Dataset Information

Systematics, Ecology, and Host Switching: Attributes Affecting Emergence of the Lassa Virus in Rodents across Western Africa.

ABSTRACT: Ever since it was established that rodents serve as reservoirs of the zoonotic Lassa virus (LASV), scientists have sought to answer the questions: which populations of rodents carry the virus? How do fluctuations in LASV prevalence and rodent abundance influence Lassa fever outbreaks in humans? What does it take for the virus to adopt additional rodent hosts, proliferating what already are devastating cycles of rodent-to-human transmission? In this review, we examine key aspects of research involving the biology of rodents that affect their role as LASV reservoirs, including phylogeography, demography, virus evolution, and host switching. We discuss how this knowledge can help control Lassa fever and suggest further areas for investigation.

SUBMITTER: Olayemi A

PROVIDER: S-EPMC7150792 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature

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Systematics, Ecology, and Host Switching: Attributes Affecting Emergence of the Lassa Virus in Rodents across Western Africa.

Olayemi Ayodeji A Fichet-Calvet Elisabeth E

Viruses 20200314 3

Ever since it was established that rodents serve as reservoirs of the zoonotic Lassa virus (LASV), scientists have sought to answer the questions: which populations of rodents carry the virus? How do fluctuations in LASV prevalence and rodent abundance influence Lassa fever outbreaks in humans? What does it take for the virus to adopt additional rodent hosts, proliferating what already are devastating cycles of rodent-to-human transmission? In this review, we examine key aspects of research invo ...[more]

PMID: 32183319

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Project description: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.

Project description:Accurate data on the Lassa virus (LASV) human case fatality rate (CFR) and the prevalence of LASV in humans, rodents and other mammals are needed for better planning of actions that will ultimately reduce the burden of LASV infection in sub-Saharan Africa. In this systematic review with meta-analysis, we searched PubMed, Scopus, Africa Journal Online, and African Index Medicus from 1969 to 2020 to obtain studies that reported enough data to calculate LASV infection CFR or prevalence. Study selection, data extraction, and risk of bias assessment were conducted independently. We extracted all measures of current, recent, and past infections with LASV. Prevalence and CFR estimates were pooled using a random-effect meta-analysis. Factors associated with CFR, prevalence, and sources of between-study heterogeneity were determined using subgroup and metaregression analyses. This review was registered with PROSPERO, CRD42020166465. We initially identified 1,399 records and finally retained 109 reports that contributed to 291 prevalence records from 25 countries. The overall CFR was 29.7% (22.3-37.5) in humans. Pooled prevalence of LASV infection was 8.7% (95% confidence interval: 6.8-10.8) in humans, 3.2% (1.9-4.6) in rodents, and 0.7% (0.0-2.3) in other mammals. Subgroup and metaregression analyses revealed a substantial statistical heterogeneity explained by higher prevalence in tissue organs, in case-control, in hospital outbreak, and surveys, in retrospective studies, in urban and hospital setting, in hospitalized patients, and in West African countries. This study suggests that LASV infections is an important cause of death in humans and that LASV are common in humans, rodents and other mammals in sub-Saharan Africa. These estimates highlight disparities between sub-regions, and population risk profiles. Western Africa, and specific key populations were identified as having higher LASV CFR and prevalence, hence, deserving more attention for cost-effective preventive interventions.

Project description:BackgroundLassa fever is caused by a viral haemorrhagic arenavirus that affects two to three million people in West Africa, causing a mortality of between 5,000 and 10,000 each year. The natural reservoir of Lassa virus is the multi-mammate rat Mastomys natalensis, which lives in houses and surrounding fields. With the aim of gaining more information to control this disease, we here carry out a spatial analysis of Lassa fever data from human cases and infected rodent hosts covering the period 1965-2007. Information on contemporary environmental conditions (temperature, rainfall, vegetation) was derived from NASA Terra MODIS satellite sensor data and other sources and for elevation from the GTOPO30 surface for the region from Senegal to the Congo. All multi-temporal data were analysed using temporal Fourier techniques to generate images of means, amplitudes and phases which were used as the predictor variables in the models. In addition, meteorological rainfall data collected between 1951 and 1989 were used to generate a synoptic rainfall surface for the same region.Methodology/principal findingsThree different analyses (models) are presented, one superimposing Lassa fever outbreaks on the mean rainfall surface (Model 1) and the other two using non-linear discriminant analytical techniques. Model 2 selected variables in a step-wise inclusive fashion, and Model 3 used an information-theoretic approach in which many different random combinations of 10 variables were fitted to the Lassa fever data. Three combinations of absenceratiopresence clusters were used in each of Models 2 and 3, the 2 absenceratio1 presence cluster combination giving what appeared to be the best result. Model 1 showed that the recorded outbreaks of Lassa fever in human populations occurred in zones receiving between 1,500 and 3,000 mm rainfall annually. Rainfall, and to a much lesser extent temperature variables, were most strongly selected in both Models 2 and 3, and neither vegetation nor altitude seemed particularly important. Both Models 2 and 3 produced mean kappa values in excess of 0.91 (Model 2) or 0.86 (Model 3), making them 'Excellent'.Conclusion/significanceThe Lassa fever areas predicted by the models cover approximately 80% of each of Sierra Leone and Liberia, 50% of Guinea, 40% of Nigeria, 30% of each of Côte d'Ivoire, Togo and Benin, and 10% of Ghana.

Dataset Information

Systematics, Ecology, and Host Switching: Attributes Affecting Emergence of the Lassa Virus in Rodents across Western Africa.

Publications

Systematics, Ecology, and Host Switching: Attributes Affecting Emergence of the Lassa Virus in Rodents across Western Africa.

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