Epidemiology and Infection

Background: Tuberculosis recurrence accounts for a substantial proportion of incident tuberculosis in many settings. Distinguishing between its mechanisms can inform public health interventions for prevention. Methods: We conducted a retrospective study of individuals with multiple culture-confirmed TB episodes and available sequential isolates from 2012 to 2023 in Dourados and Campo Grande, Mato Grosso do Sul state, Brazil. Patients were classified as having recurrent TB after treatment completion or retreatment following non-curative outcomes. Whole-genome sequencing was used to assess pairwise genetic distances between isolates, classifying relapse or persistent infection ([≤]12 single-nucleotide polymorphisms [SNPs]) versus reinfection or retreatment with reinfection (>12 SNPs). Results: Among 9,293 individuals with TB, 772 recurrent or retreatment episodes were identified. Paired isolates from 82 individuals were available for comparisons. Among individuals who completed treatment, reinfection accounted for 74.1% (40/54) of recurrent episodes, while 25.9% (14/54) were classified as relapse. Among individuals with non-curative outcomes, persistent infection (53.6%, 15/28) and retreatment with reinfection (46.4%, 13/28) occurred at similar frequencies. Persistent infection and relapse occurred earlier after the initial episode, whereas reinfection and retreatment with reinfection predominated after two years. Incarceration history was strongly associated with reinfection after treatment completion (92.5%, p=0.012) and after non-curative outcomes (76.9%, p=0.016). Conclusions: In this high-burden setting, reinfection drives TB recurrence among individuals who complete treatment, particularly at longer intervals after initial disease, reflecting sustained exposure risk. Relapse and persistent infection remain clinically important, especially following non-curative outcomes. These findings underscore the need for integrated strategies combining adherence support to prevent treatment-related recurrence with interventions to reduce transmission, particularly in high-risk settings.

BackgroundThe COVID-19 pandemic was strongly shaped by the interaction between population behaviour and transmission dynamics. Standard mathematical models do not account for this interaction, however. Objectivewe tested whether adding a mechanistic representation of population behavioural dynamics improves the ability of a mathematical model to explain and predict COVID-19 pandemic waves. MethodsWe compared a standard Susceptible-Infected-Recovered (SIR) model to a variant (SIRx) with a mechanistic representation of behavioural processes, including two-way coupling between behaviour and transmission dynamics. We used approximate Bayesian computation to parameterise the models with SARS-CoV-2 case incidence and the Oxford stringency index from 13 European countries. Models were fitted to the Spring 2020 wave, and their out-of-sample prediction for the Summer/Fall 2020 wave was tested. Outcome measures included the Akaike Information Criterion (AICc), the area between empirical and model epidemic curves, and predicted timing/magnitude of the second wave. ResultsThe average AICc for the SIRx model across all 13 countries was lower (-2638{+/-}345 versus - 2295{+/-}212 for SIR), meaning that the SIRx model explains the data more parsimoniously. The average area-between-curves was also lower (0.072{+/-}0.071 versus 0.16{+/-}0.11). The predicted peak magnitude for the SIRx model (0.0015{+/-}0.0014) was closer to the data (0.0006{+/-}0.0005) than the SIR prediction (0.0083{+/-}0.0090). The average day-of-peak for the SIRx model (283{+/-}19 days from first data point) was also closer to the data (278{+/-}25), than the SIR prediction (253{+/-}31), although the 95% credible intervals for individual countries were very large. ConclusionCoupling behavioural and disease dynamics improves the ability of mathematical models to explain and predict crucial features of pandemic waves. Research in contextO_ST_ABSEvidence before this studyC_ST_ABSMost mathematical models of infectious disease transmission do not explicitly account for behaviour, but the COVID-19 pandemic clarified the role of behavioural processes in determining the trajectory of infectious diseases in populations. On the other hand, many theoretical models of coupled behaviour-disease processes exist, although relatively few attempt to validate these models against data. We searched Google Scholar using the terms COVID-19 model, and behavio*-disease or behavio* epidem* from March 1, 2020 to October 8, 2025. We did not find any papers that compared retrospective out-of-sample model predictions of COVID-19 pandemic waves of a non-behavioural transmission model to the predictions of a coupled behaviour-disease model, in multiple populations. Added value of this studyWe carried out such a comparison for 13 European countries, by fitting models to the first COVID-19 wave in Spring 2020 and testing how well they would have predicted the second wave. We found that the coupled behaviour-disease model predicted the second wave better than the non-behavioural model, and was also more parsimonious, despite having more parameters. This study shows that feedback between disease dynamics and behavioural dynamics is a significant factor for determining the timing and magnitude of pandemic waves caused by an acute respiratory infection. It also shows that integrating population behaviour dynamics into transmission models is feasible, and can better explain observed temporal patterns in case incidence. Implications of all the available evidenceMathematical models that endogenously include the feedback between infectious disease dynamics and behavioural dynamics can add a unique and complementary tool to the public health modelling toolbox during a pandemic. Such models could help design public health interventions by improving our ability to anticipate population responses to both the interventions themselves, and a rapidly evolving epidemiological landscape.

