Short-term forecasting of the prevalence of clinical trachoma: utility of including delayed recovery and tests for infection

Fengchen Liu, Travis C Porco, Abdou Amza, Boubacar Kadri, Baido Nassirou, Sheila K West, Robin L Bailey, Jeremy D Keenan, Thomas M Lietman, Fengchen Liu, Travis C Porco, Abdou Amza, Boubacar Kadri, Baido Nassirou, Sheila K West, Robin L Bailey, Jeremy D Keenan, Thomas M Lietman

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.gov NCT00792922.

Figures

Fig. 1
Fig. 1
Forecasts of TF prevalence versus observed TF prevalence (averaged over 24 communities). The forecast distributions of average TF prevalence at 36-month in all communities from 10 models are shown by solid and dotted curves in different colors (as listed in the large legends), and their densities at 0 are listed in the small legends. The observed average TF prevalence at 36-month is shown by the dashed grey bar. For the forecast distributions of TF and the observed TF at 36-month in each of 24 communities, please see Additional file 3
Fig. 2
Fig. 2
Forecasts of the hidden (true) prevalence (averaged over 24 communities). The forecast distributions of average true prevalence at 36-month in all communities from 10 models are shown by solid and dotted curves in different colors (as listed in legends). For the forecast distributions of true prevalence at 36-month in each of 24 communities, please see Additional file 4

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