The epidemiology and clinical correlates of HIV-1 co-receptor tropism in non-subtype B infections from India, Uganda and South Africa

BACKGROUND: The introduction of C-C chemokine receptor type-5 (CCR5) antagonists as antiretroviral therapy has led to the need to study HIV co-receptor tropism in different HIV-1 subtypes and geographical locations. This study was undertaken to evaluate HIV-1 co-receptor tropism in the developing world where non-B subtypes predominate, in order to assess the therapeutic and prophylactic potential of CCR5 antagonists in these regions. METHODS: HIV-1-infected patients were recruited into this prospective, cross-sectional, epidemiologic study from HIV clinics in South Africa, Uganda and India. Patients were infected with subtypes C (South Africa, India) or A or D (Uganda). HIV-1 subtype and co-receptor tropism were determined and analyzed with disease characteristics, including viral load and CD4+ and CD8+ T cell counts. RESULTS: CCR5-tropic (R5) HIV-1 was detected in 96% of treatment-naive (TN) and treatment-experienced (TE) patients in India, 71% of TE South African patients, and 86% (subtype A/A1) and 71% (subtype D) of TN and TE Ugandan patients. Dual/mixed-tropic HIV-1 was found in 4% of Indian, 25% of South African and 13% (subtype A/A1) and 29% (subtype D) of Ugandan patients. Prior antiretroviral treatment was associated with decreased R5 tropism; however, this decrease was less in subtype C from India (TE: 94%, TN: 97%) than in subtypes A (TE: 59%; TN: 91%) and D (TE: 30%; TN: 79%). R5 virus infection in all three subtypes correlated with higher CD4+ count. CONCLUSIONS: R5 HIV-1 was predominant in TN individuals with HIV-1 subtypes C, A, and D and TE individuals with subtypes C and A. Higher CD4+ count correlated with R5 prevalence, while treatment experience was associated with increased non-R5 infection in all subtypes

Spatiotemporal Modeling of Zoonotic Arbovirus Transmission in Northeastern Florida Using Sentinel Chicken Surveillance and Earth Observation Data

The irregular timing and spatial variation in the zoonotic arbovirus spillover from vertebrate hosts to humans and livestock present challenges to predicting spillover occurrence over time and across broader geographic areas, compromising effective prevention and control strategies. The objective of this study was to quantify the effects of the landscape composition and configuration and dynamic weather events on the 2018 spatiotemporal distribution of eastern equine encephalitis virus (EEEV) (Togaviridae, Alphavirus) and West Nile virus (WNV) (Flaviviridae, Flavivirus) sentinel chicken seroconversion in northeastern Florida. We used a modeling framework that explicitly accounts for joint spatial and temporal effects and incorporates key EO (Earth Observation) information on the climate and landscape in order to more accurately quantify the environmental effects on the transmission to sentinel chickens. We investigated the environmental effects using Bernoulli generalized linear mixed effects models (GLMMs), including a site-level random effect, and then added spatial random effects and spatiotemporal random effects in subsequent runs. The models were executed using an integrated nested Laplace approximation (INLA) and a stochastic partial differential equation (SPDE) approach in R-INLA. The GLMMs that included a spatiotemporal random effect performed better relative to models that included only spatial random effects and also performed better than non-spatial models. The results indicated a strong spatiotemporal structure in the seroconversion for both viruses, but EEEV exhibited a more punctuated and compact structure at the beginning of the sampling season, while WNV exhibited a more gradual and diffuse structure across the study area toward the end of the sampling season. The percentage of cypress–tupelo wetland land cover within 3500 m of coop sites and the edge density of the forest land cover within 500 m had a strong positive effect on the EEEV seroconversion, while the best fitting model for WNV was the intercept-only model with spatiotemporal random effects. The lagged climatic variables included in our study did not have a strong effect on the seroconversion for either virus when accounting for temporal autocorrelation, demonstrating the utility of capturing this structure to avoid type I errors. The predictive accuracy for out-of-sample data for the EEEV seroconversion demonstrates the potential to develop a framework that incorporates temporal dynamics in order to better predict arbovirus transmission

Spatiotemporal Modeling of Zoonotic Arbovirus Transmission in Northeastern Florida Using Sentinel Chicken Surveillance and Earth Observation Data

The irregular timing and spatial variation in the zoonotic arbovirus spillover from vertebrate hosts to humans and livestock present challenges to predicting spillover occurrence over time and across broader geographic areas, compromising effective prevention and control strategies. The objective of this study was to quantify the effects of the landscape composition and configuration and dynamic weather events on the 2018 spatiotemporal distribution of eastern equine encephalitis virus (EEEV) (Togaviridae, Alphavirus) and West Nile virus (WNV) (Flaviviridae, Flavivirus) sentinel chicken seroconversion in northeastern Florida. We used a modeling framework that explicitly accounts for joint spatial and temporal effects and incorporates key EO (Earth Observation) information on the climate and landscape in order to more accurately quantify the environmental effects on the transmission to sentinel chickens. We investigated the environmental effects using Bernoulli generalized linear mixed effects models (GLMMs), including a site-level random effect, and then added spatial random effects and spatiotemporal random effects in subsequent runs. The models were executed using an integrated nested Laplace approximation (INLA) and a stochastic partial differential equation (SPDE) approach in R-INLA. The GLMMs that included a spatiotemporal random effect performed better relative to models that included only spatial random effects and also performed better than non-spatial models. The results indicated a strong spatiotemporal structure in the seroconversion for both viruses, but EEEV exhibited a more punctuated and compact structure at the beginning of the sampling season, while WNV exhibited a more gradual and diffuse structure across the study area toward the end of the sampling season. The percentage of cypress–tupelo wetland land cover within 3500 m of coop sites and the edge density of the forest land cover within 500 m had a strong positive effect on the EEEV seroconversion, while the best fitting model for WNV was the intercept-only model with spatiotemporal random effects. The lagged climatic variables included in our study did not have a strong effect on the seroconversion for either virus when accounting for temporal autocorrelation, demonstrating the utility of capturing this structure to avoid type I errors. The predictive accuracy for out-of-sample data for the EEEV seroconversion demonstrates the potential to develop a framework that incorporates temporal dynamics in order to better predict arbovirus transmission