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Influenza is an important public health problem and existing vaccines are not completely protective. New vaccines that protect by alternative mechanisms are needed to improve efficacy of influenza vaccines. In 2015, we did a phase 1 trial of an oral influenza vaccine, VXA-A1.1. A favourable safety profile and robust immunogenicity results in that trial supported progression of the vaccine to the current phase 2 trial. The aim of this study was to evaluate efficacy of the vaccine in a human influenza challenge model. We did a single-site, placebo-controlled and active-controlled, phase 2 study at WCCT Global, Costa Mesa, CA, USA. Eligible individuals had an initial A/California/H1N1 haemagglutination inhibition titre of less than 20 and were aged 18–49 years and in good health. Individuals were randomly assigned (2:2:1) to receive a single immunisation of either 1011 infectious units of VXA-A1.1 (a monovalent tablet vaccine) orally, a full human dose of quadrivalent inactivated influenza vaccine (IIV) via intramuscular injection, or matched placebo. Randomisation was done by computer-generated assignments with block size of five. An unmasked pharmacist provided the appropriate vaccines and placebos to the administrating nurse. Individuals receiving the treatments, investigators, and staff were all masked to group assignments. 90 days after immunisation, individuals without clinically significant symptoms or signs of influenza, an oral temperature of higher than 37·9°C, a positive result for respiratory viral shedding on a Biofire test, and any investigator-assessed contraindications were challenged intranasally with 0·5 mL wild-type A/CA/like(H1N1)pdm09 influenza virus. The primary outcomes were safety, which was assessed in all immunised participants through 365 days, and influenza-positive illness after viral challenge, which was assessed in individuals that received the viral challenge and the required number of assessments post viral challenge. This trial is registered with ClinicalTrials.gov, number NCT02918006. Between Aug 31, 2016, and Jan 23, 2017, 374 individuals were assessed for eligibility, of whom 179 were randomly assigned to receive either VXA-A1.1 (n=71 [one individual did not provide a diary card, thus the solicited events were assessed in 70 individuals]), IIV (n=72), or placebo (n=36). Between Dec 2, 2016, and April 26, 2017, 143 eligible individuals (58 in the VXA-A1.1 group, 54 in the IIV group, and 31 in the placebo group) were challenged with influenza virus. VXA-A1.1 was well tolerated with no serious or medically significant adverse events. The most prevalent solicited adverse events for each of the treatment groups after immunisation were headache in the VXA-A1.1 (in five [7%] of 70 participants) and placebo (in seven [19%] of 36 participants) groups and tenderness at injection site in the IIV group (in 19 [26%] of 72 participants) Influenza-positive illness after challenge was detected in 17 (29%) of 58 individuals in the VXA-A1.1 group, 19 (35%) of 54 in the IIV group, and 15 (48%) of 31 in the placebo group. Orally administered VXA-A1.1 was well tolerated and generated protective immunity against virus shedding, similar to a licensed intramuscular IIV. These results represent a major step forward in developing a safe and effective oral influenza vaccine. Department of Health and Human Services, Office of the Assistant Secretary for Preparedness and Response, and Biomedical Advanced Research and Development Authority.

Spatial big data have the velocity, volume, and variety of big data sources and contain additional geographic information. Digital data sources, such as medical claims, mobile phone call data records, and geographically tagged tweets, have entered infectious diseases epidemiology as novel sources of data to complement traditional infectious disease surveillance. In this work, we provide examples of how spatial big data have been used thus far in epidemiological analyses and describe opportunities for these sources to improve disease-mitigation strategies and public health coordination. In addition, we consider the technical, practical, and ethical challenges with the use of spatial big data in infectious disease surveillance and inference. Finally, we discuss the implications of the rising use of spatial big data in epidemiology to health risk communication, and public health policy recommendations and coordination across scales.

