Wastewater surveillance of dengue and chikungunya during the worst arbovirus epidemic in Brazil.

Reshaping transmission and recovery: How memory effects govern global dynamics thresholds in the SIR model.

Pinning control of an epidemic network model with time-varying weights.

Dengue fever is one of the most widely distributed vector-borne infectious diseases globally, prevalent in tropical and subtropical regions. An estimated 3.9 billion people in 128 countries are at risk of infection globally, and the 2024 outbreak was the most severe. As a tropical region, Hainan Province in China serves as a significant endemic area for dengue fever, where epidemic prevention and control remain critical. To analyze the transmission dynamics and pathogen variation characteristics of the 2024 dengue fever outbreak in Hainan Province. Acute-phase serum samples from 18 confirmed dengue cases during the 2024 outbreak in Hainan Province were collected and serotyped using TaqMan real-time PCR. The viral envelope (E) gene and representative whole genomes were sequenced and compared with domestic reference strains to assess epidemic trends. Phylogenetic and molecular clock analyses were performed based on E gene and full-genome sequences to infer spatiotemporal transmission dynamics. Sequence alignment and homology modeling were used to identify E gene mutations and evaluate their potential effects on E protein functional domains and viral pathogenicity. Among the 18 samples, 17 were dengue virus (DENV)-1, and 1 was DENV-3. Phylogenetic analysis revealed that the primary strains in this outbreak were highly homologous to those circulating in Guangdong Province; however, two strains exhibited significant genetic differences, suggesting possible independent introduction. Additionally, two unique DENV-1 E gene mutations (D147N and S338L) were identified, which we hypothesize may be associated with viral characteristics, subject to further functional verification. This study systematically reveals the epidemiological characteristics of the 2024 dengue fever outbreak and provides a scientific basis for formulating local control strategies and a research foundation for further exploring the molecular-level pathogenicity and transmissibility of DENV.

Antiretroviral therapy improves outcomes for people living with HIV (PLHIV) and prevents transmission through viral load suppression (VLS). To evaluate progress toward HIV epidemic control, VLS prevalences and correlates among PLHIV were analyzed from Tanzania HIV Impact Assessment Surveys (THIS), independent, cross-sectional using stratified-cluster design, conducted in 2016-2017 (S1) and 2022-2023 (S2). Participants 15 years and older provided consent, completed an interviewer-administered questionnaire, and tested for HIV using the national rapid testing algorithm. HIV-positive specimens were tested for viral load. Prevalence of population VLS (<1000 copies/mL) was determined irrespective of HIV-knowledge or antiretroviral therapy status. Modified Poisson Regression Model was used. We reported percentages (%) and adjusted prevalence ratios with 95% confidence intervals (CIs) accounting for complex survey design. We analyzed data from 1830 PLHIV in S1 and 1849 in S2. Population VLS prevalence increased by 26.1% (95% CI: 22.1 to 30.2, P < 0.0001) between surveys. In both surveys, women were more likely to experience VLS [S1: 1.43 (95% CI: 1.25 to 1.63), S2: 1.15 (95% CI: 1.06 to 1.24)] than men. In both surveys, PLHIV 35 years and older were more likely to experience VLS than those 15-24 years, [S1 35-49 years: 1.46 (95% CI: 1.13 to 1.90), 50 years and older: 1.69 (95% CI: 1.28 to 2.23), and S2 35-49 years: 1.31 (95% CI: 1.06 to 1.62), 50 years and older: 1.42 (95% CI: 1.15 to 1.76)]. Tanzania has made progress toward HIV epidemic control, as evidenced by the significant increase in VLS prevalence in 6 years. Targeted sex-age strategies are essential for HIV epidemic control.

