A simplified protocol for detecting two systemic bait markers (Rhodamine B and iophenoxic acid) in small mammals

We developed a method of quantifying levels of fluorescence in the whiskers of wild stoats (Mustela erminea) using fluorescence microscopy and Axiovision 3.0.6.1 software. The method allows for discrimination between natural fluorescence present in or on a whisker, and the fluorescence resulting from the ingestion of the systemic marker Rhodamine B (RB), although some visual judgement is still required. We also developed a new high performance liquid chromatography (HPLC) protocol for detecting the systemic marker iophenoxic acid (IPA) in the blood of laboratory rats (Rattus norvegicus) and wild stoats. With this method, the blood of an animal that has consumed IPA can be tested for the presence of the foreign IPA compound itself. This is a more reliable test than the previous method, which measured the raised level of natural blood protein-bound iodine correlated with IPA absorption. The quantity of blood required from animal subjects is very small (10 μl), so the testing is less intrusive and the method can be extended to smaller species. The extraction technique uses methanol, rather than acids and heavy metal salts, thereby simplifying the procedure. Recovery of IPA is quantitative, giving a highly reliable reading. In experiments on captive rats the IPA method proved successful. Of 12 positively marked carcasses, two that had not been frozen for the 24 h before blood samples were taken showed relatively lower IPA levels. The same IPA detection method, as well as the whisker analysis for RB, was applied successfully to a population of wild stoats to which both Rhodamine B and IPA were made available at bait stations. The presence of both bait markers was detectable in rats for at least 21 days and in stoats for at least 27 days

Disease effects on reproduction can cause population cycles in seasonal environments

Recent studies of rodent populations have demonstrated that certain parasites can cause juveniles to delay maturation until the next reproductive season. Furthermore, a variety of parasites may share the same host, and evidence is beginning to accumulate showing nonindependent effects of different infections.We investigated the consequences for host population dynamics of a disease-induced period of no reproduction, and a chronic reduction in fecundity following recovery from infection (such as may be induced by secondary infections) using a modified SIR (susceptible, infected, recovered) model. We also included a seasonally varying birth rate as recent studies have demonstrated that seasonally varying parameters can have important effects on long-term host–parasite dynamics. We investigated the model predictions using parameters derived from five different cyclic rodent populations.Delayed and reduced fecundity following recovery from infection have no effect on the ability of the disease to regulate the host population in the model as they have no effect on the basic reproductive rate. However, these factors can influence the long-term dynamics including whether or not they exhibit multiyear cycles.The model predicts disease-induced multiyear cycles for a wide range of realistic parameter values. Host populations that recover relatively slowly following a disease-induced population crash are more likely to show multiyear cycles. Diseases for which the period of infection is brief, but full recovery of reproductive function is relatively slow, could generate large amplitude multiyear cycles of several years in length. Chronically reduced fecundity following recovery can also induce multiyear cycles, in support of previous theoretical studies.When parameterized for cowpox virus in the cyclic field vole populations (Microtus agrestis) of Kielder Forest (northern England), the model predicts that the disease must chronically reduce host fecundity by more than 70%, following recovery from infection, for it to induce multiyear cycles. When the model predicts quasi-periodic multiyear cycles it also predicts that seroprevalence and the effective date of onset of the reproductive season are delayed density-dependent, two phenomena that have been recorded in the field

The niche of One Health approaches in Lassa fever surveillance and control

Lassa fever (LF), a zoonotic illness, represents a public health burden in West African countries where the Lassa virus (LASV) circulates among rodents. Human exposure hinges significantly on LASV ecology, which is in turn shaped by various parameters such as weather seasonality and even virus and rodent-host genetics. Furthermore, human behaviour, despite playing a key role in the zoonotic nature of the disease, critically affects either the spread or control of human-to-human transmission. Previous estimations on LF burden date from the 80s and it is unclear how the population expansion and the improvement on diagnostics and surveillance methods have affected such predictions. Although recent data have contributed to the awareness of epidemics, the real impact of LF in West African communities will only be possible with the intensification of interdisciplinary efforts in research and public health approaches. This review discusses the causes and consequences of LF from a One Health perspective, and how the application of this concept can improve the surveillance and control of this disease in West Africa

Prevalence and Risk Factors of Lassa Seropositivity in Inhabitants of the Forest Region of Guinea: A Cross-Sectional Study

