Infectious Disease Modelling

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has continuously evolved since its emergence in the human population in 2019. As of 1st August 2025, more than 1,700 Omicron subvariants have been designated by the Pango nomenclature system. The Pango nomenclature system designates a new lineage based on genetic and epidemiological information of SARS-CoV-2 strains. However, there is a possibility that strains that have similar genetic backgrounds and the same phenotype are given different Pango lineage names. In this paper, we propose a new algorithm, called FindPart-w, which can identify groups of viral lineages that share the same relative effective reproduction numbers. We introduced a new lineage replacement model, called the constrained RelRe model, which constrains groups of lineages to have the same relative effective reproduction numbers. The FindPart-w algorithm searches the equality constraints that minimise the Akaike Information Criterion of constrained RelRe models. Using hypothetical observation count data created by simulation, we found that the FindPart-w algorithm can identify groups of lineages having the same relative effective reproduction number in a practical computational time. Applying FindPart-w to actual real-world data of time-stamped lineage counts from the United States, we found that the Pango lineage nomenclature system may have given different lineage names to SARS-CoV-2 strains even if they have the same relative effective reproduction number and similar genetic backgrounds. In conclusion, this study showed that viruses that had the same relative effective reproduction number were identifiable from temporal count data of viral sequences. These findings will contribute to the future development of lineage designation systems that consider both genetic backgrounds and transmissibilities of lineages.

Despite the evolution of epidemiological analysis and modeling tools, difficulties still remain, especially in developing countries, regarding the availability and use of these tools. Often expensive, requiring high technical expertise, demanding constant connectivity of several or sometimes even significant resources, these tools, although efficient, present a major gap with the operational realities of health districts. It is in this context that we introduce Episia, an open-source Python library designed and conceived to provide a framework to facilitate epidemiological analysis and modeling. It integrates a suite of compartmental epidemic models (SIR, SEIR, SEIRD) with a sensitivity analysis using the Monte Carlo method, a complete biostatistics suite validated against the OpenEpi reference standard, as well as a native DHIS2 client for automated data ingestion. Developed in Burkina Faso, it is optimized and aims not only to address these health challenges encountered in Africa but also remains a versatile tool for global health informatics.

There is an ongoing Oropouche Fever (OF) outbreak in Brazil since 2024. There are dengue and chikungunya prediction models available, but none to help discriminate dengue, chikungunya, and OF. Objective: This study aims to develop and validate clinical prediction models for dengue, chikungunya, OF. Methods: This study uses surveillance data from Espirito Santo state / Brazil, from 2023-2025. Epidemiological investigations and biological samples were used to conclude cases as either (a) clinical-epidemiologically confirmed, (b) laboratory confirmed, or (c) discarded. The predictors were all data related to signs, symptoms, and comorbidities available in the notification forms. The analysis was performed using random forest regression models, one for each outcome, in development and validation datasets. Results: A total of 465,280 observations were analyzed, 261,691 dengue cases (56.6%), 18,676 chikungunya cases (4.0%), 12,174 OF cases (2.6%), and 179,115 discarded cases (38.6%). All three models had good discrimination and moderate to good calibration after scaling prediction. The models retained from 26 to 16 predictors each. Leukopenia and vomiting were the most discriminatory predictors for dengue, arthritis, arthralgia, and rash were the most discriminatory for chikungunya, and epidemiological features were the most relevant for OF. The dengue, chikungunya, and OF models had ROC AUC of 0.726, 0.851, and 0.896 in the validation set, respectively. Conclusion: This research identified predictors most discriminative between dengue, chikungunya, and OF. We developed and validated predictive models, one for each condition, with moderate to very good performance available at https://pedrobrasil.shinyapps.io/INDWELL/. One may use them in diagnostic work-up and arbovirus surveillance.

Despite the remarkable progress of artificial intelligence represented by large language models, how AI technologies can contribute to the construction of evidence in evidence-based medicine (EBM) remains an overlooked issue. Now, we need an AI that can be compatible with EBM. In the present paper, we aim to propose an example analysis that may contribute to this approach using variable Vision Transformer.

