Wave I (Spring and Early Summer 1918)

Wave I of the influenza pandemic was attributed at the time to Spain by France and vice versa, and to Eastern Europe by the Americans. But, whatever the truth, the disease acquired its popular name of Spanish influenza at this stage. Some of the first records of influenza activity can be traced to US Army recruits at Camp Funston, Kansas, where an epidemic of influenza first manifested in early March 1918. By the end of July, all continents had been reached. Wave I was comparatively mild.

Wave II (Autumn 1918)

During the summer of 1918, the virus mutated into a more lethal strain and a second, more severe, form of the disease emerged. Pneumonia often developed quickly, with death usually coming two days after the first indications of influenza. The exact geographical origins of Wave II are, like Wave I, unknown although western France is generally viewed as the source. The first reports can be traced to the French Atlantic port of Brest, a landing point for American troops, in late August. From here, ships appear to have carried the disease to coastal locations of North America, Africa, Latin America, South Asia and the Far East by September. New Zealand was infected in October by ships from the United States while Australia remained largely free of the disease until January 1919.

Wave III (Winter 1918–19)

In the aftermath of Wave II, a third influenza wave – of intermediate severity to the preceding waves – appeared in the winter of 1918–19. While relatively little is known of the origin and spread of this tertiary wave, the pandemic appears to have finally run its course by the spring of 1919.

Australia

Australia’s involvement in the First World War was on a massive scale in relation to its small size. From a population of some five million, over 300,000 troops served in Europe, and the problems of bringing the survivors home in the autumn and winter of 1918 – at the very height of the influenza pandemic – was on a similarly massive scale. In the early part of October 1918, with the health authorities of Australia cognisant that a virulent form of influenza was within striking distance of the country, instructions were issued for the port quarantining of all vessels with cases of influenza aboard (Cumpston, 1919, p. 7). So began Australia’s six-month maritime defence against the great influenza pandemic. The medical officer who successfully led the strategy was J.H.L. Cumpston (Figure 6.3), Director of Quarantine and later Australia’s first Commonwealth Director-General of Health.

• Click to view larger

Figure 6.3

Quarantine administration, Commonwealth of Australia, at the end of the First World War. Australia’s principal and secondary quarantine stations are marked, along with other ports of entry at which quarantine officers were stationed. The photograph is of J.H.L. Cumpston (1880–1954), Australia’s first Commonwealth Director-General of Health, 1921–45. Cumpston oversaw the health of Australia and the troops she put in arms in two world wars. He produced a number of service handbooks on diseases in Australia from 1788 to 1920. The findings were summarised in a book, Health and Disease in Australia, completed in 1928. This was not published until 1989, when the work was edited and introduced by Milton Lewis as part of Australia’s bicentennial celebrations.

Sources: (Map) drawn from The Quarantine Annual, 1930, Office International d’Hygiène Publique, League of Nations; (photograph) National Library of Australia.

The quarantine record

Records have survived for 228 vessels arriving in Australia between October 1918 and April 1919, of which 79 (35 percent) documented cases of influenza (Smallman-Raynor and Cliff, 2004, Table 11.7, p. 584). Figure 6.4 plots, by week of arrival in Australia, the distribution of the 79 infected vessels according to the inferred place of virus acquisition: Pacific Rim (chart A); Europe (B); and other locations (C). Vessels on which cases of influenza were documented after arrival in Australia and which, in the absence of quarantine, would have served as potential sources of infection for the population of Australia, are indicated by the black shading. Figure 6.4 suggests a temporal shift in the geographical source of maritime influenza, from the Pacific Rim in October–December 1918 (chart A) to Europe in January–April 1919 (chart B). This temporal shift was associated with the arrival of generally larger, and more heavily crowded, troopships from Europe via Suez in the latter period. (p. 150)

• Click to view larger

Figure 6.4

Maritime quarantine and ‘Spanish influenza’, Australia, 1918–19. Graphs plot, by overseas source of infection, the weekly count of vessels infected with influenza and quarantined on arrival at Australian ports, October 1918–May 1919. (A) Pacific Rim. (B) Europe. (C) Other. Vessels on which cases of influenza were recorded after arrival in Australia, and which may therefore be assumed to have maintained an unbroken chain of infection aboard ship, are indicated on each graph by the black shading.

