Impact of inactivated poliovirus vaccine on mucosal immunity: implications for the polio eradication endgame.

The polio eradication endgame aims to bring transmission of all polioviruses to a halt. To achieve this aim, it is essential to block viral replication in individuals via induction of a robust mucosal immune response. Although it has long been recognized that inactivated poliovirus vaccine (IPV) is incapable of inducing a strong mucosal response on its own, it has recently become clear that IPV may boost immunity in the intestinal mucosa among individuals previously immunized with oral poliovirus vaccine. Indeed, mucosal protection appears to be stronger following a booster dose of IPV than oral poliovirus vaccine, especially in older children. Here, we review the available evidence regarding the impact of IPV on mucosal immunity, and consider the implications of this evidence for the polio eradication endgame. We conclude that the implementation of IPV in both routine and supplementary immunization activities has the potential to play a key role in halting poliovirus transmission, and thereby hasten the eradication of polio

The Global Lake Ecological Observatory Network (GLEON): the evolution of grassroots network science

Nine years later, with over 380 members from 40 countries, and 50 publications to its credit, GLEON is growing at a rapid pace and pushing the boundaries of the practice of network science. GLEON is really three networks: a network of lakes, data, and peopl

ZikaPLAN: addressing the knowledge gaps and working towards a research preparedness network in the Americas.

Zika Preparedness Latin American Network (ZikaPLAN) is a research consortium funded by the European Commission to address the research gaps in combating Zika and to establish a sustainable network with research capacity building in the Americas. Here we present a report on ZikaPLAN`s mid-term achievements since its initiation in October 2016 to June 2019, illustrating the research objectives of the 15 work packages ranging from virology, diagnostics, entomology and vector control, modelling to clinical cohort studies in pregnant women and neonates, as well as studies on the neurological complications of Zika infections in adolescents and adults. For example, the Neuroviruses Emerging in the Americas Study (NEAS) has set up more than 10 clinical sites in Colombia. Through the Butantan Phase 3 dengue vaccine trial, we have access to samples of 17,000 subjects in 14 different geographic locations in Brazil. To address the lack of access to clinical samples for diagnostic evaluation, ZikaPLAN set up a network of quality sites with access to well-characterized clinical specimens and capacity for independent evaluations. The International Committee for Congenital Anomaly Surveillance Tools was formed with global representation from regional networks conducting birth defects surveillance. We have collated a comprehensive inventory of resources and tools for birth defects surveillance, and developed an App for low resource regions facilitating the coding and description of all major externally visible congenital anomalies including congenital Zika syndrome. Research Capacity Network (REDe) is a shared and open resource centre where researchers and health workers can access tools, resources and support, enabling better and more research in the region. Addressing the gap in research capacity in LMICs is pivotal in ensuring broad-based systems to be prepared for the next outbreak. Our shared and open research space through REDe will be used to maximize the transfer of research into practice by summarizing the research output and by hosting the tools, resources, guidance and recommendations generated by these studies. Leveraging on the research from this consortium, we are working towards a research preparedness network

Family Businesses and Adaptation: A Dynamic Capabilities Approach

The main objective of this research was to propose a framework centred on the dynamic capabilities approach, and to be applied in the context of family businesses’ adaption to their changing business environment. Data were gathered through interviews with ten FBs operating in Western Australia. Based on the findings, the clusters of activities, sensing, seizing, and transforming emerged as key factors for firms’ adaptation, and were reinforced by firms’ open culture, signature processes, idiosyncratic knowledge, and valuable, rare, inimitable and non-substitutable attributes. Thus, the usefulness of the proposed framework was confirmed. Implications and future research opportunities are presented. © 2018, The Author(s)

SARS-CoV-2 susceptibility and COVID-19 disease severity are associated with genetic variants affecting gene expression in a variety of tissues