Background Tuberculosis (TB) remains a critical public health challenge, with two-thirds of the global TB burden in ten Asian countries. Social vulnerabilities, comorbidities, health inequity, multi-dimensional poverty, malnutrition, and barriers to healthcare access continue to fuel TB epidemic. Inability to detect asymptomatic and sub-clinical TB, combined with passive approach in service delivery and overreliance on smear microscopy, leads to delayed diagnosis, a substantial burden of undetected cases, and continuing TB transmission in the communities. In such a context, the introduction and scale-up of active case-finding approaches - including community-based TB screening using highly sensitive screening tools and novel rapid diagnostics - becomes a strategic priority to interrupt transmission. The growing availability of multiple screening and diagnostic options makes evidence-based decision-making increasingly complex. Methods To estimate the potential epidemiological impact and cost implications of scaling up TB diagnostics and community-based screening in ten high-burden Asian countries, we constructed a mathematical model and evaluated multiple intervention scenarios. We then assessed and compared four service delivery models: 1) digital ultraportable chest x-ray (UPCXR) & Xpert/Truenat in community, 2) digital UPCXR in community and Xpert/Truenat at health facilities, 3) digital UPCXR in community and near point of care (nPOC) at health facilities, 4) nPOC in community & Xpert/Truenat at health facilities - for total investment required and projected health benefits for their cost-effectiveness. Results and conclusions The modelling study indicated that strengthening health facility capacity (with enhanced TB screening, expanded molecular diagnostics, reduced loss to follow-up, private sector standard of care, leading to increased treatment coverage & quality of active disease treatment and reduced post-treatment relapse, scale-up of TB preventive treatment (TPT), and provision of nutritional support to 80% of TB patients and their household contacts) can significantly reduce TB incidence and mortality; however, community-wide mass screening remains essential to achieving TB elimination targets . Targeted screening of vulnerable populations demonstrated greater cost-effectiveness than untargeted screening approaches. Achieving the End TB goals will ultimately require an effective TB vaccine with high population-level coverage. AI-enabled digital UPCXR-based screening combined with Xpert/Truenat testing at the community level demonstrated maximum epidemiological impact potential, while the most cost-efficient model is Digital UPCXR in the community combined with nPOC testing at health facilities. An investment of USD 12.7 billion over the next five years in community-level implementation of digital UPCXR and molecular diagnostics could avert an additional 9.8 million TB cases and 1.9 million deaths across ten Asian countries over a ten-year horizon.

Non-pharmaceutical interventions (NPIs) have been important for controlling SARS-CoV-2 transmission, particularly before and during initial vaccine rollout. During the pandemic, the US Centers for Disease Control and Prevention issued isolation and masking guidance in case of COVID-19-like illness, a positive SARS-CoV-2 test, or known exposure to SARS-CoV-2. However, the impact of this guidance on mitigating transmission in office workplaces is unclear. We used a network-based mathematical model to estimate the impact of this guidance on SARS-CoV-2 transmission among office workers and their communities. The model represented social contacts in the home, office, and community. We used data from the CorporateMix study to parametrize social contacts among office workers and calibrated the model to represent the COVID-19 epidemic in Georgia, USA from January 2021 through August 2022. In the reference scenario (58% adherence to guidance among office workers and the broader population), workplace transmission accounted for a small fraction of total infections. Reducing adherence among office workers to 0% increased workplace transmissions by 27.1% and increasing adherence to 75% reduced workplace transmission by 7.0%. Increasing adherence to 75% among office workers had minimal impact on symptomatic cases and deaths; increasing it among the broader population was more effective in reducing office worker cases and deaths. In our model, moderate adherence to recommended NPIs in workplaces was effective in reducing transmission, but increasing adherence had limited benefit given workplaces that have low contact intensity and hybrid work arrangements. These results underscore the public health benefits of community-wide adoption of recommended NPIs.

