Transactions of The Royal Society of Tropical Medicine and Hygiene

Wolbachia-based interventions were less effective in achieving introgression in Rio de Janeiro than other settings. Reduced introgression occurred in locations where dengue was historically high, raising concerns of inequity. We examined factors affecting Wolbachia introgression in Rio through trajectory modeling, spatiotemporal Bayesian models, and Ae. aegypti abundance estimation. During the intervention (2017-2020), Wolbachia introgression was 26% (median; IQR,14-44%), with four distinct trajectories. Despite receiving more releases, densely populated areas with low socioeconomic status had lower introgression. In spatiotemporally explicit multivariable regressions, higher Ae. aegypti abundance, temperature, population density and lower socioeconomic status were associated with reduced introgression. Improved characterization of Ae. aegypti abundance and other structural/environmental predictors of introgression could be used to calibrate release doses to ensure equitable benefits from Wolbachia interventions.

Brazil has one of the highest dengue burdens globally, with transmission influenced by climate events. Yet, the effects of compound climate extremes on dengue risk and prediction have not been quantified to date. Using a Bayesian spatiotemporal model of monthly dengue cases across 435 health regions from 2010 to 2025, we estimated relative risk associated with interacting long-lag and short-lag drought indicators and temperature anomalies across nine Koppen-Geiger climatic zones, adjusting for absolute temperature and El Nino-Southern Oscillation. Long-lag dry, mid-lag warm and short-lag wet conditions were associated with the highest relative risk in five climatic zones, with temperature anomalies modifying hydrometeorological dynamics more strongly in temperate than tropical areas. Climate covariates improved the prediction of case counts and outbreaks compared to baseline models at lead times of 1, 3, 6 and 12 months, and for the upcoming season 5 months ahead. Improvements were greatest in the Northeast, Southeast, and Midwest. Recent dengue incidence provided the greatest improvement at the 1-month lead, whereas combining incidence with climate information performed best at 3- and 6-month leads. We extended our framework to weekly state-level predictions of the season ahead to align with national public health planning. We proposed a multi-staged dengue forecasting approach to enhance early warning and response across Brazil.

Background. Nigeria's reported Lassa fever case fatality ratio (CFR) rose from 18.9% at week 23 of 2025 to 25.0% in 2026, prompting concerns over failing clinical care. Because surveillance artefacts can also alter CFRs, candidate mechanisms that published data can resolve were identified and evaluated. Methods. This ecological study extracted weekly state-level counts from 474 Nigeria Centre for Disease Control and Prevention situation reports (2017-2026). Per a pre-specified plan, years were compared at phase-matched points, year-on-year CFR changes were decomposed into within-state and between-state components, and tipping-point ascertainment shifts calculated. Weekly incident and cumulative published totals were compared post hoc. Findings. Of 11 candidate mechanisms, 2 could be evaluated fully and 5 in part. On weekly incident counts, 2026 did not differ from 2025 at the phase-matched midpoint (+0.3 percentage points, 95% CI -5.0 to 7.6); within-state (-1.1, 95% CI -4.7 to 3.0) and between-state (+0.5, -2.0 to 2.9) components spanned zero. Conversely, the published cumulative CFR was 5.4 to 6.2 percentage points higher because 70 deaths were added retrospectively by week 36 outside weekly reports. Reconciling cumulative figures would require 279 missed survivors (6% of suspected cases not confirmed). Interpretation. The 2026 CFR rise came from deaths added to cumulative totals outside the weekly counts, and published data cannot attribute it to care. Endemic zoonotic CFRs cannot serve as clinical outcome measures unless testing volumes, deaths added late, and post-mortem sampling are published alongside case counts. Funding. US NSF-NIH-NIFA EEID; UK BBSRC; Birgitta Sintring Fellowship (Uppsala University).

