Format

Send to

Choose Destination
Nat Commun. 2018 Jun 26;9(1):2476. doi: 10.1038/s41467-018-04577-y.

Estimating spatiotemporally varying malaria reproduction numbers in a near elimination setting.

Author information

1
MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, W2 1PG, UK. i.routledge15@imperial.ac.uk.
2
Ministry of Health (MINSAL), Calle Arce No.827, San Salvador, El Salvador.
3
MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, W2 1PG, UK.
4
Max Planck Institute for Software Systems, E1 5, Campus, 66123, Saarbrücken, Germany.
5
MACEPA, PATH, Seattle, Washington, 98121, USA.
6
Institute for Disease Modeling, Bellevue, WA, 98005, USA.

Abstract

In 2016 the World Health Organization identified 21 countries that could eliminate malaria by 2020. Monitoring progress towards this goal requires tracking ongoing transmission. Here we develop methods that estimate individual reproduction numbers and their variation through time and space. Individual reproduction numbers, Rc, describe the state of transmission at a point in time and differ from mean reproduction numbers, which are averages of the number of people infected by a typical case. We assess elimination progress in El Salvador using data for confirmed cases of malaria from 2010 to 2016. Our results demonstrate that whilst the average number of secondary malaria cases was below one (0.61, 95% CI 0.55-0.65), individual reproduction numbers often exceeded one. We estimate a decline in Rc between 2010 and 2016. However we also show that if importation is maintained at the same rate, the country may not achieve malaria elimination by 2020.

PMID:
29946060
PMCID:
PMC6018772
DOI:
10.1038/s41467-018-04577-y
[Indexed for MEDLINE]
Free PMC Article

Supplemental Content

Full text links

Icon for Nature Publishing Group Icon for PubMed Central
Loading ...
Support Center