Understanding the dynamics of HIV epidemics is important to control them effectively. Classical methods that mainly rely on occurrence data are limited by the fact that an unknown part of the epidemic eludes sampling. Since the early 2000s, phylodynamic methods have enabled the estimation of key epidemiological parameters from virus genetic sequence data. These methods have the advantage of being less sensitive to partial sampling and to provide insights about epidemic history that even predates the first samples. In this study, we analysed 2,205 HIV sequences from the French ANRS PRIMO C06 cohort. We identified and were able to reconstruct the temporal dynamics of two large clades that represent the HIV-1 epidemics in the country. Using Bayesian phylodynamic inference models, we found that the first clade, from subtype B, originated in the end of 1970s, grew rapidly during the 80s before decreasing from 2000 to 2015 and stagnating since then. The second clade, from circulating recombinant form CRF02_AG, emerged and spread in the 80s, grew again in the early 2000s, before declining slightly. We also estimated key epidemiological parameters associated with each clade. Finally, using numerical simulations, we investigated prospective scenarios and assessed the possibility to meet the 2030 UNAIDS targets. This is one of the rare studies to analyse the HIV epidemic in France using molecular epidemiology methods. It highlights the value of routine HIV sequence data for studying past epidemic trends or designing public health policies.

Tuberculosis (TB) is prevalent in Uganda and overlaps with a high rate of HIV/TB coinfection. While nearly all hospital-based TB cases in Kampala, the capital of Uganda, show clear TB symptoms, 30% or more of undiagnosed TB cases found through active screening are asymptomatic. Additionally, the host risk factors for TB in Kampala cannot be distinguished from environmental risk factors. These TB-specific challenges are just part of the complexity, especially in areas with high HIV/AIDS burden. Data science techniques, especially Artificial Intelligence (AI) and Machine Learning (ML) algorithms, could help untangle this complexity by identifying factors related to the host, pathogen, and environment, which are difficult to explain or predict with traditional/conventional methods. In this project, we will use health data science approaches (AI/ML) to identify factors driving TB transmission within households and reasons for anti-TB treatment failure. We will utilize the computational resources at Makerere University and available demographic, clinical, and laboratory data from TB patients and their contacts to develop AI and ML algorithms. These will aim to: (1) identify patients at baseline (month 0) unlikely to convert their sputum or culture results by months 2 and 5, thus at risk of failing TB treatment; (2) identify household contacts of TB cases who are at risk of developing TB disease, as well as contacts who may resist TB infection despite repeated exposure to M. tuberculosis. Achieving these objectives will provide evidence that data science methods are effective for early detection of potential TB cases and high-risk patients, thereby helping to reduce TB transmission in the community. The study protocol received approval from the School of Biomedical Sciences IRB, protocol number SBS-2023-495.

In dense urban settings, delays between diagnostic sampling and effective isolation can sustain transmission during peak infectiousness. We define a waiting-window transmission externality arising when infectious individuals remain mobile while awaiting results, formalized as E = N{middle dot}P{middle dot}TR{middle dot}D, where N is daily testing volume, P test positivity, TR transmission during the waiting period, and D turnaround time. Using Monte Carlo simulation and a susceptible-infectious-recovered (SIR) framework, we quantify excess infections per 1,000 tests/day under multiple diagnostic workflows. A surge scenario incorporates positive coupling between TR and D ({rho} = 0.45), reflecting co-occurrence of laboratory saturation and elevated contacts during system stress. Under centralized 48-hour workflows, excess infections reach [~]80 at P = 10% and [~]401 at P = 50%, increasing to [~]628 under surge conditions. In contrast, near-patient rapid testing and home sampling reduce this to [~]5 and [~]25-26, respectively. Workflows that eliminate the waiting window--either through immediate isolation at sampling or through home-based PCR that returns results at the point of collection--effectively collapse the transmission term. These findings identify diagnostic delay as a modifiable driver of epidemic dynamics. Operational redesign of testing workflows, including decentralized sampling and home-based molecular diagnostics, offers a scalable pathway to improve epidemic controllability and reduce inequities in dense urban environments.

