Development of temporal modelling for forecasting and prediction of malaria infections using time-series and ARIMAX analyses: A case study in endemic districts of Bhutan

<p>Abstract</p> <p>Background</p> <p>Malaria still remains a public health problem in some districts of Bhutan despite marked reduction of cases in last few years. To strengthen the country's prevention and control measures, this study was carried out to develop forecasting and prediction models of malaria incidence in the endemic districts of Bhutan using time series and ARIMAX.</p> <p>Methods</p> <p>This study was carried out retrospectively using the monthly reported malaria cases from the health centres to Vector-borne Disease Control Programme (VDCP) and the meteorological data from Meteorological Unit, Department of Energy, Ministry of Economic Affairs. Time series analysis was performed on monthly malaria cases, from 1994 to 2008, in seven malaria endemic districts. The time series models derived from a multiplicative seasonal autoregressive integrated moving average (ARIMA) was deployed to identify the best model using data from 1994 to 2006. The best-fit model was selected for each individual district and for the overall endemic area was developed and the monthly cases from January to December 2009 and 2010 were forecasted. In developing the prediction model, the monthly reported malaria cases and the meteorological factors from 1996 to 2008 of the seven districts were analysed. The method of ARIMAX modelling was employed to determine predictors of malaria of the subsequent month.</p> <p>Results</p> <p>It was found that the ARIMA (p, d, q) (P, D, Q)^smodel (p and P representing the auto regressive and seasonal autoregressive; d and D representing the non-seasonal differences and seasonal differencing; and q and Q the moving average parameters and seasonal moving average parameters, respectively and s representing the length of the seasonal period) for the overall endemic districts was (2,1,1)(0,1,1)¹²; the modelling data from each district revealed two most common ARIMA models including (2,1,1)(0,1,1)¹²and (1,1,1)(0,1,1)¹². The forecasted monthly malaria cases from January to December 2009 and 2010 varied from 15 to 82 cases in 2009 and 67 to 149 cases in 2010, where population in 2009 was 285,375 and the expected population of 2010 to be 289,085. The ARIMAX model of monthly cases and climatic factors showed considerable variations among the different districts. In general, the mean maximum temperature lagged at one month was a strong positive predictor of an increased malaria cases for four districts. The monthly number of cases of the previous month was also a significant predictor in one district, whereas no variable could predict malaria cases for two districts.</p> <p>Conclusions</p> <p>The ARIMA models of time-series analysis were useful in forecasting the number of cases in the endemic areas of Bhutan. There was no consistency in the predictors of malaria cases when using ARIMAX model with selected lag times and climatic predictors. The ARIMA forecasting models could be employed for planning and managing malaria prevention and control programme in Bhutan.</p

Machine Learning Methods Enable Predictive Modeling of Antibody Feature:Function Relationships in RV144 Vaccinees

The adaptive immune response to vaccination or infection can lead to the production of specific antibodies to neutralize the pathogen or recruit innate immune effector cells for help. The non-neutralizing role of antibodies in stimulating effector cell responses may have been a key mechanism of the protection observed in the RV144 HIV vaccine trial. In an extensive investigation of a rich set of data collected from RV144 vaccine recipients, we here employ machine learning methods to identify and model associations between antibody features (IgG subclass and antigen specificity) and effector function activities (antibody dependent cellular phagocytosis, cellular cytotoxicity, and cytokine release). We demonstrate via cross-validation that classification and regression approaches can effectively use the antibody features to robustly predict qualitative and quantitative functional outcomes. This integration of antibody feature and function data within a machine learning framework provides a new, objective approach to discovering and assessing multivariate immune correlates.U.S. Military HIV Research ProgramCollaboration for AIDS Vaccine Discover (OPP1032817)National Institutes of Health (U.S.) (3R01AI080289-02S1)National Institutes of Health (U.S.) (5R01AI080289-03)United States. Army Medical Research and Materiel Command (National Institute of Allergy and Infectious Diseases (U.S.) Interagency Agreement Y1-AI-2642-12)Henry M. Jackson Foundation for the Advancement of Military Medicine (U.S.) (United States. Dept. of Defense Cooperative Agreement W81XWH-07-2-0067

Spatio-temporal patterns of malaria infection in Bhutan: a country embarking on malaria elimination

<p>Abstract</p> <p>Background</p> <p>At the verge of elimination of malaria in Bhutan, this study was carried out to analyse the trend of malaria in the endemic districts of Bhutan and to identify malaria clusters at the sub-districts. The findings would aid in implementing the control activities. Poisson regression was performed to study the trend of malaria incidences at district level from 1994 to 2008. Spatial Empirical Bayesian smoothing was deployed to identify clusters of malaria at the sub-district level from 2004 to 2008.</p> <p>Results</p> <p>Trend of the overall districts and most of the endemic districts have decreased except Pemagatshel, which has an increase in the trend. Spatial cluster-outlier analysis showed that malaria clusters were mostly concentrated in the central and eastern Bhutan in three districts of Dagana, Samdrup Jongkhar and Sarpang. The disease clusters were reported throughout the year. Clusters extended to the non-transmission areas in the eastern Bhutan.</p> <p>Conclusions</p> <p>There is significant decrease in the trend of malaria with the elimination at the sight. The decrease in the trend can be attributed to the success of the control and preventive measures. In order to realize the target of elimination of malaria, the control measure needs to be prioritized in these high-risk clusters of malaria.</p