Source: Reproduced from Smallman-Raynor, M.R. and Cliff, A.D, War Epidemics: An Historical Geography of Infectious Diseases in Military Conflict and Civil Strife, Figure 11.5, p. 585, Oxford University Press, Oxford, UK, Copyright © 2004, by permission of Oxford University Press. Data from Cumpston, J. H. L., Influenza and Maritime Quarantine in Australia, Sydney, Commonwealth of Australia, Quarantine Service Publication, No. 18, Copyright © 1919.

Evidence from infected troopships

The influenza records for four troopships (Devon, Medic, Boonah and Ceramic), arriving at Australian ports between November 1918 and March 1919, are shown in Figure 6.5; the vessels involved are illustrated in Figure 6.6. Some impression of the conditions that prevailed on the ships can be gained from the eyewitness accounts of senior officers. According to one anonymous officer aboard the Medic, the scene

…was remarkable. A score or more of stalwart young men lay helpless about the after well-deck, awaiting transport to the improvised and overflowing hospitals in the troop-decks. That they lay there was not due to any neglect or delay on the part of the busy stretcher-bearers, but to the extraordinarily sudden and disabling onset of the disease. One smart, well set-up young soldier came up a companion-ladder close to where I stood, held on for a few seconds to a rail, and then sagged slowly down till he assumed the characteristic flattened sprawl on the deck. There was no pretence or “old-soldiering” about (p. 151) it. The men were literally being knocked down by a profound systematic intoxication of extraordinarily rapid onset

(Anonymous, cited in Cumpston, 1919, p. 53).

• Click to view larger

Figure 6.5

The role of ships in dispersing ‘Spanish’ influenza, 1918–19. Number of cases of influenza reported daily on four troopships repatriating troops from Europe to Australia in the aftermath of the First World War. (A) Devon. (B) Medic. (C) Boonah. (D) Ceramic.

Source: Reproduced from Cliff et al, Island Epidemics, Figure 5.12, pp. 194, Oxford University Press, Oxford, UK, Copyright © 2000 with permission from Oxford University Press.

• Click to view larger

Figure 6.6

Australia and the influenza pandemic of 1918–19. Four troopships which carried ‘Spanish’ influenza from the European Theatre or South Africa to Australia in the aftermath of the First World War. (Upper left) Troopship Devon (foreground) in First World War camouflage with troops aboard, 1915. (Upper right) Troopship Medic. HMAT (His Majesty’s Australian Transports) Medic (A7) departs Melbourne assisted by a tug, and watched by a crowd of well-wishers on the wharf, 16 December 1916. (Lower left) Troopship Boonah (HMAT A36) departing from Brisbane during World War One, 1916. Image shows a large crowd gathered to wave farewell to the troops. The ship is decorated with streamers. (Lower right) Signed photograph of the troopship HMAT A40 Ceramic, the troopship that took the 16th Battalion from Melbourne to Egypt in late December 1914. On this, the Ceramic’s first trip as a troopship, the dangers of epidemics of infectious diseases in confined spaces was illustrated. “The first death of a member of the batallion occurred this day [20 January 1915] when Private C.R…. died of pneumonia following measles” (Longmore, 1929, p. 29), and “On January 20, a sick return showed that there were 10 cases of pneumonia, 13 of measles, 7 of influenza, and 15 with other diseases in hospital, a total of 45 or 1.7 per cent. of the troops on board. During this period of the trip all hands were vaccinated and inoculated, to the obvious disgust and temporary discomfort of the troops” (Longmore, 1929, p. 21).

