Epidemiological drivers of transmissibility and severity of SARS-CoV-2 in England.

As the SARS-CoV-2 pandemic progressed, distinct variants emerged and dominated in England. These variants, Wildtype, Alpha, Delta, and Omicron were characterized by variations in transmissibility and severity. We used a robust mathematical model and Bayesian inference framework to analyse epidemiological surveillance data from England. We quantified the impact of non-pharmaceutical interventions (NPIs), therapeutics, and vaccination on virus transmission and severity. Each successive variant had a higher intrinsic transmissibility. Omicron (BA.1) had the highest basic reproduction number at 8.3 (95% credible interval (CrI) 7.7-8.8). Varying levels of NPIs were crucial in controlling virus transmission until population immunity accumulated. Immune escape properties of Omicron decreased effective levels of immunity in the population by a third. Furthermore, in contrast to previous studies, we found Alpha had the highest basic infection fatality ratio (2.9%, 95% CrI 2.7-3.2), followed by Delta (2.2%, 95% CrI 2.0-2.4), Wildtype (1.2%, 95% CrI 1.1-1.2), and Omicron (0.7%, 95% CrI 0.6-0.8). Our findings highlight the importance of continued surveillance. Long-term strategies for monitoring and maintaining effective immunity against SARS-CoV-2 are critical to inform the role of NPIs to effectively manage future variants with potentially higher intrinsic transmissibility and severe outcomes.

Quantifying the effect of delaying the second COVID-19 vaccine dose in England: a mathematical modelling study.

BACKGROUND: The UK was the first country to start national COVID-19 vaccination programmes, initially administering doses 3 weeks apart. However, early evidence of high vaccine effectiveness after the first dose and the emergence of the SARS-CoV-2 alpha variant prompted the UK to extend the interval between doses to 12 weeks. In this study, we aimed to quantify the effect of delaying the second vaccine dose in England. METHODS: We used a previously described model of SARS-CoV-2 transmission, calibrated to COVID-19 surveillance data from England, including hospital admissions, hospital occupancy, seroprevalence data, and population-level PCR testing data, using a Bayesian evidence-synthesis framework. We modelled and compared the epidemic trajectory in the counterfactual scenario in which vaccine doses were administered 3 weeks apart against the real reported vaccine roll-out schedule of 12 weeks. We estimated and compared the resulting numbers of daily infections, hospital admissions, and deaths. In sensitivity analyses, we investigated scenarios spanning a range of vaccine effectiveness and waning assumptions. FINDINGS: In the period from Dec 8, 2020, to Sept 13, 2021, the number of individuals who received a first vaccine dose was higher under the 12-week strategy than the 3-week strategy. For this period, we estimated that delaying the interval between the first and second COVID-19 vaccine doses from 3 to 12 weeks averted a median (calculated as the median of the posterior sample) of 58 000 COVID-19 hospital admissions (291 000 cumulative hospitalisations [95% credible interval 275 000-319 000] under the 3-week strategy vs 233 000 [229 000-238 000] under the 12-week strategy) and 10 100 deaths (64 800 deaths [60 200-68 900] vs 54 700 [52 800-55 600]). Similarly, we estimated that the 3-week strategy would have resulted in more infections compared with the 12-week strategy. Across all sensitivity analyses the 3-week strategy resulted in a greater number of hospital admissions. In results by age group, the 12-week strategy led to more hospitalisations and deaths in older people in spring 2021, but fewer following the emergence of the delta variant during summer 2021. INTERPRETATION: England's delayed-second-dose vaccination strategy was informed by early real-world data on vaccine effectiveness in the context of limited vaccine supplies in a growing epidemic. Our study shows that rapidly providing partial (single-dose) vaccine-induced protection to a larger proportion of the population was successful in reducing the burden of COVID-19 hospitalisations and deaths overall. FUNDING: UK National Institute for Health Research; UK Medical Research Council; Community Jameel; Wellcome Trust; UK Foreign, Commonwealth and Development Office; Australian National Health and Medical Research Council; and EU.

Climate change and communicable diseases in the Gulf Cooperation Council (GCC) countries.