We genotyped 1189 multidrug-resistant Mycobacterium tuberculosis isolates identified during 2013-2022 in Argentina, through a mixed strategy using PCR-based methods and whole-genome sequencing. Epidemiological, geographic distribution and microbiological data were integrated. Most cases belonged to a cluster (75.7%). The proportion of orphan and clustered cases varied across regions. The Euro-American lineage4 was virtually predominant. The most important clusters, M, Ra, Rb and Callao2 strains, comprised 45.9% of the newly diagnosed cases, and their relative importance varied over time. A preliminary genomic analysis showed that several local transmission chains due to the Callao2 strain, imported from Peru, were active, including a superspreading event that occurred circa 2020. A good performance of the current second-line regimes can be expected for most of the cases. Heightening suspicion of drug-resistance and enhancing timely and active surveillance in specific risk groups could contribute to the tuberculosis management in Argentina, tackling transmission and resistance amplification. BiosketchBiochemist Federico Lorenzo is a professional of the Servicio de Micobacterias, Departamento de Bacteriologia, INEI, ANLIS "C. G. Malbran" and is specialized in the microbiological diagnosis of mycobacterial diseases using next-generation sequencing technologies. His research interests are drug-resistant tuberculosis, non-tuberculous mycobacteria and bioinformatic analysis applied to diagnostics. One-sentence summaryWe evaluated the genotypes associated with multidrug-resistant tuberculosis in Argentina, 2013-2022.

Scorpionism is a public health concern in warm regions, particularly affecting children under 10 years old. Timely treatment with antivenom, provided free by the Brazilian Unified Health System, at strategic care points (PEs) is crucial to prevent avoidable deaths. Our study focused on the Sao Paulo state (SP), which has the largest population in Brazil. The objectives were to adapt a network analysis method suited to SPs context; to assess the efficiency of the SP PE network coverage, considering the 90-minute response time; and to determine the ideal number of vials to be stored at each PE. After adapting the healthcare network analysis, we applied spatial coverage models to evaluate the adequacy of PE response times. We also estimated the demand for antivenom vials at each PE based on Notifiable Diseases Information System data from 2021 to 2023, which is currently limited to the state level. We identified 12 areas lacking coverage, of which only one was suitable for a new PE. The estimated serum requirements aligned with SP's current distributions. However, the estimation carried out according to the PEs has the advantage of reducing the risk of antivenom shortages, especially in emergencies, thus ensuring timely care to prevent avoidable deaths. Our adapted method and PE serum estimates can enhance the scorpion sting care system by supporting geographic planning and optimizing resource allocation. Moreover, these findings and methodologies have potential applicability to other Brazilian regions and warm countries facing similar challenges, contributing to improved access and outcomes for scorpionism victims.

