A retrospective cohort study of Paxlovid efficacy depending on treatment time in hospitalized COVID-19 patients.

Paxlovid, a SARS-CoV-2 antiviral, not only prevents severe illness but also curtails viral shedding, lowering transmission risks from treated patients. By fitting a mathematical model of within-host Omicron viral dynamics to electronic health records data from 208 hospitalized patients in Hong Kong, we estimate that Paxlovid can inhibit over 90% of viral replication. However, its effectiveness critically depends on the timing of treatment. If treatment is initiated three days after symptoms first appear, we estimate a 17% chance of a post-treatment viral rebound and a 12% (95% CI: 0-16%) reduction in overall infectiousness for non-rebound cases. Earlier treatment significantly elevates the risk of rebound without further reducing infectiousness, whereas starting beyond five days reduces its efficacy in curbing peak viral shedding. Among the 104 patients who received Paxlovid, 62% began treatment within an optimal three-to-five-day day window after symptoms appeared. Our findings indicate that broader global access to Paxlovid, coupled with appropriately timed treatment, can mitigate the severity and transmission of SARS-Cov-2.

Optimizing COVID-19 surveillance using historical electronic health records of influenza infections.

Targeting surveillance resources toward individuals at high risk of early infection can accelerate the detection of emerging outbreaks. However, it is unclear which individuals are at high risk without detailed data on interpersonal and physical contacts. We propose a data-driven COVID-19 surveillance strategy using Electronic Health Record (EHR) data that identifies the most vulnerable individuals who acquired the earliest infections during historical influenza seasons. Our simulations for all three networks demonstrate that the EHR-based strategy performs as well as the most-connected strategy. Compared to the random acquaintance surveillance, our EHR-based strategy detects the early warning signal and peak timing much earlier. On average, the EHR-based strategy has 9.8 days of early warning and 13.5 days of peak timings, respectively, before the whole population. For the urban network, the expected values of our method are better than the random acquaintance strategy (24% for early warning and 14% in-advance for peak time). For a scale-free network, the average performance of the EHR-based method is 75% of the early warning and 109% in-advance when compared with the random acquaintance strategy. If the contact structure is persistent enough, it will be reflected by their history of infection. Our proposed approach suggests that seasonal influenza infection records could be used to monitor new outbreaks of emerging epidemics, including COVID-19. This is a method that exploits the effect of contact structure without considering it explicitly.

The Experience of 2 Independent Schools With In-Person Learning During the COVID-19 Pandemic.

BACKGROUND: In 2020, US schools closed due to SARS-CoV-2 but their role in transmission was unknown. In fall 2020, national guidance for reopening omitted testing or screening recommendations. We report the experience of 2 large independent K-12 schools (School-A and School-B) that implemented an array of SARS-CoV-2 mitigation strategies that included periodic universal testing. METHODS: SARS-CoV-2 was identified through periodic universal PCR testing, self-reporting of tests conducted outside school, and contact tracing. Schools implemented behavioral and structural mitigation measures, including mandatory masks, classroom disinfecting, and social distancing. RESULTS: Over the fall semester, School-A identified 112 cases in 2320 students and staff; School-B identified 25 cases (2.0%) in 1400 students and staff. Most cases were asymptomatic and none required hospitalization. Of 69 traceable introductions, 63 (91%) were not associated with school-based transmission, 59 cases (54%) occurred in the 2 weeks post-thanksgiving. In 6/7 clusters, clear noncompliance with mitigation protocols was found. The largest outbreak had 28 identified cases and was traced to an off-campus party. There was no transmission from students to staff. CONCLUSIONS: Although school-age children can contract and transmit SARS-CoV-2, rates of COVID-19 infection related to in-person education were significantly lower than those in the surrounding community. However, social activities among students outside of school undermined those measures and should be discouraged, perhaps with behavioral contracts, to ensure the safety of school communities. In addition, introduction risks were highest following extended school breaks. These risks may be mitigated with voluntary quarantines and surveillance testing prior to reopening.

Projecting hospital utilization during the COVID-19 outbreaks in the United States.

