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Background: The emergence of Corona virus disease (COVID-19) and wide-spread counter-measures, such as quarantine and social distancing, can have a significant impact on mental health of individuals. Methods: This online study anonymously screened 13,332 individuals worldwide for acute psychological symptoms related to Corona virus disease 2019 (COVID-19) pandemic from March 29th to April 14th, 2020. A total of n=12,817 responses were considered valid. n=1077 participants from Europe were screened a second time during May 15th to May 30th to longitudinally ascertain the persistence of psychological effects. Results: Female gender, pre-existing psychiatric condition, and prior exposure to trauma were identified as notable factors associated with increased acute psychological symptoms during COVID-19. The same factors, in addition to, being related to someone who demised due to COVID-19 and using social media more than usual predicted persistence of psychological disturbances. Optimism, ability to share concerns with family and friends like usual, positive prediction about COVID-19, and daily exercise predicted fewer acute and persistent psychological symptoms. Conclusions: Females, psychiatric patients, and individuals with previous exposure to trauma are at increased risk of being persistently psychologically affected by COVID-19.

This study anonymously screened 13,332 individuals worldwide for psychological symptoms related to Corona virus disease 2019 (COVID-19) pandemic from March 29th to April 14th, 2020. A total of n=12,817 responses were considered valid with responses from 12 featured countries and five WHO regions. Female gender, pre-existing psychiatric condition, and prior exposure to trauma were identified as notable risk factors, whereas optimism, ability to share concerns with family and friends like usual, positive prediction about COVID-19, and daily exercise predicted fewer psychological symptoms. These results could aid in dynamic optimization of mental health services during and following COVID-19 pandemic.

Summary Background To move towards ending AIDS by 2030, HIV resources should be allocated cost-effectively. We used the Optima HIV model to estimate how global HIV resources could be retargeted for greatest epidemiological effect and how many additional new infections could be averted by 2030. Methods We collated standard data used in country modelling exercises (including demographic, epidemiological, behavioural, programmatic, and expenditure data) from Jan 1, 2000, to Dec 31, 2015 for 44 countries, capturing 80% of people living with HIV worldwide. These data were used to parameterise separate subnational and national models within the Optima HIV framework. To estimate optimal resource allocation at subnational, national, regional, and global levels, we used an adaptive stochastic descent optimisation algorithm in combination with the epidemic models and cost functions for each programme in each country. Optimal allocation analyses were done with international HIV funds remaining the same to each country and by redistributing these funds between countries. Findings Without additional funding, if countries were to optimally allocate their HIV resources from 2016 to 2030, we estimate that an additional 7·4 million (uncertainty range 3·9 million–14·0 million) new infections could be averted, representing a 26% (uncertainty range 13–50%) incidence reduction. Redistribution of international funds between countries could avert a further 1·9 million infections, which represents a 33% (uncertainty range 20–58%) incidence reduction overall. To reduce HIV incidence by 90% relative to 2010, we estimate that more than a three-fold increase of current annual funds will be necessary until 2030. The most common priorities for optimal resource reallocation are to scale up treatment and prevention programmes targeting key populations at greatest risk in each setting. Prioritisation of other HIV programmes depends on the epidemiology and cost-effectiveness of service delivery in each setting as well as resource availability. Interpretation Further reductions in global HIV incidence are possible through improved targeting of international and national HIV resources. Funding World Bank and Australian NHMRC.

BackgroundWithin many sub-Saharan African countries including Malawi, HIV prevalence varies widely between regions. This variability may be related to the distribution of population groups with specific sociobehavioural characteristics that influence the transmission of HIV and the uptake of prevention. In this study, we intended to identify groups of people in Malawi with similar risk profiles.MethodsWe used data from the Demographic and Health Survey in Malawi from 2016, and stratified the analysis by sex. We considered demographic, socio-behavioural and HIV-related variables. Using Latent Class Analysis (LCA), we identified clusters of people sharing common sociobehavioural characteristics. The optimal number of clusters was selected based on the Bayesian information criterion. We compared the proportions of individuals belonging to the different clusters across the three regions and 28 districts of Malawi.ResultsWe found nine clusters of women and six clusters of men. Most women in the clusters with highest risk of being HIV infected were living in female-headed households and were formerly married or in a union. Among men, older men had the highest risk of being HIV infected, followed by young (20-25) single men. Generally, low HIV testing uptake correlated with lower risk of having HIV. However, rural adolescent girls had a low probability of being tested (48.7%) despite a relatively high HIV prevalence. Urban districts and Southern region had a higher percentage of high-prevalence and less tested clusters of individuals than other areas.ConclusionsLCA is an efficient method to find clusters of people sharing common HIV risk profiles, identify particularly vulnerable population groups, and plan targeted interventions focusing on these groups. Tailored support, prevention and HIV testing programmes should focus particularly on female household heads, adolescent girls living in rural areas, older married men, and young men who have never been married.FundingThe project was funded by the Swiss National Science Foundation (grant no 163878).

