network, yielding an illness attack rate 17% lower. We show that it is possible to significantly mitigate a more global epidemic with limited quantities of vaccine, provided that the vaccination campaign is extremely fast and it occurs within the first weeks of transmission.

Evaluating equity-promoting interventions to prevent race-based inequities in influenza outcomes.

Importance: Seasonal influenza hospitalizations pose a considerable burden in the United States, with BIPOC (Black, Indigenous, and other People of Color) communities being disproportionately affected. Objective: To determine and quantify the effects of different types of mitigation strategies on inequities in influenza outcomes (symptomatic infections and hospitalizations). Design: In this simulation study, we fit a race-stratified agent-based model of influenza transmission to demographic and hospitalization data of the United States. Participants: We consider five racial-ethnic groups: non-Hispanic White persons, non-Hispanic Black persons, non-Hispanic Asian persons, non-Hispanic American Indian or Alaska Native persons, and Hispanic or Latino persons. Setting: We tested five idealized equity-promoting interventions to determine their effectiveness in reducing inequity in influenza outcomes. The interventions assumed (i) equalized vaccination rates, (ii) equalized comorbidities, (iii) work-risk distribution proportional to the distribution of the population, (iv) reduced work contacts for all, or (v) a combination of equalizing vaccination rates and comorbidities and reducing work contacts. Main Outcomes and Measures: Reduction in symptomatic or hospitalization risk ratios, defined as the ratio of the number of symptomatic infections (hospitalizations respectively) in each age- and racial-ethnic group and their corresponding white counterpart. We also evaluated the reduction in the absolute mean number of symptomatic infections or hospitalizations in each age- and racial-ethnic group compared to the fitted scenario (baseline). Results: Our analysis suggests that symptomatic infections were equalized and reduced (by up to 17% in BIPOC adults aged 18-49) by strategies reducing work contacts or equalizing vaccination rates. Reducing comorbidities resulted in significant decreases in hospitalizations, with a reduction of over 40% in BIPOC groups. All tested interventions reduced the inequity in influenza hospitalizations in all racial-ethnic groups, but interventions reducing comorbidities in marginalized populations were the most effective. Notably, these interventions resulted in better outcomes across all racial-ethnic groups, not only those prioritized by the interventions. Conclusions and Relevance: In this simulation modeling study, equalizing vaccination rates and reducing number of work contacts (which are relatively simple strategies to implement) reduced the both the inequity in hospitalizations and the absolute number of symptomatic infections and hospitalizations in all age and racial-ethnic groups. Key Points: Question: What interventions might be effective in reducing the observed race-based disparities in seasonal influenza hospitalizations in the United States (US)?Findings: Simulated interventions equalizing comorbidity rates by in marginalized racial-ethnic groups would prevent up to 40% hospitalizations in these groups. Increasing vaccination rates among marginalized communities or reducing work contacts would reduce hospitalizations by over 10% and 20%, respectively, in most racial-ethnic groups and would similarly reduce symptomatic infections.Meaning: Increasing vaccination rates among marginalized communities, decreasing comorbidities in these groups, or reducing work contacts would help reduce the race-based disparities in seasonal influenza hospitalizations in the US.

Covid19Vaxplorer: A free, online, user-friendly COVID-19 vaccine allocation comparison tool.

