Climatic factors and cholera incidence in the Far ...

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•WHO (2011) Relationship Between Climate Variability and Occurrence Of. Diarrhoea And Cholera A Pilot Study Using Retrospective Data From Kolkata,. India ...
49th U.S. Japan Conference on Cholera and Other Enteric Bacterial Infections January 14-16, 2015- Gainesville, FL

MODELLING AND FORECASTING CHOLERA INCIDENCE IN THE FAR NORTH, CAMEROON 6750 Mouhaman Arabi [email protected] INTRODUCTION

The Higher Institute of the Sahel-The University of Maroua, Cameroon

Cholera is an acute diarrhoeal infection caused by ingestion of food or water contaminated with the bacterium Vibrio cholerae. It has a short incubation period of two hours to five days that enhances the potential explosive pattern of outbreaks (WHO 2011). Wongkoon et al (2008 and 2012) for example used a SARIMA to forecast the incidence of the dengue hemoragic fever in northern Thailand; Bhatnagar et al (2013) forecasted the incidence of dengue in Rajasthan; Cameroon experienced in 2010 one of its most severe outbreaks of cholera in decades;

Data We used weekly cholera epidemiological data per health district (1996-2011). Cholera cases were based on passive surveillance reported to the various health centres and aggregated by health district Data modelling •The Box Jenkins approach was used to model and forecast cholera incidence one year ahead using data from previous outbreaks. Box-Jenkins forecasting models (Box and Jenkins 1971) are based on statistical concepts and principles and are able to model a wide spectrum of time series behavior.

Goals a Seasonal Autoregressive Integrated Moving Average (SARIMA) model is developed to forecast the number of cholera cases at the regional scale METHOD

•The study area: The Far North of Cameroon

RESULTS As can be seen in figure 6.12, the light line represent the actual data as represented by the model, the dark solid line represents the 95% confidence limit of the forecast. Figure 1: Location of the study area

Figure 3: Forecast from SARIMA (1,1,2)(,1,1)12 (Arabi, 2014)

Exploring the regional data Five important peaks of the cholera incidence occurred in 1996, 1997, and 1998, 2010 and 2011. Small peaks can be seen in 1999 and 2001 followed by three nonepidemic years. In the years 2004, 2005 and 2006, there were three small peaks, followed by two non-epidemic years (2007 and 2008). A small peak is observed in 2009, and then a huge peak in 2010 followed by a less important peak in 2011.

Figure 4: Monthly forecast from SARIMA (1,1,2)(,1,1)12 2011 (Arabi, 2014)

DISCUSSION The SARIMA (5,0,4)(1,0,0)[52] model though well fitted the data but forecasted less than the actual cholera data. The model did not reflect the real trend of cholera cases. The monthly SARIMA (1,1,2)(1,1,1)12 model showed that the number of cholera cases in a given month can be estimated by the number of cholera cases occurring 1, 2 (p = 2) and 12 (S = 12 and p = 1) months prior. It was found that a moving-average component of order q equal to 2 is adequate for the data. We noted that the monthly SARIMA model produced relatively good estimates for each month within a 95% confidence limit, even though the time series contains periods with relatively large numbers of cholera cases. CONCLUSION AND FUTURE RESEARCH The forecast produced by the weekly model was not able to forecast the peak observed during the 35th week as opposite to the monthly SARIMA whose result well reflected the seasonality of cholera. The monthly SARIMA model was capable of representing the number of cases in a subsequent month with a relative precision. These predictions may not be credible for forecasting the exact number of cholera cases in epidemic years. •REFERENCES.

Figure 2: Time plot of cholera case 1996 to 2011

Model selection and parameter estimation The model with the lowest AIC value for this data set, and therefore the best-fit model, was SARIMA (5,0,4)(1,0,0)[52] for the weekly data and SARIMA (1,1,2)(1,1,1)12 for the monthly data were selected. Figure 5: Weekly forecast from SARIMA (5,0,4)(1,0,0)[52] in 2011(Arabi, 2014)

•Bhatnagar S, Lal V, Gupta SD, Gupta OP. (2012) “Forecasting incidence of dengue in Rajasthan, using time series analyses”. Indian Journal of Public Health; vol 56: 281-5 •WHO (2011) Relationship Between Climate Variability and Occurrence Of Diarrhoea And Cholera A Pilot Study Using Retrospective Data From Kolkata, India; Technical Report National Institute Of Cholera & Enteric Diseases (Niced) Kolkata, India Siriwan Wongkoon, Mullica Jaroensutasinee and Krisanadej Jaroensutasine (2012) Modeling of dengue fever temporal variations in central Thailand, J Bioterr Biodef 2012, 3:3