Infectious Diseases of Poverty

BackgroundDengue fever is a major neglected tropical disease with a rapidly rising global burden, and localized outbreaks are increasingly reported in southern subtropical China. Fujian Province, a coastal subtropical region with favorable ecological conditions for Aedes albopictus breeding and frequent cross-border exchanges with dengue-endemic areas, has had continuous local dengue cases for over a decade, raising concerns about the establishment of a stable natural endemic focus. Sustained local dengue transmission is defined by four core criteria, but no systematic assessment of these criteria has been conducted for Fujian using long-term multi-dimensional surveillance data. We aimed to evaluate whether a natural endemic focus for sustained local dengue transmission has been established in Fujian Province from 2014 to 2024 using four core evidence dimensions. MethodsWe extracted data on imported and locally acquired dengue cases in Fujian from 2014 to 2024 from Chinas National Notifiable Disease Reporting System (NNDRS). Serological surveillance for dengue IgG antibodies and virological surveillance for dengue virus in Aedes albopictus were conducted at seven sentinel sites. The study period was stratified into three phases based on the impact of COVID-19 non-pharmacological interventions: pre-pandemic (2014-2019), pandemic(2020-2022), and post-pandemic(2023-2024). Descriptive epidemiological analysis and data visualization were performed using R software (version 4.4.1), with t-tests for continuous variables and {chi}{superscript 2} tests for categorical variables. ResultsA total of 3,606 dengue cases were reported in Fujian during the study period, including 1,229 imported and 2,377 locally acquired cases. Key findings were as follows: (1) Temporal distribution: Local dengue transmission was completely interrupted during the 2020-2022 COVID-19 pandemic (0 local cases, only 26 imported cases), and resumed at a low level in 2023-2024 (160 local cases). (2) Serology: The overall population dengue IgG antibody positivity rate was 4.2% (66/15,736), with no statistically significant difference between pre-epidemic (3.8%, 30/7,835) and post-epidemic seasons (4.5%, 36/7,901; P=0.48), and no year with a positivity rate exceeding 10%. (3) Vector surveillance: Only one dengue virus-positive sample was detected among 385,000 Aedes albopictus mosquitoes collected during routine surveillance (Taijiang District, Fuzhou, October 2017), with no viral nucleic acid detected in all other samples. (4) Age distribution: The mean age of locally acquired cases (46.1{+/-}19.8 years) was significantly higher than that of imported cases (35.8{+/-}11.2 years, P<0.001), and local cases were concentrated in the middle-aged group (40-60 years) with no child-dominant pattern observed. ConclusionsFujian Province has not established a stable natural endemic focus for sustained local dengue transmission, and imported cases are the primary driver of local outbreaks in the region. Strengthened surveillance and early management of imported cases, integrated vector control targeting Aedes albopictus, and targeted public health education are critical and essential strategies to prevent the establishment of a dengue natural endemic focus in Fujian and other subtropical coastal regions with similar epidemiological characteristics. Author SummaryDengue fever is a rapidly spreading neglected tropical disease worldwide, and southern China faces persistent threats of local transmission due to favorable ecological conditions for mosquito breeding and frequent cross-border travel. Fujian Province, a subtropical coastal region in southeastern China, has reported annual local dengue cases for over a decade, raising public health concerns about the potential establishment of a stable natural endemic focus--where the virus circulates sustainably without relying on imported cases. To address this critical question, we conducted a comprehensive 11-year assessment (2014-2024) of dengue transmission in Fujian using four key evidence dimensions defined for identifying dengue endemic foci: the continuity of local cases independent of imported sources, population antibody levels, dengue virus detection in local mosquitoes (Aedes albopictus), and the age distribution of infected patients. We also leveraged the COVID-19 pandemic(2020-2022) as a unique natural experiment, during which strict travel restrictions drastically reduced imported dengue cases, to test whether local transmission could persist on its own. Our findings showed that local dengue transmission in Fujian completely stopped during the COVID-19 pandemic and only resumed when cross-border travel and imported cases recovered, confirming local transmission is entirely dependent on imported virus sources. Additionally, the local population had a very low dengue antibody positivity rate (4.2%), dengue virus was detected in only one mosquito sample over 11 years of surveillance, and local cases were concentrated in middle-aged adults (not children--the typical group affected in endemic areas). Together, these results confirm that Fujian Province has not established a stable natural endemic focus for dengue fever. While no endemic focus exists yet, Fujian remains at high risk of imported-driven local outbreaks due to its climate and cross-border exchanges. Our study highlights three critical strategies to prevent the future establishment of a dengue endemic focus in Fujian and other similar subtropical coastal regions: strengthening surveillance and early response for imported dengue cases, implementing targeted mosquito control measures during peak transmission seasons, and conducting public health education to raise awareness of dengue prevention. These evidence-based interventions are key to blocking the formation of sustained local dengue transmission and protecting regional population health.

