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The Advisory Committee on Immunization Practices (ACIP) recommended phased allocation of SARS-CoV-2 vaccines in December 2020. To support the development of this guidance, we used a mathematical model of SARS-CoV-2 transmission to evaluate the relative impact of three vaccine allocation strategies on infections, hospitalizations, and deaths. All three strategies initially prioritized healthcare personnel (HCP) for vaccination. Strategies of subsequently prioritizing adults aged ≥65 years, or a combination of essential workers and adults aged ≥75 years, prevented the most deaths. Meanwhile, prioritizing adults with high-risk medical conditions immediately after HCP prevented the most infections. All three strategies prevented a similar fraction of hospitalizations. While no model is capable of fully capturing the complex social dynamics which shape epidemics, exercises such as this one can be a useful way for policy makers to formalize their assumptions and explore the key features of a problem before making decisions., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Published by Elsevier Ltd.)

The most severe consequences of dengue virus infection include shock, haemorrhage, and major organ failure; however, the frequency of these manifestations varies, and the relative contribution of pre-existing anti-dengue virus antibodies, virus characteristics, and host factors (including age and comorbidities) are not well understood. Reliable characterisation of the epidemiology of severe dengue first depends on the use of consistent definitions of disease severity. As vaccine trials have shown, severe dengue is a crucial interventional endpoint, yet the infrequency of its occurrence necessitates the inclusion of thousands of study participants to appropriately compare its frequency among participants who have and have not been vaccinated. Hospital admission is frequently used as a proxy for severe dengue; however, lack of specificity and variability in clinical practices limit the reliability of this approach. Although previous infection with a dengue virus is the best characterised risk factor for developing severe dengue, the influence of the timing between dengue virus infections and the sequence of dengue virus infections on disease severity is only beginning to be elucidated. To improve our understanding of the diverse factors that shape the clinical spectrum of disease resulting from dengue virus infection, prospective, community-based and clinic-based immunological, virological, genetic, and clinical studies across a range of ages and geographical regions are needed., Competing Interests: Declaration of interests KBA reports serving as a consultant for Emergent Biosolutions and Eli Lilly, outside of the submitted work. EH reports grants from the National Institute of Allergy and Infectious Diseases (National Institutes of Health), during the conduct of this Personal View. LCK reports grants from the National Institutes of Health, during the conduct of this Personal View. ACM reports grants from the National Institutes of Health, during the conduct of this Personal View. All other authors declare no competing interests., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

Background: Since late 2015, an epidemic of yellow fever has caused more than 7334 suspected cases in Angola and the Democratic Republic of the Congo, including 393 deaths. We sought to understand the spatial spread of this outbreak to optimise the use of the limited available vaccine stock., Methods: We jointly analysed datasets describing the epidemic of yellow fever, vector suitability, human demography, and mobility in central Africa to understand and predict the spread of yellow fever virus. We used a standard logistic model to infer the district-specific yellow fever virus infection risk during the course of the epidemic in the region., Findings: The early spread of yellow fever virus was characterised by fast exponential growth (doubling time of 5-7 days) and fast spatial expansion (49 districts reported cases after only 3 months) from Luanda, the capital of Angola. Early invasion was positively correlated with high population density (Pearson's r 0·52, 95% CI 0·34-0·66). The further away locations were from Luanda, the later the date of invasion (Pearson's r 0·60, 95% CI 0·52-0·66). In a Cox model, we noted that districts with higher population densities also had higher risks of sustained transmission (the hazard ratio for cases ceasing was 0·74, 95% CI 0·13-0·92 per log-unit increase in the population size of a district). A model that captured human mobility and vector suitability successfully discriminated districts with high risk of invasion from others with a lower risk (area under the curve 0·94, 95% CI 0·92-0·97). If at the start of the epidemic, sufficient vaccines had been available to target 50 out of 313 districts in the area, our model would have correctly identified 27 (84%) of the 32 districts that were eventually affected., Interpretation: Our findings show the contributions of ecological and demographic factors to the ongoing spread of the yellow fever outbreak and provide estimates of the areas that could be prioritised for vaccination, although other constraints such as vaccine supply and delivery need to be accounted for before such insights can be translated into policy., Funding: Wellcome Trust., (Copyright © 2017 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY license. Published by Elsevier Ltd.. All rights reserved.)

In December 2013, chikungunya virus (CHIKV) transmission was reported for the first time in the Americas. Since then it has spread quickly, with more than 1 million suspected and confirmed cases being reported in one year, where previously there were only sporadic travel-related cases. Transmission patterns suggest that the epidemic in the southern hemisphere is only beginning and that chikungunya will not go away anytime soon., (Published by Elsevier Ltd.)

Vaccination reduces transmission of pathogens directly, by preventing individual infections, and indirectly, by reducing the probability of contact between infected individuals and susceptible ones. The potential combined impact of future dengue vaccines can be estimated using mathematical models of transmission. However, there is considerable uncertainty in the structure of models that accurately represent dengue transmission dynamics. Here, we review models that could be used to assess the impact of future dengue immunization programmes. We also review approaches that have been used to validate and parameterize models. A key parameter of all approaches is the basic reproduction number, R(0), which can be used to determine the critical vaccination fraction to eliminate transmission. We review several methods that have been used to estimate this quantity. Finally, we discuss the characteristics of dengue vaccines that must be estimated to accurately assess their potential impact on dengue virus transmission., (Copyright © 2011 Elsevier Ltd. All rights reserved.)