Sources: (Upper left) National Maritime Museum, Greenwich, UK (Upper right) Reproduced with permission from Australian War Memorial website, photographer Josiah Barnes; (Lower left) Reproduced with permission from John Oxley Library, State Library of Queensland, Australia; and (Lower right) Reproduced from Longmore, C., The Old Sixteenth: Being a Record of the 16th Battalion, AIF [Australian Imperial Force], during the Great War 1914–1918, pp. 18-19, History Committee of the 16th Battalion Association, Perth, Australia, 1929.

As Figure 6.5 shows, the influenza curve for the Medic followed a broadly similar course to other troopships, with two marked features of the frequency distribution of cases:

• ♦ a generally log-normal nature. Cases peaked within three to 10 days of start of voyage, with a long positive tail to the distribution;

• ♦ recurring secondary cycles of cases within general lognormal shape. Statistical analysis using the autocorrelation function (ACF) suggests that there is a periodicity in the time series of about four days, roughly corresponding with the known serial interval of the disease.

The long journey from Europe gave Cumpston time to organise a quarantine system. Boats reporting influenza were isolated in harbour, and troops were not allowed ashore until they were free of the disease. Once soldiers were landed and returned to their homes, they found that interstate travel was also restricted to inhibit transmission – by armed guards in the case of the land border between Queensland and New South Wales. The effect of these measures was to ensure that peak death rates in Australia were about an eighth of those in the United States, and a quarter of those in South Africa and New Zealand. Such was the success of the influenza control measures, concluded Cumpston, that

during the last three months of 1918, maritime quarantine had the effect of holding at the sea frontiers an intensely virulent and intensely infective form of influenza, which not only caused disastrous epidemics in New Zealand and South Africa, but actually arrived at the maritime frontiers of Australia, and caused alarming epidemics amongst the personnel of vessels detained in quarantine

(Cumpston and Lewis, 1989, pp. 318–19).

Although the effectiveness of the quarantine measures of late 1918 has been questioned (McQueen, 1975), epidemiological evidence does point to a delay of several months in the spread of virulent influenza in Australia. So, on the basis of recorded mortality, Cumpston traced the start of the Australian epidemic to (p. 152) early 1919. The first influenza deaths were reported in the state of Victoria (January), to be followed thereafter by New South Wales (February), South Australia (March), Queensland (April), Western Australia (June) and Tasmania (August). In explaining the timing of the epidemic sequence, Cumpston noted that:

Western Australia and Tasmania are the two States having the least human contact with the two [earliest] infected States, New South Wales and Victoria; they were the two States which most vigorously applied inter-State quarantine restrictions; and they were the States latest infected in the series

(Cumpston and Lewis, 1989, p. 119).

Lessons from Epidemics at Sea

A number of writers have commented on the potential epidemiological value of the Australian experience in sea transport and naval vessels in the First World War. (Cumpston 1919) argued that a troopship is a self-contained highly insulated and concentrated herd. In Australian transports, this herd remained together on the voyage to or from England for a period of eight weeks, far longer than other transports (for example, US and Canadian troops crossing the Atlantic). The herd was reasonably homogeneous in its susceptible history, and used to suffering a wide range of protective and prophylactic expedients and experiments (for example, shore quarantine, prompt diagnosis and isolation, immunisation and treatment). As (Butler 1943, p. 671) has commented:

Every new case was reported on pain of severe disciplinary action, a skilled staff, professional and clerical, was often available; and provision could easily have been made for the collecting, assembling, and manipulation of a large number of reasonably comparable experiences.

Cumpston’s assertion is borne out by (Vynnycky, et al. 2007). Using the morbidity data from the Devon, Boonah and Medic as confined settings, they compared the estimated basic and effective reproduction numbers for the 1918 pandemic with the numbers calculated for open settings in European and American cities. Several different estimation methods based on the growth rate and the final size of the pandemic were used. They found that the effective reproduction number was in the range 2.1–7.5 for confined settings like the Devon and 1.2–3.0 in community-based settings (Figure 6.7). Further, assuming that 30 percent and 50 percent of individuals were immune to Spanish influenza after the first and second waves respectively, Vynnycky, et al. estimated that, in a totally susceptible population, an infectious case could have led to 2.6–10.6 further cases in confined settings and 2.4–4.3 in community-based settings. These findings confirm the greater dangers of transmission in confined environments and the relatively low transmissibility of the 1918 Spanish influenza virus in open settings which has been found by other studies (for example, Mills, et al., 2004; Chowell, et al., 2006).

