Infectious Diseases of Poverty

West Nile virus (WNV; Flaviviridae) and Eastern Equine Encephalitis virus (EEEV; Togaviridae) represent the two most significant mosquito-borne zoonoses in the southeastern United States. While both viruses utilize avian amplifying hosts and mosquito vectors, it is unclear if they occur within distinct ecological niches. This study assessed the cumulative effects of both landscape composition and weather variables on the spatial-temporal distribution of WNV and EEEV outbreaks in Georgia over a twenty-four-year period (2001-2025). We used a modeling framework that directly accounts for both spatial and temporal effects but additionally integrates key EO variables (Earth observation). These environmental effects were investigated using Bernoulli generalized linear mixed-effects models (GLMMs) and executed with an integrated nested Laplace approximation (INLA). Our findings revealed disproportional distribution ranges of Culex quinquefasciatus, Aedes albopictus and Culiseta melanura in highly urbanized Georgian counties such as Chatham, Dekalb, and Fulton. Results showed that WNV transmission was heavily influenced by urban land cover (Posterior Mean: +0.78, 95% CrI: [0.61,0.95]) but negatively associated with lagged precipitation (Posterior Mean: -0.18, 95% CrI: [-0.29, -0.07]), confirming drought-driven amplification. Conversely, EEEV transmission was strongly influenced by wetland cover (Posterior Mean: +1.12, 95% CrI: [0.94,1.30]) and precipitation (Posterior Mean: +0.51, 95% CrI: [0.39,0.63]). These results underscore the need for improved mosquito surveillance across all Georgian counties in the face of growing vector-borne disease risks.

Background: Dengue fever has become an epidemiological concern worldwide, with 531,617 cases reported in Argentina during the 2023-2024 season. The El Nino Southern Oscillation (ENSO) phenomenon has been linked to changes in dengue dynamics. This study assessed the relationships among ENSO, local climatic variables, and dengue outbreaks in Argentina from 2018 to 2024. Methods: A nationwide spatiotemporal analysis was conducted using dengue cases and monthly mean temperature, relative humidity, and rainfall. The Southern Oscillation Index (SOI) was used as the ENSO indicator (El Nino phase=< minus0.5, neutral phase= minus0.5 to 0.5, and La Nina phase= > 0.5). We fitted Hierarchical Bayesian models with Integrated Nested Laplace Approximation (INLA). A Negative Binomial distribution of dengue cases was used to account for overdispersion. Spatial dependence was modeled by using the Besag York Mollie 2 (BYM2) structure, adjusted for population. Results: The final model explained 53.7% of the variation in Argentinas departmental monthly dengue cases during the study period. Exposure response curves of posterior probability and 95% credible intervals suggested that dengue risk increased when monthly mean temperatures were between 20 and 30 degrees Celsius during the El Nino or neutral phase, with less consistent effects during the La Nina phase. Humidity >50% was associated with increased dengue risk during the La Nina phase, with less steep or absent associations during El Nino or neutral phases. Negative SOI scores were associated with an increased risk of dengue after controlling for the covariates. Conclusion: Dengue outbreaks in Argentina were independently associated with temperatures between 20 and 30 degrees celsius during the El Nino and neutral phases, humidity > 50% during the La Nina phase, and the El Nino phase of the ENSO. Incorporating ENSO indices into predictive models could enhance early warning systems and timely public health interventions in Argentina.

In 2026, a Bundibugyo ebolavirus (BDBV) outbreak emerged in the Democratic Republic of the Congo (DRC), with 4,566 confirmed cases and 2,128 deaths reported as of 11 August, potentially becoming the largest Ebola outbreak on record globally. We developed a susceptible-exposed-infectious-deceased-recovered (SEIDR) model incorporating incorporating three categories of interventions, public self-protection, safe burial, and treatment and convalescence, to assess early transmission dynamics, the current epidemic trajectory, and cross-border spillover risk, and to inform the formulation of control strategies. Based on cumulative confirmed case data up to 31 July, sensitivity analyses across multiple candidate start dates identified 28 March as the optimal start date of sustained transmission, with 31 March to 3 April as the most likely onset window. As of 31 July, the basic reproduction number (R0) was 1.83 (95% CI: 1.81-1.84). When 58.12% of the susceptible population adopted protective behaviours, the transmission chain could be effectively interrupted. By integrating the non-dominated sorting genetic algorithm II (NSGA-II) with Pontryagin's minimum principle (PMP), we derived a time-varying optimal control strategy, with adjustments every two weeks, that could shorten the epidemic duration by approximately 7 months. Using International Migrant Stock data and Facebook IP-based mobility data with the Prophet forecasting model, we assessed spillover risk. Four countries were identified as very high risk at the end of July. Compared with the status quo scenario, the optimised control strategy could substantially reduce global importation risk. Enhanced entry screening and preparedness are warranted in neighbouring countries of the DRC in Africa, France in Europe, and Canada in North America.

