A systematic review of dengue outbreak prediction models: Current scenario and future directions.

Dengue is among the fastest-spreading vector-borne infectious disease, with outbreaks often overwhelm the health system and result in huge morbidity and mortality in its endemic populations in the absence of an efficient warning system. A large number of prediction models are currently in use globally. As such, this study aimed to systematically review the published literature that used quantitative models to predict dengue outbreaks and provide insights about the current practices. A systematic search was undertaken, using the Ovid MEDLINE, EMBASE, Scopus and Web of Science databases for published citations, without time or geographical restrictions. Study selection, data extraction and management process were devised in accordance with the 'Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies' ('CHARMS') framework. A total of 99 models were included in the review from 64 studies. Most models sourced climate (94.7%) and climate change (77.8%) data from agency reports and only 59.6% of the models adjusted for reporting time lag. All included models used climate predictors; 70.7% of them were built with only climate factors. Climate factors were used in combination with climate change factors (13.4%), both climate change and demographic factors (3.1%), vector factors (6.3%), and demographic factors (5.2%). Machine learning techniques were used for 39.4% of the models. Of these, random forest (15.4%), neural networks (23.1%) and ensemble models (10.3%) were notable. Among the statistical (60.6%) models, linear regression (18.3%), Poisson regression (18.3%), generalized additive models (16.7%) and time series/autoregressive models (26.7%) were notable. Around 20.2% of the models reported no validation at all and only 5.2% reported external validation. The reporting of methodology and model performance measures were inadequate in many of the existing prediction models. This review collates plausible predictors and methodological approaches, which will contribute to robust modelling in diverse settings and populations.

Operational feasibility of using whole blood in the rapid HIV testing algorithm of a resource-limited settings like Bangladesh.

BACKGROUND: Serum-based rapid HIV testing algorithm in Bangladesh constitutes operational challenge to scaleup HIV testing and counselling (HTC) in the country. This study explored the operational feasibility of using whole blood as alternative to serum for rapid HIV testing in Bangladesh. METHODS: Whole blood specimens were collected from two study groups. The groups included HIV-positive patients (n = 200) and HIV-negative individuals (n = 200) presenting at the reference laboratory in Dhaka, Bangladesh. The specimens were subjected to rapid HIV tests using the national algorithm with A1 = Alere Determine (United States), A2 = Uni-Gold (Ireland), and A3 = First Response (India). The sensitivity and specificity of the test results, and the operational cost were compared with current serum-based testing. RESULTS: The sensitivities [95% of confidence interval (CI)] for A1, A2, and A3 tests using whole blood were 100% (CI: 99.1-100%), 100% (CI: 99.1-100%), and 97% (CI: 96.4-98.2%), respectively, and specificities of all test kits were 100% (CI: 99.1-100%). Significant (P < 0.05) reduction in the cost of establishing HTC centre and consumables by 94 and 61%, respectively, were observed. The cost of administration and external quality assurance reduced by 39 and 43%, respectively. Overall, there was a 36% cost reduction in total operational cost of rapid HIV testing with blood when compared with serum. CONCLUSION: Considering the similar sensitivity and specificity of the two specimens, and significant cost reduction, rapid HIV testing with whole blood is feasible. A review of the national HIV rapid testing algorithm with whole blood will contribute toward improving HTC coverage in Bangladesh.