Inferring who-infected-whom-where in the 2016 Zika outbreak in Singapore—a spatio-temporal model

Abstract

Singapore experienced its first known Zika outbreak in 2016. Given the lack of herd immunity, the suitability of the climate for pathogen transmission, and the year-round presence of the vector— Aedes aegypti —Zika had the potential to become endemic, like dengue. Guillain–Barré syndrome and microcephaly are severe complications associated elsewhere with Zika and the risk of these complications makes understanding its spread imperative. We investigated the spatio-temporal spread of locally transmitted Zika in Singapore and assessed the relevance of non-residential transmission of Zika virus infections, by inferring the possible infection tree (i.e. who-infected-whom-where ) and comparing inferences using geographically resolved data on cases' home, their work, or their home and work. We developed a spatio-temporal model using time of onset and both addresses of the Zika-confirmed cases between July and September 2016 to estimate the infection tree using Bayesian data augmentation. Workplaces were involved in a considerable fraction (64.2%) of infections, and homes and workplaces may be distant relative to the scale of transmission, allowing ambulant infected persons may act as the ‘vector’ infecting distant parts of the country. Contact tracing is a challenge for mosquito-borne diseases, but inferring the geographically structured transmission tree sheds light on the spatial transmission of Zika to immunologically naive regions of the country.

Explicit characterization of human population connectivity reveals long run persistence of interregional dengue shocks

“…Substantial literature exists on using mathematical models to describe spatio-temporal patterns of disease behaviour such as hand-foot-and-mouth disease and Ebola through animal and human host movements respectively [ 11 , 12 ]. Statistical approaches include space–time Bayesian hierarchical modelling, cluster and network analysis between geographical regions [ 13 – 15 ]. However, these often have a substantial computational burden or suffer from dimensionality issues due to the temporal persistence of disease transmissions and large number of regions, which result in a large number of parameters to describe the spatial, temporal and spatio-temporal patterns.…”

Spatial connectivity in mosquito-borne disease models: a systematic review of methods and assumptions

“…We identified 50 studies that assumed spatial connectivity was related to human movement; most used mechanistic models ( n = 28, figure 4 ) which are able to include complex mobility matrices (see metapopulation and agent-based models in electronic supplementary material, technical appendix, and figure 3 for more details). Other methods used to account for human movement within models included spatial covariates based on the number of people moving between regions, random effects which assumed people were more likely to travel to neighbouring regions, and a bespoke statistical model which simulated home and work addresses based on public transport journeys [ 46 ].…”

“…Two studies applied a smoothing function to the spatial covariates within a GAM, allowing for a nonlinear relationship between the outcome and measure of connectivity [24,37]. Another study included spatial kernels, exponentially decaying correlation functions of the distance between cases' home and work addresses, estimated from public transport journeys, as spatial covariates when estimating the probability of cases being linked [46]. Spatial covariates are compatible with all statistical models identified in this review.…”

Examining the Effect of the Environment and Commuting Flow from/to Epidemic Areas on the Spread of Dengue Fever

“…This study bridges the gap and suggests that the living and working environment of commuters contribute to the source of infection diffusion. One recent study also demonstrates that an individual may often be infected at their home or other places where they spend significant amounts of time, such as their place of work, and homes and work places were suggested as possible transmission sources [58]. This finding suggests that the health management officers should pay attention to commute-related environments when combating a DF outbreak in a local place.…”