Variability in SARS-CoV-2 susceptibility and COVID-19 disease severity between individuals is partly due to genetic factors. Here, we identify 4 genomic loci with suggestive associations for SARS-CoV-2 susceptibility and 19 for COVID-19 disease severity. Four of these 23 loci likely have an ethnicity-specific component. Genome-wide association study (GWAS) signals in 11 loci colocalize with expression quantitative trait loci (eQTLs) associated with the expression of 20 genes in 62 tissues/cell types (range: 1:43 tissues/gene), including lung, brain, heart, muscle, and skin as well as the digestive system and immune system. We perform genetic fine mapping to compute 99% credible SNP sets, which identify 10 GWAS loci that have eight or fewer SNPs in the credible set, including three loci with one single likely causal SNP. Our study suggests that the diverse symptoms and disease severity of COVID-19 observed between individuals is associated with variants across the genome, affecting gene expression levels in a wide variety of tissue types

Comparative assessment of methods for short-term forecasts of COVID-19 hospital admissions in England at the local level

Background: Forecasting healthcare demand is essential in epidemic settings, both to inform situational awareness and facilitate resource planning. Ideally, forecasts should be robust across time and locations. During the COVID-19 pandemic in England, it is an ongoing concern that demand for hospital care for COVID-19 patients in England will exceed available resources. Methods: We made weekly forecasts of daily COVID-19 hospital admissions for National Health Service (NHS) Trusts in England between August 2020 and April 2021 using three disease-agnostic forecasting models: a mean ensemble of autoregressive time series models, a linear regression model with 7-day-lagged local cases as a predictor, and a scaled convolution of local cases and a delay distribution. We compared their point and probabilistic accuracy to a mean-ensemble of them all and to a simple baseline model of no change from the last day of admissions. We measured predictive performance using the weighted interval score (WIS) and considered how this changed in different scenarios (the length of the predictive horizon, the date on which the forecast was made, and by location), as well as how much admissions forecasts improved when future cases were known. Results: All models outperformed the baseline in the majority of scenarios. Forecasting accuracy varied by forecast date and location, depending on the trajectory of the outbreak, and all individual models had instances where they were the top- or bottom-ranked model. Forecasts produced by the mean-ensemble were both the most accurate and most consistently accurate forecasts amongst all the models considered. Forecasting accuracy was improved when using future observed, rather than forecast, cases, especially at longer forecast horizons. Conclusions: Assuming no change in current admissions is rarely better than including at least a trend. Using confirmed COVID-19 cases as a predictor can improve admissions forecasts in some scenarios, but this is variable and depends on the ability to make consistently good case forecasts. However, ensemble forecasts can make forecasts that make consistently more accurate forecasts across time and locations. Given minimal requirements on data and computation, our admissions forecasting ensemble could be used to anticipate healthcare needs in future epidemic or pandemic settings

The impact of COVID-19 vaccination in prisons in England and Wales : a metapopulation model

Background: High incidence of cases and deaths due to coronavirus disease 2019 (COVID-19) have been reported in prisons worldwide. This study aimed to evaluate the impact of different COVID-19 vaccination strategies in epidemiologically semi-enclosed settings such as prisons, where staff interact regularly with those incarcerated and the wider community. Methods: We used a metapopulation transmission-dynamic model of a local prison in England and Wales. Two-dose vaccination strategies included no vaccination, vaccination of all individuals who are incarcerated and/or staff, and an age-based approach. Outcomes were quantified in terms of COVID-19-related symptomatic cases, losses in quality-adjusted life-years (QALYs), and deaths. Results: Compared to no vaccination, vaccinating all people living and working in prison reduced cases, QALY loss and deaths over a one-year period by 41%, 32% and 36% respectively. However, if vaccine introduction was delayed until the start of an outbreak, the impact was negligible. Vaccinating individuals who are incarcerated and staff over 50 years old averted one death for every 104 vaccination courses administered. All-staff-only strategies reduced cases by up to 5%. Increasing coverage from 30 to 90% among those who are incarcerated reduced cases by around 30 percentage points. Conclusions: The impact of vaccination in prison settings was highly dependent on early and rapid vaccine delivery. If administered to both those living and working in prison prior to an outbreak occurring, vaccines could substantially reduce COVID-19-related morbidity and mortality in prison settings

The contribution of hospital-acquired infections to the COVID-19 epidemic in England in the first half of 2020

