Reconstructing unseen transmission events to infer dengue dynamics from viral sequences

Henrik Salje (1, 2, 3, 4) , Amy Wesolowski (4) , Tyler S. Brown (5) , Mathew V. Kiang (5, 6) , Irina Maljkovic Berry (7) , Noemie Lefrancq (1, 2) , Stefan Fernandez (8) , Richard G. Jarman (7) , Kriangsak Ruchusatsawat (9) , Sopon Iamsirithaworn (9) , Warunee P. Vandepitte (10) , Piyarat Suntarattiwong (10) , Jonathan M. Read (11) , Chonticha Klungthong (8) , Butsaya Thaisomboonsuk (8) , Kenth Engø-Monsen (12) , Caroline Buckee (5) , Simon Cauchemez (2) , Derek A. T. Cummings (3)

1 CAM - University of Cambridge [UK]
2 Modélisation mathématique des maladies infectieuses - Mathematical modelling of Infectious Diseases
3 UF - University of Florida [Gainesville]
4 Johns Hopkins Bloomberg School of Public Health [Baltimore]
5 Harvard T.H. Chan School of Public Health
6 Stanford University
7 Walter Reed Army Institute of Research
8 AFRIMS - Armed Forces Research Institute of Medical Sciences [Bangkok]
9 Ministry of Public Health [Thailand]
10 Queen Sirikit National Institute of Child Health [Bangkok]
11 Lancaster University
12 Telenor Group

For most pathogens, transmission is driven by interactions between the behaviours of infectious individuals, the behaviours of the wider population, the local environment, and immunity. Phylogeographic approaches are currently unable to disentangle the relative effects of these competing factors. We develop a spatiotemporally structured phylogenetic framework that addresses these limitations by considering individual transmission events, reconstructed across spatial scales. We apply it to geocoded dengue virus sequences from Thailand (N = 726 over 18 years). We find infected individuals spend 96% of their time in their home community compared to 76% for the susceptible population (mainly children) and 42% for adults. Dynamic pockets of local immunity make transmission more likely in places with high heterotypic immunity and less likely where high homotypic immunity exists. Age-dependent mixing of individuals and vector distributions are not important in determining spread. This approach provides previously unknown insights into one of the most complex disease systems known and will be applicable to other pathogens.

Domains: Statistics [math.ST]

Henrik Salje, Amy Wesolowski, Tyler S. Brown, Mathew V. Kiang, Irina Maljkovic Berry, et al.. Reconstructing unseen transmission events to infer dengue dynamics from viral sequences. Nature Communications, 2021, 12 (1), pp.1810. ⟨10.1038/s41467-021-21888-9⟩. ⟨pasteur-03325584⟩