Nicolas Banholzer

Reducing the global burden of disease is crucial for improving health outcomes. However, misalignment between health aid and country-level disease burden leaves vulnerable populations without the necessary support for major health challenges, particularly in the least developed countries. Here, we develop a machine learning pipeline using large language models to track flows in official development assistance (ODA) earmarked for health and identify aid–burden misalignment. We classified 3.7 million development aid projects from 2000 to 2022 (USD ~ 332 billion) into 17 major categories of communicable, maternal, neonatal, and nutritional diseases (CMNNDs) and non-communicable diseases (NCDs). We compared the rank of per capita ODA disbursement against the rank of disease burden, measured in disability-adjusted life years (DALYs). We interpret DALY-based alignment as a policy-relevant heuristic rather than a prescriptive allocation criterion. Although funding and disease burden are significantly correlated for many diseases, there are notable disparities. For example, NCDs account for 59.5% of global DALYs but received only 2.5% of health-related ODA over the study period. This is concerning because low- and middle-income countries face an increasing double burden from both CMNNDs and NCDs. Our results show aid–burden misalignment across multiple diseases in several regions, including Central Africa and parts of South Asia and West Africa. Overall, our results identify health disparities to potentially inform policy decisions on development assistance and support targeted allocation of health aid.

Introduction: Despite global progress in tuberculosis (TB) control, treatment outcomes remain suboptimal, particularly in high-burden settings and among people with HIV or drug-resistant TB. Identifying predictors of unsuccessful treatment is essential to improve TB care and policy. Methods: We evaluated TB treatment outcomes and patient characteristics associated with unsuccessful outcomes in five cohorts of the International epidemiology database to evaluate AIDS (IeDEA); Center for Infectious Disease Research, Zambia; Chiure health center, Mozambique; Martin Preuss Center, Lighthouse clinic, Malawi; Masvingo health center Zimbabwe; and Themba Lethu clinic, Hellen Joseph hospital, South Africa. We included all patients with TB aged ≥ 15 years starting TB treatment and assessed their treatment outcomes in association with sociodemographic and clinical characteristics using multivariable mixed-effects models. Unsuccessful outcomes were defined as death, loss to follow-up and treatment failure. Results: Among 1438 people with TB, median age was 39 years, 67% males, 40% with HIV, and 4% with MDR-TB; 1151 (80%) treatment outcomes were successful (606 cured and 545 completed treatment), 221 (15%) unsuccessful (89 deaths, 129 loss to follow-up and 3 treatment failures), and 66 (5%) other (49 unknown and 17 transfer-outs). Unsuccessful outcomes were more probable among people with multidrug-resistant TB (MDR-TB) and among participants without formal education. Risk of death was lower for people with bacteriologically confirmed TB (adjusted odds ratio (aOR) 0.5, 95%-credible interval [CI] 0.25-0.80), those with a secondary or higher education (aOR 0.3, 95%-CI 0.13-0.69) and BMI ≥18 kg/m² (aOR 0.6, 95%-CI 0.36-0.99). MDR-TB was associated with an increase (aOR 2.4 95%-CI 1.17-4.97) and primary and secondary or higher education with a decrease in loss to follow-up (aOR 0.3, 95%-CI 0.14-0.89 and aOR 0.3, 95%-CI 0.11-0.67, respectively). Conclusions: TB treatment outcomes fell short of the targets set by the World Health Organization of <10% unsuccessful outcomes, indicating a critical need for enhanced management strategies. Tackling loss to follow-up is crucial, especially among MDR-TB patients, including stronger retention activities and improved diagnostic capacities.

Decision-making by governments during disease outbreaks is increasingly reliant on large-scale pathogen genomics and detailed individual-level data. We use 293,841 SARS-CoV-2 genomes collected in Denmark, between September 1st 2020 and December 31st 2021, and combine these with comprehensive individual-level data on settings, including households, schools, workplaces and family relationships, to infer plausible transmission pathways. Next, we use the plausible transmission pathways to assess the effectiveness of specific non-pharmaceutical interventions (NPIs) and quantify transmission heterogeneities, providing a more detailed understanding than is possible from aggregate national estimates. School restrictions were associated with reduced community transmission; face coverings and gathering restrictions were associated with reduced transmission across all settings. We find that those vaccinated with two doses, compared to the unvaccinated, had onward transmission reduced by 23.5% (95% highest posterior density interval: 21.5-25.2%). Our proposed approach is pathogen agnostic, and can be used in future outbreaks where genomic data and data on social relationships are available.