Knowing the mortality rates associated with infection by a pathogen is essential for effective preparedness and response. Here, harnessing the flexibility of a Bayesian approach, we produce an estimate of the Infection Fatality Ratio (IFR) for A(H5N1) conditional on explicit assumptions, and quantify the uncertainty thereof. We also apply the method to first-wave COVID-19 data up to March 2020, demonstrating the estimates that could be obtained were the model available then. Our analysis uses World Development Indicators (WDI) from the World Bank, the A(H5N1) WHO confirmed cases and deaths tracker by country (2003-2024), and COVID-19 cases and deaths data from John Hopkins University (January and February 2020). Since infectious disease dynamics are typically influenced by local socio-economic factors rather than political borders, individual countries are placed within clusters of countries sharing similar WDIs relevant to respiratory viral diseases, with clusters derived by performing Hierarchical Clustering. To estimate the IFR, we fit a Negative Binomial Bayesian Hierarchical Model for A(H5N1) and COVID-19 separately. We explicitly modelled key unobserved parameters with informative priors from expert opinion and literature. By modelling underreporting, our analysis suggests lower fatality (15.3%) compared to WHO's Case Fatality Ratio estimate (54%) on lab-confirmed cases. However, credible intervals are wide ([0.5%, 64.2%] 95% CrI). Therefore, good preparedness for a potential A(H5N1) pandemic implies adopting scenario planning under our central estimate, as well as for IFRs as high as 70%. Our approach also returns a COVID-19 IFR estimate of 2.8% with [2.5%, 3.1%] 95% CrI which is consistent with literature.

Background: Nigeria bears one of the highest maternal mortality burdens globally, with skilled birth attendance (SBA) remaining critically low in many regions. Understanding the independent determinants of SBA is essential for designing targeted interventions. Methods: This cross sectional study analyzed 21,465 births from the 2018 Nigeria Demographic and Health Survey (NDHS), a nationally representative household survey using stratified two stage cluster sampling. SBA was defined as delivery attended by a doctor, nurse, midwife, or auxiliary midwife. Multivariable logistic regression was used to estimate adjusted odds ratios (aOR) with 95% confidence intervals for the associations between SBA and maternal education, household wealth, place of residence, geopolitical region, maternal age, parity, and antenatal care (ANC) utilization, after accounting for confounding. Results: The overall prevalence of SBA was 44.9%. In the fully adjusted model, higher education (aOR = 7.01, 95% CI: 5.68-8.67), richest wealth quintile (aOR = 6.27, 95% CI: 5.27-7.46), and attending [≥]4 ANC visits (aOR = 3.80, 95% CI: 3.51-4.11) were the strongest independent predictors of SBA. Regional inequalities were pronounced, with SBA prevalence ranging from 17.7% in the North West to 85.6% in the South West. Crude effect estimates for education and wealth were substantially attenuated after adjustment, indicating large confounding by correlated socioeconomic factors. Conclusions: Maternal education, household wealth, ANC utilization, and geopolitical region are independent determinants of SBA in Nigeria. Scaling up ANC programs represents the most immediately actionable intervention, while long term gains require investment in girls' education and wealth equity. Targeted strategies for the northern regions are urgently needed. Keywords: skilled birth attendance, maternal mortality, Nigeria, DHS, antenatal care, logistic regression, health equity

Introduction: Modeling when influenza epidemics typically occur can help countries optimize surveillance, time clinical and public health interventions, and reduce the burden of influenza. Methods: We used influenza virus detections reported during 2011-2024 by 180 countries to the Global Influenza Surveillance and Response System, excluding COVID-19 pandemic impacted years (2020-2023). We analyzed data by calendar year (week 1-52) or shifted year (week 30-29) time windows, based on when most influenza detections occurred in each country. For countries with sufficient data, we computed generalized additive models (GAMs) of each country's weekly influenza-positive tests to smooth and impute time series distributions. From these GAMs, we calculated each country's normalized weekly influenza burden. Country-specific normalized time series were grouped using hierarchical k-means clustering reducing the Euclidean distance between time series within clusters. We calculated cluster-specific GAMs to estimate average seasonal timing. Countries without sufficient data were assigned to a cluster based on population-weighted latitudinal distance to a cluster's mean latitude. Results: We identified five clusters, or epidemic zones, from 111 countries with sufficient data. The influenza burden in epidemic zones A and B was consistent with a northern hemisphere pattern, with most influenza detections occurring during October-April (A) and September-March (B), while epidemic zones D and E were characterized by southern hemisphere-like seasonal timing, with most influenza burden occurring during May-November. Epidemic zone C had most influenza burden occurring during September-March; most countries assigned to this cluster were in the tropics. Conclusion: Epidemic zones may serve as a useful tool to strengthen and optimize influenza surveillance for global health decision-making (e.g., during vaccine strain composition discussions) and to guide country preparedness efforts for seasonal influenza epidemics, including the timing of enhanced surveillance, as well as the procurement and delivery of vaccines and antivirals.