In the wake of community coronavirus disease 2019 (COVID-19) transmission in the United States, there is a growing public health concern regarding the adequacy of resources to treat infected cases. Hospital beds, intensive care units (ICUs), and ventilators are vital for the treatment of patients with severe illness. To project the timing of the outbreak peak and the number of ICU beds required at peak, we simulated a COVID-19 outbreak parameterized with the US population demographics. In scenario analyses, we varied the delay from symptom onset to self-isolation, the proportion of symptomatic individuals practicing self-isolation, and the basic reproduction number R0 Without self-isolation, when R0 = 2.5, treatment of critically ill individuals at the outbreak peak would require 3.8 times more ICU beds than exist in the United States. Self-isolation by 20% of cases 24 h after symptom onset would delay and flatten the outbreak trajectory, reducing the number of ICU beds needed at the peak by 48.4% (interquartile range 46.4-50.3%), although still exceeding existing capacity. When R0 = 2, twice as many ICU beds would be required at the peak of outbreak in the absence of self-isolation. In this scenario, the proportional impact of self-isolation within 24 h on reducing the peak number of ICU beds is substantially higher at 73.5% (interquartile range 71.4-75.3%). Our estimates underscore the inadequacy of critical care capacity to handle the burgeoning outbreak. Policies that encourage self-isolation, such as paid sick leave, may delay the epidemic peak, giving a window of time that could facilitate emergency mobilization to expand hospital capacity.

Impact of international travel and border control measures on the global spread of the novel 2019 coronavirus outbreak.

The novel coronavirus outbreak (COVID-19) in mainland China has rapidly spread across the globe. Within 2 mo since the outbreak was first reported on December 31, 2019, a total of 566 Severe Acute Respiratory Syndrome (SARS CoV-2) cases have been confirmed in 26 other countries. Travel restrictions and border control measures have been enforced in China and other countries to limit the spread of the outbreak. We estimate the impact of these control measures and investigate the role of the airport travel network on the global spread of the COVID-19 outbreak. Our results show that the daily risk of exporting at least a single SARS CoV-2 case from mainland China via international travel exceeded 95% on January 13, 2020. We found that 779 cases (95% CI: 632 to 967) would have been exported by February 15, 2020 without any border or travel restrictions and that the travel lockdowns enforced by the Chinese government averted 70.5% (95% CI: 68.8 to 72.0%) of these cases. In addition, during the first three and a half weeks of implementation, the travel restrictions decreased the daily rate of exportation by 81.3% (95% CI: 80.5 to 82.1%), on average. At this early stage of the epidemic, reduction in the rate of exportation could delay the importation of cases into cities unaffected by the COVID-19 outbreak, buying time to coordinate an appropriate public health response.

Future epidemiological and economic impacts of universal influenza vaccines.

The efficacy of influenza vaccines, currently at 44%, is limited by the rapid antigenic evolution of the virus and a manufacturing process that can lead to vaccine mismatch. The National Institute of Allergy and Infectious Diseases (NIAID) recently identified the development of a universal influenza vaccine with an efficacy of at least 75% as a high scientific priority. The US Congress approved $130 million funding for the 2019 fiscal year to support the development of a universal vaccine, and another $1 billion over 5 y has been proposed in the Flu Vaccine Act. Using a model of influenza transmission, we evaluated the population-level impacts of universal influenza vaccines distributed according to empirical age-specific coverage at multiple scales in the United States. We estimate that replacing just 10% of typical seasonal vaccines with 75% efficacious universal vaccines would avert ∼5.3 million cases, 81,000 hospitalizations, and 6,300 influenza-related deaths per year. This would prevent over $1.1 billion in direct health care costs compared to a typical season, based on average data from the 2010-11 to 2018-19 seasons. A complete replacement of seasonal vaccines with universal vaccines is projected to prevent 17 million cases, 251,000 hospitalizations, 19,500 deaths, and $3.5 billion in direct health care costs. States with high per-hospitalization medical expenses along with a large proportion of elderly residents are expected to receive the maximum economic benefit. Replacing even a fraction of seasonal vaccines with universal vaccines justifies the substantial cost of vaccine development.

Ebola vaccination in the Democratic Republic of the Congo.

