Modélisation, épidémiologie et surveillance des risques sanitaires

International audience

BACKGROUND: Long-term care facilities (LTCFs) are vulnerable to outbreaks of coronavirus disease 2019 (COVID-19). Timely epidemiological surveillance is essential for outbreak response, but is complicated by a high proportion of silent (non-symptomatic) infections and limited testing resources. METHODS: We used a stochastic, individual-based model to simulate transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) along detailed inter-individual contact networks describing patient-staff interactions in a real LTCF setting. We simulated distribution of nasopharyngeal swabs and reverse transcriptase polymerase chain reaction (RT-PCR) tests using clinical and demographic indications and evaluated the efficacy and resource-efficiency of a range of surveillance strategies, including group testing (sample pooling) and testing cascades, which couple (i) testing for multiple indications (symptoms, admission) with (ii) random daily testing. RESULTS: In the baseline scenario, randomly introducing a silent SARS-CoV-2 infection into a 170-bed LTCF led to large outbreaks, with a cumulative 86 (95% uncertainty interval 6-224) infections after 3 weeks of unmitigated transmission. Efficacy of symptom-based screening was limited by lags to symptom onset and silent asymptomatic and pre-symptomatic transmission. Across scenarios, testing upon admission detected just 34-66% of patients infected upon LTCF entry, and also missed potential introductions from staff. Random daily testing was more effective when targeting patients than staff, but was overall an inefficient use of limited resources. At high testing capacity (> 10 tests/100 beds/day), cascades were most effective, with a 19-36% probability of detecting outbreaks prior to any nosocomial transmission, and 26-46% prior to first onset of COVID-19 symptoms. Conversely, at low capacity (< 2 tests/100 beds/day), group testing strategies detected outbreaks earliest. Pooling randomly selected patients in a daily group test was most likely to detect outbreaks prior to first symptom onset (16-27%), while pooling patients and staff expressing any COVID-like symptoms was the most efficient means to improve surveillance given resource limitations, compared to the reference requiring only 6-9 additional tests and 11-28 additional swabs to detect outbreaks 1-6 days earlier, prior to an additional 11-22 infections. CONCLUSIONS: COVID-19 surveillance is challenged by delayed or absent clinical symptoms and imperfect diagnostic sensitivity of standard RT-PCR tests. In our analysis, group testing was the most effective and efficient COVID-19 surveillance strategy for resource-limited LTCFs. Testing cascades were even more effective given ample testing resources. Increasing testing capacity and updating surveillance protocols accordingly could facilitate earlier detection of emerging outbreaks, informing a need for urgent intervention in settings with ongoing nosocomial transmission.

With social policies increasingly directed toward enhancing equity through health programs, it is important that methods for estimating the health and economic benefits of these programs by subpopulation be developed, to assess both equity concerns and the programs' total impact. We estimated the differential health impact (measured as the number of deaths averted) and household economic impact (measured as the number of cases of medical impoverishment averted) of ten antigens and their corresponding vaccines across income quintiles for forty-one low- and middle-income countries. Our analysis indicated that benefits across these vaccines would accrue predominantly in the lowest income quintiles. Policy makers should be informed about the large health and economic distributional impact that vaccines could have, and they should view vaccination policies as potentially important channels for improving health equity. Our results provide insight into the distribution of vaccine-preventable diseases and the health benefits associated with their prevention.