This study addresses the 2025 Chikungunya outbreak in Foshan City, Guangdong Province, China, by constructing a dual-model framework based on Ordinary Differential Equations (ODE) and Petri Nets (PN) for comparative analysis of Chikungunya transmission dynamics and reproduction number estimation methods. The research employs SEICR (Susceptible-Exposed-Infectious-Chronic-Recovered) compartmental modeling to compare two formal representations under matched epidemiological assumptions, and evaluates the timing of epidemic control measures through a three-phase intervention fitting protocol. Model validation results show that both models achieve root mean square errors (RMSE) of 30.98 (ODE) and 31.05 (PN), mean absolute errors (MAE) of 15.57 and 15.78, and [Formula: see text] and 0.9498, respectively. Both models predict epidemic peaks at day 33 (406 cases), occurring 3 days earlier than the observed peak (432 cases), with a peak value error of 6.0%. Residual analysis reveals that negative residuals account for 71.4% (ODE) and 73.8% (PN) of the observation-window residuals, suggesting a structured overprediction pattern in descriptive diagnostics. Reproduction number analysis reveals that the initial transmission indicators are approximately 14.67 (ODE)/13.90 (PN), with effective values progressively decreasing through three intervention phases: 7.85/7.86 after Phase 1, 7.59/7.56 after Phase 2, and 0.059 in Phase 3, below the transmission threshold. An additional no-demography robustness check shows that removing demographic turnover changes total predicted cases by only 0.03%, suggesting that the remaining uncertainty lies mainly in omitted vector-side dynamics rather than in human-side demography. Sensitivity analysis indicates that the recovery rate (γ) is the most sensitive parameter affecting [Formula: see text] within this formulation, with a Sobol index of 0.9672, explaining 96.72% of total [Formula: see text] variation. This study provides a controlled comparison between ODE and Petri Net representations of the same epidemiological structure, offering a transparent comparative framework for outbreak fitting, intervention phase identification, and future extension toward explicit host-vector models.

The detection of Influenza A Virus (IAV), Influenza B Virus (IBV), and Respiratory Syncytial Virus (RSV) presents significant diagnostic challenges due to the high similarity of clinical symptoms with other respiratory infections, leading to the need for multiplexed, rapid testing. Herein, we have established a one-pot, multiplex CRISPR/Dx detection system based on reverse transcription-recombinase polymerase amplification (RT-RPA) and CRISPR/Cas12a. It completes the detection within 30 minutes at a constant temperature of 40 °C, with the ability to detect 10 copies per µL of IAV, 10 copies per µL of IBV, and 8 copies per µL of RSV. No cross-reactivity between these respiratory viruses was observed. When equipped with our customized miniature device, it allows visual fluorescence readout without specialized equipment. Compared with conventional RT-qPCR and two-tube RT-RPA-CRISPR/Cas12a approaches, this one-pot detection system offers a simplified workflow and shorter detection time and enables visual detection, making it especially suitable for point-of-care testing and field deployment. In essence, our CRISPR/Dx system provides a novel and practical molecular diagnostic strategy for rapid and multiplex detection of respiratory pathogens to improve patient management, rational antiviral use, and epidemic control.

Tularemia is a zoonotic disease that can cause both sporadic cases and outbreaks. This study aims to analyze a waterborne tularemia outbreak in eastern of Turkiye during January and February 2025 and to evaluate adult and pediatric tularemia patients, highlighting the differences and similarities between the two patient populations. The study included patients who presented to Malazgirt State Hospital during the tularemia outbreak and were diagnosed with tularemia. Data on age, gender, clinical and laboratory findings, antibiotic regimens/durations and complications were collected and analyzed. The study included 56 participants (mean age 24.6 ± 20.5 years, 44% female, 51% adult). No significant difference was found in the distribution of tularemia type between adult and pediatric patients (p = 0.198); the predominant type in both groups was the oropharyngeal form. Lymphadenopathy (LAP) was more frequent in adult patients (p < 0.05), abdominal pain was more frequent in pediatric patients (p < 0.001), and no significant differences were found in other clinical findings. Leukocytosis was significantly more frequent in pediatric patients compared to adults (p = 0.016). Treatment duration was statistically significantly longer in adult patients compared to pediatric patients (p < 0.001). One of the patients was pregnant who was treated with azithromycin and a healthy delivery was achieved. There were no cases resulting in sepsis or death. Our study demonstrated that tularemia can cause different clinical presentations in adult and pediatric patient groups. Despite this clinical diversity, we emphasized the critical role of recognizing the common transmission route and source in epidemic control.

Adaptive social distancing under variant-specific transmission dynamics in a spatial SEIIR model with reinforcement learning.

The end of the COVID-19 pandemic has reshaped epidemic screening and public health management, prompting a reassessment of advanced rapid testing techniques and community-wide implementation (i.e., mass testing) to track disease transmission and dynamically optimize epidemic control strategies. However, given resources and funding constraints, the joint optimization of control efforts with mass testing over time remains an open challenge. In this paper, we address a joint optimization problem of resource allocation for epidemic control and mass testing in light of evolving epidemics, which are characterized by temporarily shifting system dynamics and mixed observability of system states. To tackle this challenge, we propose a novel non-stationary Markov decision process (MDP) framework that dynamically incorporates newly acquired observational data from test results and refines control and testing strategies accordingly. We propose an efficient continual reinforcement learning (RL) approach to solve this challenging MDP within an offline-to-online paradigm. Our approach begins with offline pre-training of neural networks and subsequently fine-tunes them using newly acquired data for adaptive decision-making. The hallmark of our approach lies in the parallel learning and memory mechanism, which enables the RL agent to develop a generalized understanding of system dynamics across diverse environments, thereby improving solution optimality and robustness compared to state-of-the-art methods. This online learning-while-optimization approach broadens the applications of RL in solving MDPs with scarce historical data. Numerical experiments demonstrate the superiority of our proposed approach in parameter identifiability, as well as solution optimality and robustness under varying observability and dynamic patterns.