BackgroundBoth hemorrhagic fever with renal syndrome (HFRS) and scrub typhus (ST) are acute zoonotic infectious diseases. There is an overlap in their epidemiological characteristics and clinical manifestations, posing challenges for early differential diagnosis. This study aims to identify predictive factors for these two diseases to provide a basis for early diagnosis. Method/FindingsA retrospective analysis was conducted on the clinical data of patients diagnosed with HFRS and ST at the First Affiliated Hospital of Dali University. Logistic regression analysis was employed to explore independent risk factors for the early differential diagnosis of these two diseases, and a nomogram model was constructed based on these risk factors. The performance of the model was evaluated using the area under the receiver operating characteristic curve (AUC). The nomogram was utilized to visually present the predictive variables. Decision curve analysis (DCA) was performed to assess the clinical utility of the model. ResultsA total of 235 patients each with HFRS and ST were included in this study. After adjusting for confounding factors, the results of multivariate logistic regression analysis revealed that sex (male) (adjusted odds ratio [ajOR]: 2.093, 95% confidence interval [CI]: 1.107 - 3.957, P = 0.018), positive proteinuria (ajOR: 4.937, 95% CI: 2.427 - 10.042, P < 0.001), creatinine (CREA) (ajOR: 1.009, 95% CI: 1.003 - 1.015, P = 0.005), heart rate (ajOR: 0.981, 95% CI: 0.966 - 0.997, P = 0.018), and conjunctival congestion (ajOR: 16.167, 95% CI: 5.326 - 49.072, P < 0.001) were independent risk factors for differentiating HFRS from ST. The AUC of the model constructed based on these five independent risk factors was 0.856. ConclusionSex (male), positive proteinuria, elevated CREA, decreased heart rate, and conjunctival congestion are effective predictive factors.

BackgroundStigma - a procedures with label, stereotype, prejudice, status loss, and discrimination -has long played a role in the spread of HIV since the beginning of the epidemic. However, few researchers conducted on the HIV-related stigma and discrimination for general population in China. Consequently, we introduced translated and adapted the English version of HIV/AIDS Stigma and Discrimination Scale applied for undergraduates in China. ObjectiveThis study aimed to adapt the HIV/AIDS Public Stigma and Discrimination Scale (HPSDS) in China and to investigate its psychometric properties (e.g., reliability and validity). MethodsUsing translation, back-translation, quality evaluation, cross-cultural adaption and pre-assessment, a Chinese draft version of the HPSDS was obtained. From April 2022 to July 2022, the scale was distributed to179 universities and colleges and 2,333 college students filled out the translated and adapted questionnaires. Finally, we collected 1,604 valid questionnaires. The results were recruited to assess the psychometric characteristics of the CV-HPSDS. ResultThe CV-PHSDS consists of 3 dimensions and 14 items with Cronbachs alpha coefficient, McDonalds omega coefficient and test-retest reliability of the scale are 0.869, 0.883 and 0.857 respectively, manifesting good internal consistency and stability. To construct validity of adapted scale, an exploratory factor analysis was conducted with the cumulative variance contribution rate of 76.6% was obtained. For confirmatory factor analysis, the CFI, GFI, TLI, and IFI showed excellent fitness to the structure, with fitness indices of 0.972, 0.949, 0.965, and 0.972, respectively. Finally, a valid and reliable instrument to measure the HIV/AIDS stigma and discrimination level is obtained. ConclusionThe translated and adapted version of HPSDS shows to be a reliable and valid instrument for assessing stigma and discrimination level for undergraduates.

Effective public health planning and intervention strategies necessitate an understanding of the temporal and geographic distribution of disease incidences. This requires robust frameworks for disease incidence forecasting. However, due to variations in cases and temporal dynamics, grasping the distinct patterns of climate-sensitive diseases poses significant challenges, including identifying hotspots, trends, and seasonal variations in disease incidence. Furthermore, although most studies focus on directly predicting future incidence using historical patterns and covariates, a significant gap remains between methodological proliferation marked by diverse architectures, where models are trained and validated on benchmark datasets that are standardized and statistically stable, and epidemiological reality, which is often characterized by irregular, sparse, and highly skewed data, as well as rare but high-magnitude or bimodally distributed incidences. Hence, traditional end-to-end approaches that directly map climate and disease data often fail in these data-scarce settings due to overfitting and poor generalization. To understand disease epidemiology and mitigate the impact of incidence, we analyzed a decade of retrospective datasets in Ethiopia to examine how climate and weather conditions influence the incidence or spread of climate-sensitive diseases, including malaria and dysentery. In this study, we proposed a two-stage hybrid framework, a climate-informed disease prediction model, to forecast the likelihood of disease incidences using decades of climate and weather data. First, deep learning was applied to capture latent weather dynamics. Then, a hurdle model using Extreme Gradient Boosting (XGB) was designed for zero-inflated incidence data, combining XGBClassifier to predict incidence and XGBRegressor to estimate its size, based on weather dynamics to forecast disease incidence. Our proposed multivariate climate-driven disease incidence model incorporates both spatial (elevation, coordinates) and temporal (year, month) factors, along with key weather parameters (precipitation, sunlight, wind, relative humidity, temperature) to predict the likelihood of multiple diseases occurring in each area, serving as a foundation for future disease incidence predictions in the region. Out of 72 evaluated experiments across four categories and six targets, we found that the Transformer model showed highest number of statistically significant wins (n=18, 25.0%) comparison with Long Short-Term Memory (LSTM) (n=9, 12.5%) and the Temporal Convolutional Neural Network (TCN) (n=5, 6.9%) at climate variable forecasting using Pairwise Model Comparison Diebold-Mariano Test. The hurdle model that combines XGBClassifier and XGBRegressor outperformed the baseline in both Malaria and Dysentery forecasting. Error stratification revealed that the hurdle model provided the greatest benefit during incidence periods, as indicated by a substantially lower Mean Average Error (MAE) in both incidence and non-incidence periods than the baseline. Our proposed modular pipeline first forecasts climate variables, then predicts disease incidence, thereby enhancing interpretability and generalization in data-sparse settings. Overall, this approach provides a scalable, climate-aware forecasting tool for public health planning, particularly in regions where these diseases are endemic or where climate change may affect their prevalence, as well as in data-scarce settings.