Following the April 2018 reemergence of Ebola in a rural region of the Democratic Republic of the Congo (DRC), the virus spread to an urban center by early May. Within 2 wk of the first case confirmation, a vaccination campaign was initiated in which 3,017 doses were administered to contacts of cases and frontline healthcare workers. To evaluate the spatial dynamics of Ebola transmission and quantify the impact of vaccination, we developed a geographically explicit model that incorporates high-resolution data on poverty and population density. We found that while Ebola risk was concentrated around sites initially reporting infections, longer-range dissemination also posed a risk to areas with high population density and poverty. We estimate that the vaccination program contracted the geographical area at risk for Ebola by up to 70.4% and reduced the level of risk within that region by up to 70.1%. The early implementation of vaccination was critical. A delay of even 1 wk would have reduced these effects to 33.3 and 44.8%, respectively. These results underscore the importance of the rapid deployment of Ebola vaccines during emerging outbreaks to containing transmission and preventing global spread. The spatiotemporal framework developed here provides a tool for identifying high-risk regions, in which surveillance can be intensified and preemptive control can be implemented during future outbreaks.

Optimizing distribution of pandemic influenza antiviral drugs.

We provide a data-driven method for optimizing pharmacy-based distribution of antiviral drugs during an influenza pandemic in terms of overall access for a target population and apply it to the state of Texas, USA. We found that during the 2009 influenza pandemic, the Texas Department of State Health Services achieved an estimated statewide access of 88% (proportion of population willing to travel to the nearest dispensing point). However, access reached only 34.5% of US postal code (ZIP code) areas containing <1,000 underinsured persons. Optimized distribution networks increased expected access to 91% overall and 60% in hard-to-reach regions, and 2 or 3 major pharmacy chains achieved near maximal coverage in well-populated areas. Independent pharmacies were essential for reaching ZIP code areas containing <1,000 underinsured persons. This model was developed during a collaboration between academic researchers and public health officials and is available as a decision support tool for Texas Department of State Health Services at a Web-based interface.

Cost-effectiveness of canine vaccination to prevent human rabies in rural Tanzania.

BACKGROUND: The annual mortality rate of human rabies in rural Africa is 3.6 deaths per 100 000 persons. Rabies can be prevented with prompt postexposure prophylaxis, but this is costly and often inaccessible in rural Africa. Because 99% of human exposures occur through rabid dogs, canine vaccination also prevents transmission of rabies to humans. OBJECTIVE: To evaluate the cost-effectiveness of rabies control through annual canine vaccination campaigns in rural sub-Saharan Africa. DESIGN: We model transmission dynamics in dogs and wildlife and assess empirical uncertainty in the biological variables to make probability-based evaluations of cost-effectiveness. DATA SOURCES: Epidemiologic variables from a contact-tracing study and literature and cost data from ongoing vaccination campaigns. TARGET POPULATION: Two districts of rural Tanzania: Ngorongoro and Serengeti. TIME HORIZON: 10 years. PERSPECTIVE: Health policymaker. INTERVENTION: Vaccination coverage ranging from 0% to 95% in increments of 5%. OUTCOME MEASURES: Life-years for health outcomes and 2010 U.S. dollars for economic outcomes. RESULTS OF BASE-CASE ANALYSIS: Annual canine vaccination campaigns were very cost-effective in both districts compared with no canine vaccination. In Serengeti, annual campaigns with as much as 70% coverage were cost-saving. RESULTS OF SENSITIVITY ANALYSIS: Across a wide range of variable assumptions and levels of societal willingness to pay for life-years, the optimal vaccination coverage for Serengeti was 70%. In Ngorongoro, although optimal coverage depended on willingness to pay, vaccination campaigns were always cost-effective and lifesaving and therefore preferred. LIMITATION: Canine vaccination was very cost-effective in both districts, but there was greater uncertainty about the optimal coverage in Ngorongoro. CONCLUSION: Annual canine rabies vaccination campaigns conferred extraordinary value and dramatically reduced the health burden of rabies. PRIMARY FUNDING SOURCE: National Institutes of Health.

Dengue dynamics and vaccine cost-effectiveness in Brazil.