A review of the extant literature reveals the extent to which the spread of communicable diseases will be significantly impacted by climate change. Specific research into how this will likely be observed in the countries of the Gulf Cooperation Council (GCC) is, however, greatly lacking. This report summarises the unique public health challenges faced by the GCC countries in the coming century, and outlines the need for greater investment in public health research and disease surveillance to better forecast the imminent epidemiological landscape. Significant data gaps currently exist regarding vector occurrence, spatial climate measures, and communicable disease case counts in the GCC - presenting an immediate research priority for the region. We outline policy work necessary to strengthen public health interventions, and to facilitate evidence-driven mitigation strategies. Such research will require a transdisciplinary approach, utilising existing cross-border public health initiatives, to ensure that such investigations are well-targeted and effectively communicated.

A hierarchical Bayesian quantitative microbiological risk assessment model for Salmonella in the sheep meat food chain.

Multiple foodborne routes of Salmonella infection have been observed; however, the majority of the literature to date has been dominated by research into the most frequently observed reservoirs, such as chicken, beef, and pork. While less commonly observed, outbreaks of Salmonella within sheep meat still occur, requiring extensive investigation by food safety inspectors. Risk assessment models inform policy makers and investigators of the risks posed by pathogens at each stage of the food chain, and help suggest at which stages in the food chain outbreaks are likely induced. This work is the first risk assessment into the prevalence of Salmonella throughout the sheep meat food chain, from farm to fork. A Bayesian evidence-synthesis model is used, informed by data gathered from 27 individual studies - an exhaustive search of the existing literature, to express and enumerate the current understanding of Salmonella prevalence in the sheep meat food chain in the form of probabilities of colonisation throughout the food chain. The resulting posterior estimate projects that 9 (0-29 95% HDI) UK individuals are likely to fall ill with salmonellosis due to sheep meat every year. A variance-based sensitivity analysis reveals that the abattoir module is the stage of greatest bacterial proliferation, highlighting it as the most probable source of outbreaks, though not to the exclusion of other factors.

Mechanisms of biodiversity between Campylobacter sequence types in a flock of broiler-breeder chickens.

Commercial poultry flocks frequently harbor the dangerous bacterial pathogen Campylobacter. As exclusion efforts frequently fail, there is interest in potential ecologically informed solutions. A long-term study of Campylobacter sequence types was used to investigate the competitive framework of the Campylobacter metacommunity and understand how multiple sequence types simultaneously co-occur in a flock of chickens. A combination of matrix and patch-occupancy models was used to estimate parameters describing the competition, transmission, and mortality of each sequence type. It was found that Campylobacter sequence types form a strong hierarchical framework within a flock of chickens and occupied a broad spectrum of transmission-mortality trade-offs. Upon further investigation of how biodiversity is thus maintained within the flock, it was found that the demographic capabilities of Campylobacter, such as mortality and transmission, could not explain the broad biodiversity of sequence types seen, suggesting that external factors such as host-bird health and seasonality are important elements in maintaining biodiversity of Campylobacter sequence types.

Can good broiler flock welfare prevent colonization by Campylobacter?

Using data on rearing and welfare metrics of multiple commercial broiler flocks, we investigate how welfare measures such as hock burn, mortality, and pododermatitis, among others, impact the likelihood of a flock becoming colonized by Campylobacter. Using both logistic regression and Bayesian networks, we show that, while some welfare metrics were weakly related to Campylobacter colonization, evidence could not be found to suggest that these metrics directly exacerbated Campylobacter colonization, rather that they were both symptoms of the same parent variable - the managing company. Observed dependency on the management of the flock suggested that yet-undiscovered differences in rearing practice were the principal factor explaining both poor bird welfare and increased risk of Campylobacter, suggesting that action can be taken to improve both these factors simultaneously.

Non-pharmaceutical interventions, vaccination, and the SARS-CoV-2 delta variant in England: a mathematical modelling study.

