Quantifying the consequences of measles-induced immune modulation for whooping cough epidemiology

Abstract

Measles, an acute viral disease, continues to be an important cause of childhood mortality worldwide. Infection with the measles virus is thought to be associated with a transient but profound period of immune suppression. Recently, it has been claimed that measles-induced immune manipulation lasts for about 30 months and results in increased susceptibility to other co-circulating infectious diseases and more severe disease outcomes upon infection. We tested this hypothesis using model-based inference applied to parallel historical records of measles and whooping cough mortality and morbidity. Specifically, we used maximum likelihood to fit a mechanistic transmission model to incidence data from three different eras, spanning mortality records from 1904 to 1912 and 1922 to 1932 and morbidity records from 1946 to 1956. Our aim was to quantify the timing, severity and pathogenesis impacts of measles-induced immune modulation and their consequences for whooping cough epidemiology across a temporal gradient of measles transmission. We identified an increase in susceptibility to whooping cough following recent measles infection by approximately 85-, 10- and 36-fold for the three eras, respectively, although the duration of this effect was variable. Overall, while the immune impacts of measles may be strong and clearly evident at the individual level, their epidemiological signature in these data appears both modest and inconsistent. This article is part of the theme issue ‘Modelling infectious disease outbreaks in humans, animals and plants: approaches and important themes’. This issue is linked with the subsequent theme issue ‘Modelling infectious disease outbreaks in humans, animals and plants: epidemic forecasting and control’.

Detection, forecasting and control of infectious disease epidemics: modelling outbreaks in humans, animals and plants

“…While simple models are powerful, more complex models require detailed data regarding, for example, the distribution of hosts in the landscape (figure 2a) or data on numbers of incident cases of multiple pathogens over long timescales (figure 2b). Multi-annual data for measles provided an early example of the richness contained in many real datasets [52], and these data are still being used to understand transmission dynamics: for example, in this theme issue, Noori and Rohani investigate the interaction between childhood diseases using data dating back to 1904 [51].…”

“…[58,59]). Important themes include interactions between different pathogens [51] or different strains of the same pathogen [63], pathogen evolution to escape interventions [95], and the impact of weather or climate on the dynamics of epidemics in human and animal populations [75,96] as well as plant populations [64][65][66].…”

“…(a) Data on the locations of hosts in the landscape (here the density of cattle in the UK, adapted from[50]-see that paper for further details). (b) Inference of the values of core parameters governing epidemic dynamics, such as the basic reproduction number, requires temporal data-here, time series of the numbers of new cases in each time period (adapted from[51], see that paper for further details). (c) Schematic showing how pathogen sequence data are recorded from hosts (adapted from[54], see that paper for further details).…”

Measles and Scarlet Fever Epidemic Synergy and Evolving Pathogenic Virulence in Victoria, Australia, 1853–1916

“…Coleman argues that this pattern can be explained by measles-induced immunosuppression in combination with other factors; for example, both diseases target the respiratory epithelium and affect children at a similar age. Using model-based interference applied to historical weekly records of morbidity and mortality in London (England), Noori and Rohani (2019) similarly found support for the link between measles infection and pertussis morbidity and mortality, but variable magnitude estimates. They note that the inconsistency in their findings might reflect variability in the effects of measles infection related to factors such as age and/or the need for additional models; their study did not account for loss of previous immunity to pertussis following measles infection (ibid.…”

“…Numerous authors have argued for a change in group A Streptococcus biology in the nineteenth century as a proximal explanation for the rise of the scarlet fever pandemic and its sudden end prior to medical intervention (see Gaworewska and Colman 1988;Hardy 1993;Mathews 2005;McKeown 1976;Musser et al 1993;Swedlund and Donta 2003), opening up questions about the broader causes of such biological changes. Furthermore, previous historical work in Britain and the United States has identified associations between various "childhood" diseases, particularly the "traditional pairing" of measles and pertussis (Woods and Shelton 1997: 77; see for example Coleman 2015;Hardy 1993;Noori and Rohani 2019). Woods and Shelton (1997: 89) show that for early childhood mortality in England and Wales in the 1860s, after pertussis, measles was most closely associated with scarlet fever.…”