BackgroundTuberculosis (TB) remains a leading cause of infectious disease mortality worldwide, and treatment failure contributes to ongoing transmission, drug resistance, and poor clinical outcomes. Artificial intelligence and machine learning approaches have attracted growing interest for predicting tuberculosis treatment outcomes, but the literature is heterogeneous and lacks a comprehensive synthesis. MethodsWe conducted a systematic review and meta-analysis of studies that developed or validated machine learning models to predict TB treatment failure. We searched PubMed/MEDLINE and Embase from January 2000 to October 2025. Studies were eligible if they developed, validated, or implemented an artificial intelligence or machine learning model for the prediction of TB treatment failure or a closely related poor outcome in patients receiving anti-TB treatment. Risk of bias was assessed using the Prediction model Risk Of Bias Assessment Tool. Random-effects meta-analysis was performed to pool area under the curve values, with subgroup analyses and meta-regression to explore heterogeneity. ResultsThirty-four studies were included in the systematic review, of which 19 reported area under the curve values suitable for meta-analysis (total participants, 100,790). Studies were published between 2014 and 2025, with 91% published from 2019 onward. Tree-based methods were the most common algorithm family (52.9%), and multimodal models integrating three or more data types were used in 41.2% of studies. The pooled area under the curve was 0.836 (95% confidence interval 0.799-0.868), with substantial heterogeneity (I{superscript 2} = 97.9%). In subgroup analyses, studies including HIV-positive participants showed lower discrimination (pooled area under the curve 0.748) compared to those excluding them (0.924). Only eight studies (23.5%) performed external validation, and only one study (2.9%) was rated as low risk of bias overall, primarily due to methodological concerns in the analysis domain. Eggers test suggested publication bias (p = 0.024). Major evidence gaps included underrepresentation of high-burden countries, HIV-affected populations, social determinants, pediatric TB, and extrapulmonary disease. ConclusionsMachine learning models for predicting TB treatment failure show promising discrimination but are not yet ready for routine clinical implementation. Performance varies substantially across populations and settings, and methodological limitations, including inadequate validation, poor calibration assessment, and high risk of bias, limit confidence in current estimates. Future research should prioritize rigorous external validation, calibration assessment, and development in underrepresented populations, particularly HIV-affected and high-burden settings. Author SummaryTB kills over a million people annually. While curable, treatment failure remains common and drives ongoing transmission and drug resistance. Researchers increasingly use artificial intelligence and machine learning to predict which patients will fail treatment, but it is unclear if these models are ready for clinical use. We reviewed 34 studies including nearly 1.1 million participants from 22 countries. On average, models correctly distinguished patients who would fail treatment from those who would not 84% of the time, a performance generally considered good. However, this average hid enormous variation. Models developed in populations including HIV-positive people performed substantially worse, suggesting prediction is harder with HIV co-infection. Worryingly, only one study used high-quality methods; 97% had serious flaws in handling missing data, checking calibration, or testing in new populations. Only eight studies validated their models in different settings. To conclude, we found that machine learning is promising in predicting TB treatment failure, but it is not ready for clinical use. Researchers should prioritize validation in high-burden settings, include social determinants, and improve methodological rigor before these tools can help patients.

