Cyclic patterns of cerebral malaria admissions in Papua New Guinea for the years 1987–1996

Abstract

Data on the dynamics of malaria incidence, admissions and mortality and their best possible description are very important to better forecast and assess the implementation of programmes to register, monitor (e.g. by remote sensing) and control the disease, especially in endemic zones. Semi-annual and seasonal cycles in malaria rates have been observed in various countries and close similarity with cycles in the natural environment (temperature, heliogeophysical activity, etc.), host immunity and/or virulence of the parasite suggested. This study aimed at confirming previous results on malaria cyclicity by exploring whether trans-year and/or multiannual cycles might exist. The exploration of underlying chronomes (time structures) was done with raw data (without smoothing) by linear and nonlinear parametric regression models, autocorrelation, spectral (Fourier) and periodogram regression analysis. The strongest cyclical patterns of detrended malaria admissions were (i) annual period of 1·0 year (12 months or seasonality); (ii) quasi-biennial cycle of about 2·25 years; and (iii) infrannual, circadecennial cycle of about 10·3 years. The seasonal maximum occurred in May with the minimum in September. Notably, these cycles corresponded to similar cyclic components of heliogeophysical activity such as sunspot seasonality and solar activity cyclicities and well-known climate/weather oscillations. Further analyses are thus warranted to investigate such similarities. In conclusion, multicomponent cyclical dynamics of cerebral malaria admissions in Papua New Guinea were observed thus allowing more specific analyses and modelling as well as correlations with environmental factors of similar cyclicity to be explored. Such further results might also contribute to and provide more precise estimates for the forecasting and prevention, as well as the better understanding, of the dynamics and aetiology of this vector-borne disease.

Transyears Competing with the Seasons in Tropical Malaria Incidence

“…About 11-year (Horrebow-Schwabe) cycles of relative sunspot numbers [5][6][7] have also been reported to characterize communicable diseases such as malaria, as suggested earlier by Dimitrov et al [8]. In their analysis of cerebral malaria in Papua New Guinea (latitude between 0° and 12°S, longitude 140°-160°E), these authors also reported the presence of a strong calendar-yearly component, in the absence of a marked transyear.…”

“…Herein, we revisit published data on the incidence of malaria in different geographic locations near the equator. For this purpose, we turn to data in Papua New Guinea [8], Thailand, and Karuzi, Burundi [10], to explore any differences in terms of the relative prominence of transyears versus the calendar year. Our meta-analysis raises the question whether space weather may also contribute to communicable diseases like malaria in certain geographic…”

Cyclic variations in the dynamics of flu incidence in Azerbaijan, 1976–2000

“…As a second step, autocorrelation and spectral functions for amplitude and density were explored to examine whether the variations may exhibit cyclic patterns (Figs 2, 3). Last, a periodogram regression analysis (PRA) with determination of the cycle length and its statistical significance at P < 0·05 was applied [5,[11][12][13][14]. PRA, using the sigma method for testing the statistical significance of the linear correlation coefficients, was successfully applied in earlier studies on helioepidemiology of various cancers and infectious diseases [5,[11][12][13].…”

“…Last, a periodogram regression analysis (PRA) with determination of the cycle length and its statistical significance at P < 0·05 was applied [5,[11][12][13][14]. PRA, using the sigma method for testing the statistical significance of the linear correlation coefficients, was successfully applied in earlier studies on helioepidemiology of various cancers and infectious diseases [5,[11][12][13]. In this work we approximated the studied time series f(t) with a number of minimizing correlation-regression functions F(t) of periodic mode:…”

“…However, other more accurate quantitative approaches in modelling and forecasting flu incidence are needed [5,[8][9][10]. These methods, with minimal requirements for a specific algorithm and 95% CIs for the derived estimates and parameters [10][11][12][13] should be the following: (1) description, analysis and decomposition of temporal dynamics; nonlinear regression modelling of periodic mode; and derivation and forecasting of cyclic estimates; (2) linear and nonlinear regression modelling to independent variables (e.g. HGA) with such deterministic temporal patterns as seasonality.…”