International Journal of Environmental Research and Public Health · 2019 · 61 citations · 33 references
Veterinary MedicineInfectious Disease ControlInfectious DiseasesClimatologyInfectious Disease EpidemiologySpatial EpidemiologyMalariaPrecipitationSeasonal Transmission PatternsSeasonality In Disease OccurrenceTime-series Analysis In EpidemiologyTropical MedicineCorrelation AnalysisRainfall AmountRainfall Effects On TransmissionRainfall Time Series AnalysisRainfall-related Disease SeasonalitySpatial Cross-correlationTime Series Trend AnalysisTime-series Analysis Of Transmission
This contribution aims to investigate the influence of monthly total rainfall variations on malaria transmission in the Limpopo Province. For this purpose, monthly total rainfall was interpolated from daily rainfall data from weather stations. Annual and seasonal trends, as well as cross-correlation analyses, were performed on time series of monthly total rainfall and monthly malaria cases in five districts of Limpopo Province for the period of 1998 to 2017. The time series analysis indicated that an average of 629.5 mm of rainfall was received over the period of study. The rainfall has an annual variation of about 0.46%. Rainfall amount varied within the five districts, with the northeastern part receiving more rainfall. Spearman’s correlation analysis indicated that the total monthly rainfall with one to two months lagged effect is significant in malaria transmission across all the districts. The strongest correlation was noticed in Vhembe (r = 0.54; p-value = <0.001), Mopani (r = 0.53; p-value = <0.001), Waterberg (r = 0.40; p-value =< 0.001), Capricorn (r = 0.37; p-value = <0.001) and lowest in Sekhukhune (r = 0.36; p-value = <0.001). Seasonally, the results indicated that about 68% variation in malaria cases in summer—December, January, and February (DJF)—can be explained by spring—September, October, and November (SON)—rainfall in Vhembe district. Both annual and seasonal analyses indicated that there is variation in the effect of rainfall on malaria across the districts and it is seasonally dependent. Understanding the dynamics of climatic variables annually and seasonally is essential in providing answers to malaria transmission among other factors, particularly with respect to the abrupt spikes of the disease in the province.
33
The Power of Statistical Tests for Trend Detection
Bihrat Önöz, M. Bayazit · DergiPark (Istanbul University) · 2003 · 371 citations · Full text
Unexpected High Losses of Anopheles gambiae Larvae Due to Rainfall
Krijn P. Paaijmans, Moses O. Wandago, Andrew K. Githeko et al. · PLoS ONE · 2007 · 267 citations · Full text
Life Sciences, Ecology, Entomology +17