• Click to view larger

Figure 6.7

Effective and basic reproduction numbers for Spanish influenza, 1918. Morbidity data from cities in Europe and America for community-based and confined settings. Several different methods based on the growth rate and final size of the epidemic have been used to estimate the effective and basic reproduction numbers for the 1918 (Spanish) influenza virus. The effective reproduction number (the average number of secondary infectious cases produced by a typical infectious case in a given population) for the 1918 influenza virus was in the range 1.2–3.0 and 2.1–7.5 for community-based and confined settings, respectively. The basic reproduction number (the average number of secondary infectious cases resulting from a typical infectious case in a totally susceptible population) was in the range 2.4–4.3 and 2.6–10.6 cases in community-based and confined settings, respectively.

Source: Reproduced from Vynnycky et al, Estimates of the reproduction numbers of Spanish influenza using morbidity data, International Journal of Epidemiology, Volume 36, Issue 4, pp. 881-9, Copyright © 2007 International Epidemiological Association, by permission of Oxford University Press.

The Model

Our account is taken from (Cliff, Smallman-Raynor, Haggett, et al. 2009, pp. 555–58). A single epidemic wave in any large geographical area is a composite of the waves for each of its constituent sub-areas. That is, the composite wave at the larger geographical scale (say, a country) can in principle be broken down into a series of multiple waves for its constituent sub-areas (say, its regions) at the smaller geographical scale.

Assuming that both the sub-areas and the time periods are discrete, we use the following notation:

A = Area covered by an epidemic wave in terms of the number of sub-areas infected where a_i is a sub-area in the sequence 1, 2,…a_i…A

T = Duration of an epidemic wave, defined in terms of a number of discrete time periods, t_j, in the sequence 1, 2,…t_j…T.

Q = Total cases of a disease recorded in a single epidemic wave measured over all sub-areas and all time periods. Thus q_ij is the number of cases recorded in the cell formed by the i-th sub-area and the j-th time period of the A × T data matrix.

To illustrate the method, we assume in Figure 6.8 a simple epidemic wave where A = 12, T = 10 and Q = 122. Thus in Figure 6.8A we begin with a hypothetical map which is converted into a 12 × 10 space–time data matrix in which 122 recorded cases of a disease are distributed to simulate an array typical of an epidemic wave. Note that, whereas this overall wave is continuous (no time periods with zero cases), for individual sub-areas the record may be discontinuous with one or more time periods with zero cases.

• Click to view larger

Figure 6.8

Calculating R_0A: hypothetical 12-area example. (A) Base map with areas reference coded as a₁…a₁₂, and associated space–time data matrix with reported cases for each area given in cells. (B) Matrix rearranged to order leading edges, LE and (C) following or trailing edges, FE. (D) Leading and following edges plotted as a phase-transition diagram with susceptible (S), infected (I) and recovered (R) integrals for areas.

Source: Reproduced from Cliff et al, Infectious Diseases: Emergence and Re-Emergence: A Geographical Analysis, Figure 10.3, p.556, Oxford University Press, Oxford, UK, Copyright © 2009 by permission of Oxford University Press.

For any one of the rows in the data matrix in Figure 6.8A, two cells can be identified which mark the ‘start cell’ and the ‘end cell’ of a recorded outbreak; if the infection only lasts for one time period, the start and end cell are the same. Figures 6.8B and C analyse these start and end cells. In Figure 6.8B the 12 × 10 matrix is rearranged so as to position the start cells in an ascending temporal order. This line of cells (dark shading) defines the position of the leading edge (LE) marking the start of the epidemic wave in the different sub-areas. To the left and above this line lies a zone of cells (light shading) which have yet to be infected and thus may be regarded as areas to which the epidemic has yet to spread.

