Health
Predicting the evolution of the Lassa virus endemic area and population at risk over the next decades
Morens, D. M. et al. The origin of COVID-19 and why it matters. Am. J. Trop. Med. Hyg. 103, 955–959 (2020).
Pierson, T. C. & Diamond, M. S. The emergence of Zika virus and its new clinical syndromes. Nature 560, 573–581 (2018).
Gates, B. The next epidemic—Lessons from Ebola., https://doi.org/10.1056/NEJMp1502918 (2015).
World Health Organization. Lassa fever research and development (R&D) roadmap. https://www.who.int/publications/m/item/lassa-fever-research-and-development-(r-d)-roadmap (2018).
World Health Organization. Prioritizing diseases for research and development in emergency contexts. https://www.who.int/activities/prioritizing-diseases-for-research-and-development-in-emergency-contexts.
Akpede, G. O. et al. Caseload and case fatality of Lassa fever in Nigeria, 2001–2018: A specialist center’s experience and its implications. Front. Public Health 7, https://doi.org/10.3389/fpubh.2019.00170 (2019).
Eberhardt, K. A. et al. Ribavirin for the treatment of Lassa fever: A systematic review and meta-analysis. Int. J. Infect. Dis. 87, 15–20 (2019).
Lukashevich, I. S., Paessler, S. & de la Torre, J. C. Lassa virus diversity and feasibility for universal prophylactic vaccine. F1000Res 8, https://doi.org/10.12688/f1000research.16989.1 (2019).
Purushotham, J., Lambe, T. & Gilbert, S. C. Vaccine platforms for the prevention of Lassa fever. Immunol. Lett. 215, 1–11 (2019).
Mateo, M. et al. A single-shot Lassa vaccine induces long-term immunity and protects cynomolgus monkeys against heterologous strains. Sci. Transl. Med. 13, eabf6348 (2021).
McCormick, J. B. et al. Lassa Fever. N. Engl. J. Med. 314, 20–26 (1986).
Bell-Kareem, A. R. & Smither, A. R. Epidemiology of Lassa fever. in 1–23 (Springer, 2021). https://doi.org/10.1007/82_2021_234.
Nigeria Centre for Disease Control. https://ncdc.gov.ng/diseases/sitreps/?cat=5&name=An%20update%20of%20Lassa%20fever%20outbreak%20in%20Nigeria.
Manning, J. T., Forrester, N. & Paessler, S. Lassa virus isolates from Mali and the Ivory Coast represent an emerging fifth lineage. Front. Microbiol. 6, https://doi.org/10.3389/fmicb.2015.01037 (2015).
Dzotsi, E. K. et al. The first cases of Lassa fever in Ghana. Ghana. Med. J. 46, 166–170 (2012).
Patassi, A. A. et al. Emergence of Lassa fever disease in northern Togo: Report of two cases in Oti District in 2016. Case Rep. Infect. Dis. 2017, 8242313 (2017).
Yadouleton, A. et al. Lassa fever in Benin: Description of the 2014 and 2016 epidemics and genetic characterization of a new Lassa virus. Emerg. Microbes Infect. 9, 1761–1770 (2020).
McCormick, J. B. & Fisher-Hoch, S. P. Lassa fever. Curr. Top. Microbiol. Immunol. 262, 75–109 (2002).
Monath, T. P., Newhouse, V. F., Kemp, G. E., Setzer, H. W. & Cacciapuoti, A. Lassa virus isolation from Mastomys natalensis rodents during an epidemic in Sierra Leone. Science 185, 263–265 (1974).
Stephenson, E. H., Larson, E. W. & Dominik, J. W. Effect of environmental factors on aerosol-induced Lassa virus infection. J. Med. Virol. 14, 295–303 (1984).
Wozniak, D. M. et al. Inoculation route-dependent Lassa virus dissemination and shedding dynamics in the natural reservoir – Mastomys natalensis. Emerg. Microbes Infect. 10, 2313–2325 (2021).
Ter Meulen, J. et al. Hunting of peridomestic rodents and consumption of their meat as possible risk factors for rodent-to-human transmission of Lassa virus in the Republic of Guinea. Am. J. Trop. Med. Hyg. 55, 661–666 (1996).