There are many COVID-19 vaccines currently available, however, Low- and middle-income countries (LMIC) still have large proportions of their populations unvaccinated. Decision-makers must decide how to effectively allocate available vaccines (e.g. boosters or primary series vaccination, which age groups to target) but LMIC often lack the resources to undergo quantitative analyses of vaccine allocation, resulting in ad-hoc policies. We developed Covid19Vaxplorer (https://covid19vaxplorer.fredhutch.org/), a free, user-friendly online tool that simulates region-specific COVID-19 epidemics in conjunction with vaccination with the purpose of providing public health officials worldwide with a tool for vaccine allocation planning and comparison. We developed an age-structured mathematical model of SARS-CoV-2 transmission and COVID-19 vaccination. The model considers vaccination with up to three different vaccine products, primary series and boosters. We simulated partial immunity derived from waning of natural infection and vaccination. The model is embedded in an online tool, Covid19Vaxplorer that was optimized for its ease of use. By prompting users to fill information through several windows to input local parameters (e.g. cumulative and current prevalence), epidemiological parameters (e.g basic reproduction number, current social distancing interventions), vaccine parameters (e.g. vaccine efficacy, duration of immunity) and vaccine allocation (both by age groups and by vaccination status). Covid19Vaxplorer connects the user to the mathematical model and simulates, in real time, region-specific epidemics. The tool then produces key outcomes including expected numbers of deaths, hospitalizations and cases, with the possibility of simulating several scenarios of vaccine allocation at once for a side-by-side comparison. We provide two usage examples of Covid19Vaxplorer for vaccine allocation in Haiti and Afghanistan, which had as of Spring 2023, 2% and 33% of their populations vaccinated, and show that for these particular examples, using available vaccine as primary series vaccinations prevents more deaths than using them as boosters.

Evaluation of mpox vaccine dose-sparing strategies.

The spring-summer 2022 mpox outbreak had over 50,000 cases globally, most of them in gay, bisexual, and other men who have sex with men (MSM). In response to vaccine shortages, several countries implemented dose-sparing vaccination strategies, stretching a full-dose vaccine vial into up to five fractional-dose vaccines. Recent studies have found mixed results regarding the effectiveness of the mpox vaccine, raising the question of the utility of dose-sparing strategies. We used an age- and risk-stratified mathematical model of an urban MSM population in the United States with ∼12% high-risk MSM to evaluate potential benefits from implementing dose-sparing vaccination strategies in which a full dose is divided into 3.5 fractional doses. We found that results strongly depend on the fractional-dose vaccine effectiveness (VE) and vaccine supply. With very limited vaccines available, enough to protect with a full dose approximately one-third of the high-risk population, dose-sparing strategies are more beneficial provided that fractional doses preserved at least 40% of full-dose effectiveness (34% absolute VE), projecting 13% (34% VE) to 70% (68% absolute VE) fewer infections than full-dose strategies. In contrast, if vaccine supply is enough to cover the majority of the high-risk population, dose-sparing strategies can be outperformed by full-dose strategies. Scenarios in which fractional dosing was 34% efficacious resulted in almost three times more infections than full dosing. Our analysis suggests that when mpox vaccine supply is limited and fractional-dose vaccination retains moderate effectiveness, there are meaningful health benefits from providing a smaller dose to a larger number of people in the high-risk population. These findings should inform the public-health response to future mpox outbreaks.

Covid19Vaxplorer: a free, online, user-friendly COVID-19 Vaccine Allocation Comparison Tool.

Background: There are many COVID-19 vaccines currently available, however, Low- and middle-income countries (LMIC) still have large proportions of their populations unvaccinated. Decision-makers must decide how to effectively allocate available vaccines (e.g. boosters or primary series vaccination, which age groups to target) but LMIC often lack the resources to undergo quantitative analyses of vaccine allocation, resulting in ad-hoc policies. We developed Covid19Vaxplorer (https://covid19vaxplorer.fredhutch.org/), a free, user-friendly online tool that simulates region-specific COVID-19 epidemics in conjunction with vaccination with the purpose of providing public health officials worldwide with a tool for vaccine allocation planning and comparison. Methods: We developed an age-structured mathematical model of SARS-CoV-2 transmission and COVID-19 vaccination. The model considers vaccination with up to three different vaccine products, primary series and boosters. We simulated partial immunity derived from waning of natural infection and vaccination. The model is embedded in an online tool, Covid19Vaxplorer that was optimized for its ease of use. By prompting users to fill information through several windows to input local parameters (e.g. cumulative and current prevalence), epidemiological parameters (e.g basic reproduction number, current social distancing interventions), vaccine parameters (e.g. vaccine efficacy, duration of immunity) and vaccine allocation (both by age groups and by vaccination status). Covid19Vaxplorer connects the user to the mathematical model and simulates, in real time, region-specific epidemics. The tool then produces key outcomes including expected numbers of deaths, hospitalizations and cases, with the possibility of simulating several scenarios of vaccine allocation at once for a side-by-side comparison. Results: We provide two usage examples of Covid19Vaxplorer for vaccine allocation in Haiti and Afghanistan, which had as of Spring 2023 2% and 33% of their populations vaccinated, and show that for these particular examples, using available vaccine as primary series vaccinations prevents more deaths than using them as boosters. Covid19Vaxplorer allows users in 183 regions in the world to compare several vaccination strategies simultaneously, adjusting parameters to their local epidemics, infrastructure and logistics. Covid19Vaxplorer is an online, free, user-friendly tool that facilitates evidence-based decision making for vaccine distribution.