Background: The Human Immunodeficiency Virus and Acquired Immunodeficiency Syndrome (HIV/AIDS) remain a major public health concern in Ghana. Despite sustained progress in treatment and prevention, regional prevalence variations persist, driven by healthcare access, urbanization, and socio-economic factors. This study identifies trends and hotspots to guide effective HIV surveillance and control strategies in Ghana. Methods: A retrospective ecological study was conducted using secondary HIV data confirmed by laboratory testing, from the Ghana District Health Information Management System (DHIMS2) for the period 2020 to 2024. HIV prevalence was calculated as the number of confirmed cases per 100,000 population, using denominators from the Ghana Statistical Service 2021 Population and Housing Census. Spatiotemporal variation in prevalence was visualized using choropleth maps. Global Morans Index examined whether overall spatial dependency existed, followed by local indicators of spatial association (LISA), comprising local Morans I and the Getis-Ord Gi* statistic, to identify local clusters, outliers, and hotspots or coldspots. Results: National HIV prevalence per 100,000 population rose from 0.68 in 2020 to 0.84 in 2024. The highest burden was in the southern and middle belt regions: Western North (2.16), Bono East (1.71), Eastern (1.30), Volta (1.06), and Ahafo (1.01). Northern regions remained consistently low throughout the study period, with Northern (0.32), Upper East (0.26), and Savannah (0.30) recording averages below 0.50 per 100,000. Global Morans Index indicated a dispersed pattern in 2020 (I = -0.43), spatially random pattern between 2021 and 2023, and weak positive spatial association in 2024 (I = 0.22). Conclusions: Regional disparity in HIV prevalence in Ghana is widening, with greater burden concentrated in the more urbanized southern regions. Interventions guided by surveillance data and tailored to specific regions, including strengthened testing infrastructure and a more equitable distribution of health resources, are needed to curb transmission and support HIV in Ghana and the AIDS control programme. Keywords: HIV, AIDS, spatiotemporal analysis, Morans I, Getis-Ord Gi*, Ghana

The need to meet the World Health Organization 2030 target for elimination of lymphatic Filariasis necessitates various activities such as mass administration of medicines (MAM), pre-transmission assessment surveys (pre-TAS)/ epidemiological monitoring survey (EMS) and transmission assessment survey. However for this study, pre-TAS/EMS was conducted to validate elimination milestones and elucidate the lingering immunological consequences of chronic infection. This study conducted a comprehensive evaluation across twelve Local Government Areas (LGAs) in Osun State, screening,7388 individuals for circulating filarial antigen (CFA) using Abbott Filarial Test Strips (FTS), while simultaneously investigating immune modulation in patients with clinical elephantiasis through detailed haematological profiling. Results indicated that the prevalence of the disease at sentinel and spot check sites have been lowered below threshold levels thereby qualifying for a first transmission assessment survey (TAS1), evidenced by a 0% antigenemia rate across all surveyed LGAs, a figure significantly below the World Health Organization's <2% threshold. . The average percentage of individual participation in MDA/MAM programs was found to be 56.09%, whereby, Ola-Oluwa LGA recorded the highest at 84.70% while the list MDA program participation was found at Ilesha East with 18.97%, highlighting a potential vulnerability in vector control compliance. Furthermore, haematological assessments of chronic cases revealed a distinct state of immune suppression, characterized by significant reductions in total white blood cell and lymphocyte counts, alongside persistent monocytosis and eosinopenia. These findings suggest that while Osun State has successfully transitioned into a post-transmission phase, the lingering physiological impacts on the chronically ill and the moderate rates of preventive practice necessitate sustained surveillance and enhanced public health interventions to ensure permanent disease eradication

