搜索结果：Infectious Disease Modelling

Infectious diseases remain a persistent and evolving public health challenge, driven by factors such as antimicrobial resistance, emerging pathogens and environmental changes. In this context, this systematic review aims to analyze the analytical performance of Fourier Transform Infrared Spectroscopy (FTIR) for the microbiological identification, strain discrimination and diagnosis of human infectious diseases, based on studies published between 2015 and 2025. The review followed PRISMA guidelines and included 50 articles selected from PubMed, Scopus, and ScienceDirect databases. The findings demonstrate that FTIR is a promising analytical tool for microbial discrimination, identification, and diagnosis, particularly in studies involving cultured microorganisms combined with chemometric modelling. Most studies focused on bacteria (42%) and fungi (32%), with fewer addressing viruses (20%) and protozoa (6%). Chemometric analysis played a central role in enabling FTIR applications, particularly through the integration of unsupervised methods, such as Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA), with supervised techniques like Linear Discriminant Analysis (LDA), Support Vector Machines (SVM), and Artificial Neural Networks (ANN). Most studies aimed at microbial identification (58%) or diagnosis (14%), while 28% focused on discrimination without proposing predictive models. Overall, FTIR shows strong potential as a complementary tool for rapid diagnosis and screening of infectious diseases. However, its clinical implementation still depends on further standardization, robust validation, and the development of reliable spectral databases.

Urban transit systems, particularly those in major metropolitan areas, are becoming increasingly interconnected, making it essential to better understand passenger mobility and its implications for the spread of infectious diseases. Respiratory infectious diseases, including COVID-19 (as a case study), can spread rapidly in densely populated urban environments, particularly within public transportation systems. This study investigates the dynamics of infection spread at a subway station, and its surrounding community, focusing on both short-term and long-term transmission. Using two deterministic ordinary differential equation (ODE) models, we simulate disease transmission over a single business day and examine how daily encounters impact infection numbers over the subsequent three months. The short-term model captures localized interactions between transit passengers and community residents, while the long-term model evaluates the cumulative impact of these encounters on community infections. Parameter estimation was performed using ridership data and the least squares method. Results suggest that mobility-related factors, particularly inflow and outflow rates, have a greater impact on controlling disease spread than transmission rates in both short- and long-term dynamics. Reducing inflow eases congestion and lowers encounters in the hub but increases encounters in the community, whereas reducing outflow increases crowding in the hub while decreasing encounters in the community. Joint reductions in inflow and outflow decrease encounters in both settings. In the long term, changes in transmission rates have only a limited effect on peak infections, while reducing outflow notably decreases infections and reducing inflow slightly increases community infections. Overall, simultaneously reducing inflow and outflow is the most effective strategy for limiting encounters in the short term and infections in the long term.

Background: The global increase in ultra-processed food (UPF) consumption has been associated with individual cardio-renal-metabolic (CRM) diseases, but its role in multimorbidity progression remains unclear. Objective: This study aimed to examine the relationship between UPF intake and transitions across successive CRM disease stages. Methods: We included 115 745 UK Biobank participants free of CRM diseases at the baseline, each completing at least two 24-hour dietary assessments. Multi-state models were applied to estimate associations between UPF consumption and transitions from disease-free status to first, double, and triple CRM conditions. Substitution analyses were used to assess the impact of replacing UPFs with unprocessed or minimally processed foods (UNPFs). Results: Over a median follow-up of 13.7 years, 16 114 participants developed incident CRM disease, with 2274 progressing to double and 227 to triple multimorbidity. An interquartile range increase in UPF intake was linked to higher risks of developing a first CRM disease (HR: 1.16; 95% CI: 1.14-1.19), progressing to double disease (HR: 1.13; 95% CI: 1.08-1.20), and advancing to triple disease (HR: 1.22; 95% CI: 1.05-1.43). The associations were primarily driven by UPF beverages and processed fruits/vegetables, whereas yogurts and whole-grain cereals showed neutral or inverse associations. Replacing 15% of UPF intake with UNPFs was associated with a 13% reduction in the risk of developing a first CRM disease and a 17% reduction in the risk of progressing to triple disease, with the most significant benefits observed in lean individuals. Conclusions: UPF consumption is associated with CRM multimorbidity progression. Replacing UPFs with UNPFs may serve as a potential strategy for preventing the progression of CRM multimorbidity.

