Nearly all mathematical models of vector-borne diseases have assumed that vectors die at constant rates. However, recent empirical research suggests that mosquito mortality rates are frequently age dependent. This work develops a simple mathematical model to assess how relaxing the classical assumption of constant mortality affects the predicted effectiveness of anti-vectorial interventions. The effectiveness of mosquito control when mosquitoes die at age dependent rates was also compared across different extrinsic incubation periods. Compared to a more realistic age dependent model, constant mortality models overestimated the sensitivity of disease transmission to interventions that reduce mosquito survival. Interventions that reduce mosquito survival were also found to be slightly less effective when implemented in systems with shorter EIPs. Future transmission models that examine anti-vectorial interventions should incorporate realistic age dependent mortality rates.

The recent development of genetic markers for Bacillus anthracis has made it possible to monitor the spread and distribution of this pathogen during and between anthrax outbreaks. In Namibia, anthrax outbreaks occur annually in the Etosha National Park (ENP) and on private game and livestock farms. We genotyped 384 B. anthracis isolates collected between 1983–2010 to identify the possible epidemiological correlations of anthrax outbreaks within and outside the ENP and to analyze genetic relationships between isolates from domestic and wild animals. The isolates came from 20 animal species and from the environment and were genotyped using a 31-marker multi-locus-VNTR-analysis (MLVA) and, in part, by twelve single nucleotide polymorphism (SNP) markers and four single nucleotide repeat (SNR) markers. A total of 37 genotypes (GT) were identified by MLVA, belonging to four SNP-groups. All GTs belonged to the A-branch in the cluster- and SNP-analyses. Thirteen GTs were found only outside the ENP, 18 only within the ENP and 6 both inside and outside. Genetic distances between isolates increased with increasing time between isolations. However, genetic distance between isolates at the beginning and end of the study period was relatively small, indicating that while the majority of GTs were only found sporadically, three genetically close GTs, accounting for more than four fifths of all the ENP isolates, appeared dominant throughout the study period. Genetic distances among isolates were significantly greater for isolates from different host species, but this effect was small, suggesting that while species-specific ecological factors may affect exposure processes, transmission cycles in different host species are still highly interrelated. The MLVA data were further used to establish a model of the probable evolution of GTs within the endemic region of the ENP. SNR-analysis was helpful in correlating an isolate with its source but did not elucidate epidemiological relationships.

Summary Few studies have examined host-pathogen interactions in wildlife from an immunological perspective, particularly in the context of seasonal and longitudinal dynamics. In addition, though most ecological immunology studies employ serological antibody assays, endpoint titer determination is usually based on subjective criteria and needs to be made more objective. Despite the fact that anthrax is an ancient and emerging zoonotic infectious disease found worldwide, its natural ecology is not well understood. In particular, little is known about the adaptive immune responses of wild herbivore hosts against Bacillus anthracis. Working in the natural anthrax system of Etosha National Park, Namibia, we collected 154 serum samples from plains zebra (Equus quagga), 21 from springbok (Antidorcas marsupialis), and 45 from African elephants (Loxodonta africana) over 2-3 years, resampling individuals when possible for seasonal and longitudinal comparisons. We used enzyme-linked immunosorbent assays to measure anti-anthrax antibody titers and developed three increasingly conservative models to determine endpoint titers with more rigorous, objective mensuration. Between 52-87% of zebra, 0-15% of springbok, and 3-52% of elephants had measurable anti-anthrax antibody titers, depending on the model used. While the ability of elephants and springbok to mount anti-anthrax adaptive immune responses is still equivocal, our results indicate that zebra in ENP often survive sublethal anthrax infections, encounter most B. anthracis in the wet season, and can partially booster their immunity to B. anthracis. Thus, rather than being solely a lethal disease, anthrax often occurs as a sublethal infection in some susceptible hosts. Though we found that adaptive immunity to anthrax wanes rapidly, subsequent and frequent sublethal B. anthracis infections cause maturation of anti-anthrax immunity. By triggering host immune responses, these common sublethal infections may act as immunomodulators and affect population dynamics through indirect immunological and co-infection effects. In addition, with our three endpoint titer models, we introduce more mensuration rigor into serological antibody assays, even under the often-restrictive conditions that come with adapting laboratory immunology methods to wild systems. With these methods we identified significantly more zebras responding immunologically to anthrax than have previous studies using less comprehensive titer analyses.

BackgroundThe infectivity of the HIV-1 acute phase has been directly measured only once, from a retrospectively identified cohort of serodiscordant heterosexual couples in Rakai, Uganda. Analyses of this cohort underlie the widespread view that the acute phase is highly infectious, even more so than would be predicted from its elevated viral load, and that transmission occurring shortly after infection may therefore compromise interventions that rely on diagnosis and treatment, such as antiretroviral treatment as prevention (TasP). Here, we re-estimate the duration and relative infectivity of the acute phase, while accounting for several possible sources of bias in published estimates, including the retrospective cohort exclusion criteria and unmeasured heterogeneity in risk.Methods and FindingsWe estimated acute phase infectivity using two approaches. First, we combined viral load trajectories and viral load-infectivity relationships to estimate infectivity trajectories over the course of infection, under the assumption that elevated acute phase infectivity is caused by elevated viral load alone. Second, we estimated the relative hazard of transmission during the acute phase versus the chronic phase (RHacute) and the acute phase duration (d acute) by fitting a couples transmission model to the Rakai retrospective cohort using approximate Bayesian computation. Our model fit the data well and accounted for characteristics overlooked by previous analyses, including individual heterogeneity in infectiousness and susceptibility and the retrospective cohort's exclusion of couples that were recorded as serodiscordant only once before being censored by loss to follow-up, couple dissolution, or study termination. Finally, we replicated two highly cited analyses of the Rakai data on simulated data to identify biases underlying the discrepancies between previous estimates and our own.From the Rakai data, we estimated RHacute = 5.3 (95% credibility interval [95% CrI]: 0.79–57) and d acute = 1.7 mo (95% CrI: 0.55–6.8). The wide credibility intervals reflect an inability to distinguish a long, mildly infectious acute phase from a short, highly infectious acute phase, given the 10-mo Rakai observation intervals. The total additional risk, measured as excess hazard-months attributable to the acute phase (EHMacute) can be estimated more precisely: EHMacute = (RHacute - 1) × d acute, and should be interpreted with respect to the 120 hazard-months generated by a constant untreated chronic phase infectivity over 10 y of infection. From the Rakai data, we estimated that EHMacute = 8.4 (95% CrI: -0.27 to 64). This estimate is considerably lower than previously published estimates, and consistent with our independent estimate from viral load trajectories, 5.6 (95% confidence interval: 3.3–9.1). We found that previous overestimates likely stemmed from failure to account for risk heterogeneity and bias resulting from the retrospective cohort study design.Our results reflect the interaction between the retrospective cohort exclusion crit...