Background: Treponema pallidum quantitative polymerase chain reaction (qPCR) positivity and cycle threshold (Ct) by rapid plasma reagin (RPR), disease stage, and other characteristics are poorly characterized in routine-care specimens, leaving it unclear when qPCR of such specimens is informative for diagnosis. Methods: This Japanese multicenter cross-sectional study explored qPCR positivity and Ct by these characteristics. Nonblood specimens collected during routine care underwent qPCR at a central laboratory for this study. Bayesian logistic and normal regressions modeled positivity and mean Ct of TP0574 and polA with stage-specific continuous automated RPR terms, patient characteristics, collection site, and facility random intercepts. Estimates were standardized over the analyzed specimens. Population-group predictions were instead averaged within each group. Results: Among 154 nonblood specimens from 145 patients (71.7% non-MSM men and women) at 14 facilities, 109 (70.8%) were qPCR positive. At RPR 1, adjusted positivity was 72.2% (95% credible interval, 60.9%--82.9%) in primary and 43.8% (16.7%--69.9%) in secondary syphilis. Positivity showed a modest downward trend with RPR in primary but increased in secondary syphilis, reflecting a stage-by-RPR interaction (secondary-to-primary ratio of odds ratios, 1.78; 1.25--2.65). Predicted positivity showed an upward trend with RPR values among MSM; adjusted positivity was relatively high for genital/urethral/urinary and anal/perianal/rectal specimens. Adjusted mean Ct increased with RPR in primary and followed an inverted-U pattern in secondary syphilis. Conclusions: In primary syphilis, nonblood qPCR positivity was high even at low RPR. When serologic findings remain inconclusive in this stage, qPCR is highly recommended for securing an early diagnosis.

Artemisinin partial resistance (ART R) associated Plasmodium falciparum Kelch13 mutations is expanding across Africa. However, their population level consequences remain poorly understood, particularly in settings of moderate to high malaria transmission. We investigated associations between Kelch13 and partner drug mutation prevalence and monthly confirmed malaria incidence across 16 Ugandan districts from 2017 to 2022, using integrated molecular, epidemiological, climatic, malaria control, and antimalarial resistance data and negative binomial regression. Combined prevalence of WHO validated and candidate Kelch13 mutations increased over the study period. Each 10 percentage point increase in Kelch13 prevalence was associated with a 13.1% higher incidence after multivariable adjustment (aIRR 1.131, 95% CI 1.090 to 1.174). Furthermore, each 10% increase in pfcrt K76T was associated with a 24% increase in malaria transmission. As expected, ITN distribution >80%, IRS coverage >90%, and higher altitude were associated with lower incidence, whereas temperatures of 25 to 30C, relative humidity of 60 to 80%, and higher precipitation were associated with higher incidence. The persistence of the Kelch13 and pfcrt K76T association after adjustment suggests that expanding ART R associated parasite populations are independently associated with malaria incidence. The relationship may be bidirectional. These findings support interpreting ART R within its ecological and intervention context and integrating molecular, epidemiological, and malaria control surveillance to safeguard antimalarial efficacy.

Routine infant vaccination has helped reduce cases and deaths from diphtheria, tetanus, and pertussis globally. However, these diseases continue to pose serious health risks as protective immunity wanes over time. Three booster doses of diphtheria-tetanus-pertussis-containing vaccine (DTPCV) administered from 12-months to 15-years of age increase duration of protection beyond childhood. Growing demands on government health spending underscore the importance of understanding the health and economic impact of vaccines prior to introduction. Using an integrated multi-disease epidemiological-economic model, we estimated the health impact (cases averted, deaths averted, and changes to population immunity) and budget impact of introducing DTPCV booster doses in Uganda. Compared to no DTPCV boosters, introducing all three booster doses is projected to result in 2.2 times the proportion of the population protected against symptomatic diphtheria and 2.8 times against tetanus infection among Ugandans under 15 years of age by 2039. Compared to introduction of the early childhood booster (EC) at 18 months of age, introducing all three DTPCV boosters simultaneously is projected to avert 3.2 times the number of tetanus cases over fifteen years at 2.4 times the incremental cost. The projected government vaccination cost per planned dose was USD 0.58 for simultaneous introduction of all three boosters compared with USD 0.68 for the EC booster alone. The average annual incremental cost of all three DTPCV booster doses is USD 2.79 million. Under modelled assumptions, simultaneous introduction of all three DTPCV booster doses in Uganda provides the greatest health gains, with a lower government vaccination cost per planned dose than introduction of the EC booster alone, but may still pose affordability challenges for Ugandas government.