BackgroundRabies remains a cause of mortality in many low- and middle-income countries, with the majority of human infections resulting from dog-to-human transmission. The Integrated Bite Case Management (IBCM) model is a One Health approach that aims to strengthen rabies surveillance and response by linking the management of human bite cases with investigation of the implicated animals. This study aimed to evaluate the effectiveness of implementing IBCM in Quang Nam Province under existing resource conditions. Methodology/Principal FindingsA pre-post intervention study without a control group was conducted across the entire province. During the intervention period, 11,673 animal-bite cases were recorded; IBCM identified 75 animals suspected of having rabies, of which 40 tested positive for rabies virus by RT-PCR. Most of these animals were unvaccinated, free-roaming dogs. In communes where outbreaks were detected, the average number of registered dogs increased from 507 to 543 per commune, and vaccination coverage increased from 44.1% to 72.6% within 21 days. The average number of Post-exposure prophylaxis (PEP) courses administered per month increased from 349 to 971, the proportion of high-risk exposures increased from 9.3% to 11.9%, and the proportion of delayed PEP ([&ge;]10 days after exposure) rose slightly from 5.9% to 6.6%. At the same time, the proportion of staff with good knowledge of rabies diagnosis in animals increased substantially, from 9.1% to 55.6%. The main limitations included the pre-post design and loss to follow-up of some animals, which prevented laboratory testing. ConclusionThe implementation of IBCM within the existing health and veterinary systems substantially strengthened rabies surveillance and response in accordance with the One Health approach. IBCM was demonstrated to be feasible, resource-appropriate, and scalable, thereby contributing to progress toward the global goal of eliminating human deaths from dog-mediated rabies by 2030. Author summaryRabies is a preventable disease, yet it continues to cause deaths in many countries where dogs remain the primary reservoir and source of infection. In Vietnam, rabies surveillance remains largely separated between the human and animal health sectors. The IBCM model uses human bite cases as the "trigger point" for coordinated investigation of the implicated animals, risk assessment, and information sharing between the two sectors, thereby supporting both clinical decision-making and outbreak response. We implemented the IBCM model in Quang Nam Province and observed an increase in the number of rabid animals detected, a marked rise in dog vaccination coverage in outbreak-affected areas, and substantial improvement in the knowledge and capacity of both health and veterinary staff. Simply by strengthening collaboration and information sharing between the two sectors, the rabies surveillance system became more sensitive and effective. This represents a practical example of One Health approach in action.

A field trial was conducted using 10% lambda-cyhalothrin microcapsule suspension to provide a method for killing ticks and preventing diseases in outdoor gatherings of people or temporary resettlement places after disasters. In this study, three field experimental sites were selected, and each experimental site was set up with a test area and a control area. Before pesticide application, the tick density in three test areas and three control areas was surveyed using the flagging method. Subsequently, two methods were used for pesticide spraying: motorized fogging and electric constant-volume spraying (with the pesticide diluted 300 times). The relative density decline rate of ticks was calculated in three test sites on days 1, 7, 14, 21, and 28 after spraying, and all experimental areas achieved good tick-killing effects. Even without prohibiting wild animals, grazing sheep, and dogs (which are often infested with ticks and not treated) from entering the trial sites, spraying 10% lambda-cyhalothrin microcapsule suspension could maintain a tick-free (low-density) state for approximately 3-4 weeks. Our study provides an idea for controlling epidemics through tick elimination during the high incidence period of tick-borne diseases.

