Geographical drivers and climate-linked dynamics of Lassa fever in Nigeria.
Journal
Nature communications
ISSN: 2041-1723
Titre abrégé: Nat Commun
Pays: England
ID NLM: 101528555
Informations de publication
Date de publication:
01 10 2021
01 10 2021
Historique:
received:
28
09
2020
accepted:
08
09
2021
entrez:
2
10
2021
pubmed:
3
10
2021
medline:
16
11
2021
Statut:
epublish
Résumé
Lassa fever is a longstanding public health concern in West Africa. Recent molecular studies have confirmed the fundamental role of the rodent host (Mastomys natalensis) in driving human infections, but control and prevention efforts remain hampered by a limited baseline understanding of the disease's true incidence, geographical distribution and underlying drivers. Here, we show that Lassa fever occurrence and incidence is influenced by climate, poverty, agriculture and urbanisation factors. However, heterogeneous reporting processes and diagnostic laboratory access also appear to be important drivers of the patchy distribution of observed disease incidence. Using spatiotemporal predictive models we show that including climatic variability added retrospective predictive value over a baseline model (11% decrease in out-of-sample predictive error). However, predictions for 2020 show that a climate-driven model performs similarly overall to the baseline model. Overall, with ongoing improvements in surveillance there may be potential for forecasting Lassa fever incidence to inform health planning.
Identifiants
pubmed: 34599162
doi: 10.1038/s41467-021-25910-y
pii: 10.1038/s41467-021-25910-y
pmc: PMC8486829
doi:
Banques de données
figshare
['10.6084/m9.figshare.9777656']
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
5759Subventions
Organisme : Medical Research Council
ID : MR/R02491X/1
Pays : United Kingdom
Organisme : Wellcome Trust
ID : 220179/Z/20/Z
Pays : United Kingdom
Organisme : Wellcome Trust
Pays : United Kingdom
Organisme : World Health Organization
ID : 001
Pays : International
Organisme : Medical Research Council
ID : MR/R015600/1
Pays : United Kingdom
Informations de copyright
© 2021. The Author(s).
Références
Nigeria Centre for Disease Control. Lassa fever Situation Report, 12 April 2020. (NCDC, 2020).
Ipadeola, O. et al. Epidemiology and case-control study of Lassa fever outbreak in Nigeria from 2018 to 2019. J. Infect. 80, 578–606 (2020).
pubmed: 31926184
doi: 10.1016/j.jinf.2019.12.020
Rottingen, J. et al. New vaccines against epidemic infectious diseases. N. Engl. J. Med 376, 610–613 (2017).
pubmed: 28099066
doi: 10.1056/NEJMp1613577
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).
pubmed: 28875769
pmcid: 5694855
doi: 10.1080/20477724.2017.1369643
Siddle, K. J. et al. Genomic analysis of Lassa virus during an increase in cases in Nigeria in 2018. N. Engl. J. Med. https://doi.org/10.1056/NEJMoa1804498 (2018).
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).
pubmed: 25569707
pmcid: 4288732
doi: 10.1371/journal.pntd.0003398
Andersen, K. G. et al. Clinical sequencing uncovers origins and evolution of Lassa Virus. Cell 162, 738–750 (2015).
pubmed: 26276630
pmcid: 4537774
doi: 10.1016/j.cell.2015.07.020
Ilori, E. A. et al. Epidemiologic and clinical features of Lassa fever outbreak in Nigeria, January 1-May 6, 2018. Emerg. Infect. Dis. 25, 1066–1074 (2019).
pubmed: 31107222
pmcid: 6537738
doi: 10.3201/eid2506.181035
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).
pubmed: 17627428
doi: 10.1089/vbz.2006.0520
Bonwitt, J. et al. At home with Mastomys and Rattus: human–rodent interactions and potential for primary transmission of Lassa virus in domestic spaces. Am. J. Trop. Med. Hyg. 96, 16–0675 (2017).