Introduction Despite the significant burden of Shigellosis, routine surveillance systems in resource-limited settings lack predictive capabilities to guide proactive public health responses. Consequently, we developed and evaluated the accuracy, uncertainty and computational cost of multi-horizon forecasting models for Shigella incidence among children <5 years in rural western Kenya using 16-year longitudinal incidence time-series. Methods We estimated monthly Shigella incidence using data from three pediatric enteric studies (2008-2024), supplemented with Kenya Health Information System during inter-study gaps. Incidence estimates incorporated sampling, healthcare-seeking, stool-testing, and pathogen-specific weights, with bootstrap-derived 95% confidence intervals. Predictors included Google Trends health-related indicators, climate, temporal trends, and seasonality. Forecasts at 1-, 3-, and 6-month horizons were evaluated using rolling-origin validation. Following horizon-specific predictor selection using Boruta, 14 state-space, machine-learning, and hybrid models were compared using point-forecast and probabilistic metrics, bootstrap confidence intervals, and Model Confidence Sets. Adaptive conformal prediction recalibrated prediction intervals, while Shapley Additive exPlanations assessed predictor contributions. Results Mean monthly Shigella incidence was 2.51 per 100 child-years (standard deviation=1.85). Shigella dynamics in Kenya can be forecast beyond seasonal persistence alone for up to six months reducing absolute errors by ~ 33%. However, optimal model architectures and predictor sets shift systematically across forecasting horizons. Temporal trend and recent incidence were the dominant predictors, while predictor importance shifted with forecast horizon toward syndromic, pharmaceutical, seasonal, and lagged environmental signals. Extreme Gradient Boosting performed best at 1 month (Root mean squared error [RMSE]=1.86), remained competitive at 3 months (RMSE=1.89), whereas exponential smoothing (ETS) performed best at 6 months (RMSE=2.05). Forecast accuracy declined with increasing horizon. ETS and TBATS provided the best-calibrated prediction intervals, whereas machine-learning models generally under-covered. Model Confidence Sets demonstrated horizon-dependent model distinguishability. Conclusion Horizon-specific model selection balancing predictive accuracy, uncertainty calibration, and computational feasibility could strengthen operational early warning systems for shigellosis.

Background: Severe malaria remains a major cause of hospitalization in malaria-endemic sub-Saharan Africa, and anemia is among its most frequent severity criteria. Data on the impact of anemia on hospital length of stay (LOS) among adults with severe malaria remain limited in Central Africa. We assessed the impact of anemia on LOS among adults hospitalized for severe malaria in Brazzaville, Republic of Congo. Methods: We conducted a cross-sectional, descriptive, and analytic study of medical records from adult patients ([&ge;]18 years) diagnosed with severe malaria (WHO criteria) and admitted to four hospitals in Brazzaville between January 2020 and December 2024. We dichotomized LOS as short (<5 days) or prolonged ([&ge;]5 days). Multivariable logistic regression, using a backward stepwise procedure, identified factors independently associated with prolonged LOS; isolated hemoglobinuria served as the reference severity category. Results: Among 523 patients hospitalized for severe malaria, 296 met the inclusion criteria. Median age was 25.5 years (IQR 20-40); 51.7% were female, and 82.1% had a low socioeconomic status. Median hemoglobin was 10.55 g/dL, and median LOS was 4 days (IQR 3-5). In multivariable analysis, prolonged LOS was independently associated with delay to consultation (aOR 1.25/day, 95% CI 1.09-1.44), isolated anemia (aOR 7.07, 1.23-40.55), convulsions (aOR 5.86, 1.81-18.97), prostration (aOR 4.42, 1.21-16.2), and impaired consciousness (aOR 4.29, 1.17-15.7). The strongest associations involved combined severity criteria: anemia with prostration (aOR 8.58), anemia with jaundice or hemoglobinuria (aOR 16.21), and impaired consciousness with anemia (aOR 33.31). Conclusions: Anemia is a major, potentially modifiable determinant of prolonged hospitalization in adults with severe malaria, with a markedly amplified effect when combined with neurologic impairment. Early anemia screening, improved access to safe blood transfusion, and reduced delays to care could shorten hospital stays and ease the burden on health systems in this setting.