BackgroundThe World Health Organization has developed several global template protocols for epidemiological investigations, including for household transmission investigations (HHTIs). These investigations facilitate rapid characterisation of novel or re-emerging respiratory pathogens and support evidence-based public health actions. Beyond technical readiness, community buy-in is central to the feasibility and acceptability of HHTIs. Research is needed to determine the perceived legitimacy among the community to inform local protocol adaptation and development of implementation plans that consider community attitudes and needs. MethodsIn 2025, we conducted a convenience survey of community members living in Victoria, Australia to explore: their understanding of emerging respiratory diseases; their willingness to take part in public health surveillance activities such as HHTIs; the acceptability of clinical and epidemiological data collection and respiratory/blood sample collection as main components of HHTIs, and; participant comfort towards including their companion animals in HHTIs. ResultsWe received 282 survey responses, of which 235 were included in the analysis dataset. Compared to the general Victorian population, our participants included a higher proportion of participants who reported being female, tertiary-educated, of Aboriginal and/or Torres Strait Islander heritage, born in Australia and speaking only English at home. Participants indicated overall high levels of comfort and acceptability towards participation in HHTIs, particularly in relation to clinical and epidemiological data collection, with lesser but still high levels of comfort with providing multiple respiratory specimens in a 14-day period. Participants were least comfortable with other specimens such as urine and blood. Involving companion animals in HHTIs was similarly acceptable as human-focused components. ConclusionsDespite our survey population being non-representative of the general Victorian population, our findings provide valuable descriptive insights into the acceptability of HHTIs in Victoria, Australia from which to benchmark future local and international surveys and community engagement activities.

ObjectiveHealthcare workers (HCWs) are at increased risk of COVID-19 infection and play a critical role in influencing public vaccine acceptance. This study aimed to assess vaccination coverage and identify the determinants of vaccine uptake among healthcare workers in Cameroon, in order to inform targeted strategies to strengthen immunization programs and improve epidemic preparedness. ResultsAmong 406 participants (75.6% female, 65.5% aged 30-44 years, 61.3% nurses), 42.4% were fully vaccinated against COVID-19, while only 4.7% had completed the cholera vaccine series. Coverage varied across districts, with Biyem-Assi (53.0%) and Odza (46.0%) recording the highest COVID-19 uptake, and Nkolndongo (11.5%) leading for cholera vaccination. Independent predictors of COVID-19 uptake included being a nurse (aOR = 3.96; 95% CI: 2.07-7.81; p = 0.001) and laboratory technician professions (aOR = 8.00; 95% CI: 1.38-69.8; p =0.032). For cholera vaccination, working in internal medicine wards (aOR=11.2; 95% CI: 1.04-120; p = 0.046) and being a nurse (aOR = 1.54; p = 0.001) emerged as significant predictors. Although 62.8% of HCWs perceived their work environment as high-risk, knowledge of recommended vaccines was limited, with only 18.7% aware of cholera vaccination recommendations. Strengthening vaccine education, improving accessibility, and reducing financial barriers could enhance vaccine acceptance among HCWs. These findings provide important insights for designing targeted immunization strategies in Cameroon and similar contexts.

The COVID-19 pandemic exposed major vulnerabilities of hospital capacity and management worldwide, particularly in intensive care units (ICUs) and emergency rooms (ER), imposing prompt adaptation and resource reallocation. Although SARS-CoV-2 is no longer endangering healthcare systems, winter seasons continue to bring recurrent overload of critical care services, primarily due to respiratory infections. In France e.g., this pattern led to the reactivation of the national emergency response plan during the 2024-2025 seasonal influenza peak, highlighting the continuous need for improved predictive tools. However, forecasting hospitalization surges at a local scale remains a methodological challenge because the (very) low incidence numbers are subject to strong stochasticity and therefore require additional input of information and dedicated approaches. This study investigates the potential for early forecasting of respiratory infection peaks by analyzing ER visit trends. By clustering all-cause ER visits during the 2023-2025 winter seasons from the Nimes University Hospital (France), we identified a strong temporal correlation between early pediatric hospitalizations ([≤]5 years old) and the following weeks adult hospitalization incidence for respiratory infections. The results suggest that tracking hospital admissions of pediatric ER visits, even without hospital care needs, can serve as a valuable early warning signal for upcoming peaks in respiratory-related hospitalizations. This predictive approach could improve hospital preparedness and resource management during seasonal influenza outbreaks. Author summaryThe epidemics of respiratory viruses present a significant challenge to hospitals in the temperate zone on an annual basis. Frequently, the hospital overload is mitigated by the late reactive allocation of human and material resources that are, hence, suboptimal. This study proposes a statistical framework to assist hospitals in anticipating bed requirements during seasonal influenza waves, despite high noise at the local level, by enhancing hospitalization forecasting with emergency room (ER) visit data. The prediction of the adult epidemic peak is possible through the analysis of the respiratory pediatric ER visits, which facilitates hospital management.