The Covid-19 pandemic highlighted a critical lesson for global health systems: effective preparedness depends on timely access to reliable and interconnected health data. Across many countries, fragmented information systems limited the rapid exchange and use of data needed for outbreak detection, response, and decision-making. In Uganda, the recurrent emergence of infectious disease outbreaks, including Ebola, Marburg virus disease, and cholera, has reinforced the importance of resilient and interoperable health information systems. Although substantial progress has been made in strengthening disease surveillance and response mechanisms, persistent challenges continue to affect the country's capacity to generate and utilize high-quality data during public health emergencies. This study combined a review of published and grey literature with qualitative inquiry to explore existing challenges in Uganda's health data ecosystem. Six key informants with expertise in health information systems, disease surveillance, and outbreak response participated in in-depth interviews to provide insights into current gaps and potential solutions. The findings revealed several interconnected challenges, including fragmented and poorly integrated information systems, limited access to critical digital infrastructures, incomplete digitalization of data collection processes, and weaknesses in data quality and accessibility. These challenges often result in delays in data sharing and evidence-based decision-making during outbreaks. Participants noted that many of these barriers are well recognized and can be addressed through targeted and relatively modest investments. Key recommendations included accelerating the integration of existing health information platforms, expanding end-to-end digital data collection, increasing investments in health information systems, and strengthening coordination among government agencies and development partners. Strengthening Uganda's preparedness for future epidemics requires sustained commitment to interoperable health information systems and high-quality data management. Building on the strong foundations already established through collaboration with partners such as World Health Organization and Africa Centres for Disease Control and Prevention, targeted investments and enhanced governance mechanisms could address the most pressing system gaps. Advancing these efforts will improve the availability and use of quality health data, enabling more effective surveillance, faster outbreak response, and greater resilience against future public health threats.

Lyme borreliosis (LB), commonly referred to as Lyme disease (LD), is a prominent global health issue, exhibiting a seroprevalence rate of 14.5%. Heightened incidence levels of LD have been recorded in parts of Europe, Poland, Eastern Europe, and the Baltic States. The research aimed to inform the cost of LD and post-treatment Lyme disease syndrome (PTLDS) in Ireland through results from a patient questionnaire, disease modelling, the construction of a patient roadmap, and attempts to arrive at prevalence calculation estimates based on local data. Patient data encompassed sociodemographic particulars, disease attributes, healthcare resource utilization, and the influence on their employment status. Of 301 patients, 210 were diagnosed with LD and/or a tick-borne infection (TBI), the cohort's average age was 40.07 (SD 13.5) (N = 210; Female:Male 60:40). The mean duration of symptoms in PTLDS patients was 7.15 years. The average number of visits to other healthcare professionals was 16.8 per patient. Regarding current employment status, the data indicates that 50.2% of respondents were currently working, 10.1% were unemployed, 8.7% were retired, 5.3% had caring responsibilities, 11.1% were on sick leave, and 14.5% fell into the "Other" category. Additionally, when asked if symptoms had affected their employment status, 69% of respondents said yes, 26% said no, and 5% did not respond. Modeling efforts show that the roadmap to care for PTLDS is challenging, leading to wandering from specialty to specialty and high healthcare utilization. Utilizing a novel method of indirect reverse estimation, our lifetime risk or cumulative incidence of PTLDS estimation is at 0.003%. Lack of data collection from Irish health authorities is leaving the issue of the cost of LD and PTLDS hard to address, despite efforts from our single-site study.