BACKGROUND: Previous studies have highlighted a range of individual determinants associated with HIV testing but few have assessed the role of contextual factors. The objective of this paper is to examine the influence of both individual and community-level determinants of HIV testing uptake in Burkina Faso. METHODS: Using nationally representative cross-sectional data from the 2010 Demographic and Health Survey, the determinants of lifetime HIV testing were examined for sexually active women (n = 14,656) and men (n = 5680) using modified Poisson regression models. RESULTS: One third of women (36%; 95% Confidence Interval (CI): 33-37%) reported having ever been tested for HIV compared to a quarter of men (26%; 95% CI: 24-27%). For both genders, age, education, religious affiliation, household wealth, employment, media exposure, sexual behaviors, and HIV knowledge were associated with HIV testing. After adjustment, women living in communities where the following characteristics were higher than the median were more likely to report uptake of HIV testing: knowledge of where to access testing (Prevalence Ratio [PR] = 1.41; 95% CI: 1.34-1.48), willing to buy food from an infected vendor (PR = 2.06; 95% CI: 1.31-3.24), highest wealth quintiles (PR = 1.18; 95% CI: 1.10-1.27), not working year-round (PR = 0.90; 95% CI: 0.84-0.96), and high media exposure (PR = 1.11; 95% CI: 1.03-1.19). Men living in communities where the proportion of respondents were more educated (PR = 1.23; 95% CI: 1.07-1.41) than the median were more likely to be tested. CONCLUSIONS: This study shed light on potential mechanisms through which HIV testing could be increased in Burkina Faso. Both individual and contextual factors should be considered to design effective strategies for scaling-up HIV testing.

BACKGROUND: Therapeutic progress in patients with cystic fibrosis (CF) has resulted in improved prognosis over the past decades. We aim to reevaluate prognostic factors of CF and provide a prognostic score to predict the risk of death or lung transplantation (LT) within a 3-year period in adult patients. METHODS: We developed a logistic model using data from the French CF Registry and combined the coefficients into a prognostic score. The discriminative abilities of the model and the prognostic score were assessed by c-statistic. The prognostic score was validated using a 10-fold cross-validation. RESULTS: The risk of death or LT within 3years was related to eight characteristics. The development and the validation provided excellent results for the prognostic score; the c-statistic was 0.91 and 0.90 respectively. CONCLUSION: The score developed to predict 3-year death or LT in adults with CF might be useful for clinicians to identify patients requiring specialized evaluation for LT.

Hospital-acquired infections (HAIs), including emerging multi-drug resistant organisms, threaten healthcare systems worldwide. Efficient containment measures of HAIs must mobilize the entire healthcare network. Thus, to best understand how to reduce the potential scale of HAI epidemic spread, we explore patient transfer patterns in the French healthcare system. Using an exhaustive database of all hospital discharge summaries in France in 2014, we construct and analyze three patient networks based on the following: transfers of patients with HAI (HAI-specific network); patients with suspected HAI (suspected-HAI network); and all patients (general network). All three networks have heterogeneous patient flow and demonstrate small-world and scale-free characteristics. Patient populations that comprise these networks are also heterogeneous in their movement patterns. Ranking of hospitals by centrality measures and comparing community clustering using community detection algorithms shows that despite the differences in patient population, the HAI-specific and suspected-HAI networks rely on the same underlying structure as that of the general network. As a result, the general network may be more reliable in studying potential spread of HAIs. Finally, we identify transfer patterns at both the French regional and departmental (county) levels that are important in the identification of key hospital centers, patient flow trajectories, and regional clusters that may serve as a basis for novel wide-scale infection control strategies.

