Oliver J. Brady

Background: Safety concerns following post-licensure use of Ixchiq, a live-attenuated chikungunya vaccine, have prompted age-based restrictions in several countries. We assessed its benefit–risk profile for travel vaccination and outbreak response immunisation. Methods: We used Brazilian surveillance and post-licensure safety data from US VAERS to evaluate benefit-risk of Ixchiq among adults aged 18–64 years and ≥65 years across low, moderate, and high transmission settings. For travel vaccination, we modelled 7, 14, 30, or 90 days of exposure among travellers from areas without CHIKV transmission. For outbreak response immunisation, we assumed vaccine protection against disease only or against both disease and infection, with vaccine-attributable risk in all individuals or only in seronegative individuals. We estimated benefit–risk ratios and the probability that benefits outweighed risks for disability-adjusted life years (DALYs), deaths, and serious adverse events (SAEs). Findings: During 90-day travel to high-transmission settings, travel vaccination prevented 75.24 (6.73–760.21) DALYs per DALY caused among adults aged 18–64 years and 3.01 (0.22–37.34) among older adults aged ≥65 years. For outbreak response immunisation in high transmission settings, vaccination prevented 671.32 (68.58–5382.27) DALYs per DALY caused among adults aged 18-64 years and 34.25 (2.79–368.90) among older adults aged ≥65 years when vaccine protects against infection and disease, and seropositive individuals are protected. Across scenarios, DALY benefit-risk ratios exceeded those for SAEs and deaths. Interpretation: For travel vaccination, benefits outweighed risks for adults aged 18–64 years travelling for at least 30 days to moderate-to-high transmission settings but not for shorter trips of 7-14 days, whereas risks outweighed benefits for older adults aged ≥65 years. For outbreak response immunisation, benefits outweighed risks for adults aged 18–64 years across all transmission settings, and for older adults aged ≥65 years only in moderate-to-high transmission settings when the vaccine protects against infection and disease.

Background: Safety concerns for Ixchiq, a live-attenuated chikungunya vaccine, has led to age-based use restrictions in multiple countries. We conducted a benefit–risk analysis of Ixchiq for travel vaccination and outbreak response immunisation strategies across transmission settings.Methods: Using Brazilian surveillance and post-licensure data, we evaluated four age groups (1–11, 12–17, 18–64, and ≥65 years). We assessed travel vaccination for 7–90 days, and outbreak response immunisation for two vaccine protection assumptions — against disease only, and against both disease and infection. We estimated benefit–risk ratios and the probability that benefits outweighed risks for disability-adjusted life years (DALYs), deaths, and serious adverse events (SAEs). Findings: Vaccine-attributable SAEs were 1 per 4,939 (95% uncertainty interval 2,537–11,710) vaccinated individuals for aged 1–64 years and 1 per 892 (595–1,420) for ≥65 years. For 90-day travel in high transmission settings, 6.18 [0.14–329.02] DALYs were prevented per DALY caused by vaccination, with 82% probability that benefits outweighed risks among individuals aged 18-64 years. Outbreak response immunisation of individuals aged 1–64 years is favourable in moderate-to-high transmission settings if vaccine protects against both disease and infection, with ≥85% probability that benefits outweighed risks in terms of DALYs prevented by vaccination versus DALYs caused by vaccination. Interpretation: If Ixchiq vaccine is able to block transmission, then it could have a marginally favourable benefit-risk ratio for individuals aged <65 years in high-transmission outbreak scenarios, but in most other travel and outbreak scenarios, the potential risk of vaccination is likely to exceed the potential benefit.

Dengue is a mosquito-transmitted viral disease that has long defied control but now has a growing list of promising interventions becoming available. Major investments in these interventions are being made without a clear picture of the long-term implications of those choices. We used a mathematical model to project the impacts of alternative suites of interventions across 1,634 cities to the year 2050. We found that recently developed interventions have the potential for significant reductions in dengue hospitalisations over a period of a few years. Beyond that timeframe, our model predicts a buildup of susceptibility that diminishes intervention effectiveness over time. Routine vaccination leads to only modest reductions when layered on top of other interventions. The most effective and sustainable strategy combines short-term investments in new interventions with long-lasting changes to remove mosquito habitat in urban environments, resulting in > 90% reductions in disease burden in all cities over a 25-year period.

D-MOSS (Dengue forecasting Model Satellite-based System) was launched operationally in Vietnam in June 2019, providing near-real time dengue forecasts across all 63 provinces. Very few dengue forecasting systems have prospectively evaluated the performance of dengue forecasting under real-world operational conditions. This study comprehensively assesses D-MOSS dengue forecasting performance since operationalisation through both statistical accuracy (absolute dengue incidence, trajectory of incidence, timing of peaks), and operational utility (predictions for specific decision-making scenarios). The D-MOSS dengue forecasts in Vietnam outperformed null model baselines across almost all performance metrics.. While lead times of one month reported the highest accuracy, there was no steep linear decline in accuracy as lead times increased up to six months, and the greatest value-added over seasonal average baseline models was found for later lead times at four to six months. Higher value-added differences were observed for the second half of the year, but the unusually-early June dengue outbreak in 2022 provided a notable challenge. Spatially, larger errors were found in central and southern provinces, that report higher dengue incidence, alongside contrastingly greater value-added over null baseline models, particularly at shorter lead times. Four contextualised operational utility scenarios were tested through probabilistic classification of outbreak threshold exceedance, with accuracy ranging across probability cut-offs but peaking at 0.83 - 0.94 across scenarios.The value of waiting for the next month’s forecast and utilising different outbreak thresholds was assessed, with heterogeneous results and the distribution of false alarms or missed outbreaks clustering spatially. The results of both the global performance analysis and utility assessments continue to highlight the strong predictive ability of D-MOSS in an operational setting. Lessons should be taken from the higher-than-expected performance over long-term horizons to improve our ability to forecast further into the future, amidst the key insights these results provide into the improvement of operational dengue forecasting for key decision-making situations in Vietnam and beyond.

Dengue is an emerging infectious disease of growing global importance, yet the pace of its expansion remains poorly quantified because surveillance data are inconsistently reported across countries and over time. This undermines prioritisation of interventions and limits the ability to track progress towards international targets, including the WHO 2030 goals. Here we combine an extensive search for dengue data worldwide with Bayesian imputation and downscaling models to produce comprehensive estimates of monthly dengue case counts for 143 countries between 1990 and 2024. We estimate 73.9 million reported dengue cases worldwide over this period, including 10.6 million cases not captured in WHO digitised databases. Reported dengue incidence increased 39-fold between 1990 and 2024, corresponding to an average annual growth rate of 9.1%, with pronounced regional heterogeneity: growth was fastest in recently emerging regions in Africa, the Middle East and South Asia, and slowest in long-established endemic regions of Southeast Asia. Annual and multiannual dengue outbreaks have doubled in cyclical strength and become increasingly synchronised globally. Collectively these data show that dengue is expanding faster, more consistently and more intensely than previously thought. These data provide a foundation for improved understanding, prediction and control of dengue expansion during a period of declining incidence for most other infectious diseases.

Aedes aegypti and Aedes albopictus are primary vectors of dengue and other arboviral diseases, posing persistent challenges in Southeast Asia (SEA). Although global modelling efforts provide broad-scale estimates of Aedes distributions and arboviral risk, projections of their future suitability in SEA remain uncertain because global models rely on coarse climate simulations and occurrence data that underrepresent regional ecological niches. Here, we develop a regionally calibrated framework that augments global mosquito records with regional arboviral case locations and uses ensembles of high-resolution climate projections. We project that environmentally suitable land for both species will increase by over 10% by 2050 before stabilising toward 2080, with the strongest expansion under high-warming pathways. Our findings reveal divergent factor sensitivity, with future suitability of Aedes aegypti driven mainly by urban fraction and Aedes albopictus more responsive to climatic factors such as precipitation and humidity. At the national scale, Singapore remains highly suitable for both species, while Vietnam and Malaysia show scenario divergence, with suitability change varying by up to 20%. These findings offer a regionally calibrated evidence base for anticipating future vector dynamics and highlight the broader utility of the modelling framework as a scalable tool for climate-health risk assessment and long-term disease preparedness.

Wolbachia replacement in dengue virus vectors is a promising tool for controlling transmission, and models can explore its potential in novel and complex scenarios. We analyse how research questions in Wolbachia replacement modelling have developed over time and in response to empirical findings. A scoping review was conducted of Wolbachia replacement models. For each study, we extracted research questions, key findings and modelling methodology and thematically categorized research questions. From 726 search results, 115 studies met inclusion criteria. Four themes were identified: identifying key Wolbachia characteristics (n = 61, 53.0%), investigating effects of environmental heterogeneity (n = 21, 18.3%), estimating epidemiological impact (n = 25, 21.7%) and exploring Wolbachia replacement in combination with other arboviral control measures (n = 8, 7.0%). Models identify cytoplasmic incompatibility (CI) and maternal transmission (MT) as key for fixation success. However, less than 20% (n = 6) of studies parameterize CI and MT using empirical sources. Although models agree outbreaks may occur with fixation, few explore epidemiological outcomes of heterogeneous Wolbachia fixation or combined vector control. These findings highlight the need for empirical parameterization and incorporating environmental complexity for models to remain insightful for decision-making. Policymakers would benefit from future models exploring heterogeneous coverage and combined control strategies, given evidence of context-specific outcomes and diverse control tools.

Somaliland experienced a large dengue fever outbreak between 2023 and 2024. National laboratory surveillance identified 4971 clinically suspected cases that underwent testing (‘tested probable cases’), of which 1703 were confirmed using rapid diagnostic tests. Three deaths were reported. Cases spanned five regions of Somaliland, with 74% in Marodijeh. As surveillance data were restricted to public-sector facilities, these findings represent a lower-bound estimate of true dengue transmission. Contribution: We highlight the utility of the new laboratory surveillance and the need for mitigating dengue risks amid climatic fluctuations.

Global climate variability has been linked with some of the largest dengue outbreaks, including the record-breaking 2023–2024 epidemic, but the understanding of their mechanism and evidence for their association is lacking. By incorporating reported dengue cases and climate data from 57 countries across the Americas and Asia from 1980 to 2024, we unpacked the global climate teleconnection and quantifying its impact on dengue cases. We revealed that the heterogeneity in the association between global climate variability and dengue cases across regions is affected by the strength and types of global climate teleconnections with temperature and precipitation. By controlling for the heterogeneity, 63% of the variation in dengue cases can be attributed to El Niño–Southern Oscillation fluctuations, with higher values in endemic regions. The 1982–83, 1997–98, 2015–16, and 2023–24 El Niño events were estimated to have induced an additional 0.2, 1.4, 4.1, and 9.6 million dengue cases, respectively, over regular seasonal patterns. Due to human-induced warming, El Niño events and teleconnections may cause a 39.0–81.7% increase in cumulative cases in 2020–2099. Our findings quantify the association between global climate variability and dengue epidemics and caution about the potential future risk.

Arboviruses transmitted mainly by Aedes (Stegomyia) aegypti and Ae. albopictus, including dengue, chikungunya, and Zika viruses, and yellow fever virus in urban settings, pose an escalating global threat. Existing risk maps, often hampered by surveillance biases, may underestimate or misrepresent the true distribution of these diseases and do not incorporate epidemiological similarities despite shared vector species. We address this by generating new global environmental suitability maps for Aedes-borne arboviruses using a multi-disease ecological niche model with a nested surveillance model fit to a dataset of over 21,000 occurrence points. This reveals a convergence in suitability around a common global distribution with recent spread of chikungunya and Zika closely aligning with areas suitable for dengue. We estimate that 5.66 (95% confidence interval 5.64-5.68) billion people live in areas suitable for dengue, chikungunya and Zika and 1.54 (1.53-1.54) billion people for yellow fever. We find large national and subnational differences in surveillance capabilities with higher income more accessible areas more likely to detect, diagnose and report viral diseases, which may have led to overestimation of risk in the United States and Europe. When combined with estimates of uncertainty, these suitability maps can be used by ministries of health to target limited surveillance and intervention resources in new strategies against these emerging threats.