Equally, the 12 × 10 matrix can be organised as in Figure 6.8C, so as to arrange the end cells in ascending temporal order. This line of cells (dark shading) defines the position of the trailing edge or following edge (FE), marking the completion of the epidemic waves in each of the different sub-areas. To the right and below this line lies a zone of cells which have ceased to be infected and which thus may be regarded as areas which have recovered from infection.

Both the edges, LE and FE, can be combined as in Figure 6.8D to identify cells which are in susceptible, S, infected, I, and recovered, R, states. The resulting graph may be regarded as a phase transition or SIR diagram. It has two roles: first, it defines the boundaries of the two phase shifts from susceptible to infective status (S ⇒ I) and from infective to recovered status (I ⇒ R) and second, it integrates the three phases, S, I, and R, as areas within the graph. As discussed in (Cliff and Haggett 2006), the phase diagram assumes characteristic configurations depending upon the velocity, duration and ultimate spatial extent of an epidemic wave as it passes through a region.

Three model parameters relating to the phase diagram are especially useful. We refer to these here as V_LE, and V_FE, the velocities of the leading and following edges of the wave which are functions of the average temporal position of the edges in the phase diagram; and R_0A, here defined as the spatial basic reproduction number. The relevant equations are summarised in Appendix 6.1. (p. 155)

Spatial Basic Reproduction Number

In the notation of Figure 1.17, the aspatial basic reproduction number, R₀, is defined as the ratio between the infection rate, β, and the recovery rate, μ. That is, R₀ = β/μ and, as we have noted, it is interpreted as the average number of secondary infections produced when one infected individual is introduced into a wholly susceptible population. In the spatial domain, as shown in Figure 6.8D, the S, I and R integrals define the boundaries of the two phase shifts from susceptible to infective status (S ⇒ I) and from infective to recovered status (I ⇒ R). The spatial basic reproduction number, R_0A, is the average number of secondary infected areas produced from one infected area in a virgin region. The integral S parallels β in that a small value indicates a very rapid spread. The integral R parallels the reciprocal of μ in that a large value indicates very rapid recovery. As detailed in Appendix 6.1, we can compute R_0A as the ratio of S_A:R_A. Values of R_0A calibrate the velocity of spread (the larger the value, the greater the rate of spread).

France: A Continental Country

As described in (Cliff, Smallman-Raynor, Haggett, et al. 2009, pp. 558–9), it is possible for the 113-year time window from 1887 to create two matrices giving the time series of monthly influenza mortality for two geographical frameworks: (i) n = 51 towns with populations over 30,000 (Figure 6.9) × 168 months, 1887–1900 and (ii) n " 90 départements × 1,188 months, 1901–99. These two matrices form the basis for the analysis reported as follows.

• Click to view larger

Figure 6.9

Towns of France, 1887. Geographical distribution of 51 towns with populations in excess of 30,000 used to estimate the spatial velocity of influenza waves in France, 1887–1905.

Figure 6.10 illustrates the temporal pattern of recorded deaths from influenza in France over the period. Figure 6.10A shows the seasonal distribution of mortality, with its winter peak characteristic of northern hemisphere countries. For this reason, in the analysis which follows, we have used as our temporal unit an influenza season running from 1 July through to 30 June of the following year, rather than calendar years which split months of high influenza mortality across year boundaries. Figure 6.10B charts the annual time series. In Europe, as described by (Dowdle 1999), pandemics (marked) occurred as follows within this 113-year window:

1. (i) 1889. First deaths were reported in France in November 1889; the peak month was January 1890. The pandemic occurred in three successive waves in most parts of the world, producing mortality and morbidity greater than had been seen in decades. H2N? was the likely strain.

2. (ii) 1900. (Dowdle 1999) has queried whether this was truly a pandemic. Excess mortality was reported in North America and in England and Wales but not globally. H3N? was the likely strain.