Background: Visceral leishmaniasis (VL) is the most severe form of leishmaniasis and is caused by parasites from the Leishmania donovani complex. VL is endemic in 54 countries, with East Africa (EA) accounting for [~]70% of the global burden. Therefore, there is a pressing need to prioritize VL research in EA. Transcriptomics analysis in rodent models and patients following treatment have shown that immune homeostasis is not restored by the end of treatment. However, comprehensive proteomic studies in VL remain scarce, and the few available studies were conducted outside EA. Methodology/Principal Findings: As a part of ImmStat@cure consortium, we used the Olink Reveal proximity extension assay to profile circulating proteins in VL patients from Ethiopia, Kenya and Sudan. By comparing patients at admission and 17 days after treatment with SSG-PM, we have identified a systemic Th1-cytotoxic inflammatory profile at admission, characterized by high abundance of IFN{gamma}, IL-1B, IL-6, and the granzymes A and B. Chemokines with high abundance included CXCL8/10/11, CCL3, CCL4, CCL8, CXCL8 and CCL20, consistent with the recruitment of monocytes, macrophages, T cells, and NK cells. There were also evidence of immune regulation/exhaustion, with high levels of PD1, PD-L2, IDO1 and KYNU. Although the immune profiles were similar at admission, country specific differences were observed after treatment. Kenyan patients exhibited a sustained inflammatory profile and signatures of myeloid recruitment, characterized by high levels of IL-1B, IL-6 , IL-12A, CXCL8, CCL2, CCL3, CCL4, CCL7 and CEBPB. Sudanese patients showed signatures associated with lymphocyte activation and immune regulation. Conclusions/Significance: These results highlight the complexity of the immune changes associated with VL, identifying both core conserved profiles at admission and country-specific profiles after treatment. The data provided here will provide a valuable resource for future comparative studies across more diverse different geographic regions, for linkage studies that assess the impact of parasite genotype on immune / metabolic response and for use during the evaluation of new therapies against VL.

Population genomic studies have revealed strong regional structure in Plasmodium knowlesi across Southeast Asia. To facilitate routine genomic surveillance, we developed a targeted amplicon sequencing panel that preserves the large- and small-scale population genetic structure. We screened published genomes for short, highly polymorphic targets and a 30-SNP barcode. We validated multiplexed amplification and sequencing using a portable Oxford Nanopore Technologies MinION sequencer on 28 clinical infections from four southern Thai provinces collected between 2018-2023. Twenty-seven loci yielded sequence data and 23 infections with data from at least eight loci were retained for analysis. Across the 23 Thai infections and 104 published Malaysian genomes, 586 diverse SNPs were observed. The amplicon panel and 30-SNP barcode retained the major population structure observed in genome-wide data, including a clear distinction between Bornean and Peninsular Malaysian infections and two clusters in Borneo. Across newly sequenced Thai infections, 25 loci were diverse and showed substantial diversity (mean expected heterozygosity = 0.69). All infections were effectively monoclonal. Pairwise identity-by-descent revealed substantial recent relatedness, with 46 of 253 pairs at or above 0.5. Two Thai genetic groups (n = 12 and n = 11) were observed, mirroring previously observed structure in peninsular Malaysia. Groups were not significantly associated with province or collection year. In conclusion, targeted sequencing can reveal geographically informative population structure across scales and thus support surveillance.

Tick-borne disease risk reflects the combined influence of vector abundance, pathogen prevalence, human exposure, and seasonal activity, yet these components are rarely integrated at the county level. We developed species-specific ecological risk scores for three medically important tick species (Acari: Ixodidae): Amblyomma americanum (L.), Dermacentor variabilis (Say), and Ixodes scapularis Say, across all 102 Illinois counties using data from 2019 to 2022. County-level predicted tick abundance and pathogen prevalence were scaled and combined using equal weights to generate primary vector-level risk scores. Robustness to the equal-weight assumption was evaluated by systematically varying the relative weights assigned to abundance and pathogen prevalence, while a separate sensitivity analysis incorporated reported tick-bite incidence where available. Associations with human disease were evaluated using negative binomial regression of tick-borne disease case counts on the corresponding risk scores, with county population included as an offset. Monthly phenology indicators were constructed by integrating human disease cases, reported tick bites, and sampled tick abundance and were evaluated in relation to temperature and precipitation. Seasonal risk was estimated by combining county-level vector risk with species-specific monthly phenology, generating monthly risk surfaces that were deployed through an interactive R Shiny application. Higher risk scores were significantly associated with greater disease case rates for each tick species. County rankings were highly stable under moderate changes in abundance and prevalence weighting. Seasonal activity differed among species, with A. americanum and D. variabilis showing concentrated warm-season risk and I. scapularis exhibiting a broader seasonal profile. These county-level seasonal risk maps identify where and when relative tick-borne disease risk is elevated in Illinois, supporting targeted prevention, surveillance, and clinical awareness.