BackgroundMeasles is a highly contagious infectious disease and a leading cause of childhood morbidity and mortality worldwide. In developing country like Ethiopia, effective immunization is a proven strategy for reducing measles related illness and deaths. However, measles second dose vaccination drop out has become a major public health concern. In a densely populated city such as Addis Ababa drop rate tends to be higher than the minimum acceptable threshold, leading to increased number of cases and recurrent outbreaks. Despite of this limited evidence exists on the determinants of second dose drop out and the problem is not well investigated, as a result this study will try to identify determinants of measles second dose vaccination dropout among children 24 - 35 months of age. ObjectivesTo identify determinants of measles second dose vaccination dropout among children 24 - 35 months of age Addis Ababa, Ethiopia in 2025. MethodCommunity based unmatched case control study was conducted in Addis Ababa from September 1/2024 to October /2025 with a total of 636 participants, consisting of 212 cases and 424 controls. Data were collected using structured Quesionariie and entered into EpiData 3.1 then StataSE 18 was used for detailed analysis including Descriptive statistics. Model fitness was checked using Hosmer-Lemeshow and multicollinearity were assessed using variance inflation factor. Furthermore, Bivariable and multivariable logistic regression analyses was employed and Adjusted odds ratio with 95% confidence intervals was used to identify significant variables. ResultsA total of 620 mothers/caregivers participants respond to the study, comprising 206 (97%) cases and 414(97.6%) controls, yielding a total response rate of 97.4%. In this study, waiting time longer than 30 minutes (AOR= 3.34, 95%CI: 1.86-5.9), Lack of counseling (AOR = 2.63, 95% CI: 1.60-4.30), Lack of reminders (AOR = 2.86, 95% CI: 1.89-4.30), Previous adverse event following immunization (AOR = 2.00, 95% CI: 1.39-3.00), postnatal care visit (AOR = 0.58, 95% CI: 0.40-0.85) and family size of greater than 3 (AOR = 1.96, 95% CI: 1.29-2.98) were significantly associated with measles second dose dropout. Conclusion and recommendationIn study shows measles second dose dropout is found to be associated with long waiting time, lack of counseling, lack of reminder, history of adverse event following immunization and postnatal visit. Which suggests Strengthening Immunization Counseling, reducing waiting time, establishing effective reminding system, integrating Immunization with postnatal services and promptly addressing concerns about adverse event following immunization can help reduce measles second dose dropout.

Automated outbreak detection can enhance infectious disease surveillance by enabling early identification of outbreaks and supporting timely public health measures. However, information on its current use by national public health institutes (NPHI) remains limited. This paper provides an updated and extended overview of automated outbreak detection usage in the European Union (EU) and United Kingdom (UK). Key findings were gathered through the Joint Action United4Surveillance via an online survey of 21 countries, an in-presence workshop, and online meetings with NPHI, focusing on three objectives: assessing current demand for automated outbreak detection, examining the availability of necessary prerequisites within existing surveillance systems, and identifying challenges and requirements for implementation. Findings indicate that seven countries currently have automated outbreak detection systems (AODS) in place. While many countries have sufficient surveillance data and a clear demand for automated outbreak detection, adoption is often limited by constrained funding and lack of IT resources. While the specific methods in existing AODS differ, overall demands and outputs are similar, suggesting a single tool could serve multiple countries. Capacity building as part of EU-funded Joint Actions can bridge these gaps by developing sustainable tools and fostering cross-country collaboration.

We evaluated the probability of need for public health action against trachoma in Esmeraldas province, Ecuador. Compared to global references, seroconversion rates to Chlamydia trachomatis Pgp3 in children suggest high probability of action needed in rural villages (91%) and lower in more urban areas (32%), motivating further trachoma assessment.