BackgroundThe dynamics of HIV and ZIKV coinfection among pregnant women remain understudied, and its impacts on neonatal health still need to be defined. This gap is particularly concerning given the significant public health risks it can cause, especially in Latin America and the Caribbean, where the Zika virus is still circulating. MethodsWe conducted a transversal ecological study using aggregated data from 2015 to 2023. To do so, we developed a compartmental model that included a Susceptible-Infected-Recovered (SIR) compartment for pregnant women related to HIV and ZIKV infection status, as well as SI compartments for their newborns and ZIKV-carrying mosquitoes to perform simulations representing different epidemiological scenarios. We calculated the HIV/ZIKV basic reproduction number R0. Sensitivity analysis was performed to identify the model parameter with the greatest impact on this metric. Finally, we applied personal and sexual protection, medical treatment for Zika, and antiretroviral therapy as control measures against viral infections to evaluate the most effective strategy to improve neonatal health outcomes. ResultsThe basic reproduction number [R]0 associated with HIV/ZIKV coinfection among pregnant women varied in the range [0.09, 1.29] in the countries studied. The sensitivity analysis revealed that the [R]0 was most sensitive to the mosquito biting rate and the pregnant womens death rate. ZIKV infection rate among pregnant women had a greater impact on the number of newborns with related health problems compared to HIV infection rate. The introduction of ZIKV among pregnant women was enough to cause a surge in the number of newborns with related health issues, demonstrating a greater impact than HIV on neonatal health. When control strategies were applied to the model, simulations demonstrated that their application needed to be maintained concurrently over time and that medical treatment of Zika was the measure having the least influence on the coinfection. ConclusionSince pregnant women infected with HIV are particularly vulnerable to other infections such as ZIKV, it is crucial to improve antenatal care among them. Continued monitoring and increased prevention of HIV sexual transmission is also necessary to ensure maternal and child health. In order to optimize public health interventions, it is important to extend the research to strengthen our understanding of the implications of HIV-ZIKV coinfection among pregnant women and its effects on their children.

Malawis HIV treatment monitoring system faces serious challenges because of a shortage of experts and reliance on viral load testing every 3 to 12 months. The process causes dangerous delays in identifying treatment failure. This leads to a higher risk of disease progression, transmission, and death. To tackle this issue, this study used a machine learning model based on association rules and combined it with clustering analysis to create a machine learning framework to identify key factors and risk profiles for virological failure among children living with HIV (CLHIV) in Malawi. The methodology combines a Random Forest classifier for feature importance, association rule mining to find predictive rules, and k-Prototype clustering for risk profiling among CLHIV. The random forest feature importance results show that Body Mass Index (BMI), CD4 count, TB status, ART regimen, gender, ART adherence, and treatment duration are major drivers of virological failure. In addition to these individual factors, the analysis produced highly reliable association rules with over 90% confidence. This establishes a framework for identifying complex risk profiles and informing focused clinical interventions. The high lift values of 4.9 across the most significant rules demonstrate the models effectiveness by revealing strong, non-random associations. Clustering analysis also identified two distinct risk profiles associated with virological failure. The k-prototype clustering model performed optimally with a cluster purity of 100% and a silhouette score of 79%.

Mainland China experienced multiple waves of COVID19 pandemic during 2020 2022, driven by emerging variants and changes in public health and social measures (PHSMs). We developed a hypergraph-based Susceptible Vaccinated Exposed Infectious Recovered Susceptible (SVEIRS) model to reconstruct epidemic dynamics across 31 provinces, capturing transmission heterogeneity associated with clustered contacts. We assessed key characteristics of transmission at national and provincial levels during four outbreak periods: initial, localized predelta, Delta, and widespread Omicron, which accounted for 96.7% of all infections. We found significant diversity in transmission contributions across cluster sizes, with a small fraction of larger clusters responsible for a disproportionate share of infections. Counterfactual analyses showed that reducing clustersize heterogeneity, while holding overall exposure constant, could have lowered national infections by 11.70 to 30.79%, with the largest effects during Omicron period. Ascertainment rates increased over time but remained spatially heterogeneous with a range: (14.40, 71.93)%. Population susceptibility declined following mass vaccination (to 42.49% in Aug 2021, nationally) and rebounded (to 89.89% in Nov 2022) due to waning immunity with variations across the provinces. Effective reproduction numbers displayed marked temporal and spatial variability, with higher estimates during Omicron. Overall, these results highlight critical role of group contact heterogeneity in shaping epidemic dynamics.

Based on the 2023 Global Burden of Disease (GBD) database, this study analyzed the global burden of preterm birth from 1990 to 2023 and predicted its development trend by 2050, while exploring the disparities in disease burden across regions with different Socio-demographic Index (SDI) levels, income groups and countries. A retrospective trend analysis was conducted to collect data on preterm birth incidence, prevalence, death and disability-adjusted life years (DALYs) in 204 countries and regions worldwide from 1990 to 2023 from the GBD 2023 database. ARIMA model (p=2,d=1,q=1) and grey prediction model (GM(1,1)) were combined to predict the preterm birth burden from 2023 to 2050. In 2023, preterm birth was the primary cause of the global neonatal disease burden, with its four core indicators significantly higher than other neonatal diseases. From 1990 to 2023, the global incidence, death and DALYs of preterm birth decreased to 0.91, 0.44 and 0.52 times of the 1990 levels respectively, while the prevalence increased to 1.54 times of the baseline. Projection results showed that by 2050, the incidence, death and DALYs of preterm birth would drop to 0.79, 0.08 and 0.32 times of the 2023 levels, and the prevalence would rise to 1.23 times of 2023. Low SDI regions, lower-middle income countries, as well as India and Nigeria, bore the heaviest disease burden. Over the past three decades, the global acute health burden of preterm birth such as death has decreased notably, but the continuous rise in prevalence and severe regional and age disparities remain prominent public health challenges. The 0-6 days and 6-11 months age groups are the key time windows for preterm birth intervention. It is urgent to implement targeted prevention and control measures for low SDI regions and lower-middle income countries to reduce the global burden of preterm birth.