Recent Phase 2b dengue vaccine trials have demonstrated the safety of the vaccine and estimated the vaccine efficacy with further trials underway. In anticipation of vaccine roll-out, cost-effectiveness analysis of potential vaccination policies that quantify the dynamics of disease transmission are fundamental to the optimal allocation of available doses. We developed a dengue transmission and vaccination model and calculated, for a range of vaccination costs and willingness-to-pay thresholds, the level of vaccination coverage necessary to sustain herd-immunity, the price at which vaccination is cost-effective and is cost-saving, and the sensitivity of our results to parameter uncertainty. We compared two vaccine efficacy scenarios, one a more optimistic scenario and another based on the recent lower-than-expected efficacy from the latest clinical trials. We found that herd-immunity may be achieved by vaccinating 82% (95% CI 58-100%) of the population at a vaccine efficacy of 70%. At this efficacy, vaccination may be cost-effective for vaccination costs up to US$ 534 (95% CI $369-1008) per vaccinated individual and cost-saving up to $204 (95% CI $39-678). At the latest clinical trial estimates of an average of 30% vaccine efficacy, vaccination may be cost-effective and cost-saving at costs of up to $237 (95% CI $159-512) and $93 (95% CI $15-368), respectively. Our model provides an assessment of the cost-effectiveness of dengue vaccination in Brazil and incorporates the effect of herd immunity into dengue vaccination cost-effectiveness. Our results demonstrate that at the relatively low vaccine efficacy from the recent Phase 2b dengue vaccine trials, age-targeted vaccination may still be cost-effective provided the total vaccination cost is sufficiently low.

Controlling antimicrobial resistance through targeted, vaccine-induced replacement of strains.

Vaccination has proven effective in controlling many infectious diseases. However, differential effectiveness with regard to pathogen genotype is a frequent reason for failures in vaccine development. Often, insufficient immune response is induced to prevent infection by the diversity of existing serotypes present in pathogenic populations of bacteria. These vaccines that target a too narrow spectrum of serotypes do not offer sufficient prevention of infections, and can also lead to undesirable strain replacements. Here, we examine a novel idea to specifically exploit the narrow spectrum coverage of some vaccines to combat specific, emerging multi- and pan-resistant strains of pathogens. Application of a narrow-spectrum vaccine could serve to prevent infections by some strains that are hard to treat, rather than offer the vaccinated individual protection against infections by the pathogenic species as such. We suggest that vaccines targeted to resistant serotypes have the potential to become important public health tools, and would represent a new approach toward reducing the burden of particular multi-resistant strains occurring in hospitals. Vaccines targeting drug-resistant serotypes would also be the first clinical intervention with the potential to drive the evolution of pathogenic populations toward drug-sensitivity. We illustrate the feasibility of this approach by modeling a hypothetical vaccine that targets a subset of methicillin-resistant Staphylococcus aureus (MRSA) genotypes, in combination with drug treatment targeted at drug-sensitive genotypes. We find that a combined intervention strategy can limit nosocomial outbreaks, even when vaccine efficacy is imperfect. The broader utility of vaccine-based resistance control strategies should be further explored taking into account population structure, and the resistance and transmission patterns of the pathogen considered.

Geographic prioritization of distributing pandemic influenza vaccines.

Pandemic influenza is an international public health concern. In light of the persistent threat of H5N1 avian influenza and the recent pandemic of A/H1N1swine influenza outbreak, public health agencies around the globe are continuously revising their preparedness plans. The A/H1N1 pandemic of 2009 demonstrated that influenza activity and severity might vary considerably among age groups and locations, and the distribution of an effective influenza vaccine may be significantly delayed and staggered. Thus, pandemic influenza vaccine distribution policies should be tailored to the demographic and spatial structures of communities. Here, we introduce a bi-criteria decision-making framework for vaccine distribution policies that is based on a geospatial and demographically-structured model of pandemic influenza transmission within and between counties of Arizona in the Unites States. Based on data from the 2009-2010 H1N1 pandemic, the policy predicted to reduce overall attack rate most effectively is prioritizing counties expected to experience the latest epidemic waves (a policy that may be politically untenable). However, when we consider reductions in both the attack rate and the waiting period for those seeking vaccines, the widely adopted pro rata policy (distributing according to population size) is also predicted to be an effective strategy.

The dynamics of risk perceptions and precautionary behavior in response to 2009 (H1N1) pandemic influenza.

