Short-term forecasting of the prevalence of clinical trachoma: utility of including delayed recovery and tests for infection. Issue 1 (December 2015)

• Record Type: Journal Article
• Authors: Liu, Fengchen; Porco, Travis; Amza, Abdou; Kadri, Boubacar; Nassirou, Baido; West, Sheila; Bailey, Robin; Keenan, Jeremy; Lietman, Thomas
• Is Part Of: Parasites & vectors. Volume 8:Issue 1(2015)
• Page Start: 1
• Page End: 8
• Publication Date: 2015-12
• Subjects: Trachoma -- Model -- Mass drug administration -- Forecast -- Prediction; Parasitism -- Periodicals; Parasites -- Periodicals; Vector-pathogen relationships -- Periodicals; Animals as carriers of disease -- Periodicals; Insects as carriers of disease -- Periodicals; 616.96
• DOI: 10.1186/s13071-015-1115-8
• Languages: English
• ISSNs: 1756-3305

Abstract

Abstract Background The World Health Organization aims to control blinding trachoma by 2020. Decisions on whether to start and stop mass treatments and when to declare that control has been achieved are currently based on clinical examination data generated in population-based surveys. Thresholds are based on the district-level prevalence of trachomatous inflammation–follicular (TF) in children aged 1–9 years. Forecasts of which districts may and may not meet TF control goals by the 2020 target date could affect resource allocation in the next few years. Methods We constructed a hidden Markov model fit to the prevalence of two clinical signs of trachoma and PCR data in 24 communities from the recent PRET-Niger trial. The prevalence of TF in children in each community at 36 months was forecast given data from earlier time points. Forecasts were scored by the likelihood of the observed results. We assessed whether use of TF with additional TI and PCR data rather than just the use of TF alone improves forecasts, and separately whether incorporating a delay in TF recovery is beneficial. Results Including TI and PCR data did not significantly improve forecasts of TF. Forecasts of TF prevalence at 36 months by the model with the delay in TF recovery were significantly better than forecasts by the model without the delay in TF recovery (p = 0.003). A zero-inflated truncated normal observation model was better than a truncated normal observation model, and better than a sensitivity-specificity observation model. Conclusion The results in this study suggest that future studies could consider using just TF data for forecasting, and should include a delay in TF recovery. Trial registration Clinicaltrials.govNCT00792922