Infectious Disease Modelling

Artemisinin partial resistance has not yet been reported in southern Africa. Therefore, the magnitude of the spread of artemisinin partial resistance in this region is yet to be quantified. Using a two strain metapopulation modelling framework, we explored possible spread of artemisinin partial resistance in eight connected countries with high level of human movement. We explored three scenarios in which artemisinin partial resistance may first enter circulation: low malaria transmission level country; high malaria transmission level country and all countries and compared to an artemisinin partial resistance free scenario. Partial rank correlation coefficient sensitivity analysis was performed to identify key parameters that drive artemisinin partial resistance spread. Our model simulations show that high mobility between countries can increase the spread of mutations associated with delayed clearance. Suggesting that artemisinin partial resistance will be confirmed (>5% partial resistant cases) after 14 years of circulation if it is to appear in southern Africa. We confirm that human movement, both human-to-mosquito and mosquito-to-human probabilities of transmission, were significant and highly sensitive parameters in the spread of artemisinin partial resistance. Human mobility between countries can facilitate the spread of artemisinin partial resistance. More research is needed to identify strategies to preserve the efficacy of artemisinin-based combination therapies in the presence of partial artemisinin resistance, which may eventually lead to treatment failure and necessitate regimen replacement.

Abstract Background Malaria remains a major public health challenge in Ghana, despite recent reductions in cases due to various interventions. The endemicity of the disease varies across regions, influenced by diverse seasonal and temporal factors that support mosquito proliferation and malaria cases. This study used a Generalised Additive Models to explore the impact of weather conditions on malaria cases in Ghana. Methods Generalised Additive Models were used to examine the nonlinear effects of weather conditions on malaria cases. Monthly aggregated malaria cases from the District Health Information Management System II and average monthly rainfall and temperature data from the Ghana Meteorological Agency were analysed, covering 2012 to 2023. Regional Generalised Additive Models incorporating weather variables were developed, fitted, and validated against observed data using model diagnostics to identify the most suitable model for each region. Results The analysis revealed complex temporal patterns in malaria cases across Ghana, influenced by seasonal and long-term trends. Regions constituting the Coastal and Transitional Forest zones exhibited bimodal peak malaria seasons, while the Guinea Savannah showed a unimodal peak. Significant interactions between rainfall and temperature were identified, particularly in the Eastern region, where higher rainfall combined with temperatures around 27-28 {degrees}C were associated with higher malaria cases, reflecting the complex and region-specific nature of meteorological influences. Conclusions The findings point to the dynamic and heterogeneous nature of malaria caseloads in Ghana, emphasising the need for region-specific control strategies tailored to local climatic conditions. A key recommendation is the systematic integration of meteorological data into the National Malaria Data Repository to enable continuous monitoring of climatic influences and support timely, evidence-based intervention decisions. Future research should incorporate socio-economic factors, intervention coverage data, vector surveillance, and demographic characteristics into mathematical modelling frameworks for a more comprehensive understanding of malaria cases in Ghana.

Malaria control programs are increasingly tailored at subnational scales; however, neighboring areas remain connected through human mobility, allowing parasite importation that may undermine independently timed interventions. Although the spatial targeting of control has been the focus of extensive research, the epidemiological consequences of temporal misalignment in intervention deployment across interconnected regions remain to be elucidated. We investigate how asynchronous timing of malaria interventions affects transmission dynamics using a two-patch susceptible-infected-susceptible metapopulation model. We compare synchronous and asynchronous intervention schedules and quantify their impact using measures of excess cumulative incidence attributable to asynchrony. The measure that will be used for this purpose is referred to as Asynchrony Induced Growth (AIG). Across a range of 10,000 parameter combinations, asynchronous implementation has been observed to result in a heightened incidence compared to synchronized deployment, though the impact is typically negligible in most endemic settings. Sensitivity analyses indicate that the impact is most significant when interventions are highly effective, infectious duration is brief, and transmission intensity approaches the elimination threshold. In such circumstances, asynchrony has the potential to substantially inflate case numbers, delay transmission interruption, or even prevent elimination entirely. In illustrative scenarios that reflect realistic settings, synchronizing interventions has been shown to avert large numbers of infections and shorten elimination timelines by years to decades. These findings demonstrate that, beyond spatial targeting, temporal coordination of interventions across connected areas can meaningfully enhance malaria control and elimination. Coordinated timing may be particularly valuable for cross-border or near-elimination programs and should be considered in operational planning and resource allocation.