There is concern that influenza vaccine effectiveness (VE) may be attenuated by passage in eggs during manufacture. We compared quadrivalent cell-culture vaccine with egg-based vaccines, most of which were trivalent, against influenza A and B during 2017-2018 when A(H3N2) and B/Yamagata (present only in quadrivalent vaccines) predominated. We retrospectively examined risk of PCR-confirmed influenza A and B in members of Kaiser Permanente Northern California aged 4-64 years. We estimated the relative VE (rVE) of cell-culture vaccine versus egg-based vaccines, and the absolute VE (aVE) of each vaccine comparing vaccinated to unvaccinated individuals. Analyses used Cox regression with a calendar timeline, stratified by birth year, and adjusted for demographics, co-morbidities and utilization. One-third (1,016,965/3,053,248) of the population was vaccinated; 932,545 (91.7% of vaccinees) received egg-based and 84,420 (8.3%) received cell-culture vaccines. The rVE against influenza A was 8.0% (95% CI: -10, 23); aVE was 31.7% (CI: 18.7, 42.6) for cell-culture and 20.1% (CI: 14.5, 25.4) for egg-based vaccines. The rVE against influenza B was 39.6% (CI: 27.9, 49.3); aVE was 40.9% (CI: 30, 50.1) for cell-culture and 9.7% (CI 3.5, 15.6) for egg-based trivalent vaccines. Inclusion of the B/Yamagata lineage in the quadrivalent cell-based vaccine provided better protection against influenza B but vaccine effectiveness against influenza A was low for both the cell-culture vaccine and the egg-based vaccines. Improving influenza vaccines requires ongoing comparative vaccine effectiveness monitoring.

Using data from 12 US health departments, we estimated mean serial interval for monkeypox virus infection to be 8.5 (95% credible interval 7.3-9.9) days for symptom onset, based on 57 case pairs. Mean estimated incubation period was 5.6 (95% credible interval 4.3-7.8) days for symptom onset, based on 35 case pairs.

BackgroundThe 2022 mpox outbreak has infected over 30 000 people in the USA, with cases declining since mid-August. Infections were commonly associated with sexual contact between men. Interventions to mitigate the outbreak included vaccination and a reduction in sexual partnerships. Understanding the contributions of these interventions to decreasing cases can inform future public health efforts.MethodsWe fit a dynamic network transmission model to mpox cases reported by Washington DC through 10 January 2023. This model incorporated both vaccine administration data and reported reductions in sexual partner acquisition by gay, bisexual or other men who have sex with men (MSM). The model output consisted of daily cases over time with or without vaccination and/or behavioural adaptation.ResultsWe found that initial declines in cases were likely caused by behavioural adaptations. One year into the outbreak, vaccination and behavioural adaptation together prevented an estimated 84% (IQR 67% to 91%) of cases. Vaccination alone averted 79% (IQR 64% to 88%) of cases and behavioural adaptation alone averted 25% (IQR 10% to 42%) of cases. We further found that in the absence of vaccination, behavioural adaptation would have reduced the number of cases, but would have prolonged the outbreak.ConclusionsWe found that initial declines in cases were likely caused by behavioural adaptation, but vaccination averted more cases overall and was key to hastening outbreak conclusion. Overall, this indicates that outreach to encourage individuals to protect themselves from infection was vital in the early stages of the mpox outbreak, but that combination with a robust vaccination programme hastened outbreak conclusion.

Forecasting is beginning to be integrated into decision-making processes for infectious disease outbreak response. We discuss how technologies could accelerate the adoption of forecasting among public health practitioners, improve epidemic management, save lives, and reduce the economic impact of outbreaks.