BackgroundReliable identification of early predictors of adverse outcomes was essential during the pre-vaccination phase of the COVID-19 pandemic. Few studies have comprehensively integrated clinical presentation, laboratory parameters including arterial blood gas analysis, and chest computed tomography (CT) findings within a single well-characterized cohort, particularly in underrepresented regions of Brazil. MethodsThis retrospective cohort study included 482 consecutive adults (median age 61 years [IQR 49-73]; 64.3% men) with RT-PCR-confirmed SARS-CoV-2 infection hospitalized at a tertiary cardiac center in Central-West Brazil between March 2020 and January 2021. Demographic, clinical, laboratory (including arterial blood gas analysis), and chest CT data obtained within 48 hours of admission were analyzed. Univariable logistic regression was performed for 76 variables. Multivariable models were constructed using an a priori variable selection strategy based on clinical relevance, representation of distinct pathophysiological domains, and adherence to events-per-variable principles. Complete-case analyses were performed without imputation. ResultsIn-hospital mortality was 9.3% (45/482). Invasive mechanical ventilation was required in 74 patients (15.4%), with a mortality rate of 58.1% among those ventilated. In univariable analysis, 42 variables were associated with mortality (p < 0.05). In multivariable analysis (n = 438), five independent predictors of death were identified: age (adjusted OR 1.66 per 10 years; 95% CI 1.19-2.32; p = 0.003), arterial pH (adjusted OR 0.47 per 0.1-unit increase; 95% CI 0.25-0.89; p = 0.021), neutrophil-to-lymphocyte ratio (adjusted OR 1.30; 95% CI 1.18-1.44; p < 0.001), number of comorbidities (adjusted OR 1.59; 95% CI 1.25-2.02; p < 0.001), and serum creatinine (adjusted OR 1.37; 95% CI 1.05-1.77; p = 0.019). The model demonstrated good calibration (Hosmer-Lemeshow p > 0.05) and moderate-to-high explanatory power (Nagelkerke R{superscript 2} = 0.43). For the composite outcome of death or invasive mechanical ventilation (74 events; 15.4%), four predictors remained independently associated; serum creatinine showed a non-significant trend (p = 0.069). On chest CT (n = 424), analyzed descriptively and in univariable models only, pulmonary involvement > 50% was associated with increased odds of death (OR 2.87; 95% CI 1.42-5.79; p = 0.003). ConclusionsFive admission variables representing distinct pathophysiological domains--age, arterial pH, neutrophil-to-lymphocyte ratio, comorbidity burden, and serum creatinine--were independently associated with in-hospital mortality in this pre-vaccination cohort. Arterial pH provided independent prognostic information beyond inflammatory and renal markers. These findings support early risk stratification using routinely available clinical data.

Recent studies suggested that human cytomegalovirus (HCMV) exposure may increase tuberculosis (TB) disease risk. We assessed the association between active HCMV infection and recent HCMV exposure with tuberculosis (TB) disease among TB-presumptive South African adults. This was a nested case-control analysis that utilized stored plasma and serum samples collected from adults ([&ge;]18 years) with presumptive TB self-presenting to primary care clinics in in the Kraaifontein District in Cape Town, South Africa. Cases (n=98) and HIV status frequency matched controls (n=199) basing on mycobacterial culture and or GeneXpert Ultra were included in the study. HCMV DNAemia was detected by qPCR well as current HCMV reactivation or reinfection and recent HCMV infection, reactivation or reinfection were categorized using PCR and serology (IgM and IgG avidity ELISA) results. The median age of all participants was 37 years (IQR 29-47), 164 (55.2%) were male and 119 (40.1%) had previous TB treatment. Overall, 21 (7.1%) had HCMV DNAemia, 19 (6.4%) had positive HCMV IgM and 2 (0.7%) had low HCMV avidity. In a logistic regression model adjusting for age, gender, HIV status and BMI, TB disease was associated with current HCMV reactivation or reinfection [adjusted odds ratio (aOR) 4.88, 95%CI 1.59-16.31, p=0.007]. There was no association with recent HCMV infection, reactivation or reinfection. Unlike recent HCMV infection, reactivation or reinfection, active HCMV replication although not frequent was associated with TB disease which suggests that TB disease or an underlying common factor reactivates HCMV replication in this population.