An ongoing outbreak of Bundibugyo virus disease (BVD) in the Democratic Republic of the Congo was deemed a public health emergency of international concern in May 2026. To prevent cross-border importation, many countries, including the United States, Canada, India, Thailand, and Kenya have already proposed containment strategies, and others are likely to follow suit. How well (or poorly) are screening and quarantine containment measures are likely to work? We leverage established epidemiological theory and develop a mathematical model of traveler screening and post-arrival quarantine for BVD to answer this question. We find that traveler screening via symptom screening or molecular testing will miss the majority of infected travelers, and should be complemented by post-arrival quarantine and monitoring of sufficient duration to detect those with long incubation periods. Our findings underscore the limitations of border screening and the importance of complementary measures like post-arrival quarantine to prevent local importation of BVD.

Childhood diarrheal disease remains a leading cause of morbidity and mortality among children under five years in sub-Saharan Africa, particularly in settings affected by inadequate sanitation, climate variability, malnutrition, and limited healthcare access. Conventional forecasting approaches are often constrained by sparse surveillance data, weak spatial representation, and limited incorporation of mechanistic disease dynamics. This study presents a Physics-Informed Multimodal Artificial Intelligence Digital Twin framework that integrates Physics-Informed Neural Networks, Graph Neural Networks, diffusion-reaction epidemiological modeling, multimodal fusion learning, and Digital Twin simulation to estimate and predict childhood diarrheal disease burden in Kenya, Somaliland, and Zimbabwe. Using public epidemiological, environmental, climate, sanitation, and synthetic proof-of-concept datasets, the framework modeled temporal disease dynamics, spatial transmission, pathogen-attributed burden, and outbreak trajectories while enforcing epidemiological consistency through physics-informed optimization. Results demonstrated robust forecasting performance, enhanced spatial transmission modeling, uncertainty-aware predictions, and realistic outbreak simulations across the three countries. Rotavirus, Shigella, and Cryptosporidium were identified as major contributors to modeled mortality burden, while unsafe water exposure, poor sanitation, malnutrition, and climate-sensitive transmission substantially increased disease risk. Compared with a Bayesian baseline model, the multimodal framework achieved superior nonlinear risk characterization, geospatial learning, and temporal prediction. These findings highlight the potential of scientific machine learning and digital twin systems for infectious disease surveillance, outbreak forecasting, climate-health analytics, and evidence-based public health decision-making in low-resource African settings. Keywords: Physics-Informed Neural Networks, Graph Neural Networks, Digital Twin, Childhood Diarrheal Disease, Epidemiology, Kenya, Somaliland, Zimbabwe, Scientific Machine Learning, Spatial Epidemiology, Multimodal Fusion

Accurate outbreak forecasts are critical for timely and effective public health response. In the United States, however, most forecasts are produced at the state level, which can mask substantial sub-state heterogeneity and limit their utility for local planning. We generated and evaluated forecasts of the percentage of Emergency Department visits attributable to influenza across 173 large metropolitan Health Service Areas (HSAs) using a gradient boosting quantile regression (GBQR) model, and compared their accuracy to forecasts derived from state-level data alone. At a one-week, two-week and three-week horizon, local forecasts outperformed state-based forecasts in 98.8%, 90.8%, and 78.6% of HSAs, respectively, achieving mean weighted interval scores that were on average a 39.2% lower (95% range: 5.9% to 76.7%), 19.6% lower (-6.3% to 59.5%) , and 11.4% lower (-11.7% to 44.9%), respectively. The performance advantage of local forecasting was strongest in HSAs representing a smaller share of their state's population and increased with the proportion of the HSA population living in urban areas and the number of metropolitan areas within a state. These results, based on an analysis of HSAs with populations greater than 250,000, demonstrate that fine-scale modeling can substantially improve forecast accuracy and highlight the potential value of local forecasts for outbreak preparedness and response.