On May 15, 2026, WHO declared a Bundibugyo virus (BDBV) outbreak in Ituri Province, DR Congo with an estimated index case on April 1, 2026 (6-week pre-declaration interval). By May 24, DR Congo reported 906 suspected cases (105 confirmed and ten confirmed deaths) across three provinces. Uganda reported seven confirmed cases (three imported; four locally acquired including three health-care workers; case-fatality ratio 14%). WHO declared a Public Health Emergency of International Concern on May 17, 2026; Africa Centres for Disease Control and Prevention declared a Public Health Emergency of Continental Security on May 18, 2026. The study aimed to establish a short-term trajectory of the BDBV outbreak and probability of cross-border spillover into countries with elevated risk of importation to guide preparedness priorities. We calibrated a stochastic SEIRD (susceptible, exposed, infectious, recovered, and dead) ensemble model to the laboratory-confirmed case series, anchoring on 598 cumulative confirmed cases on June 8, 2026 (day 68) using simulation filtering (calibration window ±30%; reporting fraction 1·0 for laboratory-confirmed cases). The case-fatality ratio was drawn from a previous value centred on the observed confirmed-case ratio of approximately 19% (115 of 598). A linked daily-hazard spillover model estimated importation probability for Uganda, South Sudan, Rwanda, and Burundi over a 12-week horizon. The early suspected-case series, which peaked at 1077 on May 26, 2026, before being substantially revised downward by laboratory reclassification, is reported for context but was not used for calibration. Laboratory-confirmed DR Congo cases rose from 33 on May 18, 2026, to 598 by June 8, 2026. Calibrated to the confirmed-case anchor (598 on June 8, 2026; central basic reproduction number [R0]=1·71), the confirmed-case trajectory is most consistent with the central scenario. Under the central scenario the ensemble projected a median of 990 cumulative confirmed cases by week 12 (June 24, 2026; 90% prediction interval [PI] 709-1293) and 174 deaths; the low scenario projected 870 confirmed cases (90% PI 641-1133) and 160 deaths. The early suspected-case count (peak 1077 on 26 May 2026) was substantially revised by laboratory reclassification and is reported for context only. Cross-border spillover remained material: Uganda 94·2% importation probability (19 confirmed cases as of June 4, 2026, including five health-care worker infections and two deaths); South Sudan 69·3%; Rwanda 8·6%; and Burundi 2·0%. As of June 22, 2026, DR Congo has 1048 confirmed cases and 267 confirmed deaths and Uganda has 20 confirmed cases, two confirmed deaths, and one probable death. These numbers are changing daily and are likely to align with what is predicted in the central scenario. From the most recent laboratory-confirmed data, the outbreak is closer to what is predicted by the central scenario, even with the intensified response within DR Congo. However, uncertainty remains around reported case numbers due to low rate of contact tracing. Sustained control nonetheless remains the primary determinant of regional risk: importation into Uganda is already established, and South Sudan must continue to reinforce infection prevention and control, rapid response capacity, and cross-border surveillance under International Health Regulations 2005. These projections should be interpreted as exploratory preparedness-oriented estimates derived from a stochastic scenario-based modelling framework, rather than predictions generated from formally fitted epidemiological models using comprehensive parameter estimation and identifiability analyses. None. For the French and Swahili translations of the abstract see Supplementary Materials section. For the French and Swahili translations of the abstract see Supplementary Materials section.

Following the Bundibugyo virus disease outbreak reported in the Democratic Republic of the Congo in May 2026, we reviewed all known Ebola disease cases outside Africa and found that intercontinental transmission risk remains low. We identified 28 confirmed epidemic-linked cases outside Africa; only four involved travellers with latent infection whose symptoms were detected after border screening. Excluding medically evacuated cases, the crude overall risk since 2000 was 0.17 Ebola disease cases outside Africa per 1,000 reported cases in Africa.