Downs, I. L. et al. Natural history of aerosol induced Lassa fever in non-human primates. Viruses 12, 593 (2020).
Lecompte, E. et al. Mastomys natalensis and Lassa Fever, West Africa. Emerg. Infect. Dis. 12, 1971–1974 (2006).
Smither, A. R. & Bell-Kareem, A. R. Ecology of Lassa Virus. in 1–20 (Springer, 2021). https://doi.org/10.1007/82_2020_231.
Ogbu, O., Ajuluchukwu, E. & Uneke, C. J. Lassa fever in West African sub-region: An overview. J. Vector Borne Dis. 44, 1–11 (2007).
Fichet-Calvet, E. et al. Fluctuation of abundance and Lassa virus prevalence in Mastomys natalensis in Guinea, West Africa. Vector Borne Zoonotic Dis. 7, 119–128 (2007).
Fichet-Calvet, E., Becker-Ziaja, B., Koivogui, L. & Günther, S. Lassa serology in natural populations of rodents and horizontal transmission. Vector Borne Zoonotic Dis. 14, 665–674 (2014).
Lo Iacono, G. et al. Using modelling to disentangle the relative contributions of zoonotic and anthroponotic transmission: The case of Lassa fever. PLoS Negl. Trop. Dis. 9, e3398 (2015).
Siddle, K. J. et al. Genomic analysis of Lassa virus during an increase in cases in Nigeria in 2018. N. Engl. J. Med. 379, 1745–1753 (2018).
Kafetzopoulou, L. E. et al. Metagenomic sequencing at the epicenter of the Nigeria 2018 Lassa fever outbreak. Science 363, 74–77 (2019).
Andersen, K. G. et al. Clinical sequencing uncovers origins and evolution of Lassa virus. Cell 162, 738–750 (2015).
Lalis, A. & Wirth, T. Mice and men: An evolutionary history of Lassa fever. in Biodiversity and Evolution (eds. Grandcolas, P. & Maurel, M.-C.) 189–212, https://doi.org/10.1016/B978-1-78548-277-9.50011-5 (Elsevier, 2018).
Mylne, A. Q. N. et al. Mapping the zoonotic niche of Lassa fever in Africa. Trans. R. Soc. Trop. Med. Hyg. 109, 483–492 (2015).
Colangelo, P. et al. A mitochondrial phylogeographic scenario for the most widespread African rodent, Mastomys natalensis. Biol. J. Linn. Soc. 108, 901–916 (2013).
Gryseels, S. et al. When viruses don’t go viral: The importance of host phylogeographic structure in the spatial spread of arenaviruses. PLoS Path 13, e1006073 (2017).
Cuypers, L. N. et al. Three arenaviruses in three subspecific natal multimammate mouse taxa in Tanzania: Same host specificity, but different spatial genetic structure? Virus Evol. https://doi.org/10.1093/ve/veaa039 (2020).
Vazeille, M., Gaborit, P., Mousson, L., Girod, R. & Failloux, A.-B. Competitive advantage of a dengue 4 virus when co-infecting the mosquito Aedes aegypti with a dengue 1 virus. BMC Infect. Dis. 16, 318 (2016).
Chan, K. F. et al. Investigating viral interference between influenza A virus and human respiratory syncytial virus in a ferret model of infection. J. Infect. Dis. 218, 406–417 (2018).
Meunier, D. Y., McCormick, J. B., Georges, A. J., Georges, M. C. & Gonzalez, J. P. Comparison of Lassa, Mobala, and Ippy virus reactions by immunofluorescence test. Lancet 1, 873–874 (1985).
Howard, C. R. Antigenic diversity among the Arenaviruses. in The Arenaviridae (ed. Salvato, M. S.) 37–49, https://doi.org/10.1007/978-1-4615-3028-2_3 (Springer US, 1993).
Bhattacharyya, S., Gesteland, P. H., Korgenski, K., Bjørnstad, O. N. & Adler, F. R. Cross-immunity between strains explains the dynamical pattern of paramyxoviruses. Proc. Natl Acad. Sci. U. S. A. 112, 13396–13400 (2015).