Retrospective Analysis of Equity-Based Optimization for COVID-19 Vaccine Allocation.

Marginalized racial and ethnic groups in the United States were disproportionally affected by the COVID-19 pandemic. To study these disparities, we construct an age-and-race-stratified mathematical model of SARS-CoV-2 transmission fitted to age-and-race-stratified data from 2020 in Oregon and analyze counter-factual vaccination strategies in early 2021. We consider two racial groups: non-Hispanic White persons and persons belonging to BIPOC groups (including non-Hispanic Black persons, non-Hispanic Asian persons, non-Hispanic American Indian or Alaska Native persons, and Hispanic or Latino persons). We allocate a limited amount of vaccine to minimize overall disease burden (deaths or years of life lost), inequity in disease outcomes between racial groups (measured with five different metrics), or both. We find that, when allocating small amounts of vaccine (10% coverage), there is a trade-off between minimizing disease burden and minimizing inequity. Older age groups, who are at a greater risk of severe disease and death, are prioritized when minimizing measures of disease burden, and younger BIPOC groups, who face the most inequities, are prioritized when minimizing measures of inequity. The allocation strategies that minimize combinations of measures can produce middle-ground solutions that similarly improve both disease burden and inequity, but the trade-off can only be mitigated by increasing the vaccine supply. With enough resources to vaccinate 20% of the population the trade-off lessens, and with 30% coverage, we can optimize both equity and mortality. Our goal is to provide a race-conscious framework to quantify and minimize inequity that can be used for future pandemics and other public health interventions.

Retrospective analysis of equity-based optimization for COVID-19 vaccine allocation.

Marginalized racial and ethnic groups in the United States were disproportionally affected by the COVID-19 pandemic. To study these disparities, we construct an age-and-race-stratified mathematical model of SARS-CoV-2 transmission fitted to age-and-race-stratified data from 2020 in Oregon and analyze counterfactual vaccination strategies in early 2021. We consider two racial groups: non-Hispanic White persons and persons belonging to BIPOC groups (including non-Hispanic Black persons, non-Hispanic Asian persons, non-Hispanic American-Indian or Alaska-Native persons, and Hispanic or Latino persons). We allocate a limited amount of vaccine to minimize overall disease burden (deaths or years of life lost), inequity in disease outcomes between racial groups (measured with five different metrics), or both. We find that, when allocating small amounts of vaccine (10% coverage), there is a trade-off between minimizing disease burden and minimizing inequity. Older age groups, who are at a greater risk of severe disease and death, are prioritized when minimizing measures of disease burden, and younger BIPOC groups, who face the most inequities, are prioritized when minimizing measures of inequity. The allocation strategies that minimize combinations of measures can produce middle-ground solutions that similarly improve both disease burden and inequity, but the trade-off can only be mitigated by increasing the vaccine supply. With enough resources to vaccinate 20% of the population the trade-off lessens, and with 30% coverage, we can optimize both equity and mortality. Our goal is to provide a race-conscious framework to quantify and minimize inequity that can be used for future pandemics and other public health interventions.

Optimizing one-dose and two-dose cholera vaccine allocation in outbreak settings: A modeling study.