BACKGROUND: Overall increases in the uptake of HIV testing in the past two decades might hide discrepancies across socioeconomic groups. We used data from population-based surveys done in sub-Saharan Africa to quantify socioeconomic inequalities in uptake of HIV testing, and to establish trends in testing uptake in the past two decades. METHODS: We analysed data from 16 countries in sub-Saharan Africa where at least one Demographic and Health Survey was done before and after 2008. We assessed the country-specific and sex-specific proportions of participants who had undergone HIV testing in the previous 12 months across wealth and education groups, and quantified socioeconomic inequalities with both the relative and slope indices of inequalities. We assessed time trends in inequalities, and calculated mean results across countries with random-effects meta-analyses. FINDINGS: We analysed data for 537 784 participants aged 15-59 years (most aged 15-49 years) from 32 surveys done between 2003 and 2016 (16 before 2008, and 16 after 2008) in Cameroon, Côte d'Ivoire, DR Congo, Ethiopia, Guinea, Kenya, Lesotho, Liberia, Malawi, Mali, Niger, Rwanda, Sierra Leone, Tanzania, Zambia, and Zimbabwe. A higher proportion of female participants than male participants reported uptake of HIV testing in the previous 12 months in five of 16 countries in the pre-2008 surveys, and in 14 of 16 countries in the post-2008 surveys. After 2008, in the overall sample, the wealthiest female participants were 2·77 (95% CI 1·42-5·40) times more likely to report HIV testing in the previous 12 months than were the poorest female participants, whereas the richest male participants were 3·55 (1·85-6·81) times more likely to report HIV testing than in the poorest male participants. The mean absolute difference in uptake of HIV testing between the richest and poorest participants was 11·1 (95% CI 4·6-17·5) percentage points in female participants and 15·1 (9·6-20·6) in male participants. Over time (ie, when pre-2008 and post-2008 data were compared), socioeconomic inequalities in the uptake of HIV testing in the previous 12 months decreased in male and female participants, whereas absolute inequalities remained similar in female participants and increased in male participants. INTERPRETATION: Although relative socioeconomic inequalities in uptake of HIV testing in sub-Saharan Africa has decreased, absolute inequalities have persisted or increased. Greater priority should be given to socioeconomic equity in assessments of HIV-testing programmes. FUNDING: INSERM-ANRS (France Recherche Nord and Sud Sida-HIV Hépatites).

Abstract Despite previous evidence of high level of efficacy, no synthetic metric of yellow fever (YF) vaccine efficacy is currently available. Based on the studies identified in a recent systematic review, we conducted a random-effects meta-analysis of the serological response associated with YF vaccination. Eleven studies conducted between 1965 and 2011 representing 4,868 individual observations were included in the meta-analysis. The pooled estimate of serological response was 97.5% (95% confidence interval [CI] = 82.9–99.7%). There was evidence of between-study heterogeneity ( I 2 = 89.1%), but this heterogeneity did not appear to be related to study size, study design, or seroconversion measurement or definition. Pooled estimates were significantly higher ( P &lt; 0.0001) among studies conducted in nonendemic settings (98.9%, 95% CI = 98.2–99.4%) than among those conducted in endemic settings (94.2%, 95% CI = 83.8–98.1%). These results provide background information against which to evaluate the efficacy of fractional doses of YF vaccine that may be used in outbreak situations.

INTRODUCTION: Studies provide evidence that reduced nurse staffing resources are associated to an increase in health care-associated infections in intensive care units, but tools to assess the contribution of the mechanisms driving these relations are still lacking. We present an agent-based model of pathogen spread that can be used to evaluate the impact on nosocomial risk of alternative management decisions adopted to deal with transitory nurse shortage. MATERIALS AND METHODS: We constructed a model simulating contact-mediated dissemination of pathogens in an intensive-care unit with explicit staffing where nurse availability could be temporarily reduced while maintaining requisites of patient care. We used the model to explore the impact of alternative management decisions adopted to deal with transitory nurse shortage under different pathogen- and institution-specific scenarios. Three alternative strategies could be adopted: increasing the workload of working nurses, hiring substitute nurses, or transferring patients to other intensive-care units. The impact of these decisions on pathogen spread was examined while varying pathogen transmissibility and severity of nurse shortage. RESULTS: The model-predicted changes in pathogen prevalence among patients were impacted by management decisions. Simulations showed that increasing nurse workload led to an increase in pathogen spread and that patient transfer could reduce prevalence of pathogens among patients in the intensive-care unit. The outcome of nurse substitution depended on the assumed skills of substitute nurses. Differences between predicted outcomes of each strategy became more evident with increasing transmissibility of the pathogen and with higher rates of nurse shortage. CONCLUSIONS: Agent-based models with explicit staff management such as the model presented may prove useful to design staff management policies that mitigate the risk of healthcare-associated infections under episodes of increased nurse shortage.

