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Aim: To identify research priorities regarding the effectiveness of interventions for children and young people (CYP) with childhood neurological conditions (CNCs). These include common conditions such as epilepsies and cerebral palsy, as well as many rare conditions. Method: The National Institute for Health and Care Research (NIHR) and the James Lind Alliance (JLA) champion and facilitate priority setting partnerships (PSPs) between patients, caregivers, and clinicians (stakeholders) to identify the most important unanswered questions for research (uncertainties). A NIHR–JLA and British Paediatric Neurology Association collaboration used the JLA PSP methodology. This consisted of two surveys to stakeholders: survey 1 (to identify uncertainties) and survey 2 (a prioritization survey). The final top 10 priorities were agreed by consensus in a stakeholder workshop. Results: One hundred and thirty‐two charities and partner organizations were invited to participate. In survey 1, 701 participants (70% non‐clinicians, including CYP and parent and caregivers) submitted 1800 uncertainties from which 44 uncertainties were identified for prioritization in survey 2; from these, 1451 participants (83% non‐clinicians) selected their top 10 priorities. An unweighted amalgamated score across participant roles was used to select 26. In the final workshop, 14 health care professionals, 11 parent and caregivers, and two CYP ranked the 26 questions to finalize the top 10 priorities. Ten top priority questions were identified regarding interventions to treat CYP with CNCs and their associated comorbidities, for example, sleep, emotional well‐being, and distressing symptoms. Interpretation: The results of this study will inform research into the effectiveness of interventions for children with neurological conditions. [ABSTRACT FROM AUTHOR]

The article discusses a phylogenetic analysis on dengue virus (DENV) serotype 2 Cosmopolitan genotype in Ho Chi Minh, Vietnam. The study explored DENV lineages circulating in Ho Chi Minh City using samples from dengue patients in research studies at the Hospital for Tropical Diseases from 2017-2022 and it estimated ancestral node locations via root-to-tip regression analyses. It examined the dynamics of DENV prevalence, spread and transmission within Vietnam.

Human mobility is strongly associated with the spread of SARS-CoV-2 via air travel on an international scale and with population mixing and the number of people moving between locations on a local scale. However, these conclusions are drawn mostly from observations in the context of the global north where international and domestic connectivity is heavily influenced by the air travel network; scenarios where land-based mobility can also dominate viral spread remain understudied. Furthermore, research on the effects of nonpharmaceutical interventions (NPIs) has mostly focused on national- or regional-scale implementations, leaving gaps in our understanding of the potential benefits of implementing NPIs at higher granularity. Here, we use Chile as a model to explore the role of human mobility on disease spread within the global south; the country implemented a systematic genomic surveillance program and NPIs at a very high spatial granularity. We combine viral genomic data, anonymized human mobility data from mobile phones and official records of international travelers entering the country to characterize the routes of importation of different variants, the relative contributions of airport and land border importations, and the real-time impact of the country's mobility network on the diffusion of SARS-CoV-2. The introduction of variants which are dominant in neighboring countries (and not detected through airport genomic surveillance) is predicted by land border crossings and not by air travelers, and the strength of connectivity between comunas (Chile's lowest administrative divisions) predicts the time of arrival of imported lineages to new locations. A higher stringency of local NPIs was also associated with fewer domestic viral importations. Our analysis sheds light on the drivers of emerging respiratory infectious disease spread outside of air travel and on the consequences of disrupting regular movement patterns at lower spatial scales., (© The Author(s) 2024. Published by Oxford University Press on behalf of National Academy of Sciences.)

SARS-CoV-2 case data are primary sources for estimating epidemiological parameters and for modelling the dynamics of outbreaks. Understanding biases within case-based data sources used in epidemiological analyses is important as they can detract from the value of these rich datasets. This raises questions of how variations in surveillance can affect the estimation of epidemiological parameters such as the case growth rates. We use standardised line list data of COVID-19 from Argentina, Brazil, Mexico and Colombia to estimate delay distributions of symptom-onset-to-confirmation, -hospitalisation and -death as well as hospitalisation-to-death at high spatial resolutions and throughout time. Using these estimates, we model the biases introduced by the delay from symptom-onset-to-confirmation on national and state level case growth rates (rt) using an adaptation of the Richardson-Lucy deconvolution algorithm. We find significant heterogeneities in the estimation of delay distributions through time and space with delay difference of up to 19 days between epochs at the state level. Further, we find that by changing the spatial scale, estimates of case growth rate can vary by up to 0.13 d -1 . Lastly, we find that states with a high variance and/or mean delay in symptom-onset-to-diagnosis also have the largest difference between the rt estimated from raw and deconvolved case counts at the state level. We highlight the importance of high-resolution case-based data in understanding biases in disease reporting and how these biases can be avoided by adjusting case numbers based on empirical delay distributions. Code and openly accessible data to reproduce analyses presented here are available., Competing Interests: Conflicts of interest The authors declare no conflicts of interest., (Copyright © 2022 The Authors. Published by Elsevier B.V. All rights reserved.)