Simulation models of dengue transmission in Funchal, Madeira Island: Influence of seasonality.

The recent emergence and established presence of Aedes aegypti in the Autonomous Region of Madeira, Portugal, was responsible for the first autochthonous outbreak of dengue in Europe. The island has not reported any dengue cases since the outbreak in 2012. However, there is a high risk that an introduction of the virus would result in another autochthonous outbreak given the presence of the vector and permissive environmental conditions. Understanding the dynamics of a potential epidemic is critical for targeted local control strategies. Here, we adopt a deterministic model for the transmission of dengue in Aedes aegypti mosquitoes. The model integrates empirical and mechanistic parameters for virus transmission, under seasonally varying temperatures for Funchal, Madeira Island. We examine the epidemic dynamics as triggered by the arrival date of an infectious individual; the influence of seasonal temperature mean and variation on the epidemic dynamics; and performed a sensitivity analysis on the following quantities of interest: the epidemic peak size, time to peak, and the final epidemic size. Our results demonstrate the potential for summer and autumn season transmission of dengue, with the arrival date significantly affecting the distribution of the timing and peak size of the epidemic. Late-summer arrivals were more likely to produce large epidemics within a short peak time. Epidemics within this favorable period had an average of 11% of the susceptible population infected at the peak, at an average peak time of 95 days. We also demonstrated that seasonal temperature variation dramatically affects the epidemic dynamics, with warmer starting temperatures producing large epidemics with a short peak time and vice versa. Overall, our quantities of interest were most sensitive to variance in the date of arrival, seasonal temperature, transmission rates, mortality rate, and the mosquito population; the magnitude of sensitivity differs across quantities. Our model could serve as a useful guide in the development of effective local control and mitigation strategies for dengue fever in Madeira Island.

Predicting dengue importation into Europe, using machine learning and model-agnostic methods.

The geographical spread of dengue is a global public health concern. This is largely mediated by the importation of dengue from endemic to non-endemic areas via the increasing connectivity of the global air transport network. The dynamic nature and intrinsic heterogeneity of the air transport network make it challenging to predict dengue importation. Here, we explore the capabilities of state-of-the-art machine learning algorithms to predict dengue importation. We trained four machine learning classifiers algorithms, using a 6-year historical dengue importation data for 21 countries in Europe and connectivity indices mediating importation and air transport network centrality measures. Predictive performance for the classifiers was evaluated using the area under the receiving operating characteristic curve, sensitivity, and specificity measures. Finally, we applied practical model-agnostic methods, to provide an in-depth explanation of our optimal model's predictions on a global and local scale. Our best performing model achieved high predictive accuracy, with an area under the receiver operating characteristic score of 0.94 and a maximized sensitivity score of 0.88. The predictor variables identified as most important were the source country's dengue incidence rate, population size, and volume of air passengers. Network centrality measures, describing the positioning of European countries within the air travel network, were also influential to the predictions. We demonstrated the high predictive performance of a machine learning model in predicting dengue importation and the utility of the model-agnostic methods to offer a comprehensive understanding of the reasons behind the predictions. Similar approaches can be utilized in the development of an operational early warning surveillance system for dengue importation.

Dengue importation into Europe: A network connectivity-based approach.

The spread of dengue through global human mobility is a major public health concern. A key challenge is understanding the transmission pathways and mediating factors that characterized the patterns of dengue importation into non-endemic areas. Utilizing a network connectivity-based approach, we analyze the importation patterns of dengue fever into European countries. Seven connectivity indices were developed to characterize the role of the air passenger traffic, seasonality, incidence rate, geographical proximity, epidemic vulnerability, and wealth of a source country, in facilitating the transport and importation of dengue fever. We used generalized linear mixed models (GLMMs) to examine the relationship between dengue importation and the connectivity indices while accounting for the air transport network structure. We also incorporated network autocorrelation within a GLMM framework to investigate the propensity of a European country to receive an imported case, by virtue of its position within the air transport network. The connectivity indices and dynamical processes of the air transport network were strong predictors of dengue importation in Europe. With more than 70% of the variation in dengue importation patterns explained. We found that transportation potential was higher for source countries with seasonal dengue activity, high passenger traffic, high incidence rates, high epidemic vulnerability, and in geographical proximity to a destination country in Europe. We also found that position of a European country within the air transport network was a strong predictor of the country's propensity to receive an imported case. Our findings provide evidence that the importation patterns of dengue into Europe can be largely explained by appropriately characterizing the heterogeneities of the source, and topology of the air transport network. This contributes to the foundational framework for building integrated predictive models for bio-surveillance of dengue importation.

Africa Rising, a Narrative for Life Expectancy Gains? Evidence from a Health Production Function.