BACKGROUND: High turnover among healthcare workers is an increasingly common phenomenon in hospitals worldwide, especially in intensive care units (ICUs). In addition to the serious financial consequences, this is a major concern for patient care (disrupted continuity of care, decreased quality and safety of care, increased rates of medication errors, …). OBJECTIVE: The goal of this article was to understand how the ICU-level nurse turnover rate may be explained from multiple covariates at individual and ICU-level, using data from 526 French registered and auxiliary nurses (RANs). METHODS: A cross-sectional study was conducted in ICUs of Paris-area hospitals in 2013. First, we developed a small extension of a multi-level modeling method proposed in 2007 by Croon and van Veldhoven and validated its properties using a comprehensive simulation study. Second, we applied this approach to explain RAN turnover in French ICUs. RESULTS: Based on the simulation study, the approach we proposed allows to estimate the regression coefficients with a relative bias below 7% for group-level factors and below 12% for individual-level factors. In our data, the mean observed RAN turnover rate was 0.19 per year (SD = 0.09). Based on our results, social support from colleagues and supervisors as well as long durations of experience in the profession were negatively associated with turnover. Conversely, number of children and impossibility to skip a break due to workload were significantly associated with higher rates of turnover. At ICU-level, number of beds, presence of intermediate care beds (continuous care unit) in the ICU and staff-to-patient ratio emerged as significant predictors. CONCLUSIONS: The findings of this research may help decision makers within hospitals by highlighting major determinants of turnover among RANs. In addition, the new approach proposed here could prove useful to researchers faced with similar micro-macro data.

BACKGROUND: Hospitals are suspected of playing a key role in HCV epidemic dynamics in Egypt. This work aimed at assessing HCV prevalence and associated risk factors in patients and health-care workers (HCWs) of Ain Shams University (ASU) hospitals in Cairo. METHODS: We included 500 patients admitted to the internal medicine or surgery hospital from February to July, 2017, as well as 50 HCWs working in these same hospitals. Participants were screened for anti-HCV antibodies and HCV RNA. A questionnaire was administered to collect data on demographic characteristics and medical/surgical history. For HCWs, questions on occupational exposures and infection control practices were also included. RESULTS: The overall prevalence of anti-HCV antibodies was 19.80% (95% CI: 16.54-23.52) among participating patients, and 8.00% (95% CI: 0.48-15.52) among participating HCWs. In HCWs, the only risk factors significantly associated with anti-HCV antibodies were age and profession, with higher prevalence in older HCWs and those working as cleaners or porters. In patients, in a multivariate logistic regression, age over 50 (aOR: 3.4 [1.9-5.8]), living outside Cairo (aOR: 2.1 [1.2-3.4]), admission for liver or gastro-intestinal complaints (aOR: 4.2 [1.8-9.9]), and history of receiving parenteral anti-schistosomiasis treatment (aOR: 2.7 [1.2-5.9]) were found associated with anti-HCV antibodies. CONCLUSIONS: While HCV prevalence among patients has decreased since the last survey performed within ASU hospitals in 2008, it is still significantly higher than in the general population. These results may help better control further HCV spread within healthcare settings in Egypt by identifying at-risk patient profiles upon admission.

Healthcare facilities are vulnerable to SARS-CoV-2 introductions and subsequent nosocomial outbreaks. Antigen rapid diagnostic testing (Ag-RDT) is widely used for population screening, but its health and economic benefits as a reactive response to local surges in outbreak risk are unclear. We simulate SARS-CoV-2 transmission in a long-term care hospital with varying COVID-19 containment measures in place (social distancing, face masks, vaccination). Across scenarios, nosocomial incidence is reduced by up to 40-47% (range of means) with routine symptomatic RT-PCR testing, 59-63% with the addition of a timely round of Ag-RDT screening, and 69-75% with well-timed two-round screening. For the latter, a delay of 4-5 days between the two screening rounds is optimal for transmission prevention. Screening efficacy varies depending on test sensitivity, test type, subpopulations targeted, and community incidence. Efficiency, however, varies primarily depending on underlying outbreak risk, with health-economic benefits scaling by orders of magnitude depending on the COVID-19 containment measures in place.