BACKGROUND: A global stockpile of oral cholera vaccine (OCV) was established in 2013 for use in outbreak response and are licensed as two-dose regimens. Vaccine availability, however, remains limited. Previous studies have found that a single dose of OCV may provide substantial protection against cholera. METHODS: Using a mathematical model with two age groups paired with optimization algorithms, we determine the optimal vaccination strategy with one and two doses of vaccine to minimize cumulative overall infections, symptomatic infections, and deaths. We explore counterfactual vaccination scenarios in three distinct settings: Maela, the largest refugee camp in Thailand, with high in- and out-migration; N'Djamena, Chad, a densely populated region; and Haiti, where departments are connected by rivers and roads. RESULTS: Over the short term under limited vaccine supply, the optimal strategies for all objectives prioritize one dose to the older age group (over five years old), irrespective of setting and level of vaccination coverage. As more vaccine becomes available, it is optimal to administer a second dose for long-term protection. With enough vaccine to cover the whole population with one dose, the optimal strategies can avert up to 30% to 90% of deaths and 36% to 92% of symptomatic infections across the three settings over one year. The one-dose optimal strategies can avert 1.2 to 1.8 times as many cases and deaths compared to the standard two-dose strategy. CONCLUSIONS: In an outbreak setting, speedy vaccination campaigns with a single dose of OCV is likely to avert more cases and deaths than a two-dose pro-rata campaign under a limited vaccine supply.

Could widespread use of antiviral treatment curb the COVID-19 pandemic? A modeling study.

Despite the development of safe and effective vaccines, effective treatments for COVID-19 disease are still desperately needed. Recently, two antiviral drugs have shown to be effective in reducing hospitalizations in clinical trials. In the present work, we use an agent-based mathematical model to assess the potential population impact of the use of antiviral treatments in four countries, corresponding to four current levels of vaccination coverage: Kenya, Mexico, United States (US) and Belgium, with 1.5, 38, 57 and 74% of their populations vaccinated. For each location, we varied antiviral coverage and antiviral effect in reducing viral load (25, 50, 75 or 100% reduction). Irrespective of location, widespread antiviral treatment of symptomatic infections (≥50% coverage) is expected to prevent the majority of COVID-19 deaths. Furthermore, even treating 20% of adult symptomatic infections, is expected to reduce mortality by a third in all countries, irrespective of the assumed treatment efficacy in reducing viral load. Our results suggest that early antiviral treatment is needed to mitigate transmission, with early treatment (within two days of symptoms) preventing 50% more infections compared to late treatment (started on days 3 to 5 after developing symptoms). Our results highlight the synergistic effect of vaccination and antiviral treatment: as vaccination rate increased, antiviral treatment had a bigger impact on overall transmission. These results suggest that antiviral treatments can become a strategic tool that, in combination with vaccination, can significantly control SASRS-CoV-2 transmission and reduce COVID-19 hospitalizations and deaths.

Improving vaccination coverage and offering vaccine to all school-age children allowed uninterrupted in-person schooling in King County, WA: Modeling analysis.

The rapid spread of highly transmissible SARS-CoV-2 variants combined with slowing pace of vaccination in Fall 2021 created uncertainty around the future trajectory of the epidemic in King County, Washington, USA. We analyzed the benefits of offering vaccination to children ages 5-11 and expanding the overall vaccination coverage using mathematical modeling. We adapted a mathematical model of SARS-CoV-2 transmission, calibrated to data from King County, Washington, to simulate scenarios of vaccinating children aged 5-11 with different starting dates and different proportions of physical interactions (PPI) in schools being restored. Dynamic social distancing was implemented in response to changes in weekly hospitalizations. Reduction of hospitalizations and estimated time under additional social distancing measures are reported over the 2021-2022 school year. In the scenario with 85% vaccination coverage of 12+ year-olds, offering early vaccination to children aged 5-11 with 75% PPI was predicted to prevent 756 (median, IQR 301-1434) hospitalizations cutting youth hospitalizations in half compared to no vaccination and largely reducing the need for additional social distancing measures over the school year. If, in addition, 90% overall vaccination coverage was reached, 60% of remaining hospitalizations would be averted and the need for increased social distancing would almost certainly be avoided. Our work suggests that uninterrupted in-person schooling in King County was partly possible because reasonable precaution measures were taken at schools to reduce infectious contacts. Rapid vaccination of all school-aged children provides meaningful reduction of the COVID-19 health burden over this school year but only if implemented early. It remains critical to vaccinate as many people as possible to limit the morbidity and mortality associated with future epidemic waves.