Background: Leptospirosis is a neglected tropical disease with substantial public health impact in Brazil, closely associated with socio-environmental vulnerabilities and climatic extremes. This study analyzed the epidemiological profile, spatiotemporal distribution, and climatic influences on leptospirosis incidence and lethality in Brazil from 2015 to 2024. Methods: An ecological time-series study was conducted using secondary data from the Notifiable Diseases Information System (SINAN). Variables included geographic region, probable infection environment, occupational, and educational level (ISCED-2011). The spatiotemporal correlation between disease incidence and El Nino-Southern Oscillation (ENSO) anomalies was assessed using the Oceanic Nino Index (ONI) and Spearman's rank correlation coefficient. Results: A total of 31,397 cases were notified, with an annual average of 3,140 cases. The South and North regions exhibited the highest incidence rates, while the Northeast and Southeast presented lethality rates above the national average (9.20%). A marked reduction in notifications occurred during the COVID-19 pandemic. Contaminations occurred predominantly in the domiciliary environment (64%). Rural workers (27.45%) and civil construction workers (18.63%) were the most affected occupational groups, with a higher incidence among illiterate and low-education populations. Climatic analysis revealed a positive spatial correlation between El Nino intensification and leptospirosis incidence in the South and Southeast, and a negative correlation in specific Northeastern states. Conclusion: The dynamics of leptospirosis in Brazil are complex and multifactorial, strongly influenced by macroclimatic variations and driven by deficits in basic sanitation and urbanization. Mitigating the disease burden requires sustained, region-specific public health strategies, targeted infrastructure improvements, and enhanced epidemiological surveillance to address underreporting.

BackgroundCampylobacter is a major cause of childhood diarrhoea across Africa and asymptomatic carriage is frequently reported, however risk factors for Campylobacter presence remain poorly defined. This meta-analysis aimed to calculate the pooled prevalence of Campylobacter in diarrhoeic and non-diarrhoeic stool, assess its association with diarrhoea, identify risk factors for Campylobacter presence and antimicrobial resistance (AMR) patterns. MethodEnglish language studies on Campylobacter in children (<18 years) in Africa were searched. Prevalence of Campylobacter and AMR, Odds Ratios (OR) for Campylobacter presence in diarrhoeic stool and risk factors for Campylobacter were estimated. Heterogeneity was assessed using I2 and bias assessed via funnel plots and Eggers test. ResultsA total of 168 studies were included in the meta-analysis. The pooled prevalence of Campylobacter in diarrhoeic stool was 11.25% (9.41-13.23%), in non-diarrhoeic stool 12.56% (7.79-18.27%), and mixed stool types 33.47% (20.53-47.81%). The OR for Campylobacter presence in diarrhoeic stool versus non-diarrhoeic stool was 1.95 (95% CI: 1.62-2.33). Age affected the OR with children 0-6 months old having an OR 2.57 (1.74-3.81), 7-12 months old an OR 1.60 (1.07-2.40), 13-24 months old an OR 1.02 (0.68-1.52) and 25-60 months old an OR 1.76 (0.77-4.05). Risk factors for Campylobacter presence in stool were children living in rural areas (pooled Adjusted Odds Ratio (pAOR) = 2.59 95% CI 1.43-4.69) and having contact with animals (pAOR 4.28 95% CI: 2.40-7.61). AMR prevalence ranged from 54.85% for ampicillin to 9.85% for chloramphenicol. Heterogeneity was high across all analyses. ConclusionCampylobacter prevalence is high in symptomatic and asymptomatic children across Africa. Contact with animals and living in an urban environment are risk factors for Campylobacter presence. Risk factor identification in the African context would be strengthened with standardized risk factors. Further research is needed to clarify the public health significance of asymptomatic carriage. What is already known on this topic - Campylobacter is a significant cause of diarrhoea in children and asymptomatic carriage is common. However, the burden of asymptomatic carriage and risk factors are not well understood in Africa. What this study adds - This meta-analysis highlights the high burden of asymptomatic Campylobacter carriage, its relation to age, and identified risk factors for Campylobacter in children in Africa. How this study might affect research, practice or policy - Standardising risk factor assessments can guide future control strategies. Further research into the impact of asymptomatic carriage is warranted.

BackgroundConsanguineous unions are defined as the matrimony between individuals who are blood relatives. Researchers in all over the world worked on this issue and they checked the ratio of prevalence and effects of consanguinity in different regions of world. This research was conducted in the District Faisalabad, upper Punjab. ObjectiveTo find rate of consanguinity, coefficient of inbreeding (F) and its impacts. MethodsThe data was collected from six tehsils of District Faisalabad by interviewing the subjects. The data collected within the time span of six months. Total of 2366 subjects were interviewed after their consent approval. ResultsThe rate of consanguinity was noted 41.83% with 0.03053 coefficient of inbreeding. High rate of consanguinity (23.36%) was noted among first cousins. The distantly related and not related unions were 35.64% and 22.56% respectively. The rate of consanguineous unions in six tehsils ranged from 33.99% in Jaranwala to 53.85% in Tandlianwala. Consanguineous marriages were noted high in Punjabi speaking subjects, in housewives, in reciprocal marital types, in grand-parents and one couple family types and Rajpoot castes. There was found no significant differences of consanguinity in rural and urban areas. The rate of still births was noted high (82.25%) in consanguineous unions while neonatal, post neonatal and child mortality was low such less as 6.45%, 8.06% and 3.22% respectively. The prenatal mortality was noted slightly high 44.94% in consanguineous unions as compared to non-consanguineous unions. The congenital malformation rate was 6.29% in all marital unions but this rate was high (59.06%) in consanguineous unions as compared to non-consanguineous unions (40.93%). This is a pilot study to analyze the potential of inbreeding coefficient (F) in the District Faisalabad.

