Background Accurate estimation of the burden of Plasmodium falciparum is essential for strategic planning for control and elimination. Due in part to the extreme heterogeneity in malaria exposure, immunity, other causes of disease, direct measurements of fever and disease attributable to malaria can be difficult. This can make a comparison of epidemiological metrics both within and between populations hard to interpret. An essential part of untangling this is an understanding of the complex time-course of malaria infections.
Methods We reanalyzed malaria therapy infections in which individuals were intentionally infected with malaria parasites. In this analysis, we examined the age of an infection as a covariate describing aggregate patterns across all infections. We performed a series of piecewise linear and generalized linear regressions to highlight the infection age dependent patterns in both parasitemia and gametocytemia, and from parasitemia and gametocytemia to fever and transmission probabilities, respectively.
Results The observed duration of untreated patent infection was 130 days. As infections progressed, the fraction of infections subpatent by microscopy increased steadily. The time-averaged malaria infections had three distinct phases in parasitemia: a growth phase for the first 6 days of patency, a rapid decline from day 6 to day 18, and a slowly declining chronic phase for the remaining duration of the infection. During the growth phase, parasite densities increased sharply to a peak. Densities sharply decline for a short period of time after the peak. During the chronic phase, infections declined steadily as infections age. Gametocytemia was strongly correlated with lagged asexual parasitemia. Fever rates and transmission efficiency were strongly correlated with parasitemia and gametocytemia. The comparison between raw data and prediction from the age of infection has good qualitative agreement across all quantities of interest for predicting averaged effects.
Conclusion We established age of infection as a potentially useful covariate for malaria epidemiology. Infection age can be estimated given a history of exposure; accounting for exposure history may potentially provide a new way to estimate malaria-attributable fever rates, transmission efficiency, patent fraction, and more in immunologically naïve individuals such as children and people in low-transmission regions. Understanding how immune responses modify these statistical relationships is key for being able to apply these results more broadly.