Madariaga virus (MADV) is an understudied member of the eastern equine encephalitis virus (EEEV) complex that circulates widely in Latin America and may be geographically expanding. Spillover infections can cause severe disease in humans and equids. Despite these public and animal health concerns, no MADV vaccines or treatments are available. In this context, commercially available equine vaccines against the closely related North American EEEV (NA EEEV) could potentially provide heterologous protection against MADV. However, previous studies have shown weak or undetectable MADV-neutralizing antibody responses in equids and humans immunized with inactivated NA EEEV vaccines, and whether protection can occur despite these limited neutralizing antibody responses remains unknown. We evaluated a commercial equine trivalent inactivated NA EEEV vaccine against MADV challenge in NIH Swiss mice. Animals received two vaccine doses 14 days apart and were challenged 14 days later. Vaccination provided protection against lethal disease, whereas 36% mortality occurred in the unvaccinated group. Overall, vaccinated animals developed fewer and less severe clinical signs. MADV RNAemia was not detected following vaccination, and tissue dissemination was significantly reduced compared with the unvaccinated group. Central nervous system lesions were observed only in the unvaccinated group. Despite protection, most vaccinated mice had no detectable MADV-neutralizing antibody titers, and those that seroconverted developed only low titers, whereas most unvaccinated mice developed intermediate to high titers. These findings demonstrate that an inactivated NA EEEV-containing vaccine can protect against severe MADV disease despite limited MADV-neutralizing antibody responses.

Japanese encephalitis virus (JEV) transmission involves complex interactions among Culex mosquitoes, amplifying pig hosts, reservoir wading birds, humans, and environmental conditions, complicating quantitative assessment of transmission dynamics and intervention effectiveness. We developed a deterministic, fourteen-compartment One Health mathematical framework that integrates these interconnected host vector populations and their epidemiological states. The model incorporates temperature-dependent mosquito biting, seasonal transmission, human vaccination, pig biosecurity, environmental barriers, and mosquito-control interventions. Mathematical properties were established through analyses of non-negativity, boundedness, biologically feasible equilibria, local and global stability, and optimal control. District-specific simulations were conducted for Bellary, Udupi, Kolkata, and Purba Bardhaman during the August transmission period. Intervention scenarios were evaluated, and global sensitivity analysis was performed using 500 Latin hypercube samples with partial rank correlation coefficients. Model outputs were also compared with district-level surveillance observations. Vaccination-adjusted basic reproduction numbers were 0.905 in Bellary, 0.965 in Udupi, 1.817 in Kolkata, and 0.885 in Purba Bardhaman, with only Kolkata exceeding the epidemic threshold. Under maximum intervention, total infections decreased by 80.6%, 96.8%, 80.5%, and 72.2%, respectively, while infected mosquito populations declined to zero across all four settings. In Kolkata, vaccinating 3.6 million individuals with dose series II reduced the reproduction number from 1.817 to 0.9846, whereas population-wide dose series I vaccination alone was insufficient to reduce it below unity. Sensitivity analysis identified mosquito recruitment, temperature-dependent biting, carrying capacity, mosquito mortality, density-dependent regulation, and mosquito-to-human transmission as major determinants of peak human infection. Overall, the framework demonstrates heterogeneity in JEV transmission and intervention effectiveness and provides a mathematically grounded One Health approach for comparative evaluation of integrated control strategies.

Malaria is still one of the most dangerous causes of morbidity and mortality in tropical and subtropical regions even though it has been actively combated for many years. In 2023, there were approximately 263 million malaria cases and 597,000 deaths from this disease on a global scale, with sub-Saharan Africa being the region most affected by it [24]. Climate factors affect mosquito biology, including their abundance, survival, and biting rates, as well as parasite development, while human awareness plays a crucial role in adopting preventive measures and effective treatments. Despite the progress in both climate- and awareness-based malaria modelings, few studies integrate these factors in one comprehensive model that involves the detailed mechanisms of transmission processes. The current study develops a deterministic climate-driven SEAIR-SEI malaria transmission model that includes the impact of temperature, rainfall, and humidity on mosquito biology and endogenous community awareness. The model was proven to be well-posed by showing the positivity and boundedness of its solution and through the demonstration of the existence and uniqueness of its solution. The malaria-free equilibrium was determined, and the basic reproduction number was calculated using the next-generation matrix method. The model underwent local and global stability analyses to characterise the diseases persistence in the population. Additionally, a normalized forward sensitivity analysis was conducted, revealing the mosquito biting rate as the key force driving malaria transmission. Four time-dependent malaria interventions, namely, long-lasting insecticidal nets, community awareness campaigns, indoor residual spraying, and prompt treatment, were included in the model through optimal control theory and analysed using Pontryagins Maximum Principle. The numerical results for the optimal control problem showed that employing all four interventions leads to the best outcome by decreasing the objective functional value by 88.17%, reducing the total number of infected humans by 92.49%, and minimizing the total number of infectious mosquitoes by 93.87%. Interestingly, combining two interventions, indoor residual spraying, and prompt treatment, also yielded nearly optimal results. Therefore, the designed control strategy can serve as an efficient and affordable framework for malaria control in sub-Saharan Africa.