BACKGROUND: The narrative of Africa Rising has increasingly been called into scrutiny, not just as a debate for economic growth and development, but also as a possible link to the surge in life expectancy on the continent. Theoretically, an increase in economic development tends to result in an increase in public health spending and subsequent better health outcomes. OBJECTIVE: This paper examines the contribution of economic development and other social determinants to the health status of the African continent and to provide evidence on whether the increase in life expectancy of the past two decades can be largely attributed to the Africa Rising narrative. METHODS: We estimated an empirical health production function, with life expectancy gains as the output of the health care system, and various socio-economic, environmental and lifestyle factors as contributory factors. We fitted a generalized least squares model, using panel data from 52 African countries for the period 1995-2014. FINDINGS: The estimation shows that while increases in health care spending contributed to life expectancy gains, urbanization rates and improved water access were the major drivers of life expectancy gains with substantially larger impacts in the past two decades. CONCLUSIONS: Overall, the results provide an evidence base for iterating the need to prioritize increasing funding and examine more critically how to improve the efficiency of health spending. It also illustrates potential gains that can be achieved from an inclusive health policy agenda with a broader range of social and economic development issues.

HIV Viral Dynamics of Lopinavir/Ritonavir Monotherapy as Second-Line Treatment: A Prospective, Single-Arm Trial.

BACKGROUND: Characterizing viral response to lopinavir/ritonavir (LPV/r) monotherapy as second-line treatment may guide recommendations for resource-limited settings (RLS). METHODS: We conducted a 48-week prospective, single-arm study of LPV/r monotherapy in patients failing first-line therapy in Nigeria. The primary outcome was sustained HIV-1 viral load (VL) <400 copies/mL at 48 weeks. RESULTS: Of 30 enrolled patients, 28 (93%) achieved viral suppression on LPV/r, while 29 (96%) experienced low-level viremia. At 48 weeks, 9 (30%) met the primary outcome of sustained viral suppression; 14 (47%) patients were suppressed on LPV/r in a snapshot analysis. Detectable VLs at 12 and 24 weeks were strongly associated with treatment failure at 48 weeks. New resistance mutations were not detected. The trial was stopped early due to treatment failure. CONCLUSION: In this study, the rate of virologic failure among patients on a second-line lopinavir monotherapy regimen was relatively high and predicted by early detectable viremia. However, no LPV/r-associated resistance mutations were detected despite fluctuating low-level viremia, demonstrating the high genetic barrier to resistance of the protease inhibitor class which could be useful in RLS.

Long-term outcome of second-line antiretroviral therapy in resource-limited settings.

There is limited information on efficacy and durability of second-line antiretroviral therapy (2NL) beyond 12 months in resource-limited settings. A total of 73 patients were enrolled into a prospective 2NL observational cohort in Nigeria. Second-line antiretroviral therapy consisted of lopinavir/ritonavir plus nucleoside reverse transcriptase inhibitors. Time on 2NL ranged from 15 to 31 months. Genotypes were retrospectively done and not available to guide second-line regimen choice. At enrollment, median CD4 count was 121 cells/mm3, and median time on first-line antiretroviral therapy (ISL) was 24 months. At 6 to 9 months on 2NL, 72.6% (intention to treat [ITT]) and 88.3% (on treatment [OT]) had an undetectable viral load (UDVL). At 12 months, 65.8% (ITT) and 90.57% (OT) had UDVL. At >12 to 24 months and at >24 months, 57.5% (ITT) and 91.3% (OT) had UDVL. No statistically significant association was observed between CD4 at 2NL start, sex, genotypic sensitivity score of 2NL, or tenofovir (TDF) use in ISL and viral suppression. Two patients developed major protease inhibitor mutations while on 2NL. We observed a high degree of viral suppression at 12 months and little loss of viral suppression thereafter.

Long-term outcome of second-line antiretroviral therapy in resource-limited settings.

There is limited information on efficacy and durability of second-line antiretroviral therapy (2NL) beyond 12 months in resource-limited settings. A total of 73 patients were enrolled into a prospective 2NL observational cohort in Nigeria. Second-line antiretroviral therapy consisted of lopinavir/ritonavir plus nucleoside reverse transcriptase inhibitors. Time on 2NL ranged from 15 to 31 months. Genotypes were retrospectively done and not available to guide second-line regimen choice. At enrollment, median CD4 count was 121 cells/mm3, and median time on first-line antiretroviral therapy (ISL) was 24 months. At 6 to 9 months on 2NL, 72.6% (intention to treat [ITT]) and 88.3% (on treatment [OT]) had an undetectable viral load (UDVL). At 12 months, 65.8% (ITT) and 90.57% (OT) had UDVL. At >12 to 24 months and at >24 months, 57.5% (ITT) and 91.3% (OT) had UDVL. No statistically significant association was observed between CD4 at 2NL start, sex, genotypic sensitivity score of 2NL, or tenofovir (TDF) use in ISL and viral suppression. Two patients developed major protease inhibitor mutations while on 2NL. We observed a high degree of viral suppression at 12 months and little loss of viral suppression thereafter.