African swine fever (ASF) is a lethal, hemorrhagic, and highly contagious infectious disease caused by the African swine fever virus (ASFV). At present, ASFV has evolved into multiple variant strains with different levels of virulence in China, complicating the epidemiological landscape. In the absence of effective commercial vaccines, the development of rapid and accurate diagnostic methods has become a critical priority for epidemic prevention and control. In this study, monoclonal antibodies (mAbs) were generated by immunizing mice with inactivated ASFV. Nine newly identified mAbs targeting the p72 protein were screened to establish a colloidal gold immunochromatographic strip for ASFV detection combined with 11 previously characterized anti‐p72 mAbs developed in our laboratory. Cross‐pairing analysis was subsequently performed to identify the optimal antibody combination. Based on this evaluation, ASFV‐3 and P72‐6 were selected as the gold‐conjugated antibody and capture antibody, respectively. The assay demonstrated the capacity to effectively detect diverse ASFV genotypes, with a limit of detection (LOD) of 102.50 TCID50 per reaction against genotype Ⅰ and Ⅱ ASFVs. No cross‐reactivity was observed with five common swine pathogens including PCV, PRRSV, PRV, PEDV, and TGEV. The concordance rate with the results from commercially available test strips was 93.33%. In conclusion, a rapid, sensitive, and highly specific colloidal gold immunochromatographic strip targeting the ASFV p72 protein was successfully developed. This method is simple to operate, does not require specialized equipment, and is suitable for on‐site detection, providing a valuable tool for field surveillance and early diagnosis of ASF.

Despite substantial gains in the global HIV response, infants and young children continue to experience poorer outcomes across the HIV testing and care cascade. Rates of early infant diagnosis, timely antiretroviral therapy (ART) initiation, retention in care and viral suppression remain unacceptably low in paediatric populations. These disparities are compounded by systemic challenges, including fragmented service delivery, limited availability of child-friendly ART and other drug formulations, and health systems that fail to integrate HIV services within broader maternal and child health platforms. As a result, many children living with HIV are diagnosed and start treatment late and face high risks of mortality and morbidity, particularly in the first 2 years of life. In this commentary, we advocate for the urgent reimagining of paediatric HIV service delivery, emphasizing the need to centre infants and young children within models of care. We highlight four promising approaches: (1) Integrated mother-infant follow-up models that ensure continuity of care from pregnancy through the postpartum period and infancy until the infant's final HIV status is determined; (2) Family-centred models that treat the household as the unit of care; (3) Advanced HIV care strategies tailored to the needs of children with late presentation or treatment failure, including paediatric tailored ART regimens and diagnostics; and (4) Community-based interventions that leverage peer support, lay health workers, and provide stigma-free entry points to expand access and retention to care and treatment. These approaches have demonstrated that when services are designed to reflect the developmental, clinical and social needs of children and their caregivers, outcomes significantly improve. However, many of these models remain underutilized, fragmented or inadequately resourced. To close the paediatric HIV treatment gap, we must move beyond pilot projects towards national, integrated and adequately funded child-centred systems. This requires political will, strategic investment and prioritization of children in HIV policy, innovation and implementation research. A sustained and equitable HIV response must include infants and young children not as an afterthought, but as a core priority. Epidemic control cannot be achieved if the youngest children are left behind.

Tomato leaf curl New Delhi virus: an emerging threat to cucurbit crops in China.

Nonpharmaceutical interventions (NPIs) are essential for controlling infectious diseases during prevaccine periods, yet their success hinges on sustained population behavioral responses to restrictions. This study investigates how mobility responses to tiered restriction systems evolved over time during COVID-19 in six geographical regions across Europe, North America, Africa, and South America. Using daily mobility data and linear-mixed models, we characterized three temporal dimensions shaping these responses: cumulative exposure to restrictions, time spent under a given tier, and repeated reintroduction of the same tier. Time spent under a given tier caused the most rapid attenuation of mobility reduction, producing effects within days that cumulative exposure, required months of restrictions to achieve. Iterative application of shorter NPIs, interspersed with temporary relaxation, was associated with a restoration of mobility responses when restrictions were reintroduced, mitigating attenuation and sometimes even strengthening responses. These patterns are consistent with mechanisms discussed under the umbrella of pandemic fatigue, whereby temporary relaxation may provide psychological relief and a sense of regained autonomy that can renew public willingness to respond to restrictions when reintroduced, while remaining compatible with behavioral adjustment and structural constraints. However, they are also compatible with alternative explanations, including behavioral adaptation, structural constraints on mobility, or evolving interpretations of restrictions. These findings emphasize the dual benefits of short, strategic NPIs for epidemic control and public resilience, offering actionable insights for designing more sustainable pandemic interventions.