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.

Aedes albopictus (Skuse), the Asian tiger mosquito, is a highly competent arboviral vector whose range has expanded substantially across the United States over the past four decades. Despite predictive models placing Nebraska within the species' climatically suitable range, its establishment status in the state has remained poorly characterized. Here, we report results from a nine-year mosquito surveillance program (2017-2025) conducted across 44 Nebraska counties in collaboration with the Nebraska Department of Health and Human Services. Ae. albopictus was detected in five counties, with sustained, annually increasing populations documented in Richardson, Douglas, and Lancaster counties. Richardson County recorded continuous detections during 2017-2025, with proportional representation rising to 60.50% of collected mosquitoes by 2025. In Douglas and Lancaster counties, temporal advancement of first seasonal detection in 2024 and 2025 provide evidence consistent with successful overwintering rather than annual reintroduction. A cumulative degree-day model predicted adult emergence in mid-May across all county-year combinations, consistently preceding trap deployment by two to seven weeks and revealing a systematic early-season surveillance gap. Generalized linear mixed-effects models indicated that trap-level detection persistence, rather than urban location, was the primary predictor of yearly Ae. albopictus positivity, suggesting that current invasion dynamics are driven by focal source populations. These findings provide strong evidence for the establishment of Ae. albopictus in eastern Nebraska and highlight the need for earlier seasonal surveillance and standardized criteria to define establishment in northward-expanding vector populations.

The emergence of Highly Pathogenic Avian Influenza (HPAI 2.3.4.4b) in dairy herds in 2024 across 19 states in the United States of America has raised concerns regarding the potential national and global zoonotic impact. All recent modeling efforts implemented homogenous cattle-to-cattle (both intra and inter-herd) transmission models which did not capture the real-world heterogeneity in mixing of animals, individual variations in susceptibility and infectiousness and clinical incidences across pens and lactation groups. The aim of this study was to develop a heterogenous transmission model to estimate the epidemiological parameters for intra-herd HPAI transmission on Californian dairies. We developed a validated stochastic agent-based model to estimate the epidemiological parameters for intra-herd HPAI transmission on California dairies. The hierarchical agent-based model also parameterized stochastic cattle movements within-herd to simulate real dairy management practices. A cow-level SEIR transmission approach was assumed during the outbreak. A novel Bayesian Optimizer with Gaussian Process (BO-GP) was fitted to the agent-based model for validation which converged within 25-40 iterations (out of 100 per farm) with minimal loss over two distinct error metrics, namely, Poisson loss function and temporal distance metric. Our optimized simulations estimated an average R0 was around 10.7-10.8 across all farms within the first 15 days of observed outbreak on four dairy farms with a mean effective transmission rate of 4.5% per contact between susceptible and infectious cows within each pen. Our model demonstrated that the movement of cows between pens ensured localized clusters of outbreaks within the sub-herds (pen population) that prolonged the overall outbreak within farms. We estimated the total duration of infection between 14.5 and 28 days, which is higher than the estimates from the homogenous models. With an integrated hierarchical agent-based model combined with Bayesian approximation, we produced actionable insights on the epidemiology of intra-farm spread of HPAI within cow herds, thereby guiding both future model development and applied disease control strategy.