Importance: Coccidioidomycosis, also known as Valley fever, is a serious fungal disease caused by inhaling Coccidioides spores aerosolized from the soil. Despite rising public health concern, the distribution and incidence of coccidioidomycosis in the US remain poorly characterized as not all states are required to report cases, and case under-ascertainment is presumed to be high, particularly outside of the endemic region. Objective: To estimate monthly, county-level coccidioidomycosis incidence across the US while accounting for widespread under-ascertainment. Design, Setting, and Participants: We integrated multisource data, including human cases diagnosed in electronic health records, serological testing of dogs, and ecological and sociodemographic predictors, in a machine learning model estimating monthly, county-level coccidioidomycosis incidence. The model was trained on surveillance data from Arizona and California from 2013 to 2023, then applied to predict incidence in states across the contiguous US over the same time period. Main Outcomes and Measures: Incidence of coccidioidomycosis at the monthly county level. Results: The model predicted coccidioidomycosis incidence from 2013 to 2023 for 3,108 counties from all states in the contiguous US. The model identified 60 counties in non-reporting states where predicted incidence was [&ge;]2 cases per 100,000 population (considered moderate to high incidence). In states with mandatory reporting outside of Arizona and California, the model predicted county-level incidence rates that were 28% higher (95% bootstrap interval, 0.1%-69.8%) than those captured in national surveillance, equating to a total of 608 potentially missed cases per year (95% bootstrap interval, 340-964). Conclusions and Relevance: In this cross-sectional study, the model quantified case under-ascertainment for coccidioidomycosis relative to Arizona and California, and highlighted the potential for alternative data sources to fill in gaps in mandatory reporting. Estimating incidence while accounting for under-ascertainment and non-reporting may help efforts to improve public and physician awareness and to detect emergence in new regions.

BACKGROUND: Indigenous tribes face a disproportionately high tuberculosis (TB) burden due to deep-rooted socioeconomic disadvantage, highlighting the need for strengthened, culturally sensitive control strategies. METHODS: We conducted a 21-year retrospective study (2005-2025) among tribal and neighboring non-tribal populations attending a tribal-serving hospital in Karnataka. Pre-Nikshay data were transcribed from paper registers, and post-Nikshay data were obtained from the digital system. We assessed negative binomial interrupted time-series regression model, data completeness, TB risk factors, and used multivariable logistic regression to identify determinants of poor treatment outcomes and household contact screening yield. RESULTS: We analyzed 1,512 TB cases (tribal = 1,021; non-tribal = 491). TB detection increased significantly over time, with a steeper rise among tribal populations (p<0.001). Nikshay implementation significantly improved data completeness (Wilcoxon W = 9900, p<0.001). Among tribal patients, males had higher odds of poor treatment outcome (aOR 2.81, 95% CI 1.70- 4.85; p<0.001). Diabetes (aOR 2.10, 95% CI 1.05- 4.03; p=0.029) was independently associated with poor treatment outcome. Among 503 index cases, 1,535 household contacts were identified and 1,161 (75.6%) screened; coverage was lower among tribal contacts (69.8% vs 87.2%; p<0.001). CONCLUSIONS: Nikshay strengthened TB surveillance and improved assessment of TB burden and risk factors in tribal communities.

Brazil experienced its worst dengue epidemic in 2024, with an increase in severe outcomes among elderly individuals, highlighting the need for vaccination strategies informed by population immunity. We reconstructed state- and age-specific immune profiles by fitting an age-structured catalytic model to dengue surveillance data from 2000-2025. This inferred immune profile was incorporated into an SIR-type transmission model to project disease burden and to evaluate the public health impact of vaccine allocation strategies. We observed substantial spatial heterogeneity in accumulated immunity, which was closely associated with projected burden and optimal allocation. Strategies prioritizing states with lower historical incidence and larger susceptible populations yielded greater benefits. Under limited vaccine supply, targeting children resulted in the greatest reductions in DALYs, whereas targeting elderly populations achieved smaller DALY reductions but larger reductions in mortality. We further showed that accelerated vaccine supply markedly increased impact, while allocation choices remained critical when availability was constrained.