BACKGROUND: The trajectory of an infectious disease outbreak is affected by the behavior of individuals, and the behavior is often related to individuals' risk perception. We assessed temporal changes and geographical differences in risk perceptions and precautionary behaviors in response to H1N1 influenza. METHODS: 1,290 US adults completed an online survey on risk perceptions, interests in pharmaceutical interventions (preventive intervention and curative intervention), and engagement in precautionary activities (information seeking activities and taking quarantine measures) in response to H1N1 influenza between April 28 and May 27 2009. Associations of risk perceptions and precautionary behaviors with respondents' sex, age, and household size were analyzed. Linear and quadratic time trends were assessed by regression analyses. Geographic differences in risk perception and precautionary behaviors were evaluated. Predictors of willingness to take pharmaceutical intervention were analyzed. RESULTS: Respondents from larger households reported stronger interest in taking medications and engaged in more precautionary activities, as would be normatively predicted. Perceived risk increased over time, whereas interest in pharmaceutical preventive interventions and the engagement in some precautionary activities decreased over time. Respondents who live in states with higher H1N1 incidence per population perceived a higher likelihood of influenza infection, but did not express greater interests in pharmaceutical interventions, nor did they engage in a higher degree of precautionary activities. Perceived likelihood of influenza infection, willingness to take medications and engagement in information seeking activities were higher for women than men. CONCLUSIONS: Perceived risk of infection and precautionary behavior can be dynamic in time, and differ by demographic characteristics and geographical locations. These patterns will likely influence the effectiveness of disease control measures.

Initial human transmission dynamics of the pandemic (H1N1) 2009 virus in North America.

BACKGROUND: Between 5 and 25 April 2009, pandemic (H1N1) 2009 caused a substantial, severe outbreak in Mexico, and subsequently developed into the first global pandemic in 41 years. We determined the reproduction number of pandemic (H1N1) 2009 by analyzing the dynamics of the complete case series in Mexico City during this early period. METHODS: We analyzed three mutually exclusive datasets from Mexico City Distrito Federal which constituted all suspect cases from 15 March to 25 April: confirmed pandemic (H1N1) 2009 infections, non-pandemic influenza A infections and patients who tested negative for influenza. We estimated the initial reproduction number from 497 suspect cases identified prior to 20 April, using a novel contact network methodology incorporating dates of symptom onset and hospitalization, variation in contact rates, extrinsic sociological factors, and uncertainties in underreporting and disease progression. We tested the robustness of this estimate using both the subset of laboratory-confirmed pandemic (H1N1) 2009 infections and an extended case series through 25 April, adjusted for suspected ascertainment bias. RESULTS: The initial reproduction number (95% confidence interval range) for this novel virus is 1.51 (1.32-1.71) based on suspected cases and 1.43 (1.29-1.57) based on confirmed cases before 20 April. The longer time series (through 25 April) yielded a higher estimate of 2.04 (1.84-2.25), which reduced to 1.44 (1.38-1.51) after correction for ascertainment bias. CONCLUSIONS: The estimated transmission characteristics of pandemic (H1N1) 2009 suggest that pharmaceutical and non-pharmaceutical mitigation measures may appreciably limit its spread prior the development of an effective vaccine.

Network frailty and the geometry of herd immunity.

The spread of infectious disease through communities depends fundamentally on the underlying patterns of contacts between individuals. Generally, the more contacts one individual has, the more vulnerable they are to infection during an epidemic. Thus, outbreaks disproportionately impact the most highly connected demographics. Epidemics can then lead, through immunization or removal of individuals, to sparser networks that are more resistant to future transmission of a given disease. Using several classes of contact networks-Poisson, scale-free and small-world-we characterize the structural evolution of a network due to an epidemic in terms of frailty (the degree to which highly connected individuals are more vulnerable to infection) and interference (the extent to which the epidemic cuts off connectivity among the susceptible population that remains following an epidemic). The evolution of the susceptible network over the course of an epidemic differs among the classes of networks; frailty, relative to interference, accounts for an increasing component of network evolution on networks with greater variance in contacts. The result is that immunization due to prior epidemics can provide greater community protection than random vaccination on networks with heterogeneous contact patterns, while the reverse is true for highly structured populations.