BACKGROUND: England's COVID-19 roadmap out of lockdown policy set out the timeline and conditions for the stepwise lifting of non-pharmaceutical interventions (NPIs) as vaccination roll-out continued, with step one starting on March 8, 2021. In this study, we assess the roadmap, the impact of the delta (B.1.617.2) variant of SARS-CoV-2, and potential future epidemic trajectories. METHODS: This mathematical modelling study was done to assess the UK Government's four-step process to easing lockdown restrictions in England, UK. We extended a previously described model of SARS-CoV-2 transmission to incorporate vaccination and multi-strain dynamics to explicitly capture the emergence of the delta variant. We calibrated the model to English surveillance data, including hospital admissions, hospital occupancy, seroprevalence data, and population-level PCR testing data using a Bayesian evidence synthesis framework, then modelled the potential trajectory of the epidemic for a range of different schedules for relaxing NPIs. We estimated the resulting number of daily infections and hospital admissions, and daily and cumulative deaths. Three scenarios spanning a range of optimistic to pessimistic vaccine effectiveness, waning natural immunity, and cross-protection from previous infections were investigated. We also considered three levels of mixing after the lifting of restrictions. FINDINGS: The roadmap policy was successful in offsetting the increased transmission resulting from lifting NPIs starting on March 8, 2021, with increasing population immunity through vaccination. However, because of the emergence of the delta variant, with an estimated transmission advantage of 76% (95% credible interval [95% CrI] 69-83) over alpha, fully lifting NPIs on June 21, 2021, as originally planned might have led to 3900 (95% CrI 1500-5700) peak daily hospital admissions under our central parameter scenario. Delaying until July 19, 2021, reduced peak hospital admissions by three fold to 1400 (95% CrI 700-1700) per day. There was substantial uncertainty in the epidemic trajectory, with particular sensitivity to the transmissibility of delta, level of mixing, and estimates of vaccine effectiveness. INTERPRETATION: Our findings show that the risk of a large wave of COVID-19 hospital admissions resulting from lifting NPIs can be substantially mitigated if the timing of NPI relaxation is carefully balanced against vaccination coverage. However, with the delta variant, it might not be possible to fully lift NPIs without a third wave of hospital admissions and deaths, even if vaccination coverage is high. Variants of concern, their transmissibility, vaccine uptake, and vaccine effectiveness must be carefully monitored as countries relax pandemic control measures. FUNDING: National Institute for Health Research, UK Medical Research Council, Wellcome Trust, and UK Foreign, Commonwealth and Development Office.

Optimal COVID-19 Vaccine Sharing Between Two Nations That Also Have Extensive Travel Exchanges.

Countries around the world have observed reduced infections from the SARS-CoV-2 virus, that causes COVID-19 illness, primarily due to non-pharmaceutical interventions (NPIs) such as lockdowns and social distancing measures designed to limit physical proximity between people. However, economies and societal interactions require restarting, and so lockdowns cannot continue indefinitely. Therefore, much hope is placed in using newly developed vaccines as a route back to normality, but this raises key questions about how they are shared. There are also emerging questions regarding travel. For instance, international business and trade necessitates at least some in-person exchanges, alongside restarting travel also for tourist purposes. By utilising a Susceptible-Infected-Recovered-Vaccinated (SIRV) mathematical model, we simulate the populations of two nations in parallel, where the first nation produces a vaccine and decides the extent to which it is shared with the second. Overlaying our mathematical structure is the virus-related effects of travel between the two nations. We find that even with extensive travel, nation one minimises its total number of deaths by simply retaining vaccines, aiming for full inoculation as fast as possible, suggesting that the risks posed by travel can be mitigated by rapidly vaccinating its own population. If instead we consider the total deaths i.e., sum of deaths of both nations, then such a policy of not sharing by nation one until full vaccination is highly sub-optimal. A policy of low initial sharing causes many more deaths in nation two than lives saved in nation one, raising important ethical issues. This imbalance in the health impact of vaccination provision must be considered as some countries begin to approach the point of extensive vaccination, while others lack the resources to do so.

Data Sharing in Southeast Asia During the First Wave of the COVID-19 Pandemic.

Background: When a new pathogen emerges, consistent case reporting is critical for public health surveillance. Tracking cases geographically and over time is key for understanding the spread of an infectious disease and effectively designing interventions to contain and mitigate an epidemic. In this paper we describe the reporting systems on COVID-19 in Southeast Asia during the first wave in 2020, and highlight the impact of specific reporting methods. Methods: We reviewed key epidemiological variables from various sources including a regionally comprehensive dataset, national trackers, dashboards, and case bulletins for 11 countries during the first wave of the epidemic in Southeast Asia. We recorded timelines of shifts in epidemiological reporting systems and described the differences in how epidemiological data are reported across countries and timepoints. Results: Our findings suggest that countries in Southeast Asia generally reported precise and detailed epidemiological data during the first wave of the pandemic. Changes in reporting rarely occurred for demographic data, while reporting shifts for geographic and temporal data were frequent. Most countries provided COVID-19 individual-level data daily using HTML and PDF, necessitating scraping and extraction before data could be used in analyses. Conclusion: Our study highlights the importance of more nuanced analyses of COVID-19 epidemiological data within and across countries because of the frequent shifts in reporting. As governments continue to respond to impacts on health and the economy, data sharing also needs to be prioritised given its foundational role in policymaking, and in the implementation and evaluation of interventions.

