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Comparative Study
. 2009 Dec 7;276(1676):4111-8.
doi: 10.1098/rspb.2009.1058. Epub 2009 Sep 9.

Seasonality and comparative dynamics of six childhood infections in pre-vaccination Copenhagen

Affiliations
Comparative Study

Seasonality and comparative dynamics of six childhood infections in pre-vaccination Copenhagen

C Jessica E Metcalf et al. Proc Biol Sci. .

Abstract

Seasonal variation in infection transmission is a key determinant of epidemic dynamics of acute infections. For measles, the best-understood strongly immunizing directly transmitted childhood infection, the perception is that term-time forcing is the main driver of seasonality in developed countries. The degree to which this holds true across other acute immunizing childhood infections is not clear. Here, we identify seasonal transmission patterns using a unique long-term dataset with weekly incidence of six infections including measles. Data on age-incidence allow us to quantify the mean age of infection. Results indicate correspondence between dips in transmission and school holidays for some infections, but there are puzzling discrepancies, despite close correspondence between average age of infection and age of schooling. Theoretical predictions of the relationship between amplitude of seasonality and basic reproductive rate of infections that should result from term-time forcing are also not upheld. We conclude that where yearly trajectories of susceptible numbers are perturbed, e.g. via waning of immunity, seasonality is unlikely to be entirely driven by term-time forcing. For the three bacterial infections, pertussis, scarlet fever and diphtheria, there is additionally a strong increase in transmission during the late summer before the end of school vacations.

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Figures

Figure 1.
Figure 1.
Detail of numbers of cases reported between 1907 (1911 for varicella) and 1930; reports are weekly for all infections except mumps, where reports are monthly. See electronic supplementary material for full time series. (a) Measles; (b) pertussis; (c) mumps; (d) diphtheria; (e) varicella; (f) scarlet fever.
Figure 2.
Figure 2.
The cumulative proportion of reported cases at each age obtained by summing the data over age and dividing by the total sum to obtain proportions for (a) measles; (b) pertussis; (c) mumps; (d) diphtheria; (f) scarlet fever. Different points for the same age correspond to different years. Fitted logistic regressions are shown as a dashed line. No data are available for varicella. (e) Inferred probability density functions for different infections (black line, diphtheria; brown line, scarlet fever; green line, measles; blue line, mumps; pink line, pertussis). The grey area indicates ages between 7 and 15 years and approximately encapsulate the years of schooling. The area under each curve found between these lines is provided in table 1.
Figure 3.
Figure 3.
The seasonal transmission coefficient, β with confidence intervals (±1.96 s.e.) shown as dotted vertical lines; other parameters as in table 1. The grey rectangles show months during which the major school holidays occur. (a) Measles; (b) pertussis; (c) mumps; (d) diphtheria; (e) varicella; (f) scarlet fever.
Figure 4.
Figure 4.
Relationship between the strength of seasonality, measured as variance of β, and (a) the proportion of cases occurring in individuals starting school (p6–7), (b) the proportion of cases in school age individuals (p7–15) and (c) a measure of the expected relative magnitude of seasonality if seasonality is entirely driven by term-time forcing (i.e. if term-time forcing dominates seasonality in transmission, this relationship should be positive). Note that the scale of the y-axis is different in the third figure, since by using β̄ as a measure of R0, we can include varicella in calculations of φ. Using R0 based on the average age of infection (table 1) does not alter the basic patterns.

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