Vaccination campaigns play a pivotal role in controlling infectious diseases. Their success, however, depends not only on vaccine efficacy and availability but also significantly on public opinion and the willingness of individuals to vaccinate. This paper investigates a coupled opinion-epidemic model on heterogeneous networks, where individual opinions influence vaccination probability, and opinions themselves evolve through a combination of peer interaction and local risk perception derived from observed infection rates. Embedding the coupled dynamics in scale-free networks, particularly Barabási-Albert structures, allows us to examine the role of network heterogeneity beyond homogeneous-mixing assumptions. Using Monte Carlo simulations and a semi-analytical microscopic Markov-chain approach, we derive and numerically validate analytical expressions for the critical infection threshold and stable vaccinated population where risk perception dominated peer influence. Our results show that stronger local risk perception enhances pro-vaccination opinions and suppresses infection, while dominant peer influence can increase long-term infection levels. These findings underscore the importance of accounting for social behavior and network structure when designing effective vaccination and epidemic control strategies.

Xia Yingtang, a representative figure of the Shanghai school in traditional Chinese medicine during the Republic of China, was renowned with Ding Ganren as "Ding in the North, Xia in the South". With exceptional medical expertise, he founded the Xia-style of internal medicine lineage and actively engaged in public medical affairs. He established charitable institutions such as the Hucheng Branch of the China Red Cross Society and the Hucheng Branch Hospital, organizing and participating relief efforts in wartime and epidemic prevention and control. His contributions profoundly influenced the construction and development of China's public health system and professional education. He was dedicated to the reform and advancement of TCM education by establishing the Shanghai Traditional Chinese Medicine School and the Shanghai Women's School of Traditional Chinese Medicine. In particular, he bravely confronted the danger of traditional Chinese medicine extinction, actively participated in the opposition to the "Proposal to Abolish Traditional Chinese Medicine", promoted the promulgation of the Traditional Chinese Medicine Regulations, and advocated for the establishment of the National Traditional Chinese Medicine Day, playing a key role in securing the legitimate status of traditional Chinese medicine. This paper analysed the historical newspapers, archives, and other relevant materials and examined the life of Xia Yingtang and his medical activities, It attempts to elucidate his contributions to traditional Chinese medicine and public health, restore his historical position in the modern development of traditional Chinese medicine, and call for broader academic attention and further research on this 'overlooked' physician.

During the process of responding to epidemics stemming from sudden public health emergencies, front line medical staff often experience death anxiety to varying degrees; however, this phenomenon has not been the focus of extensive attention. In this study, using the outbreak of the COVID-19 pandemic as a case example, we aimed to investigate the status of death anxiety and corresponding coping strategies among front line medical staff within the context of public health emergencies. By convenient sampling, 170 front line medical staff were recruited into this study. The self-made general data questionnaire, the Death Attitude Profile-Revised scale, death anxiety and coping during the epidemic questionnaire were used as tools for questionnaire survey. Our results indicate that the total score of the DAP-R scale for front line medical staff was 96.64 ± 14.23. Among the five dimensions of the scale, the scores in the neutral acceptance (4.07 ± 0.52) and death avoidance (3.27 ± 0.77) dimensions are relatively high. 81.2% participants reported that combating the pandemic could lead to more positive attitudes towards death. Medical staff experienced serious death anxiety before traveling to epidemic areas and after hearing about the infection of colleagues. In coping with death anxiety, helpful information support included "Information on the disease treatment and prevention" (70.59%) and "Positive news about fighting against the COVID-19" (66.47%). "Communication between medical teams" (71.76%) and "reading supporting information" (68.24%) were considered useful interventions that could ameliorate death anxiety. Death anxiety existed throughout the entire cycle of epidemic control and management, and medical staff required support to overcome it. The data generated in this study provide a reference to inform development of death education and support programs according to the needs of the medical staff and the characteristics of public health emergencies.

