Challenges in dengue research: A computational perspective

Abstract

The dengue virus is now the most widespread arbovirus affecting human populations, causing significant economic and social impact in South America and South-East Asia. Increasing urbanization and globalization, coupled with insufficient resources for control, misguided policies or lack of political will, and expansion of its mosquito vectors are some of the reasons why interventions have so far failed to curb this major public health problem. Computational approaches have elucidated on dengue's population dynamics with the aim to provide not only a better understanding of the evolution and epidemiology of the virus but also robust intervention strategies. It is clear, however, that these have been insufficient to address key aspects of dengue's biology, many of which will play a crucial role for the success of future control programmes, including vaccination. Within a multiscale perspective on this biological system, with the aim of linking evolutionary, ecological and epidemiological thinking, as well as to expand on classic modelling assumptions, we here propose, discuss and exemplify a few major computational avenues-real-time computational analysis of genetic data, phylodynamic modelling frameworks, within-host model frameworks and GPU-accelerated computing. We argue that these emerging approaches should offer valuable research opportunities over the coming years, as previously applied and demonstrated in the context of other pathogens.

Keywords: computation; dengue; epidemiology; evolution; models.

Figure 1

Epidemiological time series of reported dengue cases for three different countries. (a) Total monthly incidence for Puerto Rico. (b) Annual dengue haemorrhagic fever incidence per 100,000 individuals in Thailand. (c) Total annual incidence for Brazil. (d) Monthly serotype, relative prevalence for Puerto Rico. (e) Yearly serotype, relative prevalence for Thailand. (f) Yearly absolute incidence for the north and south of Brazil (see Appendix: Data Description for details)

Figure 2

Dengue publication over the last five decades. Total number of dengue articles per year (bars) and the percentage of those with a computational focus (spikes). Between 1970 and 2016, a total of 15,267 dengue articles were published, including 190 modelling studies (see Appendix for details)

Figure 3

Increasing model complexity demands higher computational power. Model detail can be added by dividing a well‐mixed population into separate subpopulations, arranged in a regular spatial grid or by means of complex networks to represent geographical distribution of villages, towns and cities, with edges corresponding to major human movement patterns. Depending on data availability, more spatial and demographic detail can be added by considering individual households, places of work or schools. However, the computational demands increase significantly with more detailed information to keep track of, making the model very setting‐specific and impractical for sensitivity analyses and model fitting exercises