Lassa fever is a viral haemorrhagic fever endemic in West Africa, mainly transmitted to humans by multimammate rats. Several modes of virus transmission are suspected: aerosolisation of the virus, contact with infected rodent excreta, and consumption of rodent meat. Person-to-person transmission also occurs via contact with body fluids of infected persons (blood, urine) and is responsible for numerous outbreaks, mostly in healthcare facilities. Our objective was to precisely describe risk factors for Lassa fever in both rural and urban communities of forest Guinea. For each participant, a standardized questionnaire was completed and a blood sample tested for Lassa virus antibodies. A total of 1424 subjects were interviewed and 977 blood samples tested. The prevalence of Lassa virus antibodies was estimated at 12.9% and 10.0% in rural and urban areas, respectively. The two risk factors were: to have, in the past twelve months, undergone an injection, or lived with someone displaying a haemorrhage. Contrary to our expectation, no factors related to contact with rodents were identified. It is still probable that transmission occurs via indirect contact between rodents and humans in households, but our results highlight the importance of person-to-person transmission via close contact and nosocomial exposure

Bayesian estimation of Lassa virus epidemiological parameters: Implications for spillover prevention using wildlife vaccination

Lassa virus is a significant burden on human health throughout its endemic region in West Africa, with most human infections the result of spillover from the primary rodent reservoir of the virus, the natal multimammate mouse, M. natalensis. Here we develop a Bayesian methodology for estimating epidemiological parameters of Lassa virus within its rodent reservoir and for generating probabilistic predictions for the efficacy of rodent vaccination programs. Our approach uses Approximate Bayesian Computation (ABC) to integrate mechanistic mathematical models, remotely-sensed precipitation data, and Lassa virus surveillance data from rodent populations. Using simulated data, we show that our method accurately estimates key model parameters, even when surveillance data are available from only a relatively small number of points in space and time. Applying our method to previously published data from two villages in Guinea estimates the time-averaged R0 of Lassa virus to be 1.74 and 1.54 for rodent populations in the villages of Bantou and Tanganya, respectively. Using the posterior distribution for model parameters derived from these Guinean populations, we evaluate the likely efficacy of vaccination programs relying on distribution of vaccine-laced baits. Our results demonstrate that effective and durable reductions in the risk of Lassa virus spillover into the human population will require repeated distribution of large quantities of vaccine

Detection of Lassa Virus, Mali

To determine whether Lassa virus was circulating in southern Mali, we tested samples from small mammals from 3 villages, including Soromba, where in 2009 a British citizen probably contracted a lethal Lassa virus infection. We report the isolation and genetic characterization of Lassa virus from an area previously unknown for Lassa fever

Bridging the gap: Using reservoir ecology and human serosurveys to estimate Lassa virus spillover in West Africa

Forecasting the risk of pathogen spillover from reservoir populations of wild or domestic animals is essential for the effective deployment of interventions such as wildlife vaccination or culling. Due to the sporadic nature of spillover events and limited availability of data, developing and validating robust, spatially explicit, predictions is challenging. Recent efforts have begun to make progress in this direction by capitalizing on machine learning methodologies. An important weakness of existing approaches, however, is that they generally rely on combining human and reservoir infection data during the training process and thus conflate risk attributable to the prevalence of the pathogen in the reservoir population with the risk attributed to the realized rate of spillover into the human population. Because effective planning of interventions requires that these components of risk be disentangled, we developed a multi-layer machine learning framework that separates these processes. Our approach begins by training models to predict the geographic range of the primary reservoir and the subset of this range in which the pathogen occurs. The spillover risk predicted by the product of these reservoir specific models is then fit to data on realized patterns of historical spillover into the human population. The result is a geographically specific spillover risk forecast that can be easily decomposed and used to guide effective intervention. Applying our method to Lassa virus, a zoonotic pathogen that regularly spills over into the human population across West Africa, results in a model that explains a modest but statistically significant portion of geographic variation in historical patterns of spillover. When combined with a mechanistic mathematical model of infection dynamics, our spillover risk model predicts that 897,700 humans are infected by Lassa virus each year across West Africa, with Nigeria accounting for more than half of these human infections.</jats:p

Novel Arenavirus Sequences in Hylomyscus sp. and Mus (Nannomys) setulosus from Côte d'Ivoire: Implications for Evolution of Arenaviruses in Africa