The global ambition to end the human immunodeficiency virus (HIV) epidemic requires understanding which system-level policy levers, enacted under the framework of Universal Health Coverage (UHC), are most effective in achieving both transmission reduction and diagnostic coverage. This study addresses an important evidence gap by quantifying the within-country association between measurable UHC policy indicators and the estimated rate of new HIV infections across nine Southeast Asian countries between 2013 and 2022. Employing a Fixed-Effects panel data methodology, the analysis controls for time-invariant national heterogeneity, ensuring reliable estimates of policy impact. We found that marginal changes in total current health expenditure (CHE) as a percentage of gross domestic product (GDP) were not statistically significantly associated with changes in HIV incidence. However, increases in the UHC Infectious Disease Service Coverage Index were statistically significantly associated with concurrent reductions in HIV incidence (p < 0.001), suggesting the efficacy of targeted service implementation as the principal driver of curbing new HIV infections. In addition, the UHC Reproductive, Maternal, Newborn, and Child Health Service Coverage Index exhibited a statistically significant positive association with changes in HIV incidence (p < 0.01), which is interpreted as a vital surveillance artefact resulting from expanded detection and reporting of previously undiagnosed HIV cases. Furthermore, out-of-pocket (OOP) health expenditure as a percentage of CHE showed a counter-intuitive negative association with changes in HIV incidence (p < 0.01), suggesting this metric primarily shows ongoing indirect cost burdens on the established patient cohort, or, alternatively, presents a diagnostic access barrier that results in lower case finding. These findings suggest that policymakers should prioritise investment in targeted infectious disease service efficacy over aggregate fiscal commitment and utilise integrated sexual health platforms for strengthened HIV surveillance and case identification.

IntroductionViral load (VL) testing is the recommended approach for monitoring antiretroviral therapy (ART) effectiveness, while guidelines recommend targeted CD4 testing after ART initiation. This study examined trends in VL and CD4 testing frequencies, as well as the relationship with AIDS diagnosis and mortality among people with HIV in the Asia-Pacific region. MethodsWe included adults enrolled in the Treat Asia HIV Observational Database (TAHOD) between 2003-2018 who had been on ART for [&ge;]1 year. VL and CD4 testing rates were analysed using Poisson regression models. Associations between testing frequency and AIDS diagnosis or mortality were evaluated using Fine and Gray competing risk regression. ResultsAmong 8,446 patients, VL testing rates remained steady at 1 per person-year (PYS) between 2003-2018. Increased VL testing was associated with more frequent CD4 testing (>2 tests in the previous year; IRR=1.57, 95%CI 1.53-1.60), later follow-up years (2008-2012: IRR=1.15, 95%CI 1.12-1.18; 2013-2015: IRR=1.07, 95%CI 1.04-1.10), older age (31-40 years: IRR=1.06, 95%CI 1.03-1.08; 41-50 years: IRR=1.08, 95%CI 1.05-1.11; >50 years: IRR=1.07, 95%CI 1.03-1.11), higher current VL (401-1000 copies/mL: IRR=1.16, 95%CI 1.09-1.24; >1000 copies/mL: IRR=1.07, 95%CI 1.04-1.11), initial ART regimen (NRTI+PI: IRR=1.07, 95%CI 1.04-1.10; other combinations: IRR=1.11, 95%CI 1.05-1.17), and higher country income levels (upper-middle: IRR=2.17, 95%CI 2.11-2.23; high: IRR=3.14, 95%CI 3.03-3.26). CD4 testing rates decreased from 2.04 to 1.06/PYS over the same period. Lower CD4 testing frequency was associated with HIV exposure mode (MSM: IRR=0.94, 95%CI 0.92-0.96; IDU: IRR=0.93, 95%CI 0.90-0.97; other/unknown: IRR=0.90, 95%CI 0.87-0.93), higher current CD4 (201-350 cells/{micro}L: IRR=0.95, 95%CI 0.93-0.97; 351-500 cells/{micro}L: IRR=0.89, 95%CI 0.87-0.91; >500 cells/{micro}L: IRR=0.85, 95%CI 0.83-0.87) and receiving an NRTI+PI first-line combination (IRR=0.96, 95% CI 0.94-0.98). VL and CD4 testing frequencies were not significantly associated with AIDS diagnosis. However, having > 2 CD4 tests in the previous year was associated with higher mortality risk. ConclusionThe trends in the rates for CD4 and VL testing in the region between 2003-2018 were significantly affected by demographic, clinical and socio-economic factors. Recognizing these factors is critical to optimizing differentiated monitoring strategies and improving outcomes for PWH in the region.