Background: SARS-CoV-2 is known to transmit in hospital settings, but the contribution of infections acquired in hospitals to the epidemic at a national scale is unknown. Methods: We used comprehensive national English datasets to determine the number of COVID-19 patients with identified hospital-acquired infections (with symptom onset > 7 days after admission and before discharge) in acute English hospitals up to August 2020. As patients may leave the hospital prior to detection of infection or have rapid symptom onset, we combined measures of the length of stay and the incubation period distribution to estimate how many hospital-acquired infections may have been missed. We used simulations to estimate the total number (identified and unidentified) of symptomatic hospital-acquired infections, as well as infections due to onward community transmission from missed hospital-acquired infections, to 31st July 2020. Results: In our dataset of hospitalised COVID-19 patients in acute English hospitals with a recorded symptom onset date (n = 65,028), 7% were classified as hospital-acquired. We estimated that only 30% (range across weeks and 200 simulations: 20–41%) of symptomatic hospital-acquired infections would be identified, with up to 15% (mean, 95% range over 200 simulations: 14.1–15.8%) of cases currently classified as community-acquired COVID-19 potentially linked to hospital transmission. We estimated that 26,600 (25,900 to 27,700) individuals acquired a symptomatic SARS-CoV-2 infection in an acute Trust in England before 31st July 2020, resulting in 15,900 (15,200–16,400) or 20.1% (19.2–20.7%) of all identified hospitalised COVID-19 cases. Conclusions: Transmission of SARS-CoV-2 to hospitalised patients likely caused approximately a fifth of identified cases of hospitalised COVID-19 in the “first wave” in England, but less than 1% of all infections in England. Using time to symptom onset from admission for inpatients as a detection method likely misses a substantial proportion (> 60%) of hospital-acquired infections

Changes in social contacts in England during the COVID-19 pandemic between March 2020 and March 2021 as measured by the CoMix survey : a repeated cross-sectional study

Background During: the Coronavirus Disease 2019 (CAU OVID-19): pandemic, the United Kingdom government imposed public health policies in England to reduce social contacts in hopes of curbing virus transmission. We conducted a repeated cross-sectional study to measure contact patterns weekly from March 2020 to March 2021 to estimate the impact of these policies, covering 3 national lockdowns interspersed by periods of less restrictive policies. Methods and findings The repeated cross-sectional survey data were collected using online surveys of representative samples of the UK population by age and gender. Survey participants were recruited by the online market research company Ipsos MORI through internet-based banner and social media ads and email campaigns. The participant data used for this analysis are restricted to those who reported living in England. We calculated the mean daily contacts reported using a (clustered) bootstrap and fitted a censored negative binomial model to estimate age-stratified contact matrices and estimate proportional changes to the basic reproduction number under controlled conditions using the change in contacts as a scaling factor. To put the findings in perspective, we discuss contact rates recorded throughout the year in terms of previously recorded rates from the POLYMOD study social contact study. The survey recorded 101,350 observations from 19,914 participants who reported 466,710 contacts over 53 weeks. We observed changes in social contact patterns in England over time and by participants’ age, personal risk factors, and perception of risk. The mean reported contacts for adults 18 to 59 years old ranged between 2.39 (95% confidence interval [CI] 2.20 to 2.60) contacts and 4.93 (95% CI 4.65 to 5.19) contacts during the study period. The mean contacts for school-age children (5 to 17 years old) ranged from 3.07 (95% CI 2.89 to 3.27) to 15.11 (95% CI 13.87 to 16.41). This demonstrates a sustained decrease in social contacts compared to a mean of 11.08 (95% CI 10.54 to 11.57) contacts per participant in all age groups combined as measured by the POLYMOD social contact study in 2005 to 2006. Contacts measured during periods of lockdowns were lower than in periods of eased social restrictions. The use of face coverings outside the home has remained high since the government mandated use in some settings in July 2020. The main limitations of this analysis are the potential for selection bias, as participants are recruited through internet-based campaigns, and recall bias, in which participants may under- or over-report the number of contacts they have made