Background: TB vaccine development is hampered by the natural history of M. tuberculosis: most infected people rarely progress to disease, so trials must be very large and long to accrue sufficient disease endpoints. Current trials enrol IGRA-positive participants (IGRA: Interferon-Gamma Release Assays), but IGRAs remain positive for many years and cannot distinguish recent from remote infection. Tests for recent infection are now in development, and we argue they could make trials substantially more efficient. Here, we investigate how screening participants prior to enrolment with a test for recent M.tb infection could impact the total number of enrollees required for an adequately-powered TB vaccine trial. Methods: Using a statistical model of vaccine trial recruitment, we estimated the number needed to contact, test, and enrol to achieve an 'effective' trial (defined as 50 disease endpoints), under IGRA-gated enrolment (life-long duration of positivity) vs. enrolment with a test for recent infection. We considered five recruitment strategies: non-test-based enrolment for ages 15-49, ages 12-34, and household contacts of recent cases; and test-based enrolment using IGRA, and a test for recent infection with two-year duration of positivity. The model explored trial recruitment in South Africa, Kenya, and India. Results: Compared with an extended-positivity test, a test for recent infection reduced the median number needed to enrol by 4.2- to 6-fold under favourable conditions. It also outperformed non-test-based strategies, including enrolling household contacts. This comes at the cost of substantially more testing, but crude estimates suggest recency-based enrolment would still be cheaper overall provided the new test does not cost more than roughly 5-10 times an IGRA. Conclusions: Diagnostics for recent M.tb infection have the potential to improve vaccine trial efficiency, but more research is now needed to understand test characteristics and to evaluate their use in this setting. Our Viewpoint sets out the test attributes that would need to be established, such as time to positivity, duration of positivity, sensitivity, and specificity, before such tests could be used in vaccine trials.

We report general acceptance (79.0%) of Ebola vaccination in the Democratic Republic of the Congo (DRC) between 2023-2024 (n=5,062). Acceptance varied significantly by province with acceptance ranging from 47.6% in Kongo Central to 92.1% in Tshuapa province and was highest among healthcare workers. While Ebola vaccine acceptance and disease knowledge are relatively strong nationally in the DRC, regional differences do exist and may impact future vaccine rollout.

Background Despite substantial improvements in maternal and child health over the past two decades, preventable maternal and neonatal morbidity and mortality remain major public health concerns in rural Bangladesh. Community-based interventions involving Community Volunteers (CVs), Community Groups (CGs), and Community Support Groups (CSGs) have been introduced to strengthen maternal and child health (MCH) services through health promotion, community engagement, and improved linkage with primary healthcare facilities. This study explored the roles, challenges, and opportunities of these community-based platforms in improving maternal and child health services. Methods A qualitative study was conducted in Kalaroa Upazila of Satkhira District, Bangladesh, between July and October 2020. Data were collected through focus group discussions and in-depth interviews with mothers, Community Volunteers, Community Group members, and Community Support Group members. Participants were purposively selected to capture diverse experiences from intervention and comparison areas. Interviews were conducted using semi-structured guides, audio-recorded with informed consent, transcribed verbatim, translated into English, and analyzed thematically using an inductive approach. Results Participants from intervention areas demonstrated greater knowledge of birth preparedness, antenatal care, pregnancy danger signs, institutional delivery, postnatal care, and newborn care than those from comparison areas. Community Volunteers, CGs, and CSGs played important roles in identifying pregnant women, conducting household counselling, promoting skilled maternal healthcare, facilitating referrals, and strengthening community participation. Participants perceived that these activities increased maternal awareness, improved health-seeking behaviour, and enhanced utilization of maternal and newborn health services. However, persistent barriers, including socio-cultural beliefs, financial constraints, transportation difficulties, shortages of skilled healthcare providers, and inadequate logistical support for community volunteers, continued to limit equitable access to quality services. Participants recommended strengthening community awareness programmes, improving community clinic readiness, enhancing referral systems, and providing regular training and supportive supervision for community-based health workers. Conclusions Community-based maternal health interventions involving Community Volunteers, Community Groups, and Community Support Groups contribute to improved maternal knowledge, health-seeking behaviour, and utilization of essential maternal and newborn health services in rural Bangladesh. Sustaining these gains will require stronger primary healthcare services, continued community engagement, and targeted strategies to address persistent health system and socio-cultural barriers. These findings provide important evidence for strengthening community-based maternal and child health programmes in Bangladesh and similar resource-constrained settings.

