Spatial infectious disease epidemiology: on the cusp

Abstract

Infectious diseases continue to pose a significant public health burden despite the great progress achieved in their prevention and control over the last few decades. Our ability to disentangle the factors and mechanisms driving their propagation in space and time has dramatically advanced in recent years. The current era is rich in mathematical and computational tools and detailed geospatial information, including sociodemographic, geographic, and environmental data, which are essential to elucidate key drivers of infectious disease transmission from epidemiological and genetic data. Indeed, this paradigm shift was driven by dramatic advances in complex systems approaches along with substantial improvements in data availability and computational power. The burgeoning output of infectious disease spatial modeling suggests that we are close to a fully integrated approach for early epidemic detection and intervention. This special collection in BMC Medicine aims to bring together a broad range of quantitative investigations that improve our understanding of the spatiotemporal transmission dynamics of infectious diseases in order to mitigate their impact on the human population.

Visibility and hotspots of outdoor tobacco advertisement around educational facilities without an advertising ban: Geospatial analysis in Surabaya City, Indonesia

“…Second, since many nations have adopted a national ban on outdoor tobacco advertising since the FCTC in 2005, those studies have not employed the recently developed geospatial techniques. The hotspot analysis, for instance, that uses Getis-Ord Gi* statistics to identify clusters 12 has been increasingly used in infectious disease epidemiology research but not much in non-communicable disease, including tobacco control 13,14 .…”

Effect of daily periodic human movement on dengue dynamics: the case of the 2010 outbreak in Hermosillo, Mexico

“…Human mobility has been included in the latest generation of models in epidemiology using two main approaches: agent-based modeling and metapopulations [5]. Metapopulation models divide the population into interacting population groups defined by spatial or demographic information [6]. This mathematical modeling approach, based on ordinary differential equations, has been used to theoretically evaluate the effect of human mobility on the dynamics of infectious diseases in heterogeneous regions connected by the mobility [3,[7][8][9][10][11].…”

“…-(6) [23]. The distribution ofα h1 and α h2 has a mean equal to log(0.3) and a standard deviation of 0.4.…”

“…The distribution ofδ has a mean equal to log(0.08) and a standard deviation of 0.15. To establish the mean and standard deviation of the normal distributions for parameters (5)-(6)…”

What drives disease flows between locations?

“…The number of disease cases at a location is one of the most common epidemiological factors, as it indicates the potential of the disease flows. However, this set of factors has often been considered in an aggregated manner (e.g., the total number of disease cases over an aggregated area throughout the entire time period of an epidemic) (Chowell & Rothenberg, 2018; Eggo, Cauchemez, & Ferguson, 2011; Gog et al, 2014; Riley, Eames, Isham, Mollison, & Trapman, 2015). Few studies have been concerned with their more resolved effects on disease flows.…”