Your search found 5 records
1 Yapi, Y. G.; Briet, Olivier; Diabate, S.; Vounatsou, P.; Akodo, E.; Tanner, M.; Teuscher, T. 2005. Rice irrigation and schistosomiasis in savannah and forest areas of Cote d’Ivoire. Acta Tropica, 93:201-211.
Crop-based irrigation ; Irrigated farming ; Rice ; Schistosomiasis ; Risks ; Public health ; Waterborne diseases / Ivory Coast
(Location: IWMI-HQ Call no: IWMI 631.7.5 G204 YAP Record No: H038128)
2 Yapi, Y. G.; Briet, Olivier; Vounatsou, P.. 2006. Prevalence of geohelminths in savanna and forest areas of Cote d’Ivoire. West African Journal of Medicine, 25(2):124-125.
Public health ; Diseases ; Soils ; Villages ; Savannas ; Forests ; Helminths ; Ascariasis ; Children / Ivory Coast
(Location: IWMI-HQ Call no: IWMI 616.96 G204 YAP Record No: H038173)
3 Briet, Olivier J. T.; Vounatsou, P.; Amerasinghe, Priyanie H. 2008. Malaria seasonality and rainfall seasonality in Sri Lanka are correlated in space. Geospatial Health, 2(2):183-190.
(Location: IWMI HQ Call no: IWMI 614.532 G744 BRI Record No: H041642)
4 Briet, O. J. T.; Vounatsou, P.; Amerasinghe, Priyanie H. 2009. GSARIMA, a tool for malaria time series analysis during advanced phases of elimination campaigns with low case numbers. [MIM14819027] [Abstract only]. In Abstracts of the 5th MIM Pan-African Malaria Conference, Nairobi, Kenya, 2-6 November 2009. Dar es Salaam, Tanzania: Multilateral Initiative on Malaria (MI) Secretariat. pp.80-81.
(Location: IWMI HQ Record No: H042561)
With the renewed drive towards malaria elimination, there is a need for improved surveillance tools. While time series analysis is an important tool for surveillance, prediction and for measuring interventions' impact, approximations by standard Gassing methods are inaccuracy-prone when counts are low. Therefore, especially during "consolidation" and "pre-elimination" phases, statistical methods appropriate for count data are required.
Methods: Generalized antiprogressive moving average models (GARCIA) were extended to generalized seasonal antiprogressive integrated moving average (SABRINA) models for modeling non-stationary and / or seasonal time series counts. The models were demonstrated using monthly malaria episode time series in a district in Sri Lanka, where malaria has decreased dramatically in recent years.
Results: The malaria series showed long-term changes in the mean, unstable variance, and seasonality. After fitting negative-binomial Bayesian models, both a GSARIMA and a GARIMA deterministic seasonality model were selected. The Bayesian modeling allowed for analysis of the posterior distributions of fitted observations. Those of negative-binomial models were more appropriate than those of Gaussian models. The G(S)ARIMA models satisfactorily accounted for the autocorrelation in the series, and produced plausible prediction intervals.
Discussion: GSARIMA models may be particularly useful in the drive towards malaria elimination, since episode count series are often seasonal and non-stationary, especially when control is increased. Although building and fitting of GSARIMA models is laborious, they provide more realistic prediction intervals than Gaussian methods, and are more suitable for surveillance and the evaluation of interventions when counts are low.
Methods: Generalized antiprogressive moving average models (GARCIA) were extended to generalized seasonal antiprogressive integrated moving average (SABRINA) models for modeling non-stationary and / or seasonal time series counts. The models were demonstrated using monthly malaria episode time series in a district in Sri Lanka, where malaria has decreased dramatically in recent years.
Results: The malaria series showed long-term changes in the mean, unstable variance, and seasonality. After fitting negative-binomial Bayesian models, both a GSARIMA and a GARIMA deterministic seasonality model were selected. The Bayesian modeling allowed for analysis of the posterior distributions of fitted observations. Those of negative-binomial models were more appropriate than those of Gaussian models. The G(S)ARIMA models satisfactorily accounted for the autocorrelation in the series, and produced plausible prediction intervals.
Discussion: GSARIMA models may be particularly useful in the drive towards malaria elimination, since episode count series are often seasonal and non-stationary, especially when control is increased. Although building and fitting of GSARIMA models is laborious, they provide more realistic prediction intervals than Gaussian methods, and are more suitable for surveillance and the evaluation of interventions when counts are low.
5 Briet, O. J. T.; Amerasinghe, Priyanie H.; Vounatsou, P.. 2013. Generalized seasonal autoregressive integrated moving average models for count data with application to malaria time series with low case numbers. PLoS One, 8(6):e65761-e65761. [doi: https://doi.org/10.1371/journal.pone.0065761]
Malaria ; Time series analysis ; Statistical methods ; Regression analysis ; Models ; Rain ; Case studies / Sri Lanka / Gampaha
(Location: IWMI HQ Call no: e-copy only Record No: H045897)
http://www.plosone.org/article/fetchObject.action?uri=info%3Adoi%2F10.1371%2Fjournal.pone.0065761&representation=PDF
https://vlibrary.iwmi.org/pdf/H045897.pdf
https://vlibrary.iwmi.org/pdf/H045897.pdf
(0.90 MB) (915.78KB)
Introduction: With the renewed drive towards malaria elimination, there is a need for improved surveillance tools. While time series analysis is an important tool for surveillance, prediction and for measuring interventions’ impact, approximations by commonly used Gaussian methods are prone to inaccuracies when case counts are low. Therefore, statistical methods appropriate for count data are required, especially during ‘‘consolidation’’ and ‘‘pre-elimination’’ phases.Methods: Generalized autoregressive moving average (GARMA) models were extended to generalized seasonal autoregressive integrated moving average (GSARIMA) models for parsimonious observation-driven modelling of non Gaussian, non stationary and/or seasonal time series of count data. The models were applied to monthly malaria case time series in a district in Sri Lanka, where malaria has decreased dramatically in recent years.Results: The malaria series showed long-term changes in the mean, unstable variance and seasonality. After fitting negativebinomial Bayesian models, both a GSARIMA and a GARIMA deterministic seasonality model were selected based on different criteria. Posterior predictive distributions indicated that negative-binomial models provided better predictions than Gaussian models, especially when counts were low. The G(S)ARIMA models were able to capture the autocorrelation in the series.Conclusions: G(S)ARIMA models may be particularly useful in the drive towards malaria elimination, since episode count series are often seasonal and non-stationary, especially when control is increased. Although building and fitting GSARIMA models is laborious, they may provide more realistic prediction distributions than do Gaussian methods and may be more suitable when counts are low.
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