Luis, A. D., Douglass, R. J., Mills, J. N. & Bjørnstad, O. N. Environmental fluctuations lead to predictability in Sin Nombre hantavirus outbreaks. Ecology 96, 1691–1701 (2015).
Anderson, R. M., Jackson, H. C., May, R. M. & Smith, A. M. Population dynamics of fox rabies in Europe. Nature 289, 765–771 (1981).
Tian, H. et al. Anthropogenically driven environmental changes shift the ecological dynamics of hemorrhagic fever with renal syndrome. PLoS Pathog. 13, e1006198 (2017).
Redding, D. W., Moses, L. M., Cunningham, A. A., Wood, J. & Jones, K. E. Environmental-mechanistic modelling of the impact of global change on human zoonotic disease emergence: a case study of Lassa fever. Methods Ecol. Evol. 7, 646–655 (2016).
Peterson, A. T., Moses, L. M. & Bausch, D. G. Mapping transmission risk of Lassa fever in West Africa: the importance of quality control, sampling bias, and error weighting. PLoS One 9, e100711 (2014).
Fichet-Calvet, E. & Rogers, D. J. Risk maps of Lassa fever in West Africa. PLoS. Negl. Trop. Dis. 3, e388 (2009).
Basinski, A. J. et al. Bridging the gap: Using reservoir ecology and human serosurveys to estimate Lassa virus spillover in West Africa. PLoS Comput. Biol. 17, e1008811 (2021).
Iacono, G. L. et al. A unified framework for the infection dynamics of zoonotic spillover and spread. PLoS Negl. Trop. Dis. 10, e0004957 (2016).
Coumou, D. & Rahmstorf, S. A decade of weather extremes. Nat. Clim. Change 2, 491–496 (2012).
Coumou, D., Robinson, A. & Rahmstorf, S. Global increase in record-breaking monthly-mean temperatures. Clim. Change 118, 771–782 (2013).
Bathiany, S., Dakos, V., Scheffer, M. & Lenton, T. M. Climate models predict increasing temperature variability in poor countries. Sci. Adv. 4, eaar5809 (2018).
Arneth, A. Uncertain future for vegetation cover. Nature 524, 44–45 (2015).
Brandt, M. et al. Human population growth offsets climate-driven increase in woody vegetation in sub-Saharan Africa. Nat. Ecol. Evol. 1, 81 (2017).
Herrmann, S. M., Brandt, M., Rasmussen, K. & Fensholt, R. Accelerating land cover change in West Africa over four decades as population pressure increased. Com. Earth Envir 1, 1–10 (2020).
Gibb, R., Moses, L. M., Redding, D. W. & Jones, K. E. Understanding the cryptic nature of Lassa fever in West Africa. Pathog. Glob. Health 111, 276–288 (2017).
Elith, J., Leathwick, J. R. & Hastie, T. A working guide to boosted regression trees. J. Anim. Ecol. 77, 802–813 (2008).
Frieler, K. et al. Assessing the impacts of 1.5 °C global warming—simulation protocol of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP2b). Geosci. Model Dev. 10, 4321–4345 (2017).
Soberón, J. & Nakamura, M. Niches and distributional areas: Concepts, methods, and assumptions. Proc. Natl Acad. Sci. U. S. A. 106, 19644–19650 (2009).
Moss, R. H. et al. The next generation of scenarios for climate change research and assessment. Nature 463, 747–756 (2010).
Lemey, P., Rambaut, A., Welch, J. J. & Suchard, M. A. Phylogeography takes a relaxed random walk in continuous space and time. Mol. Biol. Evol. 27, 1877–1885 (2010).
Lukashevich, I. S. Generation of reassortants between African arenaviruses. Virology 188, 600–605 (1992).
Vijaykrishna, D., Mukerji, R. & Smith, G. J. D. RNA virus reassortment: an evolutionary mechanism for host jumps and immune evasion. PLoS Path 11, e1004902 (2015).
Whitmer, S. L. M. et al. New lineage of Lassa Virus, Togo, 2016. Emerg. Infect. Dis. 24, 599 (2018).
Ehichioya, D. U. et al. Phylogeography of Lassa virus in Nigeria. J. Virol. 93, e00929–19 (2019).