Tuberculosis (TB) is the leading global cause of death from a single infectious agent. Recent reductions in global health funding have threatened TB control, making comprehensive assessment of TB, HIV-related TB, and drug-resistant TB burdens before these disruptions essential for shaping effective responses. The WHO End TB Strategy sets targets of a 95% reduction in TB deaths and a 90% reduction in TB incidence between 2015 and 2035. Using results from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2023, this study aims to assess the burden of TB and multidrug-resistant TB (MDR-TB) across 204 countries and territories, and to evaluate progress towards the WHO End TB incidence and mortality targets. We quantified TB mortality using the Cause of Death Ensemble modelling platform with global vital registration, surveillance, verbal autopsy, and minimally invasive tissue sampling data. For TB morbidity estimation, we simultaneously modelled incidence, prevalence, and mortality by age and sex using DisMod-MR 2.1. A population attributable fraction (PAF) approach was applied to stratify morbidity and mortality estimates by HIV and drug-resistance status. We also calculated disability-adjusted life-years (DALYs) as the sum of years of life lost and years lived with disability. For the risk factor analysis, a comparative risk assessment framework was used and PAFs were derived for alcohol use, smoking, and high fasting plasma glucose to determine the proportion of TB burden associated with these risk factors. In 2023, there were an estimated 9·11 million (95% uncertainty interval 8·04-10·3) incident cases of all-form TB, 1·22 million (0·98-1·49) deaths, and 54·6 million (43·8-65·5) DALYs globally. HIV-related TB comprised 781 000 (690 000-879 000) incident cases and 210 000 (142 000-279 000) deaths, contributing 11·0 million (7·56-14·3) DALYs. MDR-TB accounted for 466 000 (198 000-1 080 000) incident cases, 102 000 (31 700-238 000) deaths, and 3·96 million (1·31-9·01) DALYs. From 2015 to 2023, global all-form TB incidence rates declined by 19·2% (17·8-20·5) and deaths declined by 22·6% (4·7-35·7); declines were larger for drug-susceptible TB than for MDR-TB. Sub-Saharan Africa and south Asia had the highest mortality burdens in 2023; reductions in all-form TB incidence and mortality were uneven between 2000 and 2023, with limited progress in both measures in Latin America and the Caribbean. Removing smoking, alcohol use, and high fasting plasma glucose would reduce global TB deaths to 768 000 (592 000-970 000) and DALYs to 34·9 million (27·8-43·8) in 2023; MDR-TB deaths would decrease to 77 200 (23 400-183 000) and DALYs to 3·12 million (1·03-7·29). Global progress towards WHO End TB targets is disparate and fragile. Although many regions achieved meaningful gains, others have stagnated in recent years. The complexity of TB prevention is amplified by divergent MDR-TB trends, the persistent burden of HIV, and growing exposure to modifiable risk factors. Recent volatility in global health financing threatens to further destabilise this vulnerable epidemiological landscape; concerted action is urgently needed to temper disruptions and preserve progress. Gates Foundation.

