Forecasting Non-Stationary Diarrhea, Acute Respiratory Infection, and Malaria Time-Series in Niono, Mali

Abstract

BackgroundMuch of the developing world, particularly sub-Saharan Africa, exhibits high levels of morbidity and mortality associated with diarrhea, acute respiratory infection, and malaria. With the increasing awareness that the aforementioned infectious diseases impose an enormous burden on developing countries, public health programs therein could benefit from parsimonious general-purpose forecasting methods to enhance infectious disease intervention. Unfortunately, these disease time-series often i) suffer from non-stationarity; ii) exhibit large inter-annual plus seasonal fluctuations; and, iii) require disease-specific tailoring of forecasting methods.Methodology/Principal FindingsIn this longitudinal retrospective (01/1996–06/2004) investigation, diarrhea, acute respiratory infection of the lower tract, and malaria consultation time-series are fitted with a general-purpose econometric method, namely the multiplicative Holt-Winters, to produce contemporaneous on-line forecasts for the district of Niono, Mali. This method accommodates seasonal, as well as inter-annual, fluctuations and produces reasonably accurate median 2- and 3-month horizon forecasts for these non-stationary time-series, i.e., 92% of the 24 time-series forecasts generated (2 forecast horizons, 3 diseases, and 4 age categories = 24 time-series forecasts) have mean absolute percentage errors circa 25%.Conclusions/SignificanceThe multiplicative Holt-Winters forecasting method: i) performs well across diseases with dramatically distinct transmission modes and hence it is a strong general-purpose forecasting method candidate for non-stationary epidemiological time-series; ii) obliquely captures prior non-linear interactions between climate and the aforementioned disease dynamics thus, obviating the need for more complex disease-specific climate-based parametric forecasting methods in the district of Niono; furthermore, iii) readily decomposes time-series into seasonal components thereby potentially assisting with programming of public health interventions, as well as monitoring of disease dynamics modification. Therefore, these forecasts could improve infectious diseases management in the district of Niono, Mali, and elsewhere in the Sahel.

The Air Quality in African Rural Environments. Preliminary Implications for Health: The Case of Respiratory Disease in the Northern Benin

“…3) shows a situation close to what the authors described in Mali with an initial increase in the number of ALRI cases during the hot and dry season and a second during the rainy season (Findley et al 2005;Medina et al 2007). A longitudinal study in a village in Burkina Faso showed that 44 % of fell ill during the 6 months of rainy season and 48 % during the 6 months of dry season.…”

Seasonality of childhood infectious diseases in Niono, Mali

“…Consequently, the proposed seasonal disease calendar ( Figure 3) and forecasts (Medina et al 2007) complement each other. [In fact, the seasonal factors described here have been employed to adjust forecasts in our previous study (Medina et al 2007). ] Forecast and calendar-based activities could be combined with the dual benefit of regular timing (seasonal disease calendars) and magnitude preparedness (forecasts).…”

“…Patient privacy was protected from inadvertent (or deliberate) violations because consultation records reflect aggregate data without personal identifiers (Medina et al 2007).…”

“…Data pre-processing The data pre-processing method employed herein has been previously described (Division des Services Socio-Sanitaires 1996Á2004, USAID 2004, Medina et al 2007). Succinctly, we digitalised monthly malaria, ARI and diarrhoea consultation records for children less than 5 years of age, from 17 community health centre (CSCOM) service areas, spanning nearly one decade (1996Á2004).…”

“…We demonstrated in a previous study that monthly malaria, ARI and diarrhoea consultation rate time-series (TS) for the district of Niono, Mali, behave seasonally and are amenable to forecasts across all age groups (Medina et al 2007). However, the scope of that study was neither to detail childhood disease seasonality nor to timely organise child health activities around an integrated seasonal disease calendar.…”