Dellicour, S., Rose, R., Faria, N. R., Lemey, P. & Pybus, O. G. SERAPHIM: studying environmental rasters and phylogenetically informed movements. Bioinformatics 32, 3204–3206 (2016).
Dellicour, S. et al. Using viral gene sequences to compare and explain the heterogeneous spatial dynamics of virus epidemics. Mol. Biol. Evol. 34, 2563–2571 (2017).
Dellicour, S. et al. Epidemiological hypothesis testing using a phylogeographic and phylodynamic framework. Nat. Commun. 11, 5620 (2020).
Dijkstra, E. W. A note on two problems in connexion with graphs. Numer. Math. 1, 269–271 (1959).
Strahler, A. N. Quantitative analysis of watershed geomorphology. Eos, Trans. Am. Geophys. Union 38, 913–920 (1957).
Kass, R. E. & Raftery, A. E. Bayes factors. J. Am. Stat. Assoc. 90, 773–795 (1995).
Ehichioya, D. U. et al. Current molecular epidemiology of Lassa virus in Nigeria. J. Clin. Microbiol. 49, 1157 (2011).
Oloniniyi, O. K. et al. Genetic characterization of Lassa virus strains isolated from 2012 to 2016 in southeastern Nigeria. PLoS Negl. Trop. Dis. 12, e0006971 (2018).
Olesen, J. E. et al. Uncertainties in projected impacts of climate change on European agriculture and terrestrial ecosystems based on scenarios from regional climate models. Clim. Change 81, 123–143 (2007).
Simo Tchetgna, H. et al. Molecular characterization of a new highly divergent Mobala related arenavirus isolated from Praomys sp. rodents. Sci. Rep. 11, 10188 (2021).
Olayemi, A. et al. New hosts of the Lassa virus. Sci. Rep. 6, 25280 (2016).
Zaidi, M. B. et al. Competitive suppression of dengue virus replication occurs in chikungunya and dengue co-infected Mexican infants. Parasit. Vectors 11, 378 (2018).
Olayemi, A. et al. Widespread arenavirus occurrence and seroprevalence in small mammals, Nigeria. Parasit. Vectors 11, 416 (2018).
Nigeria Centre for Disease Control. https://ncdc.gov.ng/diseases/sitreps/?cat=5&name=An%20update%20of%20Lassa%20fever%20outbreak%20in%20Nigeria.
Norris, K. et al. Biodiversity in a forestagriculture mosaic: the changing face of west Africa rainforests. Biol. Conserv. 143, 2341–2350 (2010).
Stocker, T. F. et al. Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the fifth assessment report of the Intergovernmental Panel on Climate Change. (Cambridge University Press, Cambridge, United Kingdom and New York, 2013).
Buba, M. I. et al. Mortality among confirmed Lassa fever cases during the 2015-2016 outbreak in Nigeria. Am. J. Public Health 108, 262–264 (2018).
Tobin, E. A. et al. Knowledge of secondary school children in Edo State on Lassa fever and its implications for prevention and control. West. Afr. J. Med. 34, 101–107 (2015).
Saez, A. M. et al. Rodent control to fight Lassa fever: Evaluation and lessons learned from a 4-year study in Upper Guinea. PLoS Negl. Trop. Dis. 12, e0006829 (2018).
Ejembi, J. et al. Contact tracing in Lassa fever outbreak response, an effective strategy for control? Online J. Public Health Inf. 11, e378 (2019).
ECHO Flash List. https://erccportal.jrc.ec.europa.eu/ECHO-Flash/ECHO-Flash-List/yy/2018/mm/2.
Pigott, D. M. et al. Local, national, and regional viral haemorrhagic fever pandemic potential in Africa: a multistage analysis. Lancet 390, 2662–2672 (2017).
Kraemer, M. U. G. et al. Past and future spread of the arbovirus vectors Aedes aegypti and Aedes albopictus. Nature Microbiology https://doi.org/10.1038/s41564-019-0376-y (2019).
Elith, J. et al. Novel methods improve prediction of species’ distributions from occurrence data. Ecography 29, 129–151 (2006).