Hantaviruses are emerging zoonotic pathogens responsible for two severe clinical syndromes: (i) haemorrhagic fever with renal syndrome (HFRS) and (ii) hantavirus cardiopulmonary syndrome (HCPS), collectively causing more than 200,000 human cases annually worldwide. Despite their public-health importance, the molecular mechanisms governing the host response and the population-level dynamics of rodent-to-human spillover remain incompletely characterised. The timeliness of this framework is underscored by the April-May 2026 outbreak of Andes orthohantavirus aboard the MV Hondius cruise ship, the first such cluster in a maritime setting, with three deaths reported across multiple countries. This event revealed critical gaps in existing models that treat humans solely as dead-end spillover hosts. Our coupled Susceptible-Exposed-Infectious-Recovered-Dead (SEIRD) model assumes no human-to-human transmission and is therefore designed for hantavirus strains where spillover does not lead to secondary human cases, specifically Hantaan virus (HTNV), Puumala virus (PUUV), Sin Nombre virus (SNV), and Dobrava-Belgrade virus (DOBV). The Andes virus (ANDV) outbreak aboard the MV Hondius is used as a real-world case study to assess the boundaries of our model and to motivate future extensions, not as a direct validation target for its quantitative predictions. Here, we present an integrated computational study combining three complementary analyses. First, we performed a preliminary phylogenetic analysis of the viral sequence, identifying Orthohantavirus andesense as the likely etiological agent responsible for the vessel-associated outbreak. Second, we carried out a downstream transcriptomic analysis of Hantaan virus (HTNV)-infected human umbilical vein endothelial cells (HUVECs), using publicly available RNA-seq data (GEO accession GSE133751, n=3 per group). This analysis identified 184 upregulated and 19 downregulated genes, highlighting a transcriptional response dominated by interferon-stimulated genes (ISGs), including CXCL10, CXCL11, MX2, DDX58, IRF7, STAT1, OASL, and CMPK2. We then constructed a protein-protein interaction (PPI) network using STRING, comprising 176 nodes and 3210 edges, and applied a composite network centrality score to rank putative regulatory hubs. This analysis identified ISG15, IRF1, CXCL10, STAT1, and DDX58 as the most central nodes. Pathway enrichment analysis confirmed a strong activation of interferon signalling (Reactome, p=1.3×10-63), antiviral defence mechanisms (Gene Ontology, p=3.8×10-58), and NF-κB-related pathways, together with a concurrent suppression of ribosomal translation. Finally, we developed a coupled SEIRD epidemiological model that explicitly represents rodent-to-rodent and rodent-to-human transmission with logistic rodent population growth. Preliminary simulation analysis demonstrates that reducing human exposure to rodent excreta is substantially more effective than rodent population control alone for reducing human disease burden, and that rodent control in isolation can paradoxically increase human cases through a dilution-like effect. The integrated framework provides molecular and epidemiological insights relevant to hantavirus surveillance, therapeutic target identification, and public-health intervention design.

Cutaneous leishmaniasis (CL), a neglected infectious disease caused by the intracellular protozoan parasite Leishmania, affects over one million people annually. The type and magnitude of the inflammatory response elicited during infection lead to skin-specific immunopathology, resulting in the clinical manifestations of CL. Systemic antileishmanial drugs are the main control measure; however, these are highly toxic, of long duration, and difficult to access for affected populations. New drugs and optimized regimens are urgently needed. Despite the known participation of immune responses in the pathology of CL, preclinical drug evaluations target parasite elimination as the efficacy measure. This overlooks the potential of host immune responses to influence therapeutic success. In this study, we evaluated the performance of non-linear pharmacokinetic/pharmacodynamic (PK/PD) models in recreating the exposure-response relationships between plasma antimony (Sb) concentrations in CL patients treated with Glucantime (SbV as meglumine antimoniate) and the gene expression dynamics of pro-inflammatory mediators in peripheral blood mononuclear cells. A one-compartment PK model, coupled to an indirect PD model with endogenous regulators, fitted the data well, explaining 80%-90% of the variance. Our results suggest a mechanism of drug-dependent immune gene regulation involving modulation of cell signalling and RNA stability in CL patients who have been cured after treatment. The model presented herein can be used for evaluating immune gene expression, alongside parasite kill, as PD endpoints of antileishmanials, both for new drug developments and in optimization of available drug regimens.

Foot-and-mouth disease (FMD) vaccines are key tools for controlling and eradicating FMD virus (FMDV) and are often used as a control measure during epidemics. Emergency vaccination decisions in previously FMD-free countries should be supported by evidence on vaccine effectiveness (VE) against transmission. Mathematical models help assess vaccination strategies but require accurate effectiveness parameters. While experimental studies have shown that FMD vaccination, particularly in cattle, effectively controls transmission, there is limited information on its effectiveness during emergency vaccination in formerly FMD-free countries. We assessed the effectiveness of emergency FMD vaccination implemented alongside other control measures such as ban on animal movements and enhanced biosecurity, estimating VE at one, two-, and three-weeks post-vaccination. VE was estimated by assessing the reduction in the between farm transmission rate parameter () following vaccination of the farms with commercial bivalent vaccines containing the O1 Campos and A24 Cruzeiro strains. To estimate, we prepared the data within an SIR modelling framework and fitted a Generalized Linear Model with a complementary log-log link (cloglog). Since vaccination was implemented by zones during the epidemic, analyses were performed separately for each zone. It was observed that VE increased over time across all vaccination zones. Notably, even within the first week post-vaccination, VE was high, with estimates of 77.65% [70.53-82.94] in zone Pre, 71.05% [21.13-87.59] in zone A, 78.50% [75.44-81.21] in zone B, 68.80% [59.55-75.83] in zone C, and 59.75% [47.68-69.12] in zone D. Evaluating vaccine performance in fully susceptible livestock populations under high-risk conditions is key to improving control strategies and preparedness. Our findings indicate that emergency vaccination, as an additional measure, effectively contributed to control the epidemic in Uruguay. These VE estimates can parameterise and refine outbreak-response models and strengthen preparedness in previously FMD-free regions.

