Polio vaccines and the eradication of poliomyelitis

Mucosal vaccines for viral diseases: Status and prospects

Virus-associated infectious diseases are highly detrimental to human health and animal husbandry. Among all countermeasures against infectious diseases, prophylactic vaccines, which developed through traditional or novel approaches, offer potential benefits. More recently, mucosal vaccines attract attention for their extraordinary characteristics compared to conventional parenteral vaccines, particularly for mucosal-related pathogens. Representatively, coronavirus disease 2019 (COVID-19), a respiratory disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), further accelerated the research and development efforts for mucosal vaccines by thoroughly investigating existing strategies or involving novel techniques. While several vaccine candidates achieved positive progresses, thus far, part of the current COVID-19 mucosal vaccines have shown poor performance, which underline the need for next-generation mucosal vaccines and corresponding platforms. In this review, we summarized the typical mucosal vaccines approved for humans or animals and sought to elucidate the underlying mechanisms of these successful cases. In addition, mucosal vaccines against COVID-19 that are in human clinical trials were reviewed in detail since this public health event mobilized all advanced technologies for possible solutions. Finally, the gaps in developing mucosal vaccines, potential solutions and prospects were discussed. Overall, rational application of mucosal vaccines would facilitate the establishing of mucosal immunity and block the transmission of viral diseases.

Spatiotemporal clustering analysis of Expanded Program on Immunization (EPI) vaccination coverage in Pakistan

Universal vaccination coverage is still far from desired targets in many global regions including Pakistan, despite the success stories and its scientifically proven benefits. EPI Pakistan vaccination coverage data 2012–2016, at district level was collected from Federal EPI Pakistan. District-wise population data were collected from Pakistan Bureau of Statistics. Descriptive statistics and sequence plots were performed in SPSS 13.0. Purely spatial scanning analysis was done in SaTScan 9.4.4 using discrete Poisson model for detection of low vaccination coverage clusters. Geographical information system (GIS) was used to display spatial patterns and clusters of low vaccination coverage districts in Pakistan. Average annual EPI vaccination coverage in each study year were; 70.98 in 2012, 69.39% in 2013, 66.74% in 2014, 61.47% in 2015, and 67.01% in 2016, respectively. Cumulative average national vaccination rate (2012–2016) for all types of EPI vaccines was 60.60%. Average national vaccination rate for BCG, OPV3, pentavalent3 and measles1 was 67.12%, 58.53%, 58.47%, and 58.29%, respectively. Spatial cluster analysis demonstrated that most of low coverage districts for BCG, OPV3 and pentavalent3 were from FATA and KPK; while measles1 low coverage districts belonged to Balochistan. Future research should probe factors involved in low vaccination coverage in high risk districts.

Engineering the Live-Attenuated Polio Vaccine to Prevent Reversion to Virulence

The live-attenuated oral poliovirus vaccine (OPV or Sabin vaccine) replicates in gut-associated tissues, eliciting mucosa and systemic immunity. OPV protects from disease and limits poliovirus spread. Accordingly, vaccination with OPV is the primary strategy used to end the circulation of all polioviruses. However, the ability of OPV to regain replication fitness and establish new epidemics represents a significant risk of polio re-emergence should immunization cease. Here, we report the development of a poliovirus type 2 vaccine strain (nOPV2) that is genetically more stable and less likely to regain virulence than the original Sabin2 strain. We introduced modifications within at the 5' untranslated region of the Sabin2 genome to stabilize attenuation determinants, 2C coding region to prevent recombination, and 3D polymerase to limit viral adaptability. Prior work established that nOPV2 is immunogenic in preclinical and clinical studies, and thus may enable complete poliovirus eradication.

Adverse events following immunization: real causality and myths

INTRODUCTION

To assure the highest safety of immunization programs, detect adverse events following immunization (AEFIs), eliminate concerns, and reduce the risk of low vaccination coverage, authorities in industrialized countries have collected years of reports of suspected AEFIs and have systematically assessed their clinical importance.

AREAS COVERED

In this paper, the methods used to assess vaccine safety and the results obtained by the analysis of reports, studies, and meta-analyses are discussed.

EXPERT OPINION

Severe AEFIs are rare, and all evaluations of safety of vaccines recommended for both children and adults have demonstrated that the advantages of vaccines are always significantly higher than the problems that they cause, and there is no need to modify recommendations. However, the definition of AEFI is dependent on the vaccines themselves, complicating the definition of an AEFI and explaining why doubts and concerns have been raised. Presently, disease epidemiology data collected in healthy people and in subjects with underlying disease, general vaccine coverage, and the vaccination status of subjects with AEFIs are managed by many independent institutions. Only strict co-operation between these institutions will lead to the successful identification of AEFIs and to a reduction of the weight of anti-vaccine arguments.