Dhingra, M. S. et al. Global mapping of highly pathogenic avian influenza H5N1 and H5Nx clade 2.3.4.4 viruses with spatial cross-validation. eLife 5, e19571 (2016).
Phillips, S. J., Anderson, R. P. & Schapire, R. E. Maximum entropy modeling of species geographic distributions. Ecol. Model. 190, 231–259 (2006).
Elith, J. et al. A statistical explanation of MaxEnt for ecologists. Divers. Distrib. 17, 43–57 (2011).
Phillips, S. J. et al. Sample selection bias and presence-only distribution models: Implications for background and pseudo-absence data. Ecol. Appl. 19, 181–197 (2009).
Valavi, R., Elith, J., Lahoz‐Monfort, J. J. & Guillera‐Arroita, G. blockCV: An r package for generating spatially or environmentally separated folds for k-fold cross-validation of species distribution models. Methods Ecol. Evol. 10, 225–232 (2019).
Suchard, M. A. et al. Bayesian phylogenetic and phylodynamic data integration using BEAST 1.10. Virus Evol. 4, vey016 (2018).
Randin, C. F. et al. Are niche-based species distribution models transferable in space? J. Biogeogr. 33, 1689–1703 (2006).
Lange, S. Bias correction of surface downwelling longwave and shortwave radiation for the EWEMBI dataset. Earth Syst. Dyn. 9, 627–645 (2018).
Dunne, J. P. et al. GFDL’s ESM2 global coupled climate–carbon earth system models. Part I: physical formulation and baseline simulation characteristics. J. Clim. 25, 6646–6665 (2012).
Jones, C. D. et al. The HadGEM2-ES implementation of CMIP5 centennial simulations. Geosci. Model Dev. 4, 543–570 (2011).
Dufresne, J.-L. et al. Climate change projections using the IPSL-CM5 Earth System Model: from CMIP3 to CMIP5. Clim. Dyn. 40, 2123–2165 (2013).
Watanabe, M. et al. Improved climate simulation by MIROC5: Mean states, variability, and climate sensitivity. J. Clim. 23, 6312–6335 (2010).
Taylor, K. E., Stouffer, R. J. & Meehl, G. A. An overview of CMIP5 and the experiment design. Bull. Am. Meteor. Soc. 93, 485–498 (2012).
Hurtt, G. C. et al. Harmonization of global land-use change and management for the period 850-2100 (LUH2) for CMIP6. Geosci. Model Dev. 1–65 https://doi.org/10.5194/gmd-2019-360 (2020)
Jones, B. & O’Neill, B. C. Spatially explicit global population scenarios consistent with the Shared Socioeconomic Pathways. Environ. Res. Lett. 11, 084003 (2016).
Katoh, K. & Standley, D. M. MAFFT Multiple Sequence Alignment Software version 7: Improvements in performance and usability. Mol. Biol. Evol. 30, 772–780 (2013).
Larsson, A. AliView: a fast and lightweight alignment viewer and editor for large datasets. Bioinformatics 30, 3276–3278 (2014).
Ayres, D. L. et al. BEAGLE 3: Improved performance, scaling, and usability for a high-performance computing library for statistical phylogenetics. Syst. Biol. https://doi.org/10.1093/sysbio/syz020 (2019).
Tavaré, S. Some probabilistic and statistical problems in the analysis of DNA sequences. Lect. Math. Life Sci. 17, 57–86 (1986).
Rambaut, A., Drummond, A. J., Xie, D., Baele, G. & Suchard, M. A. Posterior summarization in Bayesian phylogenetics using Tracer 1.7. Syst. Biol. 67, 901–904 (2018).
Laenen, L. et al. Spatio-temporal analysis of Nova virus, a divergent hantavirus circulating in the European mole in Belgium. Mol. Ecol. 25, 5994–6008 (2016).
Dellicour, S. et al. Landscape genetic analyses of Cervus elaphus and Sus scrofa: comparative study and analytical developments. Heredity 123, 228–241 (2019).
Dellicour, S. et al. Phylodynamic assessment of intervention strategies for the West African Ebola virus outbreak. Nat. Commun. 9, 2222 (2018).