Planning for the control of pandemic influenza A (H1N1) in Los Angeles County and the United States.

Mathematical and computer models can provide guidance to public health officials by projecting the course of an epidemic and evaluating control measures. The authors built upon an existing collaboration between an academic research group and the Los Angeles County, California, Department of Public Health to plan for and respond to the first and subsequent years of pandemic influenza A (H1N1) circulation. The use of models allowed the authors to 1) project the timing and magnitude of the epidemic in Los Angeles County and the continental United States; 2) predict the effect of the influenza mass vaccination campaign that began in October 2009 on the spread of pandemic H1N1 in Los Angeles County and the continental United States; and 3) predict that a third wave of pandemic influenza in the winter or spring of 2010 was unlikely to occur. The close collaboration between modelers and public health officials during pandemic H1N1 spread in the fall of 2009 helped Los Angeles County officials develop a measured and appropriate response to the unfolding pandemic and establish reasonable goals for mitigation of pandemic H1N1.

Protection afforded by previous Vibrio cholerae infection against subsequent disease and infection: A review.

BACKGROUND: Cholera is an acute, diarrheal disease caused by Vibrio cholerae O1 or 139 that is associated with a high global burden. METHODS: We analyzed the estimated duration of immunity following cholera infection from available published studies. We searched PubMed and Web of Science for studies of the long-term immunity following cholera infection. We identified 22 eligible studies and categorized them as either observational, challenge, or serological. RESULTS: We found strong evidence of protection at 3 years after infection in observational and challenge studies. However, serological studies show that elevated humoral markers of potential correlates of protection returned to baseline within 1 year. Additionally, a subclinical cholera infection may confer lower protection than a clinical one, as suggested by 3 studies that found that, albeit with small sample sizes, most participants with a subclinical infection from an initial challenge with cholera had a symptomatic infection when rechallenged with a homologous biotype. CONCLUSIONS: This review underscores the need to elucidate potential differences in the protection provided by clinical and subclinical cholera infections. Further, more studies are warranted to bridge the gap between the correlates of protection and cholera immunity. Understanding the duration of natural immunity to cholera can help guide control strategies and policy.

Quantifying the Impact of Lifting Community Nonpharmaceutical Interventions for COVID-19 During Vaccination Rollout in the United States.

Using a mathematical model, we estimated the potential impact on mortality and total infections of completely lifting community nonpharmaceutical interventions when only a small proportion of the population has been fully vaccinated in 2 states in the United States. Lifting all community nonpharmaceutical interventions immediately is predicted to result in twice as many deaths over the next 6 months as a more moderate reopening allowing 70% of prepandemic contacts.

Optimizing vaccine allocation for COVID-19 vaccines: potential role of single-dose vaccination.

Most COVID-19 vaccines require two doses, however with limited vaccine supply, policymakers are considering single-dose vaccination as an alternative strategy. Using a mathematical model combined with optimization algorithms, we determined optimal allocation strategies with one and two doses of vaccine under various degrees of viral transmission. Under low transmission, we show that the optimal allocation of vaccine vitally depends on the single-dose efficacy (SDE). With high SDE, single-dose vaccination is optimal, preventing up to 22% more deaths than a strategy prioritizing two-dose vaccination for older adults. With low or moderate SDE, mixed vaccination campaigns with complete coverage of older adults are optimal. However, with modest or high transmission, vaccinating older adults first with two doses is best, preventing up to 41% more deaths than a single-dose vaccination given across all adult populations. Our work suggests that it is imperative to determine the efficacy and durability of single-dose vaccines, as mixed or single-dose vaccination campaigns may have the potential to contain the pandemic much more quickly.