Detecting differential transmissibilities that affect the size of self-limited outbreaks

Our ability to respond appropriately to infectious diseases is enhanced by identifying differences in the potential for transmitting infection between individuals. Here, we identify epidemiological traits of self-limited infections (i.e. infections with an effective reproduction number satisfying [0 < R eff < 1) that correlate with transmissibility. Our analysis is based on a branching process model that permits statistical comparison of both the strength and heterogeneity of transmission for two distinct types of cases. Our approach provides insight into a variety of scenarios, including the transmission of Middle East Respiratory Syndrome Coronavirus (MERS-CoV) in the Arabian peninsula, measles in North America, pre-eradication smallpox in Europe, and human monkeypox in the Democratic Republic of the Congo. When applied to chain size data for MERS-CoV transmission before 2014, our method indicates that despite an apparent trend towards improved control, there is not enough statistical evidence to indicate that R eff has declined with time. Meanwhile, chain size data for measles in the United States and Canada reveal statistically significant geographic variation in R eff, suggesting that the timing and coverage of national vaccination programs, as well as contact tracing procedures, may shape the size distribution of observed infection clusters. Infection source data for smallpox suggests that primary cases transmitted more than secondary cases, and provides a quantitative assessment of the effectiveness of control interventions. Human monkeypox, on the other hand, does not show evidence of differential transmission between animals in contact with humans, primary cases, or secondary cases, which assuages the concern that social mixing can amplify transmission by secondary cases. Lastly, we evaluate surveillance requirements for detecting a change in the human-to-human transmission of monkeypox since the cessation of cross-protective smallpox vaccination. Our studies lay the foundation for future investigations regarding how infection source, vaccination status or other putative transmissibility traits may affect self-limited transmission.

Identifying postelimination trends for the introduction and transmissibility of measles in the United States

The continued elimination of measles requires accurate assessment of its epidemiology and a critical evaluation of how its incidence is changing with time. National surveillance of measles in the United States between 2001 and 2011 provides data on the number of measles introductions and the size of the resulting transmission chains. These data allow inference of the effective reproduction number, Reff, and the probability of an outbreak occurring. Our estimate of 0.52 (95% confidence interval: 0.44, 0.60) for Reff is smaller than prior results. Our findings are relatively insensitive to the possibility that as few as 75% of cases were detected. Although we confirm that measles remains eliminated, we identify an increasing trend in the number of measles cases with time. We show that this trend is likely attributable to an increase in the number of disease introductions rather than a change in the transmissibility of measles. However, we find that transmissibility may increase substantially if vaccine coverage drops by as little as 1%. Our general approach of characterizing the case burden of measles is applicable to the epidemiologic assessment of other weakly transmitting or vaccine-controlled pathogens that are either at risk of emerging or on the brink of elimination.

Emerging paradigms in anti-infective drug design

The need for new drugs to treat microbial infections is pressing. The great progress made in the middle part of the twentieth Century was followed by a period of relative inactivity as the medical needs relating to infectious disease in the wealthier nations receded. Growing realisation that anti-infectives are needed in many parts of the world, to treat neglected diseases as well as to combat the burgeoning risk of resistance to existing drugs, has galvanised a new wave of research into anti-microbial drugs. The transfer of knowledge from the Pharmaceutical industry relating to the importance of understanding how to target drugs successfully within the body, and improved understanding of how pathogens interact with their hosts, is driving a series of new paradigms in anti-infective drug design. Here we provide an overview of those processes as an introduction to a series of articles from experts in this area that emerged from a meeting entitled “Emerging Paradigms in Anti-Infective Drug Design” held in London on the 17th and 18th September 2012. The symposium was organised jointly by British Society for Parasitology (BSP) and the Biological & Medicinal Chemistry sector of the Royal Society of Chemistry (RSC) and held at the London School of Hygiene & Tropical Medicine. The symposium set out to cover all aspects of the identification of new therapeutic modalities for the treatment of neglected and tropical diseases. We aimed to bring together leading scientists from all the disciplines working in this field and cover the pharmacology, medicinal chemistry and drug delivery of potential new medicines. Sessions were held on: “Target diseases and targets for drugs”, “Target based medicinal chemistry”, “Bioavailability and chemistry”, “Targeting intracellular microbes”, “Alternative approaches and models”, and “New anti-infectives – how do we get there?”

This symposium was organised by Simon Croft (LSHTM) and Mike Barrett (University of Glasgow) for the BSP, and David Alker (David Alker Associates) and Andrew Stachulski (University of Liverpool) for the Biological & Medicinal Chemistry sector of the RSC.

