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.

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.

Dengue fever risk and burden has increased globally in the past decade, with record-breaking outbreaks driving high case numbers, outbreaks increasing in existing transmission suitable regions, and occurring in new locations. A combination of global change processes, including climate change, have provided the environmental backdrop for introductions and resurgences of mosquito-transmitted dengue virus. Understanding shifts in exposure risk is integral to public health preparedness. This study provides global mapping of the thermal suitability of dengue transmission for CMIP6 climate scenarios, across a range of general circulation models (GCMs), and we created spatially explicit demographic projections of transmission risk using year-matched RCP-SSP frameworks for demographic and emissions scenarios. Globally, poleward shifts in projected distributions of suitability for transmission for both Ae. aegypti and Ae. albopictus suitability are shown in both the near term (2030s) and longer term (2050). Under a 'middle of the road' climate scenario (CMIP6 SSP2-4.5), regions in Africa and Asia are the major areas driving increases in year-round (12 months) population at risk (PAR) through 2050, with an anticipated net gain in 932 million people at risk for Ae. aegypti transmission and 24 million for Ae. albopictus, which includes multiple regions losing areas of year-round suitability as temperatures exceed the higher thermal boundary for transmission. In contrast, the estimated net increase in PAR for one or more months of transmission suitability at a global scale by 2050 is 3.29 billion people for Ae. aegypti transmission and 3.30 billion for Ae. albopictus transmission. This snapshot of a 'middle-of-the-road' combination of climate and demographic driven increases in potential dengue transmission exposure emphasizes the importance of both expanding suitability in new areas, and growing populations in areas approaching and becoming exposed year-round. Globalization, urbanization, and shipping will continue to provide the potential for introductions into newly suitable areas as season lengths increase, sparking outbreaks in unexposed populations. This is compounded and becomes ever more probable as the number of people and places at year-round risk also increases. This project provides all global gridded outputs for onward mapping and reuse, to add to the toolkit to anticipate and prepare for prevention and response to dengue in a changing world.

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

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.

BackgroundWest Nile virus (WNV) is a vector-borne zoonotic pathogen maintained in an enzootic cycle between birds and mosquitoes which is considered a significant public health concern in Europe, particularly in relation to its recent increase in reported human cases and range expansion. While a comprehensive understanding of the viruss epidemiological dynamics is essential to inform effective prevention and control strategies, to date significant knowledge gaps remain in quantifying interspecific differences within the complex avian communities involved in WNV circulation. Globally, WNV-infection has indeed been documented across more than 300 bird species, however, whether and how inter-specific differences in avian hosts traits affect the spread of WNV is still largely unknown. A substantial body of research has investigated how epidemiological traits, such as the duration of infection and competence, influence WNV dynamics. However, much less is known about the role of avian demography. Methodology/Principal findingsWe therefore investigated through mathematical modelling the role of avian demographic traits in shaping patterns of mosquito WNV infection dynamics in northern Italy (Lombardy Region, 2016-2018). We focused on the effects of annual offspring production, timing and synchrony of breeding which ultimately affect seasonal abundance of competent avian hosts. We highlighted that timing of breeding has the greatest effect on the number of infected mosquitoes, while annual offspring production influences the timing of the infection peak. Our simulations provide evidence that non-corvid species can have a key impact on WNV transmission. Conclusion/SignificanceThese results can support future research by providing priority bird species to direct further studies and by suggesting that the acknowledgment of spatio-temporal variation in the abundance of competent avian hosts plays a key role in the development of effective surveillance strategies and mosquito control actions. Author summaryWest Nile virus (WNV) is endemic in Italy and represents a significant public health threat in Europe, with increasing cases of severe neuroinvasive disease in humans in recent years. Surveillance data reveal marked spatial and temporal variability in infection dynamics, suggesting that key drivers of WNV transmission remain poorly understood. The contribution of different bird species (over 300 are implicated in the WNV cycle) is often overlooked despite evidence that species-specific traits are critical determinants of WNV infection dynamics. Few studies have examined birds demographic traits, despite their well-established importance in shaping infection dynamics across diseases. Given the challenges in collecting detailed wildlife data, we employed mechanistic models to explore transmission scenarios and test whether avian demographic traits influence bird species roles in WNV transmission and maintenance in Lombardy. Our findings demonstrate that brood size, hatching synchrony, and hatching time significantly affect estimated WNV prevalence in mosquitoes.