Since the U.S. 2013/14 influenza season, the CDC's FluSight Challenge has provided a platform for evaluating influenza forecasting models and fostering collaboration across institutions. The Challenge aims to improve the science and enhance the utility of infectious disease forecasts for public health decision making. We analyzed ten years of submitted forecasts (2014/15-2019/20 (influenza-like illness seasons) and 2021/22-2024/25 (hospital admissions seasons)) across a range of model types, including statistical, mechanistic, machine learning, and hybrid models. Influenza-like illness (ILI) forecasts were evaluated using the exponentiated logarithmic score (skill metric) while hospital admissions forecasts were evaluated using the log transformed relative Weighted Interval Score. Corresponding potential performance differences were assessed using Wilcoxon rank-sum tests, and associations with team participation history were evaluated using Spearman's rank correlation. Model performance varied by season, and no single model type consistently outperformed others. In ILI seasons, statistical models generally performed better than mechanistic and machine learning models, though consistent differences were not observed in more recent hospital admissions seasons. Ensemble forecasts showed better overall performance across seasons, and the CDC's FluSight ensemble ranked among the top-performing forecasts every year. We also found a positive correlation between forecast accuracy and the number of years a team participated in the Challenge, with statistically significant associations in four seasons. These findings highlight the benefits of ensemble approaches and sustained engagement in improving forecasting performance, while also underscoring the continued value of forecast evaluation before and following the COVID-19 pandemic. Insights from the FluSight Challenge can guide future infectious disease forecasting efforts and support more effective public health preparedness.

Human metapneumovirus (HMPV) causes acute lower respiratory infections, primarily affecting young children and older adults, with seasonal outbreaks peaking annually in March or April in the United States and other temperate regions in the Northern hemisphere. However, the factors driving HMPV seasonality in the United States remain poorly understood. We analyzed laboratory-confirmed HMPV cases and age-specific emergency department visits across 10 US regions, fitting an age-stratified dynamic transmission model to assess spatiotemporal patterns and investigate the influence of environmental variables and viral interference from RSV on HMPV transmission rates. We found that models incorporating climate variables into the transmission rate, including vapor pressure, precipitation, potential evapotranspiration, and minimum temperature, could not capture the timing of HMPV activity across all regions. Instead, HMPV timing was associated with RSV activity, with the HMPV transmission rate reduced in the presence of RSV. We showed that, unlike RSV, only models incorporating viral interference could reproduce the biennial pattern of HMPV observed in some regions, characterized by alternating late-small and early-large epidemics. Furthermore, our model successfully reproduced post-COVID-19 HMPV and RSV epidemics and predicted that RSV interventions are not likely to lead to a substantial increase in HMPV activity despite decreasing competition from RSV. Our work unravels the spatiotemporal dynamics of HMPV and its interaction with RSV, informing future seasonal forecasting and intervention strategies for HMPV.

Background Pediatric immunizations for Respiratory Syncytial Virus (RSV), including monoclonal antibodies for infants and vaccines for pregnant people, have become broadly available and can prevent severe RSV outcomes in infants. However, quantifying the impact of RSV immunization in prevention of severe pediatric illness at the population-level is limited by lack of RSV case surveillance data. The Massachusetts Department of Public Health (DPH) conducted a modeling analysis using routine public health surveillance data to estimate the state-level impact of new RSV immunization products on Emergency Department (ED) visits and hospitalizations in Massachusetts for highest risk pediatric groups. Methods A scenario projection tool, called R.Scenario.Vax, was utilized to simulate RSV-associated ED hospital encounters by age group in the context of newly available immunizations. ED visit and hospitalization data from the National Syndromic Surveillance Program (NSSP) during the time period 10/08/2017--10/19/2024 were analyzed, scaled to account for changes in RSV testing practices over time and missing encounter volume in historic data, and utilized to inform model fit of a "typical" RSV season. RSV immunization data from the Massachusetts Immunization Information System (MIIS) for the 2023--2024 and 2024--2025 RSV seasons informed high and moderate pediatric RSV immunization coverage scenarios and their impact was compared to a counterfactual reference scenario of no new immunizations. Median projections were quantitatively and qualitatively compared to observed 2024--2025 season data. Percent reduction in hospital encounters and encounters averted per 10,000 population were calculated for each scenario as compared to the reference. Results Projections for the youngest at-risk age groups showed significantly lower RSV-associated ED visits and hospitalizations during the 2024--2025 season for both high and moderate immunization coverage scenarios. Median projections for infants under 6 months old in the highest coverage scenario, wherein nearly all infants were immunized, showed 72.6% lower ED visits and 73.4% lower hospitalizations when compared to the reference scenario, equating to 262 ED visits and 85 hospitalizations averted per 10,000 population. Conclusions Our results support the use of modeling methods for public health insights and suggest that RSV immunizations for infant populations result in significantly lower RSV-related ED encounters in Massachusetts.