The full-scale Russian invasion of Ukraine in February 2022 caused large-scale displacement and major disruption of health services. We assessed national and subnational trends in HIV testing, test positivity, number of newly registered HIV cases, and antiretroviral therapy (ART) initiation during 2020-2023, and examined whether trajectories differed by regional exposure to hostilities. This ecological time-series study analyzed routine national surveillance and program data from January 2020 through December 2023. Joinpoint regression was used to identify trend breakpoints and estimate quarterly percent change (QPC), average quarterly percent change (AQPC), monthly percent change (MPC), and average monthly percent change (AMPC), with 95% confidence intervals. Subnational analyses were stratified into three categories of exposure to hostilities: active hostilities in 2023, history of active hostilities in 2022 but not in 2023, and no active hostilities or occupation during 2022-2023. National HIV testing declined through 2021 and dropped sharply at invasion onset, followed by sustained increases after Q2 2022 (QPC + 9.14% [95% CI: 3.89; 22.47]), exceeding pre-war levels by late 2023. However, the number of positive tests decreased steadily from 5,458 in Q1 2020 to 3,590 in Q4 2023 (AQPC -3.25% [-4.86; -1.56]) and test positivity dropped from approximately 1% to 0.5% after Q2 2022 (QPC -10.48% [-22.04; -5.99]). Newly registered HIV cases were stable through 2021, declined sharply at invasion onset, briefly rebounded, and then decreased through December 2023 (MPC -1.12% [-2.39 to -0.31]). ART initiation decreased steadily from 1,495 in January 2020 to 797 in December 2023 (AMPC -1.11% [-1.40; -0.81]), without breakpoints. Post-invasion trajectories diverged regionally: regions exposed to prolonged hostilities experienced the deepest and most persistent disruptions, whereas less affected regions demonstrated gradual increases in registrations and ART initiation. The full-scale war caused substantial disruption in key HIV services. Although testing increased after the initial shock, it did not translate into higher case detection, reflecting a shift from testing high-risk populations toward war-related expanded screening of blood donors, military personnel, and displaced individuals. Regional divergence in trends was consistent with population displacement and redistribution of HIV service delivery. Interpretation of wartime HIV indicators requires attention to changes in the tested population and regional service capacity. Sustaining epidemic control under prolonged war will require combining targeted case-finding with broader screening, and strengthening cross-region linkage from diagnosis to ART initiation.

ABSTRACTOngoing monitoring of hepatitis C virus (HCV) and HIV incidence among people who inject drugs (PWID) is important for epidemic control and assessing progress towards disease elimination. We estimated trends in HIV and HCV infection incidence in a community‐based cohort of PWID in Montreal, Canada. Data from March 2011 to March 2025 were drawn from the HEPCO study of PWID aged ≥ 18 years and living in Montreal. Participants with at least two visits were included in analyses. We used a random point approach to estimate the date of each infection event, imputing each date 1000 times, and Rubin's rules to pool incidence rates and 95% CIs across imputations. A total of 743 participants contributed to HCV analyses, with 152 infection events and incidence of 6.47 per 100 person‐years (p‐y) (95% CI 6.46–6.49). Incidence peaked in 2013 and declined to 2019 before increasing again post‐pandemic. There were 61 primary HCV infection events (incidence: 6.15 per 100 p‐y; 95% CI 4.79–7.90) and 91 reinfection events (incidence: 6.71 per 100 p‐y; 95% CI 6.69–6.73). A total of 836 participants contributed to HIV analysis, with only eight infections over 3089.89 p‐y (incidence: 0.26 per 100 p‐y; 95% CI 0.13–0.52). Declining HCV incidence among PWID in Montreal may have reversed, but limited data collection during the years 2020–2021 complicates interpretation. HIV incidence was persistently low. HCV incidence remains above World Health Organization elimination targets, highlighting the need for sustained investment in prevention and linkage to care for PWID.

A class of multi-age group $SEQ_3IHR$ chickenpox epidemic models with effective vaccination in a different subpopulation size, is considered, in this paper. It is assumed that individual members of the subpopulations who recovered from the disease, may be re-infected by the disease. The models comprise of six infectious classes of subclasses and three distinct infected stages of subclasses. We establish that the global dynamics of the epidemic models are determined entirely by the basic reproduction number. In this paper, we determine that the disease-free and endemic equilibrium states, and carry out a stability analysis of the disease-free as well as the endemic equilibrium points of the model. The findings show that age heterogeneity and vaccination rate play prominent roles in the determination of epidemic spread and control of chickenpox disease in the population and also serve as the factors that influenced the basic reproduction number. We found numerically that re-infections have little or no influence on the basic reproduction number but contribute tremendously to the reduction and/or increase in the number of individuals in incubation, prodromal and active stages, as well as treatment compartment of chickenpox infection over time.