Intensive anti-malaria campaigns targeting the Anopheles population have demonstrated substantial reductions in adult mosquito density. Understanding the population dynamics of Anopheles mosquitoes throughout their whole lifecycle is important to assess the likely impact of vector control interventions alone and in combination as well as to aid the design of novel interventions.

The success of the Onchocerciasis Control Programme is undeniable and exemplary, say the authors, but it is too early to claim victory against river blindness.

The World Health Organization (WHO) has set ambitious targets for the elimination of onchocerciasis by 2020–2025 through mass ivermectin treatment. Two different mathematical models have assessed the feasibility of reaching this goal for different settings and treatment scenarios, namely the individual-based microsimulation model ONCHOSIM and the population-based deterministic model EPIONCHO. In this study, we harmonize some crucial assumptions and compare model predictions on common outputs. Using a range of initial endemicity levels and treatment scenarios, we compared the models with respect to the following outcomes: 1) model-predicted trends in microfilarial (mf) prevalence and mean mf intensity during 25 years of (annual or biannual) mass ivermectin treatment; 2) treatment duration needed to bring mf prevalence below a provisional operational threshold for treatment interruption (pOTTIS, i.e. 1.4 %), and 3) treatment duration needed to drive the parasite population to local elimination, even in the absence of further interventions. Local elimination was judged by stochastic fade-out in ONCHOSIM and by reaching transmission breakpoints in EPIONCHO. ONCHOSIM and EPIONCHO both predicted that in mesoendemic areas the pOTTIS can be reached with annual treatment, but that this strategy may be insufficient in very highly hyperendemic areas or would require prolonged continuation of treatment. For the lower endemicity levels explored, ONCHOSIM predicted that the time needed to reach the pOTTIS is longer than that needed to drive the parasite population to elimination, whereas for the higher endemicity levels the opposite was true. In EPIONCHO, the pOTTIS was reached consistently sooner than the breakpoint. The operational thresholds proposed by APOC may have to be adjusted to adequately reflect differences in pre-control endemicities. Further comparative modelling work will be conducted to better understand the main causes of differences in model-predicted trends. This is a pre-requisite for guiding elimination programmes in Africa and refining operational criteria for stopping mass treatment.

Daily mortality is an important determinant of a vector's ability to transmit pathogens. Original simplifying assumptions in malaria transmission models presume vector mortality is independent of age, infection status and parasite load. Previous studies illustrate conflicting evidence as to the importance of Plasmodium-induced vector mortality, but very few studies to date have considered the effect of infection density on mosquito survival.

The clinical outcomes associated with Chagas disease remain poorly understood. In addition to the burden of morbidity, the burden of mortality due to Trypanosoma cruzi infection can be substantial, yet its quantification has eluded rigorous scrutiny. This is partly due to considerable heterogeneity between studies, which can influence the resulting estimates. There is a pressing need for accurate estimates of mortality due to Chagas disease that can be used to improve mathematical modelling, burden of disease evaluations, and cost-effectiveness studies. A systematic literature review was conducted to select observational studies comparing mortality in populations with and without a diagnosis of Chagas disease using the PubMed, MEDLINE, EMBASE, Web of Science and LILACS databases, without restrictions on language or date of publication. The primary outcome of interest was mortality (as all-cause mortality, sudden cardiac death, heart transplant or cardiovascular deaths). Data were analysed using a random-effects model to obtain the relative risk (RR) of mortality, the attributable risk percent (ARP), and the annual mortality rates (AMR). The statistic I2 (proportion of variance in the meta-analysis due to study heterogeneity) was calculated. Sensitivity analyses and publication bias test were also conducted. Twenty five studies were selected for quantitative analysis, providing data on 10,638 patients, 53,346 patient-years of follow-up, and 2739 events. Pooled estimates revealed that Chagas disease patients have significantly higher AMR compared with non-Chagas disease patients (0.18 versus 0.10; RR = 1.74, 95 % CI 1.49–2.03). Substantial heterogeneity was found among studies (I2 = 67.3 %). The ARP above background mortality was 42.5 %. Through a sub-analysis patients were classified by clinical group (severe, moderate, asymptomatic). While RR did not differ significantly between clinical groups, important differences in AMR were found: AMR = 0.43 in Chagas vs. 0.29 in non-Chagas patients (RR = 1.40, 95 % CI 1.21–1.62) in the severe group; AMR = 0.16 (Chagas) vs. 0.08 (non-Chagas) (RR = 2.10, 95 % CI 1.52-2.91) in the moderate group, and AMR = 0.02 vs. 0.01 (RR = 1.42, 95 % CI 1.14–1.77) in the asymptomatic group. Meta-regression showed no evidence of study-level covariates on the effect size. Publication bias was not statistically significant (Egger's test p=0.08). The results indicate a statistically significant excess of mortality due to Chagas disease that is shared among both symptomatic and asymptomatic populations.