During the 2026 Bundibugyo virus disease outbreak in the Democratic Republic of the Congo and Uganda, we projected potential airline-mediated importation risk using contemporary airline network and an externally calibrated Ebola importation hazard. Effective-distance analyses identified major international hub countries, including Belgium, France, South Africa, Kenya, and the United Arab Emirates, as higher-probability gateways within 30 days. These early projections provide a reproducible framework for real-time international situational awareness, while emphasizing that importation risk does not imply local transmission risk.

Background: Reliable inference of Plasmodium vivax recurrence states - relapse, recrudescence and reinfection (the ``3Rs'') - improves estimates of antimalarial efficacy. The R package Pv3Rs features a Bayesian model designed for P. vivax molecular correction, i.e., using parasite genetic data to infer recurrence states. The model is an extension of a prototype built to analyse microsatellite data from the Vivax History (VHX) and Best Primaquine Dose (BPD) trials. Methods: We re-analysed data from 212 VHX and BPD trial participants (493 recurrences) using Pv3Rs, comparing results with those from the prototype and with genetic relatedness estimated using Dcifer, a tool for estimating relatedness based on identity-by-descent. Posterior recurrence state probabilities were computed using both uniform and time-to-event priors: artificial but equal prior probabilities facilitate posterior interpretation, while time-to-event priors leverage all available information and enable re-computation of failure rates. Relatedness estimates were used to identify and correct instances of model misspecification. Results: The Pv3Rs model generated posterior probabilities for all recurrences and was able to jointly model data on all episodes per participant for 89% of participants, compared with 73% using the prototype. Recurrence state probabilities were broadly consistent across methods, though the Pv3Rs model elevated reinfection probabilities slightly. Relatedness estimates exposed various outliers consistent with half-sibling parasites and/or genotyping errors. Outlier correction impacted some per-participant failure probabilities, but reinfection-adjusted radical-cure failure rates of high-dose primaquine remained near 3%, in line with previous findings. Conclusion: Re-analysis of VHX and BPD P. vivax genetic data restates earlier reinfection-adjusted efficacy estimates. It demonstrates the increased computational capability and misspecification sensitivity of Pv3Rs, highlighting a need for careful analyses. Using relatedness-based diagnostics alongside model-based inference, we were able to harness the advantages of model-based inference and provide a framework for future P. vivax molecular correction.

Use of zooprophylaxis as a malaria control strategy has been recommended historically, but a complex relationship exists between animal ownership and malaria infection, with mixed associations described in the literature. We sought to characterize this relationship spatially and temporally in malaria-endemic regions of Africa. We used data from 392,843 individuals from 66 Demographic and Health surveys from countries within Africa to investigate the association between household animal ownership and Plasmodium infection. We used Bayesian models with Integrated Nested Laplace Approximation to incorporate spatially varying coefficient processes, allowing the association of interest to vary over space, time, and within strata of vector species occurrence, land cover, and number of animals owned by households. Spatially varying intercept models showed that ownership of cattle, chickens/poultry, goats, horses/donkeys/mules, pigs, and sheep was broadly associated with malaria infection, with odds ratios ranging from 1.55 to 1.67. However, spatially varying slope models revealed considerable heterogeneity, with odds ratio estimates for all animal types demonstrating both protective and harmful effects varying from 0.33 to 3.33 both subnationally and across time. We found no evidence that modification by vector species, number of animals owned, and land cover fully explained the variation in estimates. Unobserved localized cultural, behavioral, or ecological factors likely modify the association between animal ownership and malaria prevalence. Further exploring the nature of this relationship over space and time will be important to understanding how context-specific One Health dynamics between humans, animals and the environment affect malaria prevention and control efforts.

