Estimating enhanced prevaccination measles transmission hotspots in the context of cross-scale dynamics

Abstract

A key question in clarifying human-environment interactions is how dynamic complexity develops across integrative scales from molecular to population and global levels. Apart from its public health importance, measles is an excellent test bed for such an analysis. Simple mechanistic models have successfully illuminated measles dynamics at the city and country levels, revealing seasonal forcing of transmission as a major driver of long-term epidemic behavior. Seasonal forcing ties closely to patterns of school aggregation at the individual and community levels, but there are few explicit estimates of school transmission due to the relative lack of epidemic data at this scale. Here, we use data from a 1904 measles outbreak in schools in Woolwich, London, coupled with a stochastic Susceptible-Infected-Recovered model to analyze measles incidence data. Our results indicate that transmission within schools and age classes is higher than previous populationlevel serological data would suggest. This analysis sheds quantitative light on the role of school-aged children in measles cross-scale dynamics, as we illustrate with references to the contemporary vaccination landscape.cross-scale dynamics | measles | mathematical model | childhood infection | vaccine refusal

Human–environment interactions in population and ecosystem health

“…Furthermore, it captures experimentally observed associations between climate and flea population dynamics in India. Similar to Becker et al (35), Lewnard and Townsend (36) demonstrate that historical datasets can yield insights into the epidemiological, ecological, and evolutionary dynamics of re-emerging disease agents, insights that will help to guide the design of preparedness and response strategies that mitigate future outbreaks.…”

“…Population dynamics not only vary at different spatial scales, as in Becker et al (35), and at different time scales, as in Hastings (28), but can also be affected by rapid evolutionary processes. Lewnard and Townsend (36) demonstrate that the evolution of disease resistance in a disease vector can drive shifts in outbreak seasonality.…”

“…Given that this relationship between climate change and vectorborne, as well as enteric diseases, is significant even in a country with high levels of sanitation and relatively low prevalence of these diseases, the influence of climate change on such diseases are expected to be even greater in developing countries. Becker et al (35) point out that modeling-coupled human-environment interactions requires understanding how natural system dynamics unfold at both small and large spatial scales, such as individual households versus entire cities. Applying a stochastic disease transmission model to a 1904 measles outbreak in London, as well as to the 2014-2015 Disneyland, California measles outbreak, Becker et al find that disease transmission within schools and within age classes is higher than has been estimated from population-level serological analyses.…”

Coexisting attractors in the context of cross-scale population dynamics: measles in London as a case study

“…The accelerating interest in digitizing historical medical records has reenergized the use of childhood diseases as a testbed for both theoretical and data-driven analysis [16,37,38]. In addition, recent statistical advances now allow for confronting complex models with data.…”

“…Seasonality in measles appears to arise from the aggregation of human populations. In the US and UK, seasonal transmission patterns have typically mirrored school calendars, with increases in transmission at the start of school terms [9,10,16,17]; other movement-related drivers have been implicated in sub-Saharan Africa [11]. In a US city-level analysis, [9] subtle changes in contact structure led to drastic differences in periodicity, suggesting that regional variation may be important.…”