BackgroundNigeria accounts for a significant share of global maternal mortality, and HIV remains a public health threat. Gombe State in northeastern Nigeria contends with profound barriers to healthcare access. This study evaluated the effectiveness of a community-based intervention using trained Community Health Workers (CHWs) to improve early identification of pregnancy and linkage to Antenatal Care (ANC) and HIV services. MethodsA quasi-experimental design was employed across six local government areas (LGAs) from January 2020 to June 2021. Three LGAs were randomly assigned to the intervention, where CHWs conducted home visits for pregnancy identification, health education, and referral facilitation. Three control LGAs received standard facility-based care. Data were collected via household surveys and facility records at baseline and endline. Analysis included Difference-in-Differences (DiD) estimation to determine the net intervention effect. ResultsThe intervention group showed significant improvements compared to the control. Early pregnancy identification (<20 weeks) increased from 45% to 78% (DiD: +29 pp, p<0.001). Attendance of at least one ANC visit rose from 58% to 85% (DiD: +22 pp, p<0.001), reducing the coverage gap by 89%. Subgroup analysis revealed the largest gains among adolescents (DiD: +31 pp) and rural residents (DiD: +27 pp). HIV testing uptake increased from 52% to 90% (DiD: +34 pp, p<0.001). Linkage to care for HIV-positive women improved from 65% to 92% (p=0.002). ConclusionA CHW-led, community-based strategy is highly effective in improving early engagement with ANC and HIV services in resource-limited settings. The intervention demonstrated a strong equity-promoting effect. Integration and scale-up of this model within primary healthcare systems is recommended.

IntroductionCholera has been a major public health concern in many parts of the world. The development of the low-cost Oral Cholera Vaccine (OCV) enabled creation of the OCV stockpile by the World Health Organization and Gavi, the Vaccine Alliance which have provided vaccine to cholera-affected countries since 2013. The current study aims to measure the impact of the OCV stockpile deployment on global health. MethodsDetailed OCV stockpile data were obtained and used to determine the size and timing of vaccination using a transmission model across all recipient countries over time. The model first estimated the impact of vaccination based on actual shipments. Considering several challenges including time lags until vaccination and the shortage of OCVs observed during the OCV stockpile implementations, multiple scenarios, so called what if scenarios were further investigated to provide guidance for the future decision-making processes on the OCV stockpile use. ResultsWith the actual OCV shipment scenario, vaccination prevented 8.1 million cases and 146,013 deaths which resulted in 6.7 million DALYs averted. If there were neither time lags nor the supply shortages, vaccination would avert 17.4 million cases, 321,730 deaths, and 15.1 million DALYs. The economic burden of cholera reduced by vaccination was estimated to be US$1.05 billion with the actual OCV shipment and US$2.33 billion without having any constraints. ConclusionOur models indicate that deployment of OCV from the stockpile may have had a significant positive impact on cholera-affected countries since its inception. Overcoming existing challenges resulting in delays in campaign implementation and shortage of vaccine will help maximize the impact of vaccination. KEY MESSAGESO_ST_ABSWhat is already known on this topicC_ST_ABSCholera outbreaks are currently ongoing in multiple countries, with the African and Eastern Mediterranean regions experiencing the most severe impacts. The Oral Cholera Vaccine (OCV) stockpile was established by World Health Organization (WHO) and Gavi since 2013 and has been widely used to control cholera outbreaks globally. Existing modeling studies assessed the potential impact of OCV and reported that OCV campaigns can contribute to effective cholera control in the near term. However, while the global OCV stockpile has been available since 2013 supporting more than 30 cholera-endemic countries, the studies mostly focused on a specific time period in selected geographical locations, missing the holistic impact of the global OCV stockpile. We further reviewed existing daily OCV stockpile shipment records thoroughly and identified that the program faced several challenges in meeting urgent demands due to administrative delays and OCV supply shortages. What this study addsWe evaluated the actual impact of deployed doses on cholera morbidity, mortality, and economic burden since the inception of the OCV stockpile in 2013. Our model further investigated what the impact of OCV would have been with no delays in implementation and no supply shortage by incorporating the detailed daily shipment data which included the number of OCV doses and dates for request, decision (approval), and delivery. The current analyses show that from 2013 to 2025, both reactive and preventive vaccination campaigns implemented through the global OCV stockpile averted more than 6.7 million DALYs, 146,013 deaths, and US$1.05 billion of the economic burden in 37 recipient countries. Our model demonstrates that vaccination impact would be further enhanced if there were no delays in the implementation of campaigns and if there were no shortage of OCV supplies. How this study might affect research, practice or policyTo our knowledge, no previous study has assessed the holistic contribution of the global OCV stockpile and investigated the optimal outcomes which may have been partially lost due to programmatic and logistical challenges. Since its inception, the impact of the OCV stockpile on global health has been substantial, and the additional benefits are expected if cholera vaccines can be deployed on time and if there is no supply constraint. In particular, the benefits induced by improving timing of vaccination does not necessarily come with additional costs associated with vaccine supplies which are known to be the major cost driver. In addition, because all stockpile recipient countries are currently required to implement a single-dose OCV campaign due to the shortage of OCVs, the continuous support for a two-dose vaccination campaign would bring much larger benefits than the single-dose vaccination scheme given a single-dose vaccination with the OCV is known to be much less efficacious among young children. Considering ongoing cholera outbreaks across multiple regions and other cholera vaccine candidates in development, our findings can be used not only to help provide guidance for the decision-making process on the future OCV stockpile deployment, but also to understand the optimal usage and potential impacts of multiple cholera vaccines which can be administered by different strategies in the future.