Background. The 2026 FIFA World Cup will bring an estimated 1--5~million international visitors to 11~US host cities between June~11 and July~19, 2026---the largest tournament in history. Large-scale international gatherings accelerate importation of infectious diseases from diverse source populations. Advance estimation of importation risk is essential for public health preparedness and surveillance prioritization. Methods. We developed a Poisson importation framework applied to five diseases (dengue fever, influenza, malaria, measles, and pertussis) across the 11~US venue cities. Three nested travel models of increasing resolution were constructed: a baseline model using routine June~2024 arrival data; a World Cup--adjusted model incorporating projected visitor growth factors; and a schedule-driven model routing WC fans to specific cities based on match assignments. WHO incidence and BTS T-100 routing fractions were combined with Monte Carlo uncertainty propagation (5,000 Uniform draws on under-reporting and travel-while-infectious parameters) to yield median importation estimates with 95\% uncertainty intervals. Results. Dengue posed the highest importation risk at most venue cities under the schedule-driven model (median $\Lambda > 10$ expected importations from Brazil alone; 95\% uncertainty interval 5.9--33.1), robust across the full literature-supported parameter range; Atlanta was the exception, where malaria probability exceeded dengue, driven by direct travel from West and Central African nations. Influenza ranked second at most cities, coinciding with the Southern Hemisphere winter peak. Pertussis showed broad geographic spread but carries the widest relative uncertainty, as the assumed detection rate sits at the upper bound of the literature range. Background tourism accounted for the dominant share of total importation risk; the World Cup fan increment contributed approximately 8.3\% of projected arrivals for WC-qualified nations. Conclusions. This Poisson importation framework, built entirely from publicly available data, provides reproducible importation risk estimates for mass gathering events. The framework extends to additional diseases, cities, and gatherings, offering a transparent baseline complementary to proprietary modeling systems.

Seroprevalence surveys reveal the extent of humoral immunity against pathogens such as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), and under some circumstances represent cumulative incidence of prior infection. However, antibody waning - or seroreversion - biases these estimates by reducing assay sensitivity in a time-varying manner. Because assay sensitivity decays over time, naively using serosurveys can substantially bias estimates of SARS-CoV-2 cumulative incidence and fatality rates. The Bayesian assay-specific, time-varying sensitivity adjustment developed in this paper can reliably correct for this bias and account for the delay between infection and serosurvey. In seroprevalence studies conducted in the United States in 2020, adjusting for time-varying sensitivity increased cumulative incidence by up to 1.4-fold, with an adjustment of 1.08 for a national study. Our estimates contrast with a previously published 2-fold adjustment that did not account for assay design. This suggests that previous analyses overestimated cumulative incidence by applying seroreversion corrections that did not account for assay-specific effects, or underestimated cumulative incidence by not applying seroreversion corrections. These biases imply fatality rate underestimation and overestimation, respectively. Our model provides a framework for design-specific time-varying sensitivity corrections in seroprevalence surveys for other pathogens.

Malaria molecular surveillance (MMS) is becoming increasingly common in endemic settings and has been proposed as a tool for monitoring parasite transmission to inform programmatic decision-making. However, the conditions under which parasite genetic metrics provide interpretable signals for broader use cases, such as assessing intervention impacts and detecting importation, remain under-characterized. We present EMOD with Full Parasite Genetics (FPG), a simulation framework designed to explore how parasite genetic metrics arise from transmission, intervention, importation, and sampling processes at programmatically relevant timescales. Using seasonal scenarios across a range of transmission intensities, we demonstrate three principal findings. First, genetic metrics can detect insecticide-treated net intervention impacts at seasonal and yearly timescales, but the strength, timing, and form of the relationship between genetic and epidemiological measures vary by metric and sampling timing. Second, importation can break the expected relationship between parasite genetic diversity from local transmission intensity at very low incidence, allowing low-transmission settings with substantial importation to maintain elevated diversity metrics. Third, convenience sampling practices, including sample size, collection timing, and the clinical composition of sampled populations, introduce non-random biases in genetic metric estimation in a way that obscures the true transmission signal. Together, these findings show that parasite genetic metrics can support operational surveillance, but that their interpretation depends on transmission context, importation, metric choice, and sampling design. EMOD FPG provides a framework for evaluating these dependencies in future setting-specific analyses and for guiding the interpretation of parasite genetic data across sites and over time.

