Enteric fever remains a major global health challenge, mainly affecting children in settings with poor sanitation and limited access to safe water. In response, over 60 million doses of typhoid conjugate vaccine (TCV) have been introduced in the past three years. However, follow-up data from trials in Malawi and Bangladesh indicate variability in the duration of vaccine-derived protection, raising important questions about the role of the force of infection (FOI) in affecting vaccine effectiveness over time. Understanding how FOI influences the waning of protection is critical, as it directly affects estimates of long-term TCV impact and cost-effectiveness, which are key parameters for policy decisions on vaccine schedules. In this modelling study, we will investigate the relationship between FOI and the duration of protection following a single dose of Vi-TT, using existing individual-level immunogenicity and efficacy data. We will then update our previous modelling analyses to assess the long-term impact and cost-effectiveness of TCV strategies, with and without booster doses, tailored to settings with varying typhoid burdens. The findings will generate essential evidence to inform WHO and national policy decisions on TCV booster recommendations and help determine whether a single-dose strategy is sufficient for sustained typhoid control in diverse contexts.

The Mekong Delta Region (MDR) of Vietnam is vulnerable to climate change which results in more frequent and intense mosquito-borne dengue outbreaks. Current dengue control measures are mostly reactive due to the absence of an early warning system (EWS) tailored to the needs of the local health systems. Local health practitioners and the community are, therefore, not adequately empowered to deploy preventive actions to reduce the impact of a dengue outbreak. We propose to develop and evaluate a digital dengue early warning system (E-DENGUE), based on a prediction model, to assist the local health systems and the local communities affected by dengue to proactively mitigate the impact of outbreaks in the MDR. The specific aims are: i) to build a predictive dengue model that accurately predicts dengue risk, at the district level, two months in advance; ii) to develop E-DENGUE––an open-source software system with a user-friendly web-based and mobile-app interface––aimed at local health practitioners to predict dengue incidence and outbreaks at the district level; iii) to evaluate the effectiveness of E-DENGUE in reducing dengue incidence using a cluster-randomised control trial based in the MDR; iv) To evaluate the cost-effectiveness of E-DENGUE for outbreak prevention in the MDR.

Imagine if we could watch multiple molecules in living cells as they move and interact. This dream may seem years away, but it is now realistic to achieve real-time dynamic super-resolution imaging of multiple tagged proteins in three dimensions (3D) in cells and in tissues. This will allow biologists to discover large-scale patterns involving diverse structures including transport vesicles, ribosomes, and chromatin domains, all previously inaccessible because they lie in the gap between the resolution of electron (1- 2 nm) and light microscopy (200-300 nm). The “big picture” of cellular organization/information processing would emerge, with advances in understanding cell function in health and disease. While we can now do this in 2D, 3D imaging is needed to follow objects as they move out of the plane. Achieving 3D imaging is a major challenge and will require two orders of magnitude more information per cellular volume, and novel algorithms to classify, analyze, and visualize patterns from massive datasets. We propose specific innovations (Table 1) that, should allow us to achieve this over the next five years, given our team’s proven track record of success. Imagine if we could watch multiple molecules in living cells as they move and interact. This dream may seem years away, but it is now realistic to achieve real-time dynamic super-resolution imaging of multiple tagged proteins in three dimensions (3D) in cells and in tissues. This will allow biologists to discover large-scale patterns involving diverse structures including transport vesicles, ribosomes, and chromatin domains, all previously inaccessible because they lie in the gap between the resolution of electron (1- 2 nm) and light microscopy (200-300 nm). The “big picture” of cellular organization/information processing would emerge, with advances in understanding cell function in health and disease. While we can now do this in 2D, 3D imaging is needed to follow objects as they move out of the plane. Achieving 3D imaging is a major challenge and will require two orders of magnitude more information per cellular volume, and novel algorithms to classify, analyze, and visualize patterns from massive datasets. We propose specific innovations (Table 1) that, should allow us to achieve this over the next five years, given our team’s proven track record of success.

Ground-breaking nature of the project Many of the expected impacts from this proposal are innovative. It aims to demonstrate that biomarkers can be used to stratify patients in the early phase of psychosis according to clinical outcomes, and that this can improve clinical care by providing a basis for more personalised treatment. Moreover, these biomarkers include measures acquired by employing novel digital technologies in mobile devices, and the prediction of outcomes will be facilitated by new tools that generate personalised estimates of a given outcome. The project will also evaluate a new class of treatment for psychosis (Cannabidiol) and assess whether existing treatments are more effective when used in specific subgroups patients that have been identified through stratification. Finally, the project aims to create a global translational research platform for the evaluation of novel treatments. The long-term impact will include the following domains: - Conceptual shift in approach to assessment in psychosis - Clinical impact of stratification - New classes of treatment and therapeutic targets - Reorganisation of clinical services - Improved care for patients, carers and families - Translation of research - Collaboration with industry - Health economics benefits - Implementation of digital technology in the early phase of psychosis The STEP (Stratification & Treatment in Early Psychosis) programme leverages cutting edge multimodal neuroscientific methods across four studies and twenty research centres worldwide with the overarching aim of improving the care for young people experiencing emerging psychotic disorders: Aim 1. Stratification of patients in the early phase of psychosis 500 individuals at clinical risk for psychosis (CHR), 100 healthy controls and 500 individuals with a first episode of psychosis (FEP) will undergo a comprehensive baseline assessment with cutting edge biomarkers and clinical follow-up. Aim 2. Evaluation of novel treatments / treatment algorithms - Trial 1. 500 CHR individuals will receive treatment as usual with placebo or cannabidiol (600mg/day orally) in a 52-weeks double blind RCT. - Trial 2. 500 FEP individuals who have not responded to an initial course of antipsychotic will be givenopen-label, treatment with Amisulpride (200-800mg/day orally) and randomly allocated to adjunctive CBD (800mg/day; orally) or Placebo for 6 weeks. - Trial 3. 250 FEP individuals who are not in symptomatic remission at the end of the trial 2 (Treatment Resistant), will be invited in an open-label 12-weeks treatment with Clozapine (300-900mg/day orally), and random allocation to adjunctive treatment with CBD (1000mg) or Placebo. Aim 3. Development of clinical tools Development of a smartphone-based tool that can predict clinical outcomes in individuals with emerging psychosis. Aim 4. Construction of a global translational research network A global clinical research network that will serve as a platform for the evaluation of biomarkers, novel treatments, and the use of stratification to facilitate treatment evaluation.