A novel mechanism linking memory stem cells with innate immunity in protection against HIV-1 infection

© 2017 The Author(s). HIV infection affects 37 million people and about 1.7 million are infected annually. Among the phase III clinical trials only the RV144 vaccine trial elicited significant protection against HIV-1 acquisition, but the efficacy and immune memory were inadequate. To boost these vaccine functions we studied T stem cell memory (TSCM) and innate immunity. TSCM cells were identified by phenotypic markers of CD4+ T cells and they were further characterised into 4 subsets. These expressed the common IL-2/IL-15 receptors and another subset of APOBEC3G anti-viral restriction factors, both of which were upregulated. In contrast, CD4+ TSCM cells expressing CCR5 co-receptors and α4β7 mucosal homing integrins were decreased. A parallel increase in CD4+ T cells was recorded with IL-15 receptors, APOBEC3G and CC chemokines, the latter downmodulating CCR5 molecules. We suggest a novel mechanism of dual memory stem cells; the established sequential memory pathway, TSCM →Central →Effector memory CD4+ T cells and the innate pathway consisting of the 4 subsets of TSCM. Both pathways are likely to be activated by endogenous HSP70. The TSCM memory stem cell and innate immunity pathways have to be optimised to boost the efficacy and immune memory of protection against HIV-1 in the clinical trial

COMPASS identifies T-cell subsets correlated with clinical outcomes.

Advances in flow cytometry and other single-cell technologies have enabled high-dimensional, high-throughput measurements of individual cells as well as the interrogation of cell population heterogeneity. However, in many instances, computational tools to analyze the wealth of data generated by these technologies are lacking. Here, we present a computational framework for unbiased combinatorial polyfunctionality analysis of antigen-specific T-cell subsets (COMPASS). COMPASS uses a Bayesian hierarchical framework to model all observed cell subsets and select those most likely to have antigen-specific responses. Cell-subset responses are quantified by posterior probabilities, and human subject-level responses are quantified by two summary statistics that describe the quality of an individual's polyfunctional response and can be correlated directly with clinical outcome. Using three clinical data sets of cytokine production, we demonstrate how COMPASS improves characterization of antigen-specific T cells and reveals cellular 'correlates of protection/immunity' in the RV144 HIV vaccine efficacy trial that are missed by other methods. COMPASS is available as open-source software

Safety and Reactogenicity of Canarypox ALVAC-HIV (vCP1521) and HIV-1 gp120 AIDSVAX B/E Vaccination in an Efficacy Trial in Thailand

A prime-boost vaccination regimen with ALVAC-HIV (vCP1521) administered intramuscularly at 0, 4, 12, and 24 weeks and gp120 AIDSVAX B/E at 12 and 24 weeks demonstrated modest efficacy of 31.2% for prevention of HIV acquisition in HIV-uninfected adults participating in a community-based efficacy trial in Thailand.Reactogenicity was recorded for 3 days following vaccination. Adverse events were monitored every 6 months for 3.5 years, during which pregnancy outcomes were recorded. Of the 16,402 volunteers, 69% of the participants reported an adverse event any time after the first dose. Only 32.9% experienced an AE within 30 days following any vaccination. Overall adverse event rates and attribution of relatedness did not differ between groups. The frequency of serious adverse events was similar in vaccine (14.3%) and placebo (14.9%) recipients (p = 0.33). None of the 160 deaths (85 in vaccine and 75 in placebo recipients, p = 0.43) was assessed as related to vaccine. The most common cause of death was trauma or traffic accident. Approximately 30% of female participants reported a pregnancy during the study. Abnormal pregnancy outcomes were experienced in 17.1% of vaccine and 14.6% (p = 0.13) of placebo recipients. When the conception occurred within 3 months (estimated) of a vaccination, the majority of these abnormal outcomes were spontaneous or elective abortions among 22.2% and 15.3% of vaccine and placebo pregnant recipients, respectively (p = 0.08). Local reactions occurred in 88.0% of vaccine and 61.0% of placebo recipients (p<0.001) and were more frequent after ALVAC-HIV than AIDSVAX B/E vaccination. Systemic reactions were more frequent in vaccine than placebo recipients (77.2% vs. 59.8%, p<0.001). Local and systemic reactions were mostly mild to moderate, resolving within 3 days.The ALVAC-HIV and AIDSVAX B/E vaccine regimen was found to be safe, well tolerated and suitable for potential large-scale use in Thailand.ClinicalTrials.govNCT00223080