Abstract Introduction Onchocerciasis is targeted for elimination with community-directed treatment with ivermectin (CDTI). Alternative strategies are needed in areas where transmission persists despite long-term CDTI and/or are co-endemic with loiasis. This study assessed the efficacy of 35-day treatment with 100mg doxycycline on Wolbachia density at 6 months and microfilaridermia and palpable nodules at 30 months post-treatment. Methods A treatment follow-up study was conducted in 20 high-transmission onchocerciasis communities in a co-endemic loiasis area of South-West Cameroon. Community-based directly observed treatment with 100mg doxycycline was administered to community members aged [≥]9 years. Wolbachia clearance at 6-months and treatment efficacy on microfilaridermia and palpable nodules were assessed at 30-months post treatment. Factors associated with reductions in microfilaridermia post treatment, including adherence to doxycycline treatment were assessed with mixed-effects logistic regression. Results Over 92% (2835/3080) of eligible participants took 35 days of 100mg doxycycline over 5 or 6 weeks. This regimen achieved a 62.8% microfilaridermia reduction and 99% palpable nodule reduction in the 720 participants included at follow-up. Wolbachia depletion was observed in 92% of skin samples at 6 months post treatment. The most important factor associated with microfilaridermia after 30 months was having missed at least 7 doxycycline consecutive doses (OR 3.11, 95%CI: 1.17-8.26). Incomplete treatment to a lesser extent was not associated with reduced efficacy at follow-up. Conclusion This large-scale community intervention shows that a 5-week treatment with 100mg doxycycline is feasible and has high curative efficacy against adult O. volvulus as measured by the dramatic reduction in the proportion of palpable nodules at 30-months post treatment. The high efficacy shows the tremendous potential of anti-Wolbachia drugs as part of the arsenal for onchocerciasis elimination and paves the way for the next generation of anti-Wolbachia drugs with shorter treatment courses, which will facilitate the implementation of alternative strategies to accelerate onchocerciasis elimination.

Pooled testing programs were introduced during the COVID-19 pandemic to expand surveillance capacity while preserving testing resources, but evidence on their epidemiological impact in schools under real-world conditions remains limited. We analyzed data from the pooled testing program implemented in public primary schools of the canton of Basel-Landschaft, Switzerland, during the Fall-Winter 2021 Delta wave. We used an agent-based transmission model informed by pooled and individual testing results, school characteristics, contact networks, and community incidence. The model was fitted to pooled positivity ratios in four clusters of administrative areas with similar epidemic trajectories. We compared pooled testing with alternative protocols in terms of school transmission, testing volume, and student-days lost. During the study period, pooled testing was offered to 21'187 students across 62 public primary schools, with high and stable participation across clusters (mean 71-79%). The fitted model reproduced observed pool positivity trends well. Compared with pooled testing, reactive class closure, reactive screening, and symptomatic testing were associated with higher in-school transmission, with excess ranging from 50% to 87%, 63% to 104%, and 72% to 133% across clusters. Weekly individual screening achieved similar reductions in transmission but required 15-25 times more tests. Relaxing class closure after depooling substantially reduced student-days lost without increasing transmission. Under real-world conditions, pooled testing provided an effective and resource-efficient strategy to reduce SARS-CoV-2 transmission in primary schools. Combining early detection of asymptomatic infections with low testing demands, pooled testing offers a scalable approach to school surveillance and control for pandemic response in educational settings.