IntroductionSocio-behavioural factors may contribute to the wide variance in HIV prevalence between and within sub-Saharan African (SSA) countries. We studied the associations between socio-behavioural variables potentially related to the risk of acquiring HIV.MethodsWe used Bayesian network models to study associations between socio-behavioural variables that may be related to HIV. A Bayesian network consists of nodes representing variables, and edges representing the conditional dependencies between variables. We analysed data from Demographic and Health Surveys conducted in 29 SSA countries between 2010 and 2016. We predefined and dichotomized twelve variables, including factors related to age, literacy, HIV knowledge, HIV testing, domestic violence, sexual activity, and women’s empowerment. We analysed data on men and women for each country separately and then summarised the results across the countries. We conducted a second analysis including also the individual HIV status in a subset of 23 countries where this information was available. We presented summary graphs showing associations that were present in at least six countries (five in the analysis with HIV status).ResultsWe analysed data from 190,273 men (range across countries 2,295–17,359) and 420,198 women (6,621–38,948). The two variables with the highest total number of edges in the summary graphs were literacy and rural/urban location. Literacy was negatively associated with false beliefs about AIDS and, for women, early sexual initiation, in most countries. Literacy was also positively associated with ever being tested for HIV and the belief that women have the right to ask their husband to use condoms if he has a sexually transmitted infection. Rural location was positively associated with false beliefs about HIV and the belief that beating one’s wife is justified, and negatively associated with having been tested for HIV. In the analysis including HIV status, being HIV positive was associated with female-headed household, older age and rural location among women, and with no variables among men.ConclusionsLiteracy and urbanity were strongly associated with several factors that are important for HIV acquisition. Since literacy is one of the few variables that can be improved by interventions, this makes it a promising intervention target.

Introduction With limited funds available, meeting global health targets requires countries to both mobilize and prioritize their health spending. Within this context, countries have recognized the importance of allocating funds for HIV as efficiently as possible to maximize impact. Over the past six years, the governments of 23 countries in Africa, Asia, Eastern Europe and Latin America have used the Optima HIV tool to estimate the optimal allocation of HIV resources. Methods Each study commenced with a request by the national government for technical assistance in conducting an HIV allocative efficiency study using Optima HIV. Each study team validated the required data, calibrated the Optima HIV epidemic model to produce HIV epidemic projections, agreed on cost functions for interventions, and used the model to calculate the optimal allocation of available funds to best address national strategic plan targets. From a review and analysis of these 23 country studies, we extract common themes around the optimal allocation of HIV funding in different epidemiological contexts. Results and discussion The optimal distribution of HIV resources depends on the amount of funding available and the characteristics of each country's epidemic, response and targets. Universally, the modelling results indicated that scaling up treatment coverage is an efficient use of resources. There is scope for efficiency gains by targeting the HIV response towards the populations and geographical regions where HIV incidence is highest. Across a range of countries, the model results indicate that a more efficient allocation of HIV resources could reduce cumulative new HIV infections by an average of 18% over the years to 2020 and 25% over the years to 2030, along with an approximately 25% reduction in deaths for both timelines. However, in most countries this would still not be sufficient to meet the targets of the national strategic plan, with modelling results indicating that budget increases of up to 185% would be required. Conclusions Greater epidemiological impact would be possible through better targeting of existing resources, but additional resources would still be required to meet targets. Allocative efficiency models have proven valuable in improving the HIV planning and budgeting process.

This chapter provides an overview of research on image-dependent gamut mapping. A color gamut includes all colors that can be rendered by a device or are contained in an image. Mapping the colors from one color gamut into another one is called gamut mapping. To understand the objectives and problems of gamut mapping several aspects need to be covered, namely, the basics of color science, typical image reproduction workflows, psychovisual testing and scaling, and image-quality measures. Data from psychovisual tests and image quality measures allow image-dependent gamut mapping to be considered as an optimization problem, namely, finding the best mapping either as perceived by users or in terms of image quality. We show that many popular gamut-mapping algorithms can be cast in the language of an abstract optimization framework and discuss the aspects optimized by the different algorithms.

The measurement of image quality requires the judgement by the human visual system. This paper describes a psycho-visual test technique that uses the internet as a test platform to identify image quality in a more time-effective manner, comparing the visual response data with the results from the same test in a lab-based environment and estimate the usefulness of the internet as a platform for scaling studies.

In this article the authors show that image quality measures can be successfully used to develop image-individualized gamut mapping algorithms. First the, authors compare different image quality measures for the gamut mapping problem and then validate them using psychovisual data from four recent gamut mapping studies. The scoring function used to validate the quality measures is the hit rate, i.e., the percentage of correct choice predictions on data from psychovisual tests. Some of the image quality measures predict the observer's preferences as good as scaling methods such as Thurstone's method, which is used to evaluate the psychovisual tests. This is remarkable because the scaling methods are based on the experimental data, whereas the quality measures are independent of these data. The best performing image quality measure is used to automatically select the optimal gamut mapping algorithm for an individual image.