Content area
Full Text
Malaria transmission is influenced by climate, land use and deliberate interventions. Recent declines have been observed in malaria transmission. Here we show that the African continent has witnessed a long-term decline in the prevalence of Plasmodium falciparum from 40% prevalence in the period 1900-1929 to 24% prevalence in the period 2010-2015, a trend that has been interrupted by periods of rapidly increasing or decreasing transmission. The cycles and trend over the past 115 years are inconsistent with explanations in terms of climate or deliberate intervention alone. Previous global initiatives have had minor impacts on malaria transmission, and a historically unprecedented decline has been observed since 2000. However, there has been little change in the high transmission belt that covers large parts of West and Central Africa. Previous efforts to model the changing patterns of P. falciparum transmission intensity in Africa have been limited to the past 15 years1,2 or have used maps drawn from historical expert opinions3. We provide quantitative data, from 50,424 surveys at 36,966 geocoded locations, that covers 115 years of malaria history in sub-Saharan Africa; inferring from these data to future trends, we would expect continued reductions in malaria transmission, punctuated with resurgences.
Although short-term seasonal cycles are fundamental to malaria epidemiology, longer-term climate anomalies and shifting environmental and intervention landscapes also alter the likelihood of contact between mosquitoes and humans or the duration of host infection. The supra-seasonal, long-term cycles of transmission are poorly defined for P. falciparum malaria in Africa.
To provide an empirical basis for defining the long-term nature of malaria transmission cycles, we used data on the P. falciparum parasite rate (the proportion of persons positive for malaria infection among those examined). These data were derived from a repository assembled over the past 21 years (see Supplementary Information 1). To our knowledge, these data (available through the Harvard Dataverse, http://dx.doi.org/10.7910/DVN/Z29FR0) represent the largest repository assembled for any parasitic disease in Africa, derived from over 50,000 community-based surveys across sub-Saharan Africa since 19004 (Extended Data Figs 1, 2, Supplementary Information 1). We have used the space-time cube of data to leverage power from neighbouring areas and preceding data points in time5 in a conditional autoregressive spatial and temporal model, in order to compute a smoothed median estimate for...