A Mathematical Modeling Approach to Uncover Factors Influencing the Spread of Campylobacter in a Flock of Broiler-Breeder Chickens.

Despite continued efforts to improve biosecurity protocols, Campylobacter continues to be detected in the majority of commercial chicken flocks across Europe. Using an extensive data set of Campylobacter prevalence within a chicken breeder flock for over a year, multiple Bayesian models are presented to explore the dynamics of the spread of Campylobacter in response to seasonal variation, species-specificity, bird health, and total colonization prevalence. These models indicated that birds within the flock varied greatly in their response to bacterial challenge, and that this phenomenon had a large impact on the overall prevalence of different species of Campylobacter. Campylobacter jejuni appeared more frequently in the summer, while Campylobacter coli persisted for a longer duration, amplified by the most susceptible birds in the flock. Our study suggests that strains of Campylobacter that appear most frequently likely possess no demographic advantage, but are instead amplified due to the health of the birds that ingest it.

How and When to End the COVID-19 Lockdown: An Optimization Approach.

Countries around the world are in a state of lockdown to help limit the spread of SARS-CoV-2. However, as the number of new daily confirmed cases begins to decrease, governments must decide how to release their populations from quarantine as efficiently as possible without overwhelming their health services. We applied an optimal control framework to an adapted Susceptible-Exposure-Infection-Recovery (SEIR) model framework to investigate the efficacy of two potential lockdown release strategies, focusing on the UK population as a test case. To limit recurrent spread, we find that ending quarantine for the entire population simultaneously is a high-risk strategy, and that a gradual re-integration approach would be more reliable. Furthermore, to increase the number of people that can be first released, lockdown should not be ended until the number of new daily confirmed cases reaches a sufficiently low threshold. We model a gradual release strategy by allowing different fractions of those in lockdown to re-enter the working non-quarantined population. Mathematical optimization methods, combined with our adapted SEIR model, determine how to maximize those working while preventing the health service from being overwhelmed. The optimal strategy is broadly found to be to release approximately half the population 2-4 weeks from the end of an initial infection peak, then wait another 3-4 months to allow for a second peak before releasing everyone else. We also modeled an "on-off" strategy, of releasing everyone, but re-establishing lockdown if infections become too high. We conclude that the worst-case scenario of a gradual release is more manageable than the worst-case scenario of an on-off strategy, and caution against lockdown-release strategies based on a threshold-dependent on-off mechanism. The two quantities most critical in determining the optimal solution are transmission rate and the recovery rate, where the latter is defined as the fraction of infected people in any given day that then become classed as recovered. We suggest that the accurate identification of these values is of particular importance to the ongoing monitoring of the pandemic.

Optimal control approaches for combining medicines and mosquito control in tackling dengue.

Dengue is a debilitating and devastating viral infection spread by mosquito vectors, and over half the world's population currently live at risk of dengue (and other flavivirus) infections. Here, we use an integrated epidemiological and vector ecology framework to predict optimal approaches for tackling dengue. Our aim is to investigate how vector control and/or vaccination strategies can be best combined and implemented for dengue disease control on small networks, and whether these optimal strategies differ under different circumstances. We show that a combination of vaccination programmes and the release of genetically modified self-limiting mosquitoes (comparable to sterile insect approaches) is always considered the most beneficial strategy for reducing the number of infected individuals, owing to both methods having differing impacts on the underlying disease dynamics. Additionally, depending on the impact of human movement on the disease dynamics, the optimal way to combat the spread of dengue is to focus prevention efforts on large population centres. Using mathematical frameworks, such as optimal control, are essential in developing predictive management and mitigation strategies for dengue disease control.

A Mathematical Model of Campylobacter Dynamics Within a Broiler Flock.