Background Brucellosis is a severe zoonotic disease with pronounced seasonality and regional heterogeneity in high-incidence areas of China. Reliable forecasting tools are needed to inform prevention strategies, but the optimal modeling approach across different regions remains unclear. Principal Findings We collected monthly brucellosis incidence and 17 environmental variables from 2014 to 2024 across five high-incidence provinces: Inner Mongolia, Xinjiang, Shanxi, Heilongjiang, and Hebei. A three-step procedure--cross-correlation analysis, multicollinearity diagnostics, and stepwise regression--was used to select exogenous predictors. We then compared four time-series models: seasonal autoregressive integrated moving average (SARIMA), SARIMA with exogenous variables (SARIMAX), long short-term memory (LSTM), and LSTM with exogenous variables (LSTMX). All five provinces showed a unimodal seasonal pattern with peaks between April and July, though environmental drivers and optimal lag periods varied substantially by region, ranging from 1 to 6 months. In forecasting performance, LSTM achieved the highest accuracy in Shanxi (R2=0.925), Hebei (R2=0.876), and Xinjiang (R2=0.829), outperforming SARIMA and SARIMAX. LSTMX performed best in Inner Mongolia (R2=0.759) and Heilongjiang (R2=0.772) but showed weaker performance than LSTM in Shanxi and Hebei. Overall, adding exogenous variables did not consistently improve predictions across provinces. Conclusions Our findings demonstrate that LSTM-based models offer clear advantages for brucellosis forecasting in most high-incidence provinces, but the value of incorporating environmental predictors is region-dependent. These results support the development of tailored early warning systems and precision prevention strategies for brucellosis in high-risk areas of China.

Abstract BACKGROUND West Nile virus (WNV) is a growing health burden in Italy. Anticipating human infection risk is hampered by the pathogen's complex ecology, highlighting the need for comprehensive early-warning tools. AIM We aimed to model municipal-level WNV risk in Italy and characterize its decadal expansion in Italy, providing a comprehensive ecological understanding of viral emergence. METHODS We applied a machine learning framework to annual human WNV case data from 2014 to 2024. The model integrated a suite of environmental, socio-economic, and macroecological predictors to generate risk projections. We evaluated the model's performance through multiple validation settings. We also performed an anticipation test for the 2025 epidemic season, using 2024 environmental data to assess the model's predictive accuracy against observed 2025 human cases. RESULTS Our model achieved robust performance (True Skill Statistic > 0.4) and captured WNV progressive expansion from 184 predicted positive municipalities in 2014 to 2,012 in 2024 (an 11-fold increase in 11 years). Seasonal minimum temperature was the primary risk driver, followed by monitoring year and population density, indicating active spatial spread. Environmental suitability consistently preceded clinical detection. Municipalities with cases in 2023-2024 exhibited significantly higher predicted suitability during 2018-2022 than those without cases (average risk 0.58 vs 0.20). Our model successfully identified emerging risk hotspots along the Adriatic coast and southern Italy before the official human spillover of 2025. CONCLUSION Embedding macroecological drivers into WNV risk modelling provides an improved understanding of drivers of rapid WNV expansion. Our model enables proactive risk mapping, surveillance efforts, and targeted public health measures.

Wolbachia-mediated incompatible-insect technique (IIT) via wAlbB, wMel or wPip/wAlbA/wAlbB strains are promising approaches for suppressing wildtype Aedes mosquitoes and therefore Aedes-borne diseases. Yet, the effectiveness of this technique under climate change remains uncertain. Here, we evaluate the long-term robustness of male Wolbachia-infected mosquito releases to suppress wildtype Aedes aegypti and Ae. albopictus populations across future climate scenarios across diverse geographical regions. We compiled large publicly available datasets on Aedes abundance across Singapore, China, the European Union and the United States, historical and projected climatic conditions in these regions and conducted experiments to test the thermal stability of cytoplasmic incompatibility in Wolbachia-infected male Aedes aegypti and albopictus. A climatically-driven entomological model was developed and calibrated using a Bayesian approach to model observed Aedes population dynamics and infer area-specific climate-driven variation in mosquito life-history traits. We back-inferred historical mosquito abundance and projected mosquito abundance in future climate change scenarios incorporating experimental and locally inferred entomological parameters and then simulated the counterfactual implementation of IIT in these regions. We find that Aedes populations are projected to increase in most regions across all climate change scenarios from 2050-2100 even under high heat conditions in the absence of interventions. While we found that IIT can suppress wild-type populations effectively across all future scenarios and in high heat conditions, effectiveness was found to depend heavily on mosquito emigration rates, overflooding ratios, release intervals and release strategies Extensive robustness checks confirmed that the model reproduced historical temporal trends, captured the influence of individual parameters on outcome and was sensitive to changes in values of inferred parameters and implement policy. These findings demonstrate that IIT may be a robust vector control tool under future climate conditions.