Dzingirai, V. et al. Structural drivers of vulnerability to zoonotic disease in. Afr. Philos. Trans. R. Soc. B 372, 20160169 (2017).
doi: 10.1098/rstb.2016.0169
Fichet-Calvet, E. & Rogers, D. J. Risk maps of Lassa fever in West Africa. PLoS Negl. Trop. Dis. 3, e388 (2009).
pubmed: 19255625
pmcid: 2644764
doi: 10.1371/journal.pntd.0000388
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).
doi: 10.1111/2041-210X.12549
McCormick, J. B., Webb, P. A., Krebs, J. W., Johnson, K. M. & Smith, E. S. A prospective study of the epidemiology and ecology of Lassa fever. J. Infect. Dis. 155, 437–444 (1987).
pubmed: 3805771
doi: 10.1093/infdis/155.3.437
Bausch, D. G. et al. Lassa fever in Guinea: I. Epidemiology of human disease and clinical observations. Vector Borne Zoonotic Dis. 1, 269–281 (2001).
pubmed: 12653127
doi: 10.1089/15303660160025903
Wilkinson, A. In One Health: Science, Politics and Zoonotic Disease in Africa (ed. Bardosh, K.) Ch. 7 (Routledge, 2016).
Sogoba, N. et al. Lassa Virus seroprevalence in Sibirilia Commune, Bougouni District, Southern Mali. Emerg. Infect. Dis. 22, 657–663 (2016).
pubmed: 26981786
pmcid: 4806955
doi: 10.3201/eid2204.151814
Akpede, G. O., Asogun, D. A., Okogbenin, S. A. & Okokhere, P. O. Lassa fever outbreaks in Nigeria. Expert Rev. Anti. Infect. Ther. 16, 663–666 (2018).
pubmed: 30111178
doi: 10.1080/14787210.2018.1512856
Asogun, D. A. et al. Molecular diagnostics for Lassa fever at Irrua Specialist Teaching Hospital, Nigeria: lessons learnt from two years of laboratory operation. PLoS Negl. Trop. Dis. 6, e1839 (2012).
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 Heal. 7, 170 (2019).
doi: 10.3389/fpubh.2019.00170
Shaffer, J. G. et al. Lassa fever in post-conflict Sierra Leone. PLoS Negl. Trop. Dis. 8, e2748 (2014).
pubmed: 24651047
pmcid: 3961205
doi: 10.1371/journal.pntd.0002748
Ilori, E. A. et al. Increase in Lassa fever cases in Nigeria, January–March 2018. Emerg. Infect. Dis. 24, 2018–2019 (2019).
NCDC. First Annual Report of the Nigeria Centre for Disease Control. http://www.ncdc.gov.ng/themes/common/docs/protocols/78_1515412191.pdf (2016).
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).
pubmed: 25229705
pmcid: 4170823
doi: 10.1089/vbz.2013.1484
Fichet-Calvet, E., Lecompte, E., Koivogui, L., Daffis, S. & Meulen, J. T. Reproductive characteristics of Mastomys natalensis and Lassa virus prevalence in Guinea, West Africa. Vector-Borne Zoonotic Dis. 8, 41–48 (2008).
pubmed: 18237265
doi: 10.1089/vbz.2007.0118
Olayemi, A. et al. Arenavirus diversity and phylogeography of Mastomys natalensis rodents, Nigeria. Emerg. Infect. Dis. 22, 687–690 (2016).
doi: 10.3201/eid2204.150155
Olayemi, A. et al. Small mammal diversity and dynamics within Nigeria, with emphasis on reservoirs of the lassa virus. Syst. Biodivers. 16, 118–127 (2018).
doi: 10.1080/14772000.2017.1358220
Ehichioya, D. U. et al. Phylogeography of Lassa virus in Nigeria. J. Virol. 93, e00929–19 (2019).
pubmed: 31413134
pmcid: 6803284
doi: 10.1128/JVI.00929-19
Leirs, H., Verhagen, R., Verheyen, W., Mwanjabe, P. & Mbise, T. Forecasting rodent outbreaks in Africa: an ecological basis for Mastomys control in Tanzania. J. Appl. Ecol. 33, 937–943 (1996).
doi: 10.2307/2404675
Massawe, A. W., Rwamugira, W., Leirs, H., Makundi, R. H. & Mulungu, L. S. Do farming practices influence population dynamics of rodents? A case study of the multimammate field rats, Mastomys natalensis, in Tanzania. Afr. J. Ecol. 45, 293–301 (2007).
doi: 10.1111/j.1365-2028.2006.00709.x
Ballester, J., Lowe, R., Diggle, P. J. & Rodó, X. Seasonal forecasting and health impact models: challenges and opportunities. Ann. N. Y. Acad. Sci. 1382, 8–20 (2016).
pubmed: 27428726
doi: 10.1111/nyas.13129
Lowe, R. et al. Evaluating probabilistic dengue risk forecasts from a prototype early warning system for Brazil. Elife 5, 1–18 (2016).