Since its inception in 2003, the United States President's Emergency Plan for AIDS Relief (PEPFAR) has played a central role in HIV/AIDS prevention and treatment across Sub-Saharan Africa. Following the January 2025 suspension of United States foreign aid, which includes PEPFAR funding, countries across Sub-Saharan Africa demonstrated varying governmental responses to the resulting HIV funding disruptions. This study evaluates whether national characteristics were associated with a country's ability to respond to this funding crisis. Countries in Sub-Saharan Africa receiving PEPFAR funding were classified into one of three response categories: Level 1 (minimal or no governmental response), Level 2 (government response without sufficient plans or resources to fully offset funding losses), or Level 3 (high governmental response with strategies to maintain HIV/AIDS prevention and treatment services). Response categories were compared with national Human Development Index (HDI), GDP per capita, HIV prevalence, total population living with HIV, annual HIV mortality, and historical PEPFAR funding. Univariable proportional-odds ordinal logistic regression was used to identify factors associated with response level. Five countries (17.2%) were classified as Level 1, 10 (34.5%) as Level 2, and 14 (48.3%) as Level 3. Higher HDI was significantly associated with stronger governmental response (OR = 4.05; 95% CI, 1.49-11.04; Holm-adjusted p = 0.0376). GDP per capita (OR = 2.35; 95% CI, 1.00-5.51; unadjusted p = 0.0489) and HIV prevalence (OR = 3.53; 95% CI, 1.03-12.11; unadjusted p = 0.0452) demonstrated positive associations before adjustment for multiple comparisons, but neither remained significant after Holm correction (adjusted p = 0.2261 for both). Historical PEPFAR funding, total population living with HIV, and annual HIV mortality were not significantly associated with response level after adjustment. Countries with higher levels of human development demonstrated stronger governmental responses to the 2025 PEPFAR funding disruptions. Several countries with minimal documented responses were also affected by active conflict or political instability, potentially compounding their vulnerability to treatment interruptions. These findings suggest that broader national development may play an important role in governmental responses to funding disruptions and highlight the need to strengthen resilient health systems, diversify HIV financing, and prioritize support for countries at greatest risk of future funding shocks.

Background Women living with Human Immunodeficiency Virus (WLHIV) are at high risk of cervical cancer. While Human Papilloma Virus Deoxyribonucleic acid (HPV DNA) testing is the standard of care, its cost limits widespread use in resource-limited settings, therefore visual inspection with acetic acid (VIA) remains the primary screening method. This study therefore evaluated the diagnostic accuracy of VIA compared to HPV DNA testing for cervical cancer screening among WLHIV in Nigeria. Methods This cross-sectional analytical study was conducted between November 2022 and November 2024 in Osogbo, Nigeria among 300 WLHIV on antiretroviral therapy aged 25-49 years underwent cervical cancer screening using VIA and HPV DNA testing (Ampfire HPV test kits, ATILA Biosystems(R) USA). All 309 participants underwent colposcopy and biopsy regardless of their screening results, with histological examination serving as the gold standard. Data were analyzed using Stata, with diagnostic performance measures calculated using histology as the gold standard. Results The mean age of participants was 42.6 {+/-} 6.4 years. Thirty-six of 309 women (11.7%) were VIA-positive, while 91 (29.4%) were HPV-positive. Histology confirmed CIN2+ in 22 participants (7.1%). HPV testing demonstrated significantly higher sensitivity than VIA for detecting CIN2+ (91.2%, 95% CI: 76.3-98.1 vs. 44.1%, 95% CI: 26.7-62.6; p<0.001) and higher negative predictive value (98.9%, 95% CI: 96.3-99.8 vs. 92.0%, 95% CI: 88.0-95.0; p<0.001). However, VIA showed higher specificity (91.7%, 95% CI: 87.8-94.7 vs. 83.3%, 95% CI: 78.4-87.6; p=0.003). The agreement between the two tests was fair (kappa = 0.20, p<0.001). Both HPV positivity (adjusted OR: 42.1, 95% CI: 9.5-186.2; p<0.001) and VIA positivity (adjusted OR: 6.9, 95% CI: 2.6-18.4; p<0.001) were independently associated with CIN2+. Conclusion HPV DNA testing demonstrated superior sensitivity and negative predictive value compared to VIA for detecting CIN2+ among WLHIV, while VIA showed higher specificity. Keywords: Cervical cancer screening, visual inspection with acetic acid, Human Papilloma Virus Women living with HIV, Nigeria