Background Nepal has set a goal to eliminate lymphatic filariasis (LF) by 2030. As of 2024, Nepal has stopped the mass drug administration (MDA) in 56 of the 64 endemic districts and completed two rounds of MDA in six districts with persistent LF ([&ge;]2% antigen prevalence) using the three-drug regimen of Ivermectin, Diethylcarbamazine, and Albendazole (IDA), exceeding 65% coverage. We subsequently conducted an Epidemiological Monitoring Survey (EMS) to assess the impact of the MDA in reduction of LF infection prevalence below the transmission threshold and examine the factors associated with it. Methods We conducted a cross-sectional EMS nine months after MDA in 12 evaluation units (EUs) across six districts, with two sites per EU. We recruited a total of 7,343 individuals aged [&ge;]20 years, sampled using multi-stage sampling, ensuring at least 300 blood samples collected per site. We collected data on demographics and MDA participation. We performed the LF antigen testing for all participants, followed by night blood microfilariae testing in antigen-positive individuals. Statistical analyses included non-parametric tests, Chi-square and Fishers Exact tests, and multivariable logistic regression to assess outcomes after adjusting for potential confounders. Results Nine of 12 evaluation units (EUs) recorded <1% microfilaremia, meeting the WHO threshold for passing EMS, while three EUs failed with [&ge;]1% prevalence in at least one site. Antigen and MF prevalence were 4.47% and 0.34%, respectively (ratio 13:1). Both Antigen and MF prevalences were significantly associated with female sex (AOR= 0.564, 95% CI: 0.441-0.721 and AOR = 0.326, 95% CI: 0.129-0.826 respectively) and participation in the most recent MDA round (AOR = 0.477; 95% CI: 0.385-0.591 and AOR = 0.089; 95% CI: 0.017-0.464 respectively). MDA uptake was influenced by age (<40 years, AOR = 0.72; 95% CI: 0.653-0.793), sex (female, AOR = 1.438; 95% CI: 1.29-1.603), cross-border residence (AOR = 0.616; 95% CI: 0.558-0.681), and occupation (agriculture and housewife, AOR = 1.144; 95% CI: 1.008-1.298). MF prevalence was also associated with younger age (<40 years, AOR = 0.211; 95% CI: 0.071-0.626). Conclusion The survey indicates progress toward LF elimination, with nine of twelve EUs achieving WHOs <1% microfilaremia threshold after two rounds of IDA-MDA. However, transmission persists in three sites, likely linked to poor MDA participation among specific subgroups--particularly males, younger adults, and cross-border populations. Strengthening MDA coverage and compliance across all demographic and occupational groups, with special focus on border areas, is essential to achieve LF elimination in Nepal.

ObjectiveThis study aimed to assess the effectiveness of SMS and voice message reminders in reducing the dropout rate in Lome-Togo, in 2026. MethodsWe conducted a cross-sectional study between October 2025 and March 2026 in the Grand Lome region. The intervention consisted of an integrated digital system used by health facilities to send automated SMS. Categorical variables were described in terms of frequency and proportion; Fishers exact test was used to compare proportions. Quantitative variables were described by their means accompanied by their standard deviation; the Wilcoxon rank-sum test was used to compare means. The significance level for statistical tests was set at 5%. ResultsA total of 30 health facilities were included. Seventy percent (70.0%) of the health facilities used messages associated with calls. Ninety percent (90.0%) of participants found the reminders useful, and 60.0% reported an improvement in Expanded Program on Immunization services related to their use. Among participants who received a reminder, 51.0% kept their vaccination appointments. The Penta 1/3 dropout rate decreased from 3.2% before the intervention to 1.3% (p < 0.001). Among the 323 parents of children included, only 20.74% reported receiving a reminder by phone. Sixty-point-five percent (60.5%) preferred to receive both text messages and voice calls. ConclusionThis study demonstrates the operational feasibility of an SMS/call-based reminder system in reducing dropout rate for childhood vaccination in Togo.