doi: 10.7554/eLife.11285
Dan-Nwafor, C. C. et al. Measures to control protracted large Lassa fever outbreak in Nigeria, 1 January to 28 April 2019. Euro Surveill. 24, 1–4 (2019).
doi: 10.2807/1560-7917.ES.2019.24.20.1900272
Zhao, S. et al. Large-scale Lassa fever outbreaks in Nigeria: quantifying the association between disease reproduction number and local rainfall. Epidemiol Infect. 148: e4 (2020).
pubmed: 31918780
pmcid: 7019145
doi: 10.1017/S0950268819002267
Akhmetzhanov, A. R., Asai, Y. & Nishiura, H. Quantifying the seasonal drivers of transmission for Lassa fever in Nigeria. Philos. Trans. R. Soc. B 374, 20180268 (2019).
doi: 10.1098/rstb.2018.0268
Gotway, C. A. & Young, L. J. Combining incompatible spatial data. J. Am. Stat. Assoc. 97, 632–648 (2002).
doi: 10.1198/016214502760047140
Lindgren, F. & Rue, H. Bayesian statistical modelling with R-INLA. J. Stat. Softw. 63, 1–26 (2010).
Hijmans, R. J. raster: geographic data analysis and modelling. R package v.2.5-8. https://CRAN.R-project.org/package=raster (2016).
Hunziker, P. velox: fast raster manipulation and extraction. R package v.0.2.0. https://CRAN.R-project.org/package=velox (2017).
Rue, H., Martino, S. & Chopin, N. Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations. J. R. Stat. Soc. Ser. B Stat. Methodol. 71, 319–392 (2009).
doi: 10.1111/j.1467-9868.2008.00700.x
Redding, D. W., Lucas, T. C. D., Blackburn, T. M. & Jones, K. E. Evaluating Bayesian spatial methods for modelling species distributions with clumped and restricted occurrence data. PLoS ONE 12, e0187602 (2017).
pubmed: 29190296
pmcid: 5708625
doi: 10.1371/journal.pone.0187602
Funk, C. et al. The climate hazards infrared precipitation with stations - A new environmental record for monitoring extremes. Sci. Data 2, 1–21 (2015).
doi: 10.1038/sdata.2015.66
Ogbu, K. N., Hounguè, N. R., Gbode, I. E. & Tischbein, B. Performance evaluation of satellite-based rainfall products over Nigeria. Climate 8, 1–23 (2020).
doi: 10.3390/cli8100103
Beguería, S. & Vicente-Serrano, S. M. SPEI: calculation of the standardised precipitation-evapotranspiration index (2017).
Tusting, L. S. et al. Mapping changes in housing in sub-Saharan Africa from 2000 to 2015. Nature https://doi.org/10.1038/s41586-019-1050-5 (2019).
Weiss, D. J. et al. A global map of travel time to cities to assess inequalities in accessibility in 2015. Nature https://doi.org/10.1038/nature25181 (2018).
Maina, J. et al. A spatial database of health facilities managed by the public health sector in sub Saharan Africa. Sci. Data 6, 134 (2019).
pubmed: 31346183
pmcid: 6658526
doi: 10.1038/s41597-019-0142-2
Watanabe, S. Asymptotic equivalence of Bayes cross validation and widely applicable information criterion in singular learning theory. J. Mach. Learn. Res. 11, 3571–3594 (2010).
Gelman, A., Hwang, J. & Vehtari, A. Understanding predictive information criteria for Bayesian models. Stat. Comput. 24, 997–1016 (2014).
doi: 10.1007/s11222-013-9416-2
Fletcher, I. K. et al. The relative role of climate variation and control interventions on Malaria elimination efforts in El Oro, Ecuador: a modeling study. Front. Environ. Sci. 8, 1–16 (2020).
doi: 10.3389/fenvs.2020.00135
Kelly, A. H. & Marí Sáez, A. Shadowlands and dark corners: an anthropology of light and zoonosis. Med. Anthropol. Theory 5, 21–47 (2018).
doi: 10.17157/mat.5.3.382
Makundi, R. H., Massawe, A. W. & Mulungu, L. S. Reproduction and population dynamics of Mastomys natalensis Smith, 1834 in an agricultural landscape in the Western Usambara Mountains, Tanzania. Integr. Zool. 2, 233–238 (2007).
pubmed: 21396040
doi: 10.1111/j.1749-4877.2007.00063.x
FEWSNET. Famine Early Warning Systems Network: Revised livelihoods zone map and descriptions for Nigeria. https://fews.net/west-africa/nigeria/livelihood-description/september-2018 (2018).