Abstract Background Chikungunya re-emerged in Mauritius in 2025 after a 19-year period with no outbreak. As precipitation and temperature play a key role in mosquito proliferation, the climatic conditions prevailing in Mauritius in 2025 may have influenced the emergence and spread of the outbreak. Our aim in this study was to investigate the spatial distribution of the 2025 chikungunya outbreak in Mauritius, and to determine the association between chikungunya incidence and selected predictor variables using Geographic Information Systems (GIS). Methods The variables considered were mean altitude, mean air temperature and mean precipitation, population density, road density, number of houses near rivers and the relative deprivation index (RDI) per village council area (VCA) and municipal ward (MW). Rainfall and temperature data were obtained from the climate bulletins of the Mauritius Meteorological Services and were also extracted from the ERA5 Monthly Averaged Data on Single levels available from the platform of Copernicus Climate Data Store. The U.S. Geological Survey Earth Explorer platform was used to access elevation data for Mauritius. In the absence of publicly available, spatially disaggregated official case data, reported chikungunya cases were reconstructed from multiple publicly accessible sources supplemented by communications with professionals involved in the outbreak. Information was collected contemporaneously as the outbreak evolved during 2025 and was supplemented by searches of archived material. Cases were georeferenced and aggregated to VCAs and MWs. All data was mapped using QGIS version 3.40.13.Page 2 of 17 Results The main findings were that the 2025 chikungunya outbreak was significantly associated with elevation and precipitation. Temporal analysis demonstrated that rainfall in the preceding month was the strongest climatic predictor of monthly chikungunya cases. Spatial analysis, however, showed that mean precipitation had a significant (p-value < 0.001) and negative association with chikungunya incidence, indicating lower disease occurrence in areas of high precipitation. Mean elevation was significantly (p-value < 0.001) and negatively associated with chikungunya incidence, indicating lower disease occurrence in higher-altitude areas. Temperature did not have any significant relationship with chikungunya incidence. Conclusions Rainfall had opposite associations depending on the analytical scale: preceding-month rainfall was positively associated with temporal case variation, whereas spatially averaged precipitation was negatively associated with incidence. Thus, rainfall exerted distinct temporal and spatial influences on chikungunya transmission, while higher elevation was associated with lower chikungunya incidence. These findings highlight the importance of integrating climatic and spatial analyses to improve surveillance Keywords Chikungunya; Elevation; Precipitation; Temperature; Geographical Information Systems; Mauritius.

Dengue presents a major public health challenge in Vietnam, driven by biological, behavioural, and environmental factors. Compound climate extremes, such as sequential hydrometeorological events, can influence dengue risk yet remain understudied. We evaluated the effects of compound climate extremes, socioeconomic conditions, and human mobility on dengue relative risk across 670 districts in Vietnam over 20 years, stratifying by eight subregions spanning emerging to endemic transmission. Dengue risk was greatest following dry-then-wet conditions in seven subregions. Higher temperatures increased risk across North and Central Vietnam, but had limited effect in the South. Mobility associations followed an urban-rural gradient, with increased risk in highly rural districts where residents travelled more frequently to fewer destinations, and in urban districts with more visitors and dispersed outgoing mobility. Stratifying climatic and socioeconomic effects by subregion improved predictive skill at lead times of 1 to 6 months over unstratified and baseline models, with substantial spatial variation. Accounting for compound extremes across distinct spatial contexts could strengthen disease early warning systems in Vietnam and beyond.

Invasive mosquito species pose substantial risks to human and animal health. Since 2004, Culex coronator, a mosquito vector species of public health concern, has shown rapid range expansion within the United States, spreading from a historically limited distribution in southern Texas to across the Gulf Coast region and into eastern and mid-Atlantic states. However, changes in environmental suitability associated with this expansion across historical, contemporary, and future climate conditions have not been evaluated. Here, we used species distribution models (SDMs) to compare predictions of abiotic suitability for Cx. coronator under historic (1960-1989) and recent (2000-2024) climate conditions calibrated on the historical range in the United States. We also created a contemporary SDM based on occurrence records prior to and following species range expansion (1960-2024), and further, to predict potential distributions under current and future climate conditions. Models calibrated on the historical range predicted only modest changes in suitability along the Gulf Coast region and failed to identify large areas of the humid subtropical eastern United States that are now occupied. In contrast, the contemporary model predicted widespread suitability across much of the southern and eastern United States. Future projections under the mid-range SSP3 scenario predicted increasing suitability at higher latitudes and elevations. Across all models, suitability was consistently low in arid and semi-arid regions, including along the historical western range limit, suggesting that moisture availability may constrain Cx. coronator distributions. Together, these results highlight the need to incorporate updated occurrence records when modeling invasive mosquito species to strengthen surveillance and control strategies.