Background: Hospitals in high-burden tuberculosis (TB) settings are important sites of Mycobacterium tuberculosis ( Mtb) transmission, yet the impact of infection prevention and control (IPC) measures targeting crowding is poorly understood. We assessed the effects of simple interventions to reduce spatial clustering and airborne transmission in a Zambian hospital. Methods and findings: From June to August 2024, we prospectively collected clinical data on presumptive (symptom-based screening) and confirmed (Xpert-positive, chest X-ray) TB patients, indoor CO ₂ levels, bioaerosol samples, and continuous person movements (optical sensors) in the hospital’s main waiting hall. Airborne Mtb DNA was detected using hourly cyclonic bioaerosol sampling. Using a spatiotemporal Wells–Riley model integrating ventilation, proximity between visitors, and movement patterns, we estimated Mtb transmission risk under routine conditions and during two IPC interventions: (1) an optimised waiting-area layout with physical distancing measures; and (2) an added one-way patient flow system. During 52 days, 668 presumptive and 45 confirmed TB patients visited the hospital, and 671 840 person movements were recorded. Despite excellent natural ventilation (median CO ₂ 455 ppm; 8.0 air changes per hour), airborne Mtb was detected on six days. The first intervention reduced spatial clustering by an estimated 24% (95% credible interval [CrI] 13–32) and the second by 13% (95% CrI 1–23). The interventions lowered Mtb transmission risk by an estimated 39% (95% CrI 29–48) and 21% (95% CrI 9–32), respectively. Over the four weeks of implementation, they collectively averted an estimated 16 (95% CrI 8–26) infections. Conclusions: In this high-burden, well-ventilated hospital, short-range exposure remained a driver of airborne Mtb transmission. Simple, low-cost operational changes to reduce clustering substantially decreased proximity-driven transmission risk. Integrating proximity-focused strategies can meaningfully strengthen IPC in resource-limited healthcare settings.

Close-proximity interactions are considered a key risk factor for respiratory virus transmission, but their importance relative to shared space and air quality remains unclear. We conducted a six-week longitudinal study in a Swiss secondary school (67 students, aged 14–15). We detected 87 infections in saliva samples and recorded absences to identify plausible transmissions, excluding implausible ones through genomic analysis. Time in close proximity (within 1.5 metres) was measured using wearable sensors and air quality via CO ₂ monitors. Students spent 21.2 minutes per day in close proximity (interquartile range 7.8–44.2) and 5.3 hours in shared classrooms (IQR 3.8–6.2), during which air quality was suboptimal for 1.9 hours (IQR 1.2–3.0). Using pairwise survival models, we found that transmission was more likely within than between classes. Close proximity was modestly associated with higher transmission risk overall (rate ratio 1.16 per doubling daily time, 95%-CI 1.01–1.33), while time in shared classrooms (RR 3.17, 95%-CI 1.96–5.17) and suboptimal air quality (RR 1.90 95%-CI 1.23–2.94) also predicted within-class risk. Prolonged exposure in shared, poorly ventilated spaces, which potentially includes several infectious sources, drives respiratory virus transmission more than close contact.

Close-proximity interactions are considered a key risk factor for respiratory virus transmission, but their importance relative to shared space and air quality remains unclear. We conducted a six-week longitudinal study in a Swiss secondary school (67 students, aged 14–15). We detected 87 infections in saliva samples and recorded absences to identify plausible transmissions, excluding implausible ones through genomic analysis. Time in close proximity (within 1.5 metres) was measured using wearable sensors and air quality via CO₂ monitors. Students spent 21.2 minutes per day in close proximity (interquartile range 7.8–44.2) and 5.3 hours in shared classrooms (IQR 3.8–6.2), during which air quality was suboptimal for 1.9 hours (IQR 1.2–3.0). Using pairwise survival models, we found that transmission was more likely within than between classes. Close proximity was modestly associated with higher transmission risk overall (rate ratio 1.16 per doubling daily time, 95%-CI 1.01–1.33), while time in shared classrooms (RR 8.96, 95%-CI 4.85–16.88) and suboptimal air quality (RR 5.59 95%-CI 3.25–9.83) also predicted within-class risk. Prolonged exposure in shared, poorly ventilated spaces drives respiratory virus transmission more than close contact. Supplementary Information Supplementary methods and results for the genetic and pairwise survival analysis as well as genomic sequence accession codes.

Background: In our research, we investigated student state and habitual well‐being in school during and in the aftermath of the COVID‐19 pandemic (after schools reopened) and analyzed their associations to measures in schools aimed at preventing ongoing infectious diseases.Methods and Measures: We conducted two interventional field studies in Swiss lower secondary education (Grades 8 and 9, students aged 13–15 years) to compare trait and state well‐being during three different study conditions: no infection control measure, facial mask mandates, and the temporary installation of portable air cleaners in classrooms.Results: In Study 1, student enjoyment in school significantly decreased over time and their physical complaints increased. Students reported significantly higher negative activation with masks and air filters when compared to no infectious control measures. However, their negative activation significantly decreased over time with the mask, whereas it increased without measures. In Study 2, student worries in school significantly decreased over time. In one class, students reported an increase in positive activation and valence without air cleaners. In the other class, positive activation and concentration increased with air cleaners and negative activation decreased without air filters.Implications for School Health and Conclusions: Results suggest that infection control measures in school can have short term positive and negative effects on students' self‐reported state well‐being while not significantly impeding student's habitual well‐being. For school health, students might benefit psychologically in times of crisis when they understand the implementation of measures as actions of care and safety.