BACKGROUND: COVID-19 outbreaks still occur in English care homes despite the interventions in place. METHODS: We developed a stochastic compartmental model to simulate the spread of SARS-CoV-2 within an English care home. We quantified the outbreak risk with baseline non-pharmaceutical interventions (NPIs) already in place, the role of community prevalence in driving outbreaks, and the relative contribution of all importation routes into a fully susceptible care home. We also considered the potential impact of additional control measures in care homes with and without immunity, namely: increasing staff and resident testing frequency, using lateral flow antigen testing (LFD) tests instead of polymerase chain reaction (PCR), enhancing infection prevention and control (IPC), increasing the proportion of residents isolated, shortening the delay to isolation, improving the effectiveness of isolation, restricting visitors and limiting staff to working in one care home. We additionally present a Shiny application for users to apply this model to their facility of interest, specifying care home, outbreak and intervention characteristics. RESULTS: The model suggests that importation of SARS-CoV-2 by staff, from the community, is the main driver of outbreaks, that importation by visitors or from hospitals is rare, and that the past testing strategy (monthly testing of residents and daily testing of staff by PCR) likely provides negligible benefit in preventing outbreaks. Daily staff testing by LFD was 39% (95% 18-55%) effective in preventing outbreaks at 30 days compared to no testing. CONCLUSIONS: Increasing the frequency of testing in staff and enhancing IPC are important to preventing importations to the care home. Further work is needed to understand the impact of vaccination in this population, which is likely to be very effective in preventing outbreaks.

The human microbiome can protect against colonization with pathogenic antibiotic-resistant bacteria (ARB), but its impacts on the spread of antibiotic resistance are poorly understood. We propose a mathematical modeling framework for ARB epidemiology formalizing within-host ARB-microbiome competition, and impacts of antibiotic consumption on microbiome function. Applied to the healthcare setting, we demonstrate a trade-off whereby antibiotics simultaneously clear bacterial pathogens and increase host susceptibility to their colonization, and compare this framework with a traditional strain-based approach. At the population level, microbiome interactions drive ARB incidence, but not resistance rates, reflecting distinct epidemiological relevance of different forces of competition. Simulating a range of public health interventions (contact precautions, antibiotic stewardship, microbiome recovery therapy) and pathogens ( Clostridioides difficile , methicillin-resistant Staphylococcus aureus , multidrug-resistant Enterobacteriaceae) highlights how species-specific within-host ecological interactions drive intervention efficacy. We find limited impact of contact precautions for Enterobacteriaceae prevention, and a promising role for microbiome-targeted interventions to limit ARB spread.

BACKGROUND: Deaths due to vaccine preventable diseases cause a notable proportion of mortality worldwide. To quantify the importance of vaccination, it is necessary to estimate the burden averted through vaccination. The Vaccine Impact Modelling Consortium (VIMC) was established to estimate the health impact of vaccination. METHODS: We describe the methods implemented by the VIMC to estimate impact by calendar year, birth year and year of vaccination (YoV). The calendar and birth year methods estimate impact in a particular year and over the lifetime of a particular birth cohort, respectively. The YoV method estimates the impact of a particular year's vaccination activities through the use of impact ratios which have no stratification and stratification by activity type and/or birth cohort. Furthermore, we detail an impact extrapolation (IE) method for use between coverage scenarios. We compare the methods, focusing on YoV for hepatitis B, measles and yellow fever. RESULTS: We find that the YoV methods estimate similar impact with routine vaccinations but have greater yearly variation when campaigns occur with the birth cohort stratification. The IE performs well for the YoV methods, providing a time-efficient mechanism for updates to impact estimates. CONCLUSIONS: These methods provide a robust set of approaches to quantify vaccination impact; however it is vital that the area of impact estimation continues to develop in order to capture the full effect of immunisation.

