Inferring person-to-person networks of Plasmodium falciparum transmission: are analyses of routine surveillance data up to the task?

John H Huber; Michelle S Hsiang; Nomcebo Dlamini; Maxwell Murphy; Sibonakaliso Vilakati; Nomcebo Nhlabathi; Anita Lerch; Rasmus Nielsen; Nyasatu Ntshalintshali; Bryan Greenhouse; T Alex Perkins

doi:10.1186/s12936-022-04072-2

Inferring person-to-person networks of Plasmodium falciparum transmission: are analyses of routine surveillance data up to the task?

Malar J. 2022 Feb 21;21(1):58. doi: 10.1186/s12936-022-04072-2.

Authors

John H Huber¹, Michelle S Hsiang^{2

3

4}, Nomcebo Dlamini⁵, Maxwell Murphy⁶, Sibonakaliso Vilakati⁵, Nomcebo Nhlabathi⁵, Anita Lerch⁷, Rasmus Nielsen⁸, Nyasatu Ntshalintshali⁹, Bryan Greenhouse^{6

10}, T Alex Perkins¹¹

Affiliations

¹ Department of Biological Sciences, University of Notre Dame, Notre Dame, IN, USA. jhuber3@nd.edu.
² Department of Pediatrics, University of Texas Southwestern Medical Center, Dallas, TX, USA.
³ Malaria Elimination Initiative, Global Health Group, University of California, San Francisco, CA, USA.
⁴ Department of Pediatrics, University of California, San Francisco,, CA, USA.
⁵ National Malaria Elimination Programme, Ministry of Health, Manzini, Eswatini.
⁶ Department of Medicine, University of California, San Francisco, CA, USA.
⁷ Department of Biological Sciences, University of Notre Dame, Notre Dame, IN, USA.
⁸ Department of Integrative Biology and Statistics, University of California, Berkeley, CA, USA.
⁹ Clinton Health Access Initiative, Eswatini Country Office, Mbabane, Eswatini.
¹⁰ Chan Zuckerberg Biohub, San Francisco, CA, USA.
¹¹ Department of Biological Sciences, University of Notre Dame, Notre Dame, IN, USA. taperkins@nd.edu.

Abstract

Background: Inference of person-to-person transmission networks using surveillance data is increasingly used to estimate spatiotemporal patterns of pathogen transmission. Several data types can be used to inform transmission network inferences, yet the sensitivity of those inferences to different data types is not routinely evaluated.

Methods: The influence of different combinations of spatial, temporal, and travel-history data on transmission network inferences for Plasmodium falciparum malaria were evaluated.

Results: The information content of these data types may be limited for inferring person-to-person transmission networks and may lead to an overestimate of transmission. Only when outbreaks were temporally focal or travel histories were accurate was the algorithm able to accurately estimate the reproduction number under control, R_c. Applying this approach to data from Eswatini indicated that inferences of R_c and spatiotemporal patterns therein depend upon the choice of data types and assumptions about travel-history data.

Conclusions: These results suggest that transmission network inferences made with routine malaria surveillance data should be interpreted with caution.

MeSH terms

Disease Outbreaks
Humans
Malaria* / epidemiology
Malaria, Falciparum* / epidemiology
Plasmodium falciparum
Reproduction

Abstract

MeSH terms

Grants and funding