Background: Although the hemagglutination inhibition (HAI) titer remains the gold standard correlate of protection against influenza, it does not fully capture the broader antibody responses that contribute to immunity. Methods: We analyzed immune responses in paired pre-infection and convalescent sera from 306 RT-PCR-confirmed A/H3N2 infections from two household studies (2014-18) in Managua, Nicaragua. Antibody responses were measured by HAI and enzyme-linked immunosorbent assays (ELISAs) against full-length hemagglutinin (HA), the HA stalk, and neuraminidase (NA). Participants were classified as HAI responders ([&ge;]4-fold HAI rise), alternate responders (no HAI rise but [&ge;]4-fold boost in [&ge;]1 ELISA), or no-response individuals (no [&ge;]4-fold rise in any assay). We compared demographic, clinical, and pre-infection antibody characteristics across these groups. We also analyzed predictors of an NA response. Results: Overall, 77% of participants had HAI seroconversion or a 4-fold rise. Among the 23% HAI non-responders, 62% had alternate antibody responses. No-response individuals had the highest pre-infection HAI and full-length HA titers (p < 0.0001), the lowest viral loads, and the fewest fever or influenza like illness (ILI) symptoms (p < 0.01). An NA response was more common among symptomatic individuals (p = 0.0483) and those with low or high baseline NA titers. Conclusions: High baseline HAI titers can limit detectable 4-fold rises and are associated with milder illness. Evaluating additional immune responses may capture a more complete picture of the host response to infection, thereby improving surveillance and informing vaccine development. Keywords: Influenza A/H3N2; Hemagglutination inhibition (HAI); Neuraminidase antibodies; symptomatic vs asymptomatic infection; correlates of protection.

Foodborne illness is a significant public health concern worldwide. Shiga toxin-producing Escherichia coli and Campylobacter species are recognized as important zoonotic bacterial pathogens contributing to human infections through the food chain, particularly via foods of animal origin. Although goat meat is in high demand in the southern region of Thailand, studies on foodborne pathogens in this livestock species remain limited. The current study aimed to (i) determine the antimicrobial susceptibility of Campylobacter spp. and STEC isolated from goats, and (ii) analyze the genetic relationships among Campylobacter spp. And E. coli O157 isolates obtained from different sources. Campylobacter jejuni and C. coli isolates were characterized based on sequences of seven housekeeping genes using the Achtman multilocus sequence typing scheme. For E. coli O157:H7, core genome multilocus sequence typing analysis was performed using whole-genome sequencing data. Genetic diversity was observed among C. jejuni, whereas a clonal population structure was detected in C. coli and E. coli O157:H7. Overlapping genetic characteristics were observed between C. jejuni isolates from goats and those previously reported in livestock and humans in Thailand. Among Campylobacter species, resistance to fluoroquinolones, including ciprofloxacin and nalidixic acid, was observed, whereas resistance to fosfomycin was most frequently detected in Shiga toxin-producing E. coli. Tetracycline-resistant isolates were identified in both Campylobacter species and Shiga toxin-producing E. coli at moderate levels. A multidrug-resistant pattern was observed only in C. coli, whereas no multidrug-resistant C. jejuni or Shiga toxin-producing E. coli isolates were detected. These findings indicate that healthy goats may serve as potential reservoirs of zoonotic pathogens and antimicrobial resistance in southern Thailand, where goat meat is frequently consumed.

Numerous factors may influence the optimal rollout of new gonococcal antibiotics. We compared eight rollout strategies using a gonorrhea transmission model and ranked strategies by the number of gonococcal infections and clinically useful antibiotic lifespan. Rankings were most sensitive to the starting ceftriaxone resistance prevalence and screening frequency.

