Etiology of interepidemic periods of mosquito-borne disease

Abstract

Dengue viruses and malaria protozoa are of increasing global concern in public health. The diseases caused by these pathogens often show regular seasonal patterns in incidence because of the sensitivity of their mosquito vectors to climate. Between years in endemic areas, however, there can be further significant variation in case numbers for which public health systems are generally unprepared. There is an acute need for reliable predictions of within-year and between-year epidemic events. The prerequisite for developing any system of early warning is a detailed understanding of the factors involved in epidemic genesis. In this report we discuss the potential causes of the interepidemic periods in dengue hemorrhagic fever in Bangkok and of Plasmodium falciparum malaria in a highland area of western Kenya. The alternative causes are distinguished by a retrospective analysis of two unique and contemporaneous 33-year time series of epidemiological and associated meteorological data recorded at these two sites. We conclude that intrinsic population dynamics offer the most parsimonious explanation for the observed interepidemic periods of disease in these locations.

Figure 1

(a) A line graph showing the monthly incidence (cases per 100,000) of DHF in Bangkok from January 1966 to December 1998. The dashed line shows a moving average of 61 months and the bold line, the stationary DHF incidence series (original value − moving average) on which SDA was performed. (b–d) The spectral density plots of mean monthly temperature (b), total monthly rainfall (c), and total DHF incidence (d) for Bangkok are shown. A Tukey–Hamming window of three points was applied to smooth the spectral density plots. Details of the variance structure from the periodograms (unsmoothed spectral density plots of frequency) show that annual and short frequencies account for 31.5% of the total variance in the DHF time series and superannual frequencies account for 68.5%. For the temperature and rainfall time series, the annual/superannual variance split is 92.5:7.5 and 94.0:6.0, respectively.

Figure 2

(a) A line graph showing the monthly incidence (cases per 100,000) of P. falciparum malaria incidence (cases per 100,000) in Kericho from January 1966 to December 1998. (b–d) The SDA of mean monthly temperature (b), total monthly rainfall (c), and total malaria incidence (d) for Kericho are shown. As for Fig. 1 b–d, except that annual and shorter frequencies account for 69.8% of the total variance in the malaria time series and superannual frequencies account for 30.2%. For the temperature and rainfall time series the annual/superannual variance split is 82.1:17.9 and 89.1:10.9, respectively.

Figure 3

(a) A line graph showing the monthly MENSOI from January 1966 to December 1998. (b) SDA plot of the MENSOI. As for Fig. 1 b–d, except that details of the variance structure from the periodogram show that annual and shorter frequencies account for 4.5% of the total variance in the MENSOI time series and superannual frequencies account for 95.5%.