Globally, the bacterial genus Campylobacter is one of the leading causes of human gastroenteritis, with its primary route of infection being through poultry meat. The application of biosecurity measures is currently limited by a lack of understanding of the transmission dynamics within a flock. Our work is the first to undertake a mathematical modeling approach to Campylobacter population dynamics within a flock of broilers (chickens bred specifically for meat). A system of stochastic differential equations is used to model the routes of infection between co-housed birds. The presented model displays the strong correlation between housing density and Campylobacter prevalence, and shows how stochastic variation is the driving factor determining which strains of Campylobacter will emerge first within a flock. The model also shows how the system will rapidly select for phenotypic advantages, to quickly eliminate demographically-weaker strains. A global sensitivity analysis is performed, highlighting that the growth and death rate of other native bacterial species likely contributes the greatest to preventing flock outbreaks, presenting a promising approach to hypothesizing new methods of combatting disease transmission.

A class of 2,4-bisanilinopyrimidine Aurora A inhibitors with unusually high selectivity against Aurora B.

The two major Aurora kinases carry out critical functions at distinct mitotic stages. Selective inhibitors of these kinases, as well as pan-Aurora inhibitors, show antitumor efficacy and are now under clinical investigation. However, the ATP-binding sites of Aurora A and Aurora B are virtually identical, and the structural basis for selective inhibition has therefore not been clear. We report here a class of bisanilinopyrimidine Aurora A inhibitors with excellent selectivity for Aurora A over Aurora B, both in enzymatic assays and in cellular phenotypic assays. Crystal structures of two of the inhibitors in complex with Aurora A implicate a single amino acid difference in Aurora B as responsible for poor inhibitory activity against this enzyme. Mutation of this residue in Aurora B (E161T) or Aurora A (T217E) is sufficient to swap the inhibition profile, suggesting that this difference might be exploited more generally to achieve high selectivity for Aurora A.

A pentacyclic aurora kinase inhibitor (AKI-001) with high in vivo potency and oral bioavailability.

Aurora kinase inhibitors have attracted a great deal of interest as a new class of antimitotic agents. We report a novel class of Aurora inhibitors based on a pentacyclic scaffold. A prototype pentacyclic inhibitor 32 (AKI-001) derived from two early lead structures improves upon the best properties of each parent and compares favorably to a previously reported Aurora inhibitor, 39 (VX-680). The inhibitor exhibits low nanomolar potency against both Aurora A and Aurora B enzymes, excellent cellular potency (IC50 < 100 nM), and good oral bioavailability. Phenotypic cellular assays show that both Aurora A and Aurora B are inhibited at inhibitor concentrations sufficient to block proliferation. Importantly, the cellular activity translates to potent inhibition of tumor growth in vivo. An oral dose of 5 mg/kg QD is well tolerated and results in near stasis (92% TGI) in an HCT116 mouse xenograft model.

A selective, slow binding inhibitor of factor VIIa binds to a nonstandard active site conformation and attenuates thrombus formation in vivo.

The serine protease factor VIIa (FVIIa) in complex with its cellular cofactor tissue factor (TF) initiates the blood coagulation reactions. TF.FVIIa is also implicated in thrombosis-related disorders and constitutes an appealing therapeutic target for treatment of cardiovascular diseases. To this end, we generated the FVIIa active site inhibitor G17905, which displayed great potency toward TF.FVIIa (Ki = 0.35 +/- 0.11 nM). G17905 did not appreciably inhibit 12 of the 14 examined trypsin-like serine proteases, consistent with its TF.FVIIa-specific activity in clotting assays. The crystal structure of the FVIIa.G17905 complex provides insight into the molecular basis of the high selectivity. It shows that, compared with other serine proteases, FVIIa is uniquely equipped to accommodate conformational disturbances in the Gln217-Gly219 region caused by the ortho-hydroxy group of the inhibitor's aminobenzamidine moiety located in the S1 recognition pocket. Moreover, the structure revealed a novel, nonstandard conformation of FVIIa active site in the region of the oxyanion hole, a "flipped" Lys192-Gly193 peptide bond. Macromolecular substrate activation assays demonstrated that G17905 is a noncompetitive, slow-binding inhibitor. Nevertheless, G17905 effectively inhibited thrombus formation in a baboon arterio-venous shunt model, reducing platelet and fibrin deposition by approximately 70% at 0.4 mg/kg + 0.1 mg/kg/min infusion. Therefore, the in vitro potency of G17905, characterized by slow binding kinetics, correlated with efficacious antithrombotic activity in vivo.