This study aimed to identify new arenaviruses and gather insights in the evolution of arenaviruses in Africa. During 2003 through 2005, 1,228 small mammals representing 14 different genera were trapped in 9 villages in south, east, and middle west of Côte d'Ivoire. Specimens were screened by pan-Old World arenavirus RT-PCRs targeting S and L RNA segments as well as immunofluorescence assay. Sequences of two novel tentative species of the family Arenaviridae, Menekre and Gbagroube virus, were detected in Hylomyscus sp. and Mus (Nannomys) setulosus, respectively. Arenavirus infection of Mus (Nannomys) setulosus was also demonstrated by serological testing. Lassa virus was not found, although 60% of the captured animals were Mastomys natalensis. Complete S RNA and partial L RNA sequences of the novel viruses were recovered from the rodent specimens and subjected to phylogenetic analysis. Gbagroube virus is a closely related sister taxon of Lassa virus, while Menekre virus clusters with the Ippy/Mobala/Mopeia virus complex. Reconstruction of possible virus–host co-phylogeny scenarios suggests that, within the African continent, signatures of co-evolution might have been obliterated by multiple host-switching events

Assessment of Three Mitochondrial Genes (16S, Cytb, CO1) for Identifying Species in the Praomyini Tribe (Rodentia: Muridae)

The Praomyini tribe is one of the most diverse and abundant groups of Old World rodents. Several species are known to be involved in crop damage and in the epidemiology of several human and cattle diseases. Due to the existence of sibling species their identification is often problematic. Thus an easy, fast and accurate species identification tool is needed for non-systematicians to correctly identify Praomyini species. In this study we compare the usefulness of three genes (16S, Cytb, CO1) for identifying species of this tribe. A total of 426 specimens representing 40 species (sampled across their geographical range) were sequenced for the three genes. Nearly all of the species included in our study are monophyletic in the neighbour joining trees. The degree of intra-specific variability tends to be lower than the divergence between species, but no barcoding gap is detected. The success rate of the statistical methods of species identification is excellent (up to 99% or 100% for statistical supervised classification methods as the k-Nearest Neighbour or Random Forest). The 16S gene is 2.5 less variable than the Cytb and CO1 genes. As a result its discriminatory power is smaller. To sum up, our results suggest that using DNA markers for identifying species in the Praomyini tribe is a largely valid approach, and that the CO1 and Cytb genes are better DNA markers than the 16S gene. Our results confirm the usefulness of statistical methods such as the Random Forest and the 1-NN methods to assign a sequence to a species, even when the number of species is relatively large. Based on our NJ trees and the distribution of all intraspecific and interspecific pairwise nucleotide distances, we highlight the presence of several potentially new species within the Praomyini tribe that should be subject to corroboration assessments

Environmental-mechanistic modelling of the impact of global change on human zoonotic disease emergence: A case study of Lassa fever

1. Human infectious diseases are a significant threat to global human health and economies (e.g., Ebola, SARs), with the majority of infectious diseases having an animal source (zoonotic). Despite their importance, the lack of a quantitative predictive framework hampers our understanding of how spill-overs of zoonotic infectious diseases into the human population will be impacted by global environmental stressors. 2. Here, we create an environmental-mechanistic model for understanding the impact of global change on the probability of zoonotic disease reservoir host-human spill-over events. As a case study, we focus on Lassa fever virus (LAS). We firstly quantify the spatial determinants of LAS outbreaks, including the phylogeographic distribution of its reservoir host Natal multimammate rat (Mastomys natalensis) (LAS host). Secondly, we use these determinants to inform our environmental-mechanistic model to estimate present day LAS spill-over events and the predicted impact of climate change, human population growth, and land use by 2070. 3. We find phylogeographic evidence to suggest that LAS is confined to only one clade of LAS host (Western clade Mastomys natalensis), and that the probability of its occurrence was a major determinant of the spatial variation in LAS historical outbreaks (69.8%), along with human population density (20.4%). Our estimates for present day LAS spill-over events from our environmental-mechanistic model were consistent with observed patterns, and we predict an increase in events per year by 2070 from 195,125 to 406,725 within the LAS endemic western African region. Of the component drivers, climate change and human population growth are predicted to have the largest effects by increasing landscape suitability for the host and human-host contact rates, while land use change has only a weak impact on the number of future events. 4. LAS spill-over events did not respond uniformly to global environmental stressors, and we suggest that understanding the impact of global change on zoonotic infectious disease emergence requires an understanding of how reservoir host species respond to environmental change. Our environmental-mechanistic modelling methodology provides a novel generalizable framework to understand the impact of global change on the spill-over of zoonotic diseases