Pulmonary tuberculosis (PTB) and chronic respiratory diseases (CRDs) are closely linked. Affected groups present with similar symptoms and share many risk factors (eg, poverty-related factors, smoking, occupational exposures). PTB is itself an independent risk factor for chronic lung disease. However, in many high TB-incidence settings health services for these conditions are provided separately, with little integration of prevention, diagnosis or care. We describe a transdisciplinary programme of research investigating strategies for integrated TB-CRD care in Arusha, Tanzania, Nairobi, Kenya and Lagos, Nigeria, using clinical, health economic, health systems and qualitative research methods. A prospective clinical cohort study will describe the burden and impact of non-TB respiratory disease (eg, asthma, chronic obstructive pulmonary disease, post-TB lung disease) among adolescents and adults presenting to primary and secondary health facilities with chronic cough who would normally be managed via TB care pathways. Health economics methods will explore patient costs of non-TB respiratory disease, facility-level costs of integrated TB/respiratory diagnostics and will develop a modelling framework to estimate the costs and consequences of integration more broadly. In-depth interviews, focus group discussions, observations and participatory methods will be used to explore lived experiences of chronic respiratory symptoms, disease and exposures among patients and providers, and to identify and address challenges around respiratory health and care. Lastly, existing TB and CRD healthcare services and systems in our three research sites will be described, and local, national and policy level understandings of 'integration' of TB and CRD care will be explored. Together, the findings of this work will be used to develop context-informed model(s) of integrated TB-CRD care and a theory of change and framework for evaluation in future implementation studies. Ethical approval has been obtained from Imperial College London in the UK, the Scientific Ethics Review Unit in Kenya, University of Lagos in Nigeria and the National Institute of Medical Research in Tanzania. Findings of this study will be presented in research publications and symposia, and will be shared with local communities and stakeholders.

For infectious diseases where people can experience multiple infections during their lifetime, the time between observed infections in individuals (or "time to recurrence") can provide valuable information on infection and transmission dynamics. Routinely collected data, such as electronic health records, are a potential source of time to recurrence data. However, they are challenging to analyse because patients can drop out of the data set in a way which is not visible to the data collection process. Standard epidemiological approaches, such as parametric survival analysis with imputation, cannot be applied to such data. In this study, we explored the feasibility of interrogating routinely collected time to recurrence data by calibrating mechanistic transmission models with explicit dropout mechanisms. We identified model structures and parameter regimes where the method could precisely and accurately estimate important epidemiological quantities. Application of our method to real data of malaria infections routinely collected in Papua, Indonesia, was able to estimate the forces of infection for different malaria species, the rate of dropout and recrudescence for P. falciparum, and the probability of treatment success. Our method has the potential to increase the value of existing and new data sets for informing public health research.