Introduction: Malaria, primarily transmitted by Anopheles mosquitoes, remains a major public health concern in Maiduguri, Borno State, Nigeria. While conventional control methods (e.g., ITNs) face challenges due to insecticide resistance and accessibility constraints, many communities rely on locally sourced natural products. This study aimed to assess the prevalence, usage patterns, and associated factors of these natural alternatives. Methods: A cross-sectional survey was conducted across three purposefully selected communities in Maiduguri (Mairi, Furi, Lagos Street). A total of 450 household heads were interviewed using a structured questionnaire, collecting data on socio-demographics, specific natural products used, method of application, frequency, and perceived efficacy. Data were analyzed using descriptive statistics and binary logistic regression. Results: Overall usage prevalence of natural products was high at 68.4%. The most common products identified were Neem (Azadirachta indica) extract (45.9%) and burnt Lemon Grass (Cymbopogon citratus) (31.2%). Usage pattern was predominantly indoor fumigation (burning), and over 70% of users prepared the products crudely at home. Logistic regression revealed that rural residence (Odds Ratio (OR): 2.1; p<0.01) and low education level (OR: 1.8; p<0.05) were significant independent predictors of higher natural product reliance. Conclusion: Natural products constitute a widely adopted, community-driven vector control method in Borno State. The high prevalence and association with vulnerable populations suggest an urgent need to standardize the preparation and application of these products for potential integration into regional malaria control programs. Keywords: Anopheles, Adulticides, Borno State, Malaria, Natural Repellents, Vector Control, Usage Pattern.

The 2026 Bundibugyo Ebola outbreak in eastern Democratic Republic of the Congo (DRC) has already generated international spread to Uganda, raising concerns about further regional and international dissemination. Using International Air Transport Association origin-destination passenger flows, we assessed relative exposure to Ebola virus disease importation into Europe under six outbreak expansion scenarios reflecting plausible pathways of geographical spread, including cross-border transmission and amplification in highly connected regional capitals. Relative exposure patterns remained largely unchanged under localized transmission in eastern DRC and border-spillover scenarios. Expansion into South Sudan generated a first structural increase in importation pressure to Europe through the connectivity associated with Juba, while hypothetical amplification in Kampala, Kigali, and Kinshasa substantially increased importation pressure and reshaped exposure patterns across Europe. Across all scenarios, France, Italy, and the United Kingdom remained among the most exposed countries. Mobility-informed scenario analyses support preparedness as the geography of the outbreak evolves.

Effective filtration and concentration of stool specimens is an essential pre-analytical step for reducing fecal debris and improving organism recovery using microscopy-based ova and parasite (O&P) examination. This study evaluated three commercially available fecal sedimentation-based filtration/concentration systems, ParaPak SpinCon (Meridian Bioscience), Mini Parasep SF (Apacor), and the newly-available ParadeviceReingenuity), for qualitative parasite detection and workflow logistics using conventional and artificial intelligence (AI)-assisted microscopy. Forty clinical stool specimens (20 parasite-positive and 20 parasite-negative) were processed with the 3 devices, and the resultant 120 wet mount and 120 trichrome stained smear preparations were examined using conventional microscopy. Trichrome-stained slides were also scanned at 40x magnification using a Hamamatsu NanoZoomerS360 flatbed digital slide scanner and images were analyzed using the Techcyte Fusion Human Fecal Trichrome AI algorithm. Positive and indeterminate digital findings were confirmed by conventional glass slide microscopy. Slides and digital images were reviewed in a blinded manner. Concordance was assessed among the 360 initial evaluations (microscopy and AI-assisted), and discrepant parasitology results were resolved through re-review and specimen reprocessing as needed. Final qualitative agreement across slide/image evaluations using all three concentration systems was 100%. Minor discrepancies in protozoan and white/red blood cell detection/identification were noted in 5 and 7 cases, respectively, and likely reflected sampling and observer variability. While the three concentration systems produced equivalent qualitative results, the Paradevice and Mini Parasep SF offered the most streamlined workflows. These findings support the Paradevice and Mini Parasep SF as efficient, analytically equivalent systems that are compatible with traditional and AI-assisted O&P workflows.