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

ObjectivesTo estimate the pooled prevalence of Wolbachia infection in biting midges (Ceratopogonidae) across Southwest Asia and to evaluate ecological and biological factors associated with infection patterns. Study DesignSystematic review and meta-analysis. MethodsA comprehensive search of international and regional databases (PubMed, Scopus, Web of Science, Embase, SID, MagIran) was conducted without date restriction. Eligible studies included those using molecular techniques to detect Wolbachia in Ceratopogonidae collected from Southwest Asia. Pooled prevalence was calculated using a random-effects model. Subgroup and meta-regression analyses were performed to assess variations by country, species, altitude, habitat type, and sex. Heterogeneity and publication bias were evaluated using I{superscript 2}, Cochrans Q, and Eggers tests in accordance with PRISMA guidelines. ResultsTwenty-four studies comprising 14,832 midges from six countries were included. The pooled prevalence of Wolbachia infection was 32.6% (95% CI: 28.4-36.9%; I{superscript 2}=78.3%). Iran showed the highest prevalence (38.2%), and Culicoides imicola was the most frequently infected species (36.8%). Higher prevalence was associated with lower altitudes (<500 m; P=0.012), rural habitats (P=0.034), and female midges (P=0.008). Limited evidence suggested the presence of cytoplasmic incompatibility and reduced bluetongue virus competence in infected midges. ConclusionsWolbachia infection is common among Ceratopogonidae in Southwest Asia and is influenced by ecological and biological factors. These findings highlight the potential of Wolbachia as a biocontrol tool in regional vector management, underscoring the need for further experimental and strain-level studies.

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 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.

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.

The mosquito Aedes albopictus is a competent vector for the transmission of several arboviruses and is currently spreading across many continents. Since conventional control methods, like insecticides, often lead to environmental problems and the emergence of resistance, scientists developed alternative mosquito control strategies. One of the most used is the Sterile Insect Technique (SIT), which involves the mass release of males sterilized through irradiation. The Toxic Male Technique (TMT) is instead based on the release of genetically modified males expressing toxic proteins that kill females when they mate. Control strategies are often intended as methods to eradicate mosquito populations, yet a less ambitious and more cost-effective task is to reduce them such that the probability of transmission of viruses to humans becomes negligible. To compare the efficacy of these control strategies, we develop a mathematical model with two communicating compartments: a mosquito population and epidemiological model coupled with a human epidemiological model. As a proof-of-concept, we test the model using meteorological and entomological data for the Emilia-Romagna region. Our results indicate that the TMT strategy is more effective in lowering the probability of transmission and provides indication for the deployment of control strategies.

Conventional mosquito surveillance typically relies on contemporaneous data, making it challenging to anticipate future vector surges. To support proactive vector management, this study evaluates a multi-model forecasting framework designed to generate probabilistic 1- to 4-week-ahead forecasts of Aedes aegypti relative abundance per trap night. The framework was validated using multi-year surveillance data across four US jurisdictions spanning varied environments (from subtropical to temperate and arid). We found that an ensemble approach aggregating statistical and machine learning models generally achieved the best performance across all locations and forecast horizons. Relative forecast performance improved as the forecast horizon extended from 1 to 4 weeks ahead. The most challenging data to forecast were primarily restricted to low mosquito activity periods or atypical population peaks with unusual timing or magnitude. While full integration into routine vector management workflows represents a long-term process requiring operational adaptation, this work advances forecasting research and establishes a baseline for translating these approaches into real-time applications for public health authorities, with downstream effects in mitigating the risks of mosquito-borne diseases.