Dellicour, S. et al. Phylogeographic and phylodynamic approaches to epidemiological hypothesis testing. bioRxiv https://doi.org/10.1101/788059 (2020).
Dellicour, S., Rose, R. & Pybus, O. G. Explaining the geographic spread of emerging epidemics: a framework for comparing viral phylogenies and environmental landscape data. BMC Bioinform 17, 1–12 (2016).
McRae, B. H. Isolation by resistance. Evolution 60, 1551–1561 (2006).
Jacquot, M., Nomikou, K., Palmarini, M., Mertens, P. & Biek, R. Bluetongue virus spread in Europe is a consequence of climatic, landscape and vertebrate host factors as revealed by phylogeographic inference. Proc. R. Soc. Lond. B 284, 20170919 (2017).
Gill, M. S. et al. Improving Bayesian population dynamics inference: A coalescent-based model for multiple loci. Mol. Biol. Evol. 30, 713–724 (2013).
Karcher, M. D., Palacios, J. A., Bedford, T., Suchard, M. A. & Minin, V. N. Quantifying and mitigating the effect of preferential sampling on phylodynamic inference. PLoS Comput. Biol. 12, e1004789 (2016).
Sources 2/ https://www.nature.com/articles/s41467-022-33112-3 The mention sources can contact us to remove/changing this article |
What Are The Main Benefits Of Comparing Car Insurance Quotes Online
LOS ANGELES, CA / ACCESSWIRE / June 24, 2020, / Compare-autoinsurance.Org has launched a new blog post that presents the main benefits of comparing multiple car insurance quotes. For more info and free online quotes, please visit https://compare-autoinsurance.Org/the-advantages-of-comparing-prices-with-car-insurance-quotes-online/ The modern society has numerous technological advantages. One important advantage is the speed at which information is sent and received. With the help of the internet, the shopping habits of many persons have drastically changed. The car insurance industry hasn't remained untouched by these changes. On the internet, drivers can compare insurance prices and find out which sellers have the best offers. View photos The advantages of comparing online car insurance quotes are the following: Online quotes can be obtained from anywhere and at any time. Unlike physical insurance agencies, websites don't have a specific schedule and they are available at any time. Drivers that have busy working schedules, can compare quotes from anywhere and at any time, even at midnight. Multiple choices. Almost all insurance providers, no matter if they are well-known brands or just local insurers, have an online presence. Online quotes will allow policyholders the chance to discover multiple insurance companies and check their prices. Drivers are no longer required to get quotes from just a few known insurance companies. Also, local and regional insurers can provide lower insurance rates for the same services. Accurate insurance estimates. Online quotes can only be accurate if the customers provide accurate and real info about their car models and driving history. Lying about past driving incidents can make the price estimates to be lower, but when dealing with an insurance company lying to them is useless. Usually, insurance companies will do research about a potential customer before granting him coverage. Online quotes can be sorted easily. Although drivers are recommended to not choose a policy just based on its price, drivers can easily sort quotes by insurance price. Using brokerage websites will allow drivers to get quotes from multiple insurers, thus making the comparison faster and easier. For additional info, money-saving tips, and free car insurance quotes, visit https://compare-autoinsurance.Org/ Compare-autoinsurance.Org is an online provider of life, home, health, and auto insurance quotes. This website is unique because it does not simply stick to one kind of insurance provider, but brings the clients the best deals from many different online insurance carriers. In this way, clients have access to offers from multiple carriers all in one place: this website. On this site, customers have access to quotes for insurance plans from various agencies, such as local or nationwide agencies, brand names insurance companies, etc. "Online quotes can easily help drivers obtain better car insurance deals. All they have to do is to complete an online form with accurate and real info, then compare prices", said Russell Rabichev, Marketing Director of Internet Marketing Company. CONTACT: Company Name: Internet Marketing CompanyPerson for contact Name: Gurgu CPhone Number: (818) 359-3898Email: [email protected]: https://compare-autoinsurance.Org/ SOURCE: Compare-autoinsurance.Org View source version on accesswire.Com:https://www.Accesswire.Com/595055/What-Are-The-Main-Benefits-Of-Comparing-Car-Insurance-Quotes-Online View photos
to request, modification Contact us at Here or [email protected]