BackgroundThe COVID-19 pandemic significantly changed the daily routines of frontline health workers (FLHW), particularly those directly caring for COVID-19 patients. This study explores the lived experiences and coping strategies of FLHW at a Zonal Hospital in Kilimanjaro, Tanzania MethodThe study used a qualitative exploratory descriptive design, to retrospectively capture psychological impacts, challenges, coping strategies, and professional dilemmas faced by FLHW. Participants were purposively sampled from KCMC Hospital departments directly involved in COVID-19 patient care. FindingsThe FLHW experienced considerable fear, stress, and stigma. The fear of infection and high mortality rates among patients and colleagues heightened their anxiety. Stress was exacerbated by long working hours, uncomfortable personal protective equipment (PPE), poor remuneration, and the emotional toll of witnessing numerous deaths. FLHW also experienced being stigmatized and discriminated against, both within their workplaces, within the family and in the broader community. Some FLHW considered quitting their jobs due to the overwhelming workload, fear of being infected, and emotional strain. Additionally, absenteeism and avoidance of COVID-19 duties were common, driven by fear and inadequate hospital capacity. Coping strategies among FLHW to manage their stress and maintain resilience included acceptance, faith, family support, rest, and, the use of recreational substances. ConclusionThe FLHW experienced considerable fear, stress, and stigma. The study highlights the need for better psychological support, improved communication, adequate training, and resources to support FLHW before, during, and post-pandemic. Enhanced resilience and confidence, along with a greater appreciation for protective measures and compassion for patients, were some of the key lessons learned from their experiences during the pandemic informing more effective preparedness care in future pandemics.

BackgroundAlthough the impact of COVID-19 vaccination is widely documented in the general population, the evidence on its effectiveness in children under 5 years of age is still limited. In this context, the continuation of vaccination programs in this age group has been debated globally. Consequently, we estimated the effectiveness of the 3-dose series of BNT162b2 (Pfizer-BioNTech) in children aged 6 months to 4 years and the complete 2-dose series of CoronaVac (Sinovac) in children aged 3 to 4 in reducing the risk of hospitalizations due to COVID-19-attributed severe acute respiratory infection (SARI) in Brazil. MethodsWe conducted a retrospective cohort study in 24 Brazilian municipalities, using surveillance data. We evaluated vaccine effectiveness in reducing the incidence rate of COVID-19-attributed SARI hospitalizations from July 2023 to December 2024. Covariate adjustments, defined a priori based on a conceptual model represented by a directed acyclic graph (DAG), were implemented using random-effects Poisson regression models. We also analyzed alternative vaccination schedules and obtained age-specific estimates of effectiveness. ResultsThe cohort comprised 37.6 million person-months of follow-up and 1,384 COVID-19-attributed SARI hospitalizations, including 27 associated deaths. The 3-dose series of BNT162b2 vaccine had an effectiveness of 97% (IRR 0.03, 95%CI 0.01-0.10) in the group aged 6 months to 4 years, with no significant differences among age-specific estimates. No deaths occurred among children who completed the 3-dose series, whereas four deaths were observed among those with fewer doses. The effectiveness of CoronaVac was small and not statistically significant (IRR 0.96, 95%CI 0.57-1.62). However, no deaths were recorded among children who received any number of CoronaVac doses, and no COVID-19-attributed SARI hospitalizations were observed among those who received a third dose of this vaccine. ConclusionsThe 3-dose series of the mRNA vaccine (BNT162b2) had high and consistent effectiveness in protecting against severe COVID-19 in children aged 6 months to 4 years. These findings support the maintenance of routine COVID-19 vaccination in this age group.