Assessment of causality of individual adverse events following immunization (AEFI): a WHO tool for global use

Serious illnesses or even deaths may rarely occur after childhood vaccinations. Public health programs are faced with great challenges to establish if the events presenting after the administration of a vaccine are due to other conditions, and hence a coincidental presentation, rather than caused by the administered vaccines. Given its priority, the Global Advisory Committee for Vaccine Safety (GACVS) commissioned a group of experts to review the previously published World Health Organization (WHO) Adverse Event Following Immunization (AEFI) causality assessment methodology and aide-memoire, and to develop a standardized and user friendly tool to assist health care personnel in the processing and interpretation of data on individual events, and to assess the causality after AEFIs. We describe a tool developed for causality assessment of individual AEFIs that includes: (a) an eligibility component for the assessment that reviews the diagnosis associated with the event and identifies the administered vaccines; (b) a checklist that systematically guides users to gather available information to feed a decision algorithm; and (c) a decision support algorithm that assists the assessors to come to a classification of the individual AEFI. Final classification generated by the process includes four categories in which the event is either: (1) consistent; (2) inconsistent; or (3) indeterminate with respect of causal association; or (4) unclassifiable. Subcategories are identified to assist assessors in resulting public health decisions that can be used for action. This proposed tool should support the classification of AEFI cases in a standardized, transparent manner and to collect essential information during AEFI investigation. The algorithm should provide countries and health officials at the global level with an instrument to respond to vaccine safety alerts, and support the education, research and policy decisions on immunization safety.

Comparing methods for estimating R0 from the size distribution of subcritical transmission chains

Many diseases exhibit subcritical transmission (i.e. 0<R0<1) so that infections occur as self-limited 'stuttering chains'. Given an ensemble of stuttering chains, information about the number of cases in each chain can be used to infer R0, which is of crucial importance for monitoring the risk that a disease will emerge to establish endemic circulation. However, the challenge of imperfect case detection has led authors to adopt a variety of work-around measures when inferring R0, such as discarding data on isolated cases or aggregating intermediate-sized chains together. Each of these methods has the potential to introduce bias, but a quantitative comparison of these approaches has not been reported. By adapting a model based on a negative binomial offspring distribution that permits a variable degree of transmission heterogeneity, we present a unified analysis of existing R0 estimation methods. Simulation studies show that the degree of transmission heterogeneity, when improperly modeled, can significantly impact the bias of R0 estimation methods designed for imperfect observation. These studies also highlight the importance of isolated cases in assessing whether an estimation technique is consistent with observed data. Analysis of data from measles outbreaks shows that likelihood scores are highest for models that allow a flexible degree of transmission heterogeneity. Aggregating intermediate sized chains often has similar performance to analyzing a complete chain size distribution. However, truncating isolated cases is beneficial only when surveillance systems clearly favor full observation of large chains but not small chains. Meanwhile, if data on the type and proportion of cases that are unobserved were known, we demonstrate that maximum likelihood inference of R0 could be adjusted accordingly. This motivates the need for future empirical and theoretical work to quantify observation error and incorporate relevant mechanisms into stuttering chain models used to estimate transmission parameters.

Inference of R(0) and transmission heterogeneity from the size distribution of stuttering chains

For many infectious disease processes such as emerging zoonoses and vaccine-preventable diseases, [Formula: see text] and infections occur as self-limited stuttering transmission chains. A mechanistic understanding of transmission is essential for characterizing the risk of emerging diseases and monitoring spatio-temporal dynamics. Thus methods for inferring [Formula: see text] and the degree of heterogeneity in transmission from stuttering chain data have important applications in disease surveillance and management. Previous researchers have used chain size distributions to infer [Formula: see text], but estimation of the degree of individual-level variation in infectiousness (as quantified by the dispersion parameter, [Formula: see text]) has typically required contact tracing data. Utilizing branching process theory along with a negative binomial offspring distribution, we demonstrate how maximum likelihood estimation can be applied to chain size data to infer both [Formula: see text] and the dispersion parameter that characterizes heterogeneity. While the maximum likelihood value for [Formula: see text] is a simple function of the average chain size, the associated confidence intervals are dependent on the inferred degree of transmission heterogeneity. As demonstrated for monkeypox data from the Democratic Republic of Congo, this impacts when a statistically significant change in [Formula: see text] is detectable. In addition, by allowing for superspreading events, inference of [Formula: see text] shifts the threshold above which a transmission chain should be considered anomalously large for a given value of [Formula: see text] (thus reducing the probability of false alarms about pathogen adaptation). Our analysis of monkeypox also clarifies the various ways that imperfect observation can impact inference of transmission parameters, and highlights the need to quantitatively evaluate whether observation is likely to significantly bias results.