The robust estimate and forecast capability of random forests (RF) has been widely recognized, however this ensemble machine learning method has not been widely used in mosquito-borne disease forecasting. In this study, two sets of RF models were developed at the national (pooled department-level data) and department level in Colombia to predict weekly dengue cases for 12-weeks ahead. A pooled national model based on artificial neural networks (ANN) was also developed and used as a comparator to the RF models. The various predictors included historic dengue cases, satellite-derived estimates for vegetation, precipitation, and air temperature, as well as population counts, income inequality, and education. Our RF model trained on the pooled national data was more accurate for department-specific weekly dengue cases estimation compared to a local model trained only on the department's data. Additionally, the forecast errors of the national RF model were smaller to those of the national pooled ANN model and were increased with the forecast horizon increasing from one-week-ahead (mean absolute error, MAE: 9.32) to 12-weeks ahead (MAE: 24.56). There was considerable variation in the relative importance of predictors dependent on forecast horizon. The environmental and meteorological predictors were relatively important for short-term dengue forecast horizons while socio-demographic predictors were relevant for longer-term forecast horizons. This study demonstrates the potential of RF in dengue forecasting with a feasible approach of using a national pooled model to forecast at finer spatial PLOS NEGLECTED TROPICAL DISEASES

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Background Colombia has one of the highest burdens of arboviruses in South America. The country was in a state of hyperendemicity between 2014 and 2016, with co-circulation of several Aedes-borne viruses, including a syndemic of dengue, chikungunya, and Zika in 2015. Methodology/Principal findings We analyzed the cases of dengue, chikungunya, and Zika notified in Colombia from January 2014 to December 2018 by municipality and week. The trajectory and velocity of spread was studied using trend surface analysis, and spatio-temporal high-risk clusters for each disease in separate and for the three diseases simultaneously (multivariate) were identified using Kulldorff’s scan statistics. During the study period, there were 366,628, 77,345 and 74,793 cases of dengue, chikungunya, and Zika, respectively, in Colombia. The spread patterns for chikungunya and Zika were similar, although Zika’s spread was accelerated. Both chikungunya and Zika mainly spread from the regions on the Atlantic coast and the south-west to the rest of the country. We identified 21, 16, and 13 spatio-temporal clusters of dengue, chikungunya and Zika, respectively, and, from the multivariate analysis, 20 spatio-temporal clusters, among which 7 were simultaneous for the three diseases. For all disease-specific analyses and the multivariate analysis, the most-likely cluster was identified in the south-western region of Colombia, including the Valle del Cauca department. Conclusions/Significance The results further our understanding of emerging Aedes-borne diseases in Colombia by providing useful evidence on their potential site of entry and spread trajectory within the country, and identifying spatio-temporal disease-specific and multivariate high-risk clusters of dengue, chikungunya, and Zika, information that can be used to target interventions.

Although servant leadership has been acknowledged as an important predictor of employees’ behavioral outcomes in the service industry, there is still no cohesive understanding of the positive association between servant leadership and employees’ customer-oriented behavior (COB). This research, drawing on cognitive affective processing system theory (CAPS), empirically investigates the influence of servant leadership on employees’ COB by exploring two mediators (i.e., organizational identification and vitality). We conducted two studies in China, using a cross-sectional design to survey employees in service-oriented technical organizations (Study 1) and a time-lagged design to survey hospitality employees with frontline service jobs in star-level hotels (Study 2). Across both samples, we found that servant leadership enhanced employees’ COB by simultaneously increasing their organizational identification and vitality. We discuss the implications of these results for future research and practice.

Lyme disease is a growing public health problem in Québec. Its emergence over the last decade is caused by environmental and anthropological factors that favour the survival of Ixodes scapularis, the vector of Lyme disease transmission. The objective of this study was to estimate the speed and direction of human Lyme disease emergence in Québec and to identify spatiotemporal risk patterns. A surface trend analysis was conducted to estimate the speed and direction of its emergence based upon the first detected case of Lyme disease in each municipality in Québec since 2004. A cluster analysis was also conducted to identify at-risk regions across space and time. These analyses were reproduced for the date of disease onset and date of notification for each case of Lyme disease. It was estimated that Lyme disease is spreading northward in Québec at a speed varying between 18 and 32 km/year according to the date of notification and the date of disease onset, respectively. A significantly high risk of disease was found in seven clusters identified in the south-west of Québec in the sociosanitary regions of Montérégie and Estrie. The results obtained in this study improve our understanding of the spatiotemporal patterns of Lyme disease in Québec, which can be used for proactive, targeted interventions by public and clinical health authorities.