ABSTRACT Background: Malaria remains a leading public health burden in sub-Saharan Africa, disproportionately affecting children under five years. In response, Kenya introduced the RTS,S/AS01 malaria vaccine in selected regions, including Siaya County where malaria transmission is endemic. Despite this milestone, uptake has been inconsistent, with hesitancy emerging as a significant barrier. Objective: This study aimed to determine factors associated with malaria vaccine hesitancy among caregivers of children 6-59 months in Ugenya Subcounty, Siaya County. Methodology: A cross-sectional mixed methods design was employed involving 425 caregivers and 15 healthcare workers and County health officials between January to February 2025. Quantitative data were collected using structured questionnaires and analyzed in Stata version 17 through descriptive statistics, bivariate analysis at 20% significance threshold, and multivariable logistic regression at 5% level to determine key factors associated with malaria vaccine hesitancy. Qualitative data from 15 key informant interviews were transcribed verbatim and thematically analyzed using NVivo. Thematic analysis, guided by a predefined codebook, was used to identify recurring patterns and extract key themes, which were illustrated with direct quotations from participants Results: Overall, 42.9% of caregivers (n=181; 95% CI: 38.9%-47.3%) reported hesitancy. Significant predictors included caregiver age, marital status, family size, access to health facilities, and vaccine availability. Single caregivers, those from smaller households, and those facing health facility access challenges were more likely to be hesitant to malaria vaccine. Despite high levels of knowledge, misconceptions and misinformation about vaccine safety, often spread via social media persisted. Conversely, caregivers relying on healthcare workers and mainstream media showed greater acceptance of malaria vaccine. Conclusion and Recommendations: Malaria vaccine hesitancy remains significant at 42.9%, driven by demographic factors such as younger age, single status, and smaller household size. Structural barriers including limited vaccine availability and poor access to health facilities further contribute to reluctance. Although knowledge and awareness were high, misinformation, particularly from social media, persisted, while information from healthcare workers improved acceptance. Addressing these gaps through targeted community engagement, improved access, and trusted communication channels is essential to increase uptake of malaria vaccine.

BackgroundScrub typhus is a major yet neglected vector-borne disease in Thailand, where it has been nationally notifiable for over two decades. However, long-term changes in its epidemiology, including reporting rates, transmission intensity, disease severity, and seasonal patterns, have not been comprehensively characterised at the national level. MethodologyWe analysed 22 years of national surveillance data for scrub typhus in Thailand (2003-2024) using a latent process model that jointly fits reported cases with published nationwide seroprevalence data and antibody kinetics to estimate reporting rates and underlying transmission dynamics across all 77 provinces of Thailand. FindingsOver the 22-year study period, 143096 cases and 119 deaths were reported nationally. Estimated reporting proportion broadly mirrored transmission intensity, being higher in high-burden regions and lower elsewhere. A synchronous decline in detection was observed across all regions during the COVID-19 pandemic, followed by rapid rebound by 2024. After accounting for these reporting dynamics, the force of infection was highest in the northern provinces but also substantial in the northeast and south, with upward trends in some provinces. Susceptibility among older adults aged 65 and above increased progressively over the study period, reversing the pattern observed two decades earlier. Case-fatality in the 25-35-year reference group was low and declined from 0.14% (95% Credible Interval [CrI]: 0.06-0.29%) to 0.06% (95% CrI: 0.02-0.12%), but relative case-fatality remained consistently highest among adults above 65 across all periods. Three geographically distinct seasonal patterns were identified, all stable over time. ConclusionOver two decades, scrub typhus transmission in Thailand has been shown to extend well beyond its traditionally recognised northern focus, with substantial burden in previously underappreciated regions, while the demographic profile of those most affected has shifted progressively toward older adults. These findings support the need for regionally tailored surveillance, age-targeted clinical preparedness, and sustained investment in understanding the ecological drivers of transmission. Key messagesScrub typhus is a common but neglected cause of fever in Thailand, where it has been reported through the national surveillance system for over two decades. However, trends in reported cases can be misleading because they reflect not only true changes in transmission but also variation in diagnosis and reporting over time and across regions. We developed a model that combines surveillance data with seroprevalence surveys and antibody kinetics to separate true changes in transmission from variation in reporting, allowing us to estimate how transmission intensity, disease severity, and seasonal patterns have evolved from 2003 to 2024 across all 77 provinces. We found that substantial transmission occurs not only in the well-studied northern provinces but also in the northeast and south, where the disease has received less attention. Susceptibility has progressively shifted toward older adults, who also face the highest case-fatality, while three distinct seasonal patterns vary by region but have remained stable over time. These findings suggest that scrub typhus control in Thailand requires a shift from a predominantly northern focus toward regionally tailored strategies that account for local transmission timing, an ageing at-risk population, and the ecological drivers that sustain transmission in each setting.