Another Approach to Detect Malaria Epidemics and to Evaluate the Impact of their Control Measures in Situation of Lack of Information
DOI:
https://doi.org/10.17140/EPOJ-2-108Keywords:
Autoregressive integrated moving average modeling (ARIMA), Malaria, Malaria epidemiologic surveillance systemAbstract
The purpose of this study is to establish a theoretical epidemiologic threshold for the rate of reported malaria cases in order to detect epidemics and evaluate the impact of control measures. To create this epidemiologic threshold it has been used an multiple cross-over time series autoregressive integrated moving average forecasting model that reflects the dependence of the magnitude of the rate of malaria reports on the past levels of rain, temperature and vegetation density according to factors related with malaria transmission described by the MacDonald’s theory. Information available for the model derivation and accuracy testing was obtained from Médecins Sans Frontières in Karuzi, a Burundi’s province, with a health network of a 100-bed hospital and 11 health centers, that consists in the monthly malaria incidence rate estimated from clinical diagnostics in medical consultations with a 5-20% of clinical cases
with microbiological confirmation in non-epidemic periods and less than 2% during outbreaks, the cumulative monthly level of precipitation and the minimum and maximum mean monthly temperature recorded by the local meteorological stations, as well as the NDVI provided by the National Oceanographic and Atmospheric Administration satellites. This available information covers the 1997-2003 period. The obtained model makes it possible to create a curve of expected non-epidemic case reports with a reliability of 95%. This model identified four epidemics in the 7-year study period and detected the impact of a malaria control campaign in the last year. The application of this methodological tool permits the timely detection of malaria epidemics and the evaluation of the impact of measures for its control.
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