In November 2025, Marburg virus caused the first outbreak of Marburg virus disease (MVD) in Ethiopia. By December 15, a total of 14 laboratory-confirmed cases were reported, including 9 deaths, corresponding to a case fatality ratio of 64.3% among confirmed cases. In the absence of licenced vaccines or antivirals, non-pharmaceutical interventions (NPIs) were implemented to control transmission. We developed a stochastic epidemic model incorporating a stochastic exponential growth process with reporting adjustment to assess the effectiveness of NPIs. The reporting ratio was modelled using a beta distribution, and transmission parameters were estimated via Markov chain Monte Carlo with particle filtering for likelihood calculation. Using pre-NPI data (November 12-23), we estimated an exponential growth rate of 0.012 (95% credible interval [CrI]: 0.008, 0.014) per day, corresponding to an initial reproduction number of 1.13 (95% CrI: 1.09, 1.16). We projected 64 cases (95% CrI: 19, 189) and 41 deaths (95% CrI: 12, 122) during post-NPI period (November 24 to December 15) without interventions. Compared with 4 observed cases, NPIs were associated with an estimated effectiveness of 93.7% (95% CrI: 78.9, 97.9). These findings indicated moderate transmissibility of MVD, and a potential transmission risk reduction following the implementation of control measures, underscoring the importance of timely intervention and sustained surveillance regarding local MVD activities.

Bluetongue virus (BTV) is a vector-transmitted orbivirus that infects ruminant livestock and wildlife, causing substantial economic losses owing to its impact on animal health and trade. Despite extensive study, the mechanisms underlying infection and immune dynamics remain unclear. Here, we develop and calibrate a mechanistic within-host model of BTV infection in experimentally infected sheep to quantify the contributions of innate and adaptive immune responses to viral control. The model integrates longitudinal data on viral RNA dynamics reverse transcriptase quantitative polymerase chain reaction, CD8+ T-cell and antibody dynamics, and midge infection outcomes within a Bayesian inference framework using Hamiltonian Monte Carlo. Our results reveal that CD8+ T lymphocytes mediate over 90% of viral clearance, establishing them as the primary drivers of disease control. Interferons exert an early but moderate antiviral effect, whereas antibodies contribute minimally to acute clearance yet sustain long-term immunity. Sensitivity analyses identify viral replication and infection rates as key determinants of peak viral RNA load. Collectively, these findings provide a quantitative framework linking immune kinetics, viral dynamics and transmission potential, advancing mechanistic understanding of BTV pathogenesis, and informing strategies for vaccine and antiviral development in ruminant hosts.

To quantify the impact of tropical cyclones on dengue transmission dynamics and related disease burden by integrating field entomology with dynamic modelling in Guangzhou, China. Guangzhou was selected as the study site considering that it is China's most dengue-endemic coastal megacity with frequent tropical cyclones and the country's largest recorded dengue outbreak in 2014. Mark-release-recapture experiments with laboratory simulations were performed in 2024 to assess the flight distance, reproduction and survival of Aedes albopictus under cyclone and baseline conditions to estimate human-mosquito contact rates. These data informed a dynamic susceptible-incubation-infected-recovered model simulating the 2014 dengue epidemic during Typhoons Rammasun and Kalmaegi. We estimated excess cases and economic losses against counterfactual baselines and projected the impact of enhanced vector control. During cyclones, Aedes albopictus maximum flight distance increased by 63.0% and human-mosquito contact rate by 30.1%. Estimated R0 increased from 1.10 (baseline) to 1.43 (Rammasun) and 1.29 (Kalmaegi). The estimated excess burden reached 217 excess cases (US$76 400) for Rammasun and 5131 cases (US$1.93 million) for Kalmaegi. Enhanced vector control during Kalmaegi was projected to avert 59 360 additional cases (US$20.91 million). Tropical cyclones amplify dengue transmission by expanding mosquito dispersal and intensifying human-vector contact. Early vector control, guided by warnings, substantially reduces cases and costs.