3. (iii) 1918–19. This occurred in three waves as follows – Wave I: April–July 1918; Wave II: August–November 1918; Wave III: March 1919 (Patterson and Pyle, 1991). It produced mortality (p. 156) unequalled in recorded history – up to 50 millions or more worldwide. H1N1 was the likely strain.

4. (iv) 1957. This pandemic occurred in two main waves – Wave I: February–December 1957; Wave II: October 1957–January 1958 (Cliff, et al., 2004). H2N2 was the causative virus.

5. (v) 1968–69. Wave I: 1968–69 (30 percent of deaths); Wave II: 1969–70 (70 percent of deaths). Wave I primarily affected North America and to a lesser extent Europe. In Wave II the situation was reversed and was associated with significant drift in the N surface antigen (Viboud, et al., 2005). H3N2 was the causative strain.

• Click to view larger

Figure 6.10

Influenza mortality in France, 1887–1999. (A) Radar chart showing the proportion of deaths reported in each month over the period (dark shading). The average monthly proportion is marked by light shading. (B) Time series of annual deaths from influenza reported in French records. For 1901–5, the national series of deaths by cause was recorded for towns whose populations exceeded 5,000 rather than 30,000.

Figure 6.10B shows that, as in most countries of the world, the 1918–19 pandemic stood alone in France in terms of mortality caused. While the long-term trend in mortality is steadily downwards, nevertheless it is the case that pandemic years generally experienced heightened mortality as compared with adjacent years.

The time series illustrated in Figure 6.10B reinforces the need for robust methods of analysis. Influenza mortality appears to have run at a much higher level between 1887 and 1900, than in the rest of the time series. However, this is an artefact of the recording method; from 1887–1900, influenza mortality in France was estimated from excess pneumonia deaths, whereas in the rest of the series, it was recorded directly.

Figure 6.11A plots the results for the spatial basic reproduction number, R_0A, and the two edge parameters, V_LE, and V_FE, along with the linear regression trend lines. It shows that the long term trend in R_0A was slowly downwards over the last century; i.e. the rate of geographical propagation of influenza epidemics gradually diminished, probably reflecting less intense epidemics arising from (i) improved standards of living and healthcare, and (ii) no shift of virus strain since 1969. This century-long declining trend in R_0A is reflected in a similar decline in the leading edge (LE) velocity parameter, and a rising trend in the following edge (FE) parameter implying that epidemics have arrived later and ended sooner in each influenza season; influenza seasons have become of shorter duration.

• Click to view larger

Figure 6.11

Swash model parameters for influenza in France, 1887–1999. (A) Time series plots of calculated values for the spatial basic reproductive number, R_0A, and for the edge velocity parameters, V_LE and V_FE. (B) Time series of the infected (I), susceptible (S) and recovered (R) integrals. Linear regression trend lines are also shown.

Within these long term trends, however, the pandemic seasons of 1900, 1918–19, 1957 and 1968–69 showed a locally raised R_0A. In the case of 1889, this is not evident, while the rise to a higher level in 1969 persisted for four seasons. In the latter case, this is consistent with the description by (Viboud, et al. 2005) of the 1968–69 pandemic as a “smouldering pandemic”. From the late 1980s, R_0A oscillated wildly, suggestive of locally intense epidemics in each influenza season involving only a few départements rather than the entire country.

Table 6.1 highlights the difference between pandemic and non-pandemic seasons in France. The 103 seasons for which data were available have been classified into three groups: (a) pandemic-affected (p. 157) (11), (b) high-intensity inter-pandemic (54), with death rates greater than the lowest pandemic season, and (c) low-intensity inter-pandemic (38), with death rates lower than the lowest pandemic season. The average velocity of the leading edge ( equation A1 in Appendix 6.1) for the three groups is (a) = 2.39 months, (b) = 3.59 months and (c) = 4.93 months. The results for V_LE in Table 6.