Epidemiology and seasonality of human parainfluenza serotypes 1-3 in Australian children.

BACKGROUND: Parainfluenza viruses are significant contributors to childhood respiratory illness worldwide, although detailed epidemiological studies are lacking. Few recent Australian studies have investigated serotype-specific PIV epidemiology, and there is a paucity of southern hemisphere PIV reports. We report age-stratified PIV hospitalisation rates and a mathematical model of PIV seasonality and dynamics in Western Australia (WA). METHODS: We used linked perinatal, hospital admission and laboratory diagnostic data of 469 589 children born in WA between 1996 and 2012. Age-specific rates of viral testing and PIV detection in hospitalised children were determined using person time-at-risk analysis. PIV seasonality was modelled using a compartmental SEIRS model and complex demodulation methods. RESULTS: From 2000 to 2012, 9% (n = 43 627) of hospitalised children underwent PIV testing, of which 5% (n = 2218) were positive for PIV-1, 2 or 3. The highest incidence was in children aged 1-5 months (PIV-1:62.6 per 100 000 child-years, PIV-2:26.3/100 000, PIV-3:256/100 000), and hospitalisation rates were three times higher for Aboriginal children compared with non-Aboriginal children overall (IRR: 2.93). PIV-1 peaked in the autumn of even-numbered years, and PIV-3 annually in the spring, whereas PIV-2 had inconsistent peak timing. Fitting models to the higher incidence serotypes estimated reproduction numbers of 1.24 (PIV-1) and 1.72 (PIV-3). CONCLUSION: PIV-1 and 3 are significant contributors towards infant respiratory hospitalisations. Interventions should prioritise children in the first 6 months of life, with respect to the observed autumn PIV-1 and spring PIV-3 activity peaks. Continued surveillance of all serotypes and investigation into PIV-1 and 3 interventions should be prioritised.

Inferring a complete genotype-phenotype map from a small number of measured phenotypes.

Understanding evolution requires detailed knowledge of genotype-phenotype maps; however, it can be a herculean task to measure every phenotype in a combinatorial map. We have developed a computational strategy to predict the missing phenotypes from an incomplete, combinatorial genotype-phenotype map. As a test case, we used an incomplete genotype-phenotype dataset previously generated for the malaria parasite's 'chloroquine resistance transporter' (PfCRT). Wild-type PfCRT (PfCRT3D7) lacks significant chloroquine (CQ) transport activity, but the introduction of the eight mutations present in the 'Dd2' isoform of PfCRT (PfCRTDd2) enables the protein to transport CQ away from its site of antimalarial action. This gain of a transport function imparts CQ resistance to the parasite. A combinatorial map between PfCRT3D7 and PfCRTDd2 consists of 256 genotypes, of which only 52 have had their CQ transport activities measured through expression in the Xenopus laevis oocyte. We trained a statistical model with these 52 measurements to infer the CQ transport activity for the remaining 204 combinatorial genotypes between PfCRT3D7 and PfCRTDd2. Our best-performing model incorporated a binary classifier, a nonlinear scale, and additive effects for each mutation. The addition of specific pairwise- and high-order-epistatic coefficients decreased the predictive power of the model. We evaluated our predictions by experimentally measuring the CQ transport activities of 24 additional PfCRT genotypes. The R2 value between our predicted and newly-measured phenotypes was 0.90. We then used the model to probe the accessibility of evolutionary trajectories through the map. Approximately 1% of the possible trajectories between PfCRT3D7 and PfCRTDd2 are accessible; however, none of the trajectories entailed eight successive increases in CQ transport activity. These results demonstrate that phenotypes can be inferred with known uncertainty from a partial genotype-phenotype dataset. We also validated our approach against a collection of previously published genotype-phenotype maps. The model therefore appears general and should be applicable to a large number of genotype-phenotype maps.