Background: Unexplained spatial heterogeneity in field-observed Human Blood Index (HBI) hinders vector-borne disease control. We synthesized micro-morphological sensory structure densities across primary disease vectors to formulate a novel Proboscis Sensory Index (PSI) from micro-morphological sensory structure densities to evaluate its capacity to predict vector anthropophily, zooprophylactic modulation, and spatial transmission risk across Northern Nigeria. Methods: A systematic review of 42 studies (N = 18,647 blood-fed mosquitoes across 126 georeferenced sites, 2005 to 2025) synthesized baseline species-specific PSI metrics from electron microscopy sensilla counts. A multi-variable binomial Generalized Linear Mixed Model (GLMM) evaluated interactions between PSI, livestock, human density, and environmental covariates. Predictive accuracy was validated against field-derived Entomological Inoculation Rates (EIR) and historical outbreak records. Results: The overall pooled HBI was 0.642 (95% CI: 0.588 to 0.694), exhibiting significant variation across the Sudan (0.728), Guinea (0.615), and Sahel (0.584) savannas. Species specific PSI values graded from Anopheles gambiae s.s. (8.91 {+/-} 0.34), Anopheles funestus (8.12 - 0.38), and Anopheles arabiensis (7.68 {+/-} 0.41) to Culex quinquefasciatus (4.87 {+/-} 0.52). In GLMM analysis, PSI was the strongest predictor of anthropophily ({beta} = 0.89, aOR = 2.44, 95% CI: 2.32 to 2.56, p < 0.001), with fixed effects explaining 78% of total field variance (Marginal R2 = 0.78). High PSI species maintained elevated anthropophily regardless of livestock availability, whereas low-PSI species displayed steep zooprophylactic diversion ({Delta}HBI = -0.71, p < 0.001) under high cattle density. Incorporating PSI reduced localized EIR prediction error by 64.2% (RMSE = 15.0 vs. 41.8 infective bites/person/month), and high-resolution (1 km2) predictive mapping correctly identified 87.1% (27/31) of historically documented outbreak Local Government Areas. Conclusion: Micro-sensory organ density dictates vector host preference and livestock-mediated zooprophylactic potential. Integrating PSI into spatial models resolves localized transmission heterogeneity, guiding targeted vector control prioritizing indoor residual spraying and dual-ingredient nets in high PSI foci, while leveraging endectocides and larval management in low-PSI pastoral corridors.

Background Dengue is a major public health challenge, and predictive models are crucial for early warning systems. However, many current modeling practices rely exclusively on climatic factors or employ complex algorithms that lack the interpretability needed for informed public health decision-making. To address these shortcomings, we developed and validated a multidimensional, interpretable statistical model to predict monthly dengue incidence. Methodology/Principal Findings We used a Generalized Linear Mixed Model (GLMM) with a Negative Binomial distribution to analyze 14 years (2010-2023) of spatiotemporal data from 37 municipalities in Huila, Colombia, an endemic region. The model integrates non-linear and lagged effects of climatic, demographic, and socioeconomic factors. The final model underwent rigorous external validation on an independent test set (2021-2023). Our model demonstrated high predictive discrimination (R2 = 0.743, Spearman's {rho} = 0.657), accurately capturing the timing of epidemic outbreaks. Key findings include the identification of an optimal thermal window for transmission at 27-28{degrees}C, a threshold effect for precipitation above 800 mm, and a saturation dynamic in outbreak autocorrelation. Conclusions/Significance This mechanistically-informed statistical approach provides a robust and transparent tool for epidemiological surveillance, successfully balancing high predictive performance with the explanatory power needed for effective, data-driven public health interventions.