To aid understanding of the effect of antiviral treatment on population-level influenza transmission, we used a novel pharmacokinetic–viral kinetic transmission model to test the correlation between nasal viral load and infectiousness, and to evaluate the impact that timing of treatment with the antivirals oseltamivir or baloxavir has on influenza transmission. The model was run under three candidate profiles whereby infectiousness was assumed to be proportional to viral titer on a natural-scale, log-scale, or dose–response model. Viral kinetic profiles in the presence and absence of antiviral treatment were compared for each individual (N = 1000 simulated individuals); subsequently, viral transmission mitigation was calculated. The predicted transmission mitigation was greater with earlier administration of antiviral treatment, and with baloxavir versus oseltamivir. When treatment was initiated 12–24 hours post symptom onset, the predicted transmission mitigation was 39.9–56.4% for baloxavir and 26.6–38.3% for oseltamivir depending on the infectiousness profile. When treatment was initiated 36–48 hours post symptom onset, the predicted transmission mitigation decreased to 0.8–28.3% for baloxavir and 0.8–19.9% for oseltamivir. Model estimates were compared with clinical data from the BLOCKSTONE post-exposure prophylaxis study, which indicated the log-scale model for infectiousness best fit the observed data and that baloxavir affords greater reductions in secondary case rates compared with neuraminidase inhibitors. These findings suggest a role for baloxavir and oseltamivir in reducing influenza transmission when treatment is initiated within 48 hours of symptom onset in the index patient.

Background : Emerging pathogens such as Zika, chikungunya, Ebola, and dengue viruses are serious threats to national and global health security. Accurate forecasts of emerging epidemics and their severity are critical to minimizing subsequent mortality, morbidity, and economic loss. The recent introduction of chikungunya and Zika virus to the Americas underscores the need for better methods for disease surveillance and forecasting. Methods : To explore the suitability of current approaches to forecasting emerging diseases, the Defense Advanced Research Projects Agency (DARPA) launched the 2014–2015 DARPA Chikungunya Challenge to forecast the number of cases and spread of chikungunya disease in the Americas. Challenge participants ( n =38 during final evaluation) provided predictions of chikungunya epidemics across the Americas for a six-month period, from September 1, 2014 to February 16, 2015, to be evaluated by comparison with incidence data reported to the Pan American Health Organization (PAHO). This manuscript presents an overview of the challenge and a summary of the approaches used by the winners. Results : Participant submissions were evaluated by a team of non-competing government subject matter experts based on numerical accuracy and methodology. Although this manuscript does not include in-depth analyses of the results, cursory analyses suggest that simpler models appear to outperform more complex approaches that included, for example, demographic information and transportation dynamics, due to the reporting biases, which can be implicitly captured in statistical models. Mosquito-dynamics, population specific information, and dengue-specific information correlated best with prediction accuracy. Conclusion : We conclude that with careful consideration and understanding of the relative advantages and disadvantages of particular methods, implementation of an effective prediction system is feasible. However, there is a need to improve the quality of the data in order to more accurately predict the course of epidemics.

Assessing the pandemic risk posed by specific non-human influenza A viruses is an important goal in public health research. As influenza virus genome sequencing becomes cheaper, faster, and more readily available, the ability to predict pandemic potential from sequence data could transform pandemic influenza risk assessment capabilities. However, the complexities of the relationships between virus genotype and phenotype make such predictions extremely difficult. The integration of experimental work, computational tool development, and analysis of evolutionary pathways, together with refinements to influenza surveillance, has the potential to transform our ability to assess the risks posed to humans by non-human influenza viruses and lead to improved pandemic preparedness and response.

We prove a generalization of N. Ozawa’s Kurosh-type theorem to the setting of free products of semiexact II1\\text {II}_1 factors with respect to arbitrary (non-tracial) faithful normal states. We are thus able to distinguish certain resulting type III factors. For example, if M=LFn⊗LFmM = LF_n \\otimes LF_m and {φi}\\{\\varphi _i\\} is any sequence of faithful normal states on MM, then the ll-various (M,φ1)∗...∗(M,φl)(M,\\varphi _1) * ... * (M,\\varphi _l) are all mutually non-isomorphic.