AbstractsO_ST_ABSBackgroundC_ST_ABSAccurate dengue forecasting is vital for public health preparedness. Despite a surge in forecasting approaches, a quantitative ranking of the relative performance and practical utility of dengue forecasting is lacking. MethodsA systematic review and Network Meta-Analysis (NMA) of studies comparing dengue forecasting methods (2014-2024) was conducted. Models were categorised into five groups: Time Series, Deep Learning (DL), Machine Learning (excluding DL), Hybrid, and Ensembles. NMA was applied to the logarithm of the most common forecast error metric to rank relative performance--an "Implementability Score" quantified analyst and data requirements, and computational costs. Results59 studies were included. NMA of Root Mean Squared Error identified k-Nearest Neighbour (k-NN) models as achieving the highest predictive accuracy, followed closely by Vector Autoregression, Kalman Filtering, Generalised Linear Model and Autoregressive Neural Network (ARNN). While DL models showed high potential, they scored lowest in implementability due to poor interpretability and high data requirements. Most studies utilised meteorological covariates, with significant gaps in the use of socio-economic and entomological predictors. ConclusionsAlthough there was some trade-off between accuracy and implementability, traditional statistical models were often comparable in accuracy to machine learning approaches, with advantages in interpretability and data needs. Under-explored areas for future research include the use of ensemble models and the use of socio-economic and entomological data. RegistrationPROSPERO CRD420251016662. Author SummaryDengue is a critical global health threat affecting the worlds population. While many forecasting models exist to help officials prepare for outbreaks, there has been no standardised way to compare their performance. This leaves health experts in resource-limited areas uncertain about which tools are truly reliable or easy to use under their specific local conditions. We conducted a network meta-analysis of studies comparing dengue forecasting methods accuracy, grouping them into five categories: Machine Learning, Deep Learning, Time Series, Ensemble, and Hybrid. Beyond ranking their accuracy, we developed an "Implementability Score" to evaluate the practical feasibility of each model, accounting for technical complexity, data requirements, and software accessibility. Our analysis identified the top-performing models. Notably, traditional statistical models often performed as well as complex Deep Learning algorithms. While advanced models show potential, they are often difficult to implement or explain to decision-makers. There is no "one-size-fits-all" solution; the best model depends on capacity and data in each setting. This study provides a roadmap for public health officials to select tools that are both accurate and feasible.

Sylvatic Yellow Fever (YF) is an infectious mosquito-borne disease with significant epidemiological relevance due to its widespread distribution and high lethality for human and non-human primates, particularly in tropical regions of the planet such as in Brazil. Identifying regions and periods of high environmental suitability for the occurrence of YF is essential for preventing or mitigating its burden, as it enables the efficient allocation of surveillance efforts, prevention, and implementation of control measures. Environmental modeling of YF occurrence has proven to be an effective approach toward this goal; however, its effectiveness strongly depends on the modeling framework's capabilities as well as the spatial and temporal precision of all associated data. We propose a fine-scale geospatial modeling of YF environmental suitability that is based on a generative machine-learning ensemble method built on a large set of high-resolution environmental covariates. First, we take the spatiotemporal statistical description of the environment of each of the 545 YF cases from 2019--2024 up to 30 m/monthly resolution at three buffer scales: 100 m, 500 m, and 1000 m ratios. Then, we perform a feature selection and train hundreds of One-Class Support Vector Machine submodels to form a robust ensemble model, whose predictions are projected to a 1x1 km resolution grid of Brazil under several metrics, exceeding seven million ensemble evaluations. The predictions ranked the Southern Brazil region with the highest mean suitability for YF, with a level of 0.64; Southeast comes next with 0.46, followed closely by Central-West region (0.44), North (0.39), and finally Northeast (0.28). The model exhibited high uncertainty for the North region, indicating that data collection efforts are much needed in this region. As for the environmental covariates, a feature analysis pointed out that Land use and cover accounts for the largest influence in the model output.

People who inject drugs (PWID) in India continue to experience high HIV incidence while coverage of HIV and harm reduction services within this population remains suboptimal in many settings, highlighting the need to identify novel service delivery points. To evaluate the effectiveness of spatially focused upscaling of interventions at observed venues where PWID injected drugs together, we developed an individual-based dynamic transmission model of HIV informed by detailed injection network, service engagement, and injection venue attendance data collected in a sociometric study of PWID (n = 2512) in New Delhi, India. HIV incidence was simulated for different spatial targeting strategies and with increasing service coverage at injection venues according to UNAIDS/UNODC goals. We identified significant decreases in predicted HIV incidence when deploying interventions at frequently visited injection venues (from 6.8 cases/100 person-years to 2.7/100PY for full service coverage at the most-visited venue, and further down to 1.3/100PY for 12 most-visited venues). Prioritizing the most visited venues stratified by spatial clusters provided services to a larger number of individuals versus prioritizing the overall most visited venues, suggesting that service expansion at venues that are spatially distinct with minimal population overlap has a slightly larger impact on reducing HIV incidence.