Chikungunya virus (CHIKV), an emerging arbovirus, is transmitted by Aedes mosquitoes. Climate change and increasing population mobility have driven recent outbreaks beyond traditional endemic regions. Since early July 2025, Guangdong province in southern China has faced an unprecedented outbreak of chikungunya fever. We aim to in-depth describe the epidemic features and theoretically assess the potential impact of vaccination campaigns. In total, the outbreak reported more than 25,000 cases. Foshan and Jiangmen successively emerged as epicenters of the outbreak. Through stringent public health interventions, the outbreak was controlled within two months in these two epicenters, respectively. Phylogenetic analysis revealed close genetic relation of the CHIKV isolates from this outbreak to the recent isolates in Réunion and Mayotte, but distant from the ones previously identified in China. Pre-emptive vaccination, achieving 20, 40%, 60%, and 80% coverage pre-outbreak, would avert up to 57%, 81%, 92%, and 97% infections shown by mathematical modelling, respectively. Achieving a daily vaccination rate comparable to the COVID-19 rollout, covering approximately 4% of the population in Foshan and 2% of the population in Jiangmen per day, could lower cumulative CHIKV infections by 68.7% in Foshan within two months and by 98.4% in Jiangmen within four months, respectively. In summary, the 2025 chikungunya outbreak in Guangdong was likely sparked by case importation. Implementation of stringent public health interventions is possible to control the outbreak, but can provoke significant public concerns. Vaccine campaigns are expected to be effective in both preparedness and response to future chikungunya outbreaks.

Global human papillomavirus (HPV) prevalence among women rose significantly from 14% (2019) to 24% (2024), underscoring the need to understand transmission dynamics and public health impact. Although multi-genotype infections are increasingly documented, evidence remains limited on their combined effect on cervical lesion severity and transmission, especially in regional populations. Using clinical datasets from Xiamen, China, this study evaluates co-infection patterns through integrated statistical and dynamical models to quantify associations with histological severity and transmissibility. Data were sourced from positive HPV nucleic acid tests at two hospitals in Xiamen. Genotyping employed multiplex PCR-based flow fluorescence hybridisation. Cumulative link models (CLMs) were used to assess associations between multi-genotype infections and cervical lesion severity. Concurrently, an ordinary differential equation transmission model was developed to estimate reproduction numbers, comparing transmission potential across infection types. Of 1&#x2009;33&#x2009;438 samples, 15&#x2009;939 (11.9%) were HPV-positive, covering 27 genotypes. HPV 16, 52 and 58 were the most prevalent high-risk types. Co-infections involving these genotypes showed strong inter-hospital correlation in pairing patterns (Pearson's r=0.851). Co-infection severity association was context-specific: the number of genotypes predicted severity in the screening population (eg, quadruple infections OR=1.47, 95%&#x2009;CI 1.23 to 1.71, p<0.01) but not in the referral population. Conversely, co-infections exhibited consistently higher relative transmissibility indices in both settings (eg, high-low-risk co-infection median model-derived : 2.57-6.82). HPV co-infection impacts are modulated by patient population: relative transmission potential is broadly elevated, whereas histological severity effects are marked in screening cohorts but minimal in referral groups. Context-aware public health strategies-adapting co-infection screening and interventions to clinical setting-are urged for more effective and efficient disease control.

Imported malaria is a critical obstacle to achieving elimination in low transmission settings, but importation classification tools combining human mobility and parasite genomics are lacking. A Bayesian model combining epidemiological, human mobility, and parasite genetic data was developed to estimate malaria importation and geographic origins of Plasmodium falciparum cases. Using microhaplotype-based genetic relatedness from 1605 samples across nine Mozambican provinces in 2022, the study focused on two low-transmission districts in the south: Magude and Matutuine. Parasites from southern Mozambique showed lower genetic relatedness to those from northern/central regions (0.021) than the national average (0.034, p<0.001), indicating limited connectivity. Overall, 42% (88/207) of infections in these districts were classified as imported, mainly originating from Inhambane province (63% [55/88]). Imported cases showed higher parasite complexity than local ones (odds ratios [OR] = 1.3). Importation rates differed markedly between districts - Matutuine (48.60%, 87/179) was far more affected than Magude (10.71%, 3/28) - highlighting the need for localised rather than uniform elimination strategies. In Matutuine, importation appears to be actively sustaining transmission, suggesting that reducing malaria burden in source regions (particularly Inhambane) and targeting travellers from central and northern Mozambique would have the greatest elimination impact.