BackgroundIn Ethiopia as other Sub-Sharan African countries malaria in pregnancy is a major public health threat particularly in the rural communities. Immunosuppression during pregnancy increases the risk of placental malaria; this, in turn, leads to anemia, low birth weight, preterm delivery, and stillbirth, causing severe complications that pose a life-threatening risk to both the mother and fetus. Hence, this study is aimed to assess the prevalence of malaria, and associated factors among pregnant women attending antenatal clinics in Damot Gale district, South Ethiopia. MethodAn institutional-based cross sectional study was conducted from March 1 to May 30, 2024. A multistage sampling technique and pre-tested semi-structured questionnaires were used for data collection. Using standard parasitological methods, blood samples collected from 389 participants were analyzed through microscopic examination of Giemsa-stained thick and thin blood films. Logistic regression was used to assess factors associated with malaria. Adjusted odds ratio with 95% confidence interval was calculated and P-value < 0.05 was considered statistically significant. ResultsIn this study the prevalence of malaria among pregnant women was 10.5% (95% CI: 7.7-13.6%). Among the malaria-positive pregnant women (9%) were P. falciparum infected, followed by P. vivax (1.3) and mixed infections (0.2%). In the multivariate analysis, multigravdae (AOR = 0.358; 95% CI: 0.14-0.92), utilization of LLINs (AOR = 0.286; 95% CI: 0.119-0.687) and use of IRS (AOR = 0.068; 95% CI: 0.027-0.172) were significantly associated with malaria among pregnant women. ConclusionMalaria is still a public health problem among pregnant women in Damote-gale district. The present study indicated that adequate distribution and proper utilization of bed nets In addition seasonal proper spray of IRS might be helpful to prevent and control malaria.

BackgroundWolbachia-based control methods reduce dengue transmission by suppressing Aedes aegypti populations or blocking viral replication, yet their effectiveness across the climatic conditions of endemic regions remains poorly understood. Seasonal and interannual temperature changes shape mosquito dynamics, but how Wolbachia releases perform during anomalous events such as El Nino or heatwaves is largely unknown--a gap that limits our ability to optimize release strategies and predict intervention success across different climates. Methodology/Principal FindingsTo address this gap, we built a mathematical modeling framework that explicitly incorporates temperature-dependent Wolbachia parameters together with the seasonal and interannual climate variability characteristic of dengue-endemic regions, coupling a detailed Wolbachia dynamics model with a susceptible-infectious-recovered (SIR) epidemiological model to evaluate Wolbachia establishment, persistence, and stability under different thermal regimes and trace their downstream impact on disease spread. Higher temperatures eroded both Wolbachia establishment and long-term persistence, sharply narrowing the range of effective release strategies as conditions approached 30 {degrees}C. Seasonality added a further layer of complexity: the timing of thermal stress relative to Wolbachia frequency, not merely its magnitude, determined whether population replacement succeeded. Interannual shifts, progressive warming, widening seasonal swings, and displaced thermal peaks, each eroded Wolbachia prevalence and stability, with effects that compounded over successive years. Dengue transmission tracked these dynamics closely, with warmer conditions producing larger, earlier outbreaks, and intervention success hinging on how release timing, frequency, and targeting were matched to local thermal conditions. Conclusions/SignificanceAs extreme heat events become more frequent under climate change, release programs that ignore local thermal conditions risk falling short where dengue control is most needed. By elucidating the mechanistic interplay among temperature, Wolbachia, and dengue, our findings help refine Wolbachia release programs to suit different climatic conditions, thereby strengthening dengue control as climate variability intensifies. Author summaryDengue sickens hundreds of millions of people each year, and releasing mosquitoes carrying the naturally occurring bacterium Wolbachia, which blunts the viruss spread, has become one of the most promising tools for fighting it. Yet one factor that decides whether these releases succeed or fail is routinely overlooked: temperature. We built a mathematical model linking Wolbachia biology and mosquito population dynamics to temperature, to ask a practical question: which release strategies work under real-world, changing climate conditions? Heat narrowed the margin for success. Higher temperatures shrank the range of effective interventions and slowed the replacement of wild mosquitoes with Wolbachia-carrying ones. Timing mattered just as much as intensity: releases launched before peak heat allowed Wolbachia to establish at higher levels, before thermal stress eroded its fitness benefits. And success was not permanent--growing year-to-year temperature swings could destabilize Wolbachia populations even after they had become established, threatening long-term disease control. The public-health stakes were stark: Wolbachia cut peak dengue cases by 54.6% at 25{degrees}C, but by only 10% at 30{degrees}C. For the communities most burdened by dengue, these results carry an urgent message. As climate change drives temperatures upward across endemic regions, Wolbachia programs designed without accounting for local thermal conditions risk underperforming precisely where they are needed most. Effective deployment requires climate-sensitive planning, adaptive release schedules, and continued investment in field-validated models that reflect the realities of a warming world.