Female sex workers (FSW) in sub-Saharan Africa experience disproportionately high risks of HIV infection. Mathematical models are widely used to assess the contribution of sex workers and other key populations to HIV transmission dynamics and to inform targeted programmes. However, many rely on simplifying assumptions, such as stable sex worker characteristics and constant HIV transmission risk over time. These assumptions may be unrealistic and could bias modelled estimates. We used the South African Thembisa model to assess how alternative assumptions about FSW age, duration of sex work, and client-to-FSW transmission risk affect modelled HIV outcomes. We compared six scenarios that combined constant and increasing FSW age and sex work duration with constant and early-epidemic declining (exponentially or exposure-dependent) transmission risk. Each scenario was calibrated to HIV prevalence data from population-based and sex worker-specific surveys. Scenarios that allowed both FSW characteristics and transmission risk to vary over time showed the best agreement with external data, most closely reproducing HIV incidence, prevalence, and viral suppression estimates from a 2019 national sex worker survey (incidence [~]5 per 100 person-years, prevalence 61-62%, viral suppression [~]60%), and producing incidence rate ratios more consistent with estimates from the broader eastern and southern Africa region. By contrast, the scenario assuming constant FSW characteristics and transmission risk overestimated HIV incidence and underestimated prevalence and viral suppression. At the same time, this time-invariant specification attributed a much larger share of new HIV infections to sex work, with commercial sex work accounting for more than 20% of new infections in 2025, compared with 9-13% under time-varying assumptions. Overall, our findings show that HIV model estimates for sex workers are highly sensitive to modelling assumptions. Incorporating time-varying FSW parameters yields estimates that are more consistent with empirical data and support more reliable programme planning and evaluation. Author SummaryFemale sex workers in sub-Saharan Africa face much higher risks of HIV infection than other women. Mathematical models are often used to understand why and to guide prevention programmes. Yet many of these models make simple assumptions about sex workers - for example, that their average age stays the same over time, that they spend a fixed number of years in sex work, or that the chance of HIV passing from a client to a sex worker never changes. In reality, these factors changed over time. In this study, we used South Africas national HIV model to test how changing these assumptions affects the results. We compared different versions of the model and checked which ones best matched national sex worker survey data. We found that the model worked better when we allowed sex workers to become older over time, to spend longer in sex work, and the risk of passing on HIV to decline. Our findings show that mathematical models can give very different answers depending on how they represent the lives and experiences of sex workers. More realistic assumptions lead to more accurate estimates and can help ensure that programmes focus support where it is most needed.

BackgroundHIV exposed infants (HEIs) are at a higher risk of infant mortality compared to their counterparts who are not HIV exposed. Early Infant Diagnosis (EID) is the critical first step in reducing HIV-related infant mortality through prompt identification of HIV-infected infants and subsequent initiation of antiretroviral therapy. However, there is limited information on Uptake of EID and factors associated with its timely completion among HIV exposed infants. Therefore, this study aimed at determining the uptake of EID and factors associated with its timely completion among HIV exposed infants at Lira Regional Referral Hospital (LRRH). MethodsThe study was a retrospective cohort of 252 HEIs born in the period of 1st January 2021 to 31st December 2021 chosen through consecutive sampling. Data abstraction tools were used to collect data on uptake of 1st, 2nd, 3rd DNA-PCR and final rapid test from mother-baby pair files and EID register. The main outcome was Uptake of EID and classified as timely and untimely according to the PMTCT guideline. Data was analyzed using descriptive statistics and generalized estimating equations (GEE) with poisson family, log link and unstructured correlation structure. ResultsThe timely uptake of EID among HIV exposed infants at 4-6 weeks, 9 months, 6 weeks after cessation of breastfeeding and 18 months were 80.1% (95% CI:74.5-84.7), 84.2% (95% CI:79.0-88.3), 3.7% (95% CI:2.0-7.0) and 78.8% (95% CI:73.2-83.6) respectively. Having cotrimoxazole given was associated with timely completion of EID [aRR=2.974, 95% CI (1.45-6.10)] ConclusionUptake of EID among HEIs was sub-optimal, below the Ministry of Healths 90% target. Timely cotrimoxazole administration was associated with EID completion,

Monkeypox viral disease has been and continues to be a global public health concern. Currently, there are existing, though minimal measures to manage mpox and any future outbreaks. Relying on data-driven modeling for early detection of mpox and prediction of possible cases and deaths in the presence of an outbreak is thus imperative. The present study forecasted global mpox virus cases and deaths in Asia, Africa, Australia, Europe, North America, Oceania, and South America. Three forecasting models (deep neural network, gradient boosting, and polynomial regression) were trained on data from the seven geographical regions. The performance of the three models was assessed using coefficient of determination, mean squared error, root mean squared error, and mean absolute scaled error across each region. Prediction using the deep neural network revealed a potential of higher mpox deaths in Africa and higher mpox cases in South America. Prediction using gradient boosting showed a potential of mpox deaths in Africa and higher mpox cases in Asia and North America. Prediction using polynomial regression revealed a potential of higher mpox deaths in Africa and Asia while rapid rises in mpox cases from 2025 to 2028 were anticipated in all regions except Asia in case of a monkeypox outbreak. For the three models, the tree-based ML model (gradient boosting) outperformed the statistical model and deep learning model by R2 and MSE in predicting mpox case counts across all the seven geographical regions. This study showcases the worth in using data-driven modelling to predict emerging and re-emerging infectious diseases such as mpox.