The COVID-19 pandemic renewed interest in airborne transmission of respiratory infections, particularly in congregate indoor settings, such as schools. We modeled transmission risks of tuberculosis (caused by Mycobacterium tuberculosis, Mtb) and COVID-19 (caused by SARS-CoV-2) in South African, Swiss and Tanzanian secondary schools. We estimated the risks of infection with the Wells-Riley equation, expressed as the median with 2.5% and 97.5% quantiles (credible interval [CrI]), based on the ventilation rate and the duration of exposure to infectious doses (so-called quanta). We computed the air change rate (ventilation) using carbon dioxide (CO₂) as a tracer gas and modeled the quanta generation rate based on reported estimates from the literature. The share of infectious students in the classroom is determined by country-specific estimates of pulmonary TB. For SARS-CoV-2, the number of infectious students was estimated based on excess mortality to mitigate the bias from country-specific reporting and testing. Average CO₂ concentration (parts per million [ppm]) was 1,610 ppm in South Africa, 1,757 ppm in Switzerland, and 648 ppm in Tanzania. The annual risk of infection for Mtb was 22.1% (interquartile range [IQR] 2.7%-89.5%) in South Africa, 0.7% (IQR 0.1%-6.4%) in Switzerland, and 0.5% (IQR 0.0%-3.9%) in Tanzania. For SARS-CoV-2, the monthly risk of infection was 6.8% (IQR 0.8%-43.8%) in South Africa, 1.2% (IQR 0.1%-8.8%) in Switzerland, and 0.9% (IQR 0.1%-6.6%) in Tanzania. The differences in transmission risks primarily reflect a higher incidence of SARS-CoV-2 and particularly prevalence of TB in South Africa, but also higher air change rates due to better natural ventilation of the classrooms in Tanzania. Global comparisons of the modeled risk of infectious disease transmission in classrooms can provide high-level information for policy-making regarding appropriate infection control strategies.

BACKGROUND: Given their high-risk resident population, nursing homes were critical institutions in the COVID-19 pandemic, calling for continued monitoring and vaccine administration to healthcare workers and residents. Here, we studied long-term severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) immunity in vaccinated and unvaccinated healthcare workers and residents of a nursing home in Switzerland between February 2021 and June 2022. METHODS: Our study comprised 45 participants, of which 39 were healthcare workers and six were residents. All participants were offered a maximum of three mRNA vaccine doses (Pfizer/BioNTech, BNT162b2) in December 2020, January 2021, and November/December 2021. Thirty-five participants received three vaccinations, seven either one or two, and three remained unvaccinated. We collected four blood samples: one in March 2021 and three during follow-ups in November 2021, February 2022, and June 2022. We performed a multifactorial serological SARS-CoV-2 assay (ABCORA) for immunoglobulin G, A, and M responses to spike (receptor-binding domain, S1, and S2) and nucleocapsid (N) proteins. Furthermore, we assessed predicted neutralisation activity based on signal over cutoff in ABCORA. We collected epidemiological data from participants via a standardised questionnaire. RESULTS: Thirty-two (71%) of the 45 participants showed hybrid immunity from combined vaccination and previous infection; 10 (22%) had only vaccine-induced immunity; and three (7%) had only post-infection immunity. Participants with hybrid immunity showed the highest predicted neutralisation activity at the end of the study period (median Sum S1 = 273), and unvaccinated participants showed the lowest (median Sum S1 = 41). Amongst participants who reported a SARS-CoV-2 infection, median Sum S1 levels increased with the number of vaccinations (p = 0.077). The healthcare worker group showed a significant time-dependent decrease in median Sum S1 after base immunisation (93% decrease, p = 0.0005) and the booster dose (26% decrease, p = 0.010). Predicted neutralisation activity was lower amongst residents (adjusted ratio of means [AM] = 0.7, 95% confidence interval [CI] = 0.3–1.0) and amongst smokers (AM = 0.5, 95% CI 0.3–0.8). Activity increased with the number of vaccinations (booster: AM = 3.6, 95% CI 1.5–8.8; no booster: AM = 2.3, 95% CI 0.9–2.5). Positive SARS-CoV-2 infection status tended to confer higher predicted neutralisation levels (AM = 1.5, 95% CI 0.9–2.5). CONCLUSIONS: Our study of the long-term serological course of SARS-CoV-2 in a nursing home showed that the first SARS-CoV-2 booster vaccine was essential for maintaining antiviral antibody levels. Hybrid immunity sustained SARS-CoV-2 immunity at the highest level. In critical settings such as nursing homes, monitoring the SARS-CoV-2 immune status may guide booster vaccinations.