Introduction: Current tuberculosis (TB) screening tools, such as the WHO four-symptom screen (W4SS), lack sufficient sensitivity and specificity for effective community-based active case finding, contributing to both missed diagnoses and unnecessary diagnostic evaluations. This study aimed to develop and validate a machine learning (ML) model to improve TB risk prediction among persons aged >=15 years in community settings of Zambia and South Africa. Methods: A large, harmonized dataset was created from four community-based TB prevalence surveys in South Africa and Zambia (N=169,813), restricted to individuals not under treatment at the time of survey. A binary reference outcome was defined based on available microbiological and radiographic data, grouping individuals as either 'Possible TB' or 'Unlikely TB'. An XGBoost model was trained on 80% (N=135,854) of the data using demographic, clinical, and socio-economic variables, and model interpretability was assessed using SHapley Additive exPlanations (SHAP) values. Internal validation was performed using a 20% hold-out test set (N=33,959). Model performance was assessed using discrimination, calibration, and clinical utility measures compared to the W4SS and against WHO's 2025 Target Product Profile (TPP) for a tool in a two-step screening algorithm. Results: Overall, 16,413 (9.7%) of individuals were labelled as 'Possible TB'. On the test set, the XGBoost model yielded an area under the curve (AUC) of 79.7% (95% CI: 78.7, 80.7), outperforming the W4SS (AUC 57.0%; 95% CI: 56.1, 57.8). The XGBoost model achieved 81.5% sensitivity (95% CI: 77.6, 84.9) at a 60% specificity threshold. This exceeded the W4SS, which achieved only 38.2% sensitivity (95% CI: 36.5, 39.9) on the same dataset. SHAP analysis identified age, previous TB treatment, times treated for TB and unemployment as the primary contributors to risk. Conclusion: The ML XGBoost model shows promise as a screening tool to support community-based active case finding activities prior to diagnostic testing. However, as performance remained below TPP targets, and adding variables, e.g. on geolocation, could be considered.

With significant population fractions in many societies who refuse vaccines, it is important to reconsider how vaccination is incorporated into compartmental epidemiology models. It is still most common to apply the vaccination rate to the entire class of susceptibles, rather than to use the more realistic assumption that the vaccination rate function should depend only on the population of susceptibles who are willing and able to receive a vaccination. This study uses a simple generic disease model to address two questions: (1) How much error is introduced in key model outcomes by neglecting vaccine unwillingness?, and (2) Can the error be reduced by incorporating vaccine unwillingness into the vaccination rate constant rather than the rate diagram? The answers depend greatly on the time scale of interest. For the endemic time scale, where longterm behavior is studied with equilibrium point analysis, the error in neglecting unwillingess is large and cannot be improved upon by decreasing the vaccination rate constant. For the epidemic time scale, where the first big epidemic wave is studied with numerical simulations, the error can still be significant, particularly for diseases that are relatively less infectious and vaccination programs that are relatively slow.

Mycoplasmopsis [Mycoplasma] agassizii is one of the principal pathogens associated with upper respiratory tract disease (URTD) in tortoises, yet its epidemiology in European wild chelonian populations remains poorly understood. The pathogen has been linked to population declines in some wild tortoise populations and is frequently detected in captive tortoises, where infections may persist subclinically and prolonged contact can facilitate transmission. In this context, the pet trade and the release or escape of captive individuals represent potential pathways for pathogen exchange between captive and wild populations. We assessed the presence and prevalence of M. agassizii in wild Mediterranean tortoises in Spain and compared infection patterns with captive populations. A total of 259 tortoises were sampled between 2020 and 2025, including spur thighed tortoises (Testudo graeca; 127 wild; 63 captive) and Hermanns tortoises (Testudo hermanni; 46 wild; 23 captive). Detection of M. agassizii was performed using PCR. The pathogen was detected in both species, but prevalence patterns differed markedly between captivity status and species. High prevalence was consistently observed in captive individuals of both species. In contrast, wild populations showed species-specific patterns: T. graeca exhibited very low or absent prevalence across wild populations, whereas T. hermanni showed comparatively higher prevalence in the wild. These results provide the first baseline assessment of M. agassizii occurrence in Mediterranean tortoises in Spain and highlight the importance of incorporating pathogen surveillance into conservation and management strategies for European chelonian populations.