Background The United Nations through universal health coverage, including sexual and reproductive health (SRH), pledges to include all people, leaving no one behind. However, people with disabilities continue to experience multiple barriers in accessing SRH services. Studies analysing the impacts of disability in conjunction with other social identities and health determinants reveal a complex pattern in SRH service use. Framed within a larger mixed methods study conducted in Uganda, we examined how disability, among other key social determinants of health (SDH), was associated with the use of SRH services. Methods We analysed data from repeated cross-sectional national surveys, the Uganda Demographic and Health Surveys (DHS) of 2006, 2011, and 2016. The three outcomes of interest were antenatal care visits, HIV testing, and modern contraception use. Our main exposure of interest was the type of disability, classified according to six functional dimensions: seeing, hearing, walking/climbing steps, remembering/concentrating, communicating, and self-care. We performed descriptive and multivariable logistic regression analyses, which controlled for covariates such as survey year, sex, age, place of residence, education, and wealth index. Interaction terms between disability and other factors such as sex, education, and wealth index were explored. Regression analyses were informed by an intersectionality framework to highlight social and health disparities within groups. Results From 2006 to 2016, 15.5-18.5% of study participants lived with some form of disability. Over the same period, the overall prevalence of at least four antenatal care visits increased from 48.3 to 61.0%, while overall HIV testing prevalence rose from 30.8 to 92.4% and the overall prevalence of modern contraception use increased from 18.6 to 34.2%. The DHS year, highest education level attained, and wealth index were the most consistent determinants of SRH service utilisation. People with different types of disabilities did not have the same SRH use patterns. Interactions between disability type and wealth index were associated with neither HIV testing nor the use of modern contraception. Women who were wealthy with hearing difficulty (Odds Ratio (OR) = 0.15, 95%CI 0.03 – 0.87) or with communication difficulty (OR = 0.17, 95%CI 0.03 – 0.82) had lower odds of having had optimal antenatal care visits compared to women without disabilities who were poorer. Conclusion This study provided evidence that SRH service use prevalence increased over time in Uganda and highlights the importance of studying SRH and the different disability types when examining SDH. The SDH are pivotal to the attainment of universal health coverage, including SRH services, for all people irrespective of their social identities.

29 30 31 32 33 34 35 36 2 37 Abstract:38 The robust estimate and forecast capability of random forests (RF) has been widely recognized, 39 however this ensemble machine learning method has not been widely used in mosquito-borne 40 disease forecasting. In this study, two sets of RF models were developed for the national and 41 departmental levels in Colombia to predict weekly dengue cases at 12-weeks ahead. A national 42 model based on artificial neural networks (ANN) was also developed and used as a comparator 43 to the RF models. The various predictors included historic dengue cases, satellite-derived 44 estimates for vegetation, precipitation, and air temperature, population counts, income inequality, 45 and education. Our RF model trained on the national data was more accurate for department-46 specific weekly dengue cases estimation compared to a local model trained only on the 47 department's data. Additionally, the forecast errors of the national RF model were smaller to 48 those of the national ANN model and were increased with the forecast horizon increasing from 49 one-week ahead (mean absolute error, MAE: 5.80; root mean squared error, RMSE: 11.10) to 50 12-weeks ahead (MAE: 13.38; RMSE: 26.82). There was considerable variation in the relative 51 importance of predictors dependent on forecast horizon. The environmental and meteorological 52 predictors were relatively important for short-term dengue forecast horizons while socio-53 demographic predictors were relevant for longer-term forecast horizons. This study showed the 54 potential of RF in dengue forecasting with also demonstrating the feasibility of using a national 55 model to forecast at finer spatial scales. Furthermore, sociodemographic predictors are important 56 to include to capture longer-term trends in dengue. 57 58 59 3 60 Author summary:61 Dengue virus has the highest disease burden of all mosquito-borne viral diseases, infecting 390 62 million people annually in 128 countries. Forecasting is an important warning mechanism that 63 can help with proactive planning and response for clinical and public health services. In this 64 study, we compare two different machine learning approaches to dengue forecasting: random 65 forest (RF) and neural networks (NN). National and local (departmental-level) models were 66 compared and used to predict dengue cases in the future. The results showed that the counts of 67 future dengue cases were more accurately estimated by RF than by NN. It was also shown that 68 environmental and meteorological predictors were more important for forecast accuracy for 69 shorter-term forecasts while socio-demographic predictors were more important for longer-term 70 forecasts. Finally, the national model applied to local data was more accurate in dengue 71 forecasting compared to the local model. This research contributes to the field of disease 72 forecasting and highlights different considerations for future forecasting studies. 73 74 75 76 77 78 79 80 81 4 82 Introduction83 Dengue virus is most prevalent of the mos...

Lyme disease is a growing public health problem in Québec. Its emergence over the last decade is caused by environmental and anthropological factors that favour the survival of Ixodes scapularis, the vector of Lyme disease transmission. The objective of this study was to estimate the speed and direction of Lyme disease emergence in Québec and to identify spatiotemporal risk patterns. A surface trend analysis was conducted to estimate the speed and direction of its emergence based upon the first detected case of Lyme disease in each municipality in Québec since 2004. A cluster analysis was also conducted to identify at-risk regions across space and time. These analyses were reproduced for the date of disease onset and date of notification for each case of Lyme disease. It was estimated that Lyme disease is spreading northward in Québec at a speed varying between 18 and 32 km/year according to the date of notification and the date of disease onset, respectively. A high rate of disease risk was found in seven clusters identified in the south-west of Québec in the sociosanitary regions of Montérégie and Estrie. The results obtained in this study improve our understanding of the spatiotemporal patterns of Lyme disease in Québec, which can be used for proactive, targeted interventions by public and clinical health authorities.