The spread of carbapenemase-producing Enterobacteriaceae (CPE) in healthcare settings is a major public health threat that has been associated with cross-border and local patient transfers between healthcare facilities. Since the impact of transfers on spread may vary, our study aimed to assess the contribution of a patient transfer network on CPE incidence and spread at a countrywide level, with a case study of France from 2012 to 2015. Our results suggest a transition in 2013 from a CPE epidemic sustained by internationally imported episodes to an epidemic sustained by local transmission events through patient transfers. Incident episodes tend to occur within close spatial distance of their potential infector. We also observe an increasing frequency of multiple spreading events, originating from a limited number of regional hubs. Consequently, coordinated prevention and infection control strategies should focus on transfers of carriers of CPE to reduce regional and inter-regional transmission.

Antimicrobial resistance (AMR) has become a major threat worldwide, especially in countries with inadequate sanitation and low antibiotic regulation. However, adequately prioritizing AMR interventions in such settings requires a quantification of the relative impacts of environmental, animal, and human sources in a One-Health perspective. Here, we propose a stochastic quantitative risk assessment model for the different components at interplay in AMR selection and spread. The model computes the incidence of AMR colonization in humans from five different sources: water or food consumption, contacts with livestock, and interhuman contacts in hospitals or the community, and combines these incidences into a per-year acquisition risk. Using data from the literature and Monte-Carlo simulations, we apply the model to hypothetical Asian-like settings, focusing on resistant bacteria that may cause infections in humans. In both scenarios A, illustrative of low-income countries, and B, illustrative of high-income countries, the overall individual risk of becoming colonized with resistant bacteria at least once per year is high. However, the average predicted incidence of colonization was lower in scenario B at 0.82 (CrI [0.13, 5.1]) acquisitions/person/year, versus 1.69 (CrI [0.66, 11.13]) acquisitions/person/year for scenario A. A high percentage of population with no access to improved water on premises and a high percentage of population involved in husbandry are shown to strongly increase the AMR acquisition risk. The One-Health AMR risk assessment framework we developed may prove useful to policymakers throughout Asia, as it can easily be parameterized to realistically reproduce conditions in a given country, provided data are available.

