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A spatio-temporal approach to short-term prediction of visceral leishmaniasis diagnoses in India

Author

Listed:
  • Emily S Nightingale
  • Lloyd A C Chapman
  • Sridhar Srikantiah
  • Swaminathan Subramanian
  • Purushothaman Jambulingam
  • Johannes Bracher
  • Mary M Cameron
  • Graham F Medley

Abstract

Background: The elimination programme for visceral leishmaniasis (VL) in India has seen great progress, with total cases decreasing by over 80% since 2010 and many blocks now reporting zero cases from year to year. Prompt diagnosis and treatment is critical to continue progress and avoid epidemics in the increasingly susceptible population. Short-term forecasts could be used to highlight anomalies in incidence and support health service logistics. The model which best fits the data is not necessarily most useful for prediction, yet little empirical work has been done to investigate the balance between fit and predictive performance. Methodology/Principal findings: We developed statistical models of monthly VL case counts at block level. By evaluating a set of randomly-generated models, we found that fit and one-month-ahead prediction were strongly correlated and that rolling updates to model parameters as data accrued were not crucial for accurate prediction. The final model incorporated auto-regression over four months, spatial correlation between neighbouring blocks, and seasonality. Ninety-four percent of 10-90% prediction intervals from this model captured the observed count during a 24-month test period. Comparison of one-, three- and four-month-ahead predictions from the final model fit demonstrated that a longer time horizon yielded only a small sacrifice in predictive power for the vast majority of blocks. Conclusions/Significance: The model developed is informed by routinely-collected surveillance data as it accumulates, and predictions are sufficiently accurate and precise to be useful. Such forecasts could, for example, be used to guide stock requirements for rapid diagnostic tests and drugs. More comprehensive data on factors thought to influence geographic variation in VL burden could be incorporated, and might better explain the heterogeneity between blocks and improve uniformity of predictive performance. Integration of the approach in the management of the VL programme would be an important step to ensuring continued successful control. Author summary: This paper demonstrates a statistical modelling approach for forecasting of monthly visceral leishmaniasis (VL) incidence at block level in India, which could be used to tailor control efforts according to local estimates and monitor deviations from the currently decreasing trend. By fitting a variety of models to four years of historical data and assessing predictions within a further 24-month test period, we found that the model which best fit the observed data also showed the best predictive performance, and predictive accuracy was maintained when making rolling predictions up to four months ahead of the observed data. Since there is a two-month delay between reporting and processing of the data, predictive power more than three months ahead of current data is crucial to make forecasts which can feasibly be acted upon. Some heterogeneity remains in predictive power across the study region which could potentially be improved using unit-specific data on factors believed to be associated with reported VL incidence (e.g. age distribution, socio-economic status and climate).

Suggested Citation

  • Emily S Nightingale & Lloyd A C Chapman & Sridhar Srikantiah & Swaminathan Subramanian & Purushothaman Jambulingam & Johannes Bracher & Mary M Cameron & Graham F Medley, 2020. "A spatio-temporal approach to short-term prediction of visceral leishmaniasis diagnoses in India," PLOS Neglected Tropical Diseases, Public Library of Science, vol. 14(7), pages 1-21, July.
  • Handle: RePEc:plo:pntd00:0008422
    DOI: 10.1371/journal.pntd.0008422
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    References listed on IDEAS

    as
    1. Meyer, Sebastian & Held, Leonhard & Höhle, Michael, 2017. "Spatio-Temporal Analysis of Epidemic Phenomena Using the R Package surveillance," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 77(i11).
    2. Anteneh Asmare Godana & Samuel Musili Mwalili & George Otieno Orwa, 2019. "Dynamic spatiotemporal modeling of the infected rate of visceral leishmaniasis in human in an endemic area of Amhara regional state, Ethiopia," PLOS ONE, Public Library of Science, vol. 14(3), pages 1-21, March.
    3. Sebastian Funk & Anton Camacho & Adam J Kucharski & Rachel Lowe & Rosalind M Eggo & W John Edmunds, 2019. "Assessing the performance of real-time epidemic forecasts: A case study of Ebola in the Western Area region of Sierra Leone, 2014-15," PLOS Computational Biology, Public Library of Science, vol. 15(2), pages 1-17, February.
    4. Tilmann Gneiting & Fadoua Balabdaoui & Adrian E. Raftery, 2007. "Probabilistic forecasts, calibration and sharpness," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 69(2), pages 243-268, April.
    5. Claudia Czado & Tilmann Gneiting & Leonhard Held, 2009. "Predictive Model Assessment for Count Data," Biometrics, The International Biometric Society, vol. 65(4), pages 1254-1261, December.
    6. Luis Fernando Chaves & Mercedes Pascual, 2007. "Comparing Models for Early Warning Systems of Neglected Tropical Diseases," PLOS Neglected Tropical Diseases, Public Library of Science, vol. 1(1), pages 1-6, October.
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