Background: Snakebite envenoming is a major cause of preventable death and disability in the Brazilian Amazon, where long distances, sparse roads, and dependence on river transport delay access to antivenom. We developed location-allocation models to identify community health centers that could strategically expand access to antivenom in Amazonas State, Brazil. Methodology/Principal Findings: We conducted an ecological geospatial study using a 2025 WorldPop population surface, locations of existing and candidate health facilities, and a multimodal road-and-river transportation network derived from OpenStreetMap and HydroSHEDS. Population demand was represented by 7,065 populated centroids, including 1,586 within Indigenous territories. We applied a maximize-coverage algorithm with a six-hour travel-time threshold. Two models were developed: one for Amazonas excluding Manaus and one for populations living in Indigenous territories. Both models began with 77 facilities already providing antivenom and progressively added candidate community health centers until coverage gains plateaued. The plateau occurred at 110 facilities, corresponding to 33 additional centers. In the model excluding Manaus, this configuration covered 1,118,831 people, or 75.11% of the target population; 87.61% of those covered could reach care within three hours. In Indigenous territories, coverage increased from 50.55% to 69.50%, reaching 50,434 people, of whom 81.39% were within three hours of care. Validation used 3,595 snakebite notifications from the 30 highest-burden municipalities in the Brazilian Notifiable Diseases Information System during 2023-2025. The median proportion reaching care within six hours was 40.81% in observed data and 72.17% in model estimates. Conclusions/Significance: Strategically equipping 33 additional existing community health centers could substantially expand timely access to antivenom, particularly in rural and Indigenous areas. Location-allocation modeling that incorporates river transportation can support evidence-based decentralization of time-sensitive health services in geographically complex settings.

Real-time nowcasting enhances situational awareness by mitigating reporting delays that obscure transmission dynamics. We applied Nowcasting by Bayesian Smoothing (NobBS) to the 2024 dengue outbreak in Puerto Rico (PR), using case surveillance data from the PR Department of Health. The method accurately captured the epidemic trajectory and consistently outperformed a baseline model, although reporting anomalies occasionally reduced performance. We also conducted analyses by dengue virus serotype and health region, as well as previous years. For analyses with few dengue cases, a model in which parameters are jointly estimated across groups generally achieved better performance than the independent one. Historical analyses revealed that years with higher variability in reporting delays generally exhibited higher uncertainty. The findings here underscore key lessons for real-time dengue nowcasting: alternative models may be needed in complex circumstances, but with stable reporting patterns and continuous evaluation, nowcasts can be a reliable and valuable public health tool.

BackgroundSevere dengue remains difficult to predict because patients with different clinical trajectories may present with overlapping features, and conventional severity classifications may not fully capture underlying biological heterogeneity. In this study, we applied an integrated clinical and proteomic endotyping approach to dissect dengue disease heterogeneity and identify molecular signatures associated with severity. MethodsPlasma proteomic profiles were analyzed together with detailed clinical, biochemical, hematological, coagulation, and immunological parameters from healthy controls and dengue patients classified according to WHO 2009 severity criteria. High-throughput proteomic analysis, unsupervised clustering, pathway enrichment, and machine-learning-based classification were used to identify dengue endotypes and define molecular features associated with predicted severe disease. ResultsIncreasing dengue severity was associated with progressive abnormalities in liver function, coagulation parameters, hematological indices, and inflammatory mediators, including IL-6, IL-15, HGF, and MUC-16. However, proteomic profiling revealed substantial overlap across conventional severity categories, indicating that clinical classification alone does not fully resolve dengue host-response heterogeneity. Integrated clinical-proteomic clustering identified distinct dengue endotypes, including a predicted severe endotype enriched for inflammatory, antiviral, and cytotoxic lymphocyte-associated pathways. This high-risk endotype was characterized by elevated IL-15, IFN-{gamma}, and granzymes, consistent with coordinated activation of cytotoxic lymphocyte-associated antiviral responses. Machine-learning analysis further showed that proteomic features were strong discriminators of this endotype, supporting their potential utility as biomarkers of severe host-response states. ConclusionIntegrated clinical-proteomic endotyping provides molecular resolution beyond conventional severity grading and identifies immune pathways associated with severe dengue. This framework may improve biological understanding of dengue progression and support future risk stratification and biomarker development.