BACKGROUND: Circulation of multidrug-resistant bacteria (MRB) in healthcare facilities is a major public health problem. These settings have been greatly impacted by the Coronavirus Disease 2019 (COVID-19) pandemic, notably due to surges in COVID-19 caseloads and the implementation of infection control measures. We sought to evaluate how such collateral impacts of COVID-19 impacted the nosocomial spread of MRB in an early pandemic context. METHODS AND FINDINGS: We developed a mathematical model in which Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) and MRB cocirculate among patients and staff in a theoretical hospital population. Responses to COVID-19 were captured mechanistically via a range of parameters that reflect impacts of SARS-CoV-2 outbreaks on factors relevant for pathogen transmission. COVID-19 responses include both "policy responses" willingly enacted to limit SARS-CoV-2 transmission (e.g., universal masking, patient lockdown, and reinforced hand hygiene) and "caseload responses" unwillingly resulting from surges in COVID-19 caseloads (e.g., abandonment of antibiotic stewardship, disorganization of infection control programmes, and extended length of stay for COVID-19 patients). We conducted 2 main sets of model simulations, in which we quantified impacts of SARS-CoV-2 outbreaks on MRB colonization incidence and antibiotic resistance rates (the share of colonization due to antibiotic-resistant versus antibiotic-sensitive strains). The first set of simulations represents diverse MRB and nosocomial environments, accounting for high levels of heterogeneity across bacterial parameters (e.g., rates of transmission, antibiotic sensitivity, and colonization prevalence among newly admitted patients) and hospital parameters (e.g., rates of interindividual contact, antibiotic exposure, and patient admission/discharge). On average, COVID-19 control policies coincided with MRB prevention, including 28.2% [95% uncertainty interval: 2.5%, 60.2%] fewer incident cases of patient MRB colonization. Conversely, surges in COVID-19 caseloads favoured MRB transmission, resulting in a 13.8% [-3.5%, 77.0%] increase in colonization incidence and a 10.4% [0.2%, 46.9%] increase in antibiotic resistance rates in the absence of concomitant COVID-19 control policies. When COVID-19 policy responses and caseload responses were combined, MRB colonization incidence decreased by 24.2% [-7.8%, 59.3%], while resistance rates increased by 2.9% [-5.4%, 23.2%]. Impacts of COVID-19 responses varied across patients and staff and their respective routes of pathogen acquisition. The second set of simulations was tailored to specific hospital wards and nosocomial bacteria (methicillin-resistant Staphylococcus aureus, extended-spectrum beta-lactamase producing Escherichia coli). Consequences of nosocomial SARS-CoV-2 outbreaks were found to be highly context specific, with impacts depending on the specific ward and bacteria evaluated. In particular, SARS-CoV-2 outbreaks significantly impacted patient MRB colonization only in settings with high underlying risk of bacterial transmission. Yet across settings and species, antibiotic resistance burden was reduced in facilities with timelier implementation of effective COVID-19 control policies. CONCLUSIONS: Our model suggests that surges in nosocomial SARS-CoV-2 transmission generate selection for the spread of antibiotic-resistant bacteria. Timely implementation of efficient COVID-19 control measures thus has 2-fold benefits, preventing the transmission of both SARS-CoV-2 and MRB, and highlighting antibiotic resistance control as a collateral benefit of pandemic preparedness.

Objectives: Energy transition scenarios are prospective outlooks describing combinations of changes in socio-economic systems that are compatible with climate targets. These changes could have important health co-benefits. We aimed to quantify the health benefits of physical activity caused by active transportation on all-cause mortality in the French negaWatt scenario over the 2021–2050 period. Methods; Relying on a health impact assessment framework, we quantified the health benefits of increased walking, cycling and E-biking projected in the negaWatt scenario. The negaWatt scenario assumes increases of walking and cycling volumes of +11% and +612%, respectively, over the study period. Results: As compared to a scenario with no increase in volume of active travel, we quantified that the negaWatt scenario would prevent 9,797 annual premature deaths in 2045 and translate into a 3-month increase in life expectancy in the general population. These health gains would generate €34 billion of economic benefits from 2045 onwards. Conclusion: Increased physical activity implied in the negaWatt transition scenario would generate substantial public health benefits, which are comparable to the gain expected by large scale health prevention interventions.

Recent yellow fever (YF) outbreaks have highlighted the increasing global risk of urban spread of the disease. In context of recurrent vaccine shortages, preventive vaccination activities require accurate estimates of existing population-level immunity. We present POLICI (POpulation-Level Immunization Coverage - Imperial), an interactive online tool for visualising and extracting YF vaccination coverage estimates in Africa. We calculated single year age-disaggregated sub-national population-level vaccination coverage for 1950-2050 across the African endemic zone by collating vaccination information and inputting it into a demographic model. This was then implemented on an open interactive web platform. POLICI interactively displays age-disaggregated, population-level vaccination coverages at the first subnational administrative level, through numerous downloadable and customisable visualisations. POLICI is available at https://polici.shinyapps.io/yellow_fever_africa/. POLICI offers an accessible platform for relevant stakeholders in global health to access and explore vaccination coverages. These estimates have already been used to inform the WHO strategy to Eliminate Yellow fever Epidemics (EYE).