Introduction Cancer incidence is rising rapidly in low- and middle-income countries (LMICs), where diagnostic pathology capacity is constrained. AI applied to routine histopathology could strengthen diagnostic capacity and offer leapfrogging potential by embedding AI into diagnostic systems as they are built up. However, without empirically identifying implementation determinants in LMIC cancer-diagnostic settings, AI solutions risk being poorly aligned with local constraints, limiting impact or exacerbating existing disparities. Methods We conducted an exploratory mixed-methods pilot study across three phases: 1) a structured survey characterising the diagnostic and technical landscape and AI priorities; 2) focus group discussions (FGDs) to inductively identify implementation determinants and 3) a second survey quantifying FGD-derived themes. Twelve physicians were purposively recruited (10 from LMIC, 2 from high-income countries for reference) and findings integrated through narrative triangulation. Results There was substantial heterogeneity in diagnostic and AI infrastructure that did not track consistently with country income classification. H&E staining was universally available, immunohistochemistry capacity was rudimentary at 7 of 10 sites and molecular testing absent or minimal at 6 of 10 sites. Whole-slide-imaging scanners were available at 8 of 10 sites, while graphical processing units, required for efficient AI inference, were available at only 4 of 10 sites. The participants would prioritise use of AI for cancer screening, cancer diagnosis, and biomarker prediction from H&E slides. Six implementation determinants were identified: AI literacy, governance frameworks, funding, evidence generation, clinical deployment, and collaboration. Financial constraints emerged as the dominant barrier across all determinants, and collaboration as an important enabling mechanism. Conclusion AI implementation readiness in LMIC cancer-diagnostic settings is highly heterogeneous and cannot be inferred from income classification, underscoring the need for institution-level assessment. Realising the leapfrogging potential of AI in this context requires coordinated progress across all determinants, with sustainable financing, and collaboration enabling the remaining determinants.

Japanese encephalitis virus (JEV) is a leading cause of viral encephalitis in humans in Asia. Prior to 2021, only sporadic outbreaks had been detected in Australia's tropical northern extremes. However, during 2021-23, JEV infection was identified in 45 people and in more than 85 piggeries, together with many detections in feral pigs, mosquitoes, and other livestock across Australia, including in the temperate far south. Here we estimated the spatial extent of virus transmission during the outbreak (the realised niche) and the area environmentally suitable for transmission (the potential niche). We collated a national dataset of JEV surveillance and detection in humans, mosquitoes, domestic pigs, feral pigs, and sentinel chickens from 2021-23. We combined these data with (1) previously published predictions of habitat suitability for vertebrate host and vector species, and (2) estimated densities of humans, domestic pigs, and feral pigs, in a multipart assessment of spatial risk. Our model projected broad potential environmental suitability over much of northern and eastern Australia during the outbreak period, but floodplain wetland areas in the north and the inland east of Australia had both the highest estimated environmental suitability for transmission, and the strongest evidence for virus circulation.

Abstract Background: Cyprus has transitioned into a high-alert zone for mosquito-borne viruses. This necessitates a proactive assessment of its future physicians. Following the 2022 re-establishment of Aedes aegypti in Larnaca and Aedes albopictus in Limassol, Cyprus, the island is now biologically primed for local outbreaks of dengue, Zika, and chikungunya, alongside Culex-borne West Nile virus. The study assessed medical students' knowledge and risk perception. Methods: A cross-sectional online survey administered the validated MosquitoWise Health Belief Model instrument to undergraduate-entry and graduate-entry students at a university in Cyprus in mid-2026. Results: Of 1023 students, 119 responded (11.6%), with pre-clinical students overrepresented (p<0.001). Mean knowledge was 5.43/9, stronger on mosquito biology (72.5%) than virus knowledge (54.2%). Median severity was 6.00/7 against susceptibility 4.25/7. Knowledge differed by study stage (p<0.001), correlated with severity (rs = 0.399) but not susceptibility. Conclusions: The study establishes a critical baseline for pre-clinical medical students in Cyprus. Overall, participants performed better on mosquito biology when compared to mosquito-borne viruses and how they spread. Our findings highlight the value of integrating theoretical microbiology with clinical practice in medical education to enhance physicians' preparedness for the changing landscape of newly emerging infections in Cyprus and across Europe as a whole. Key Words: Arboviruses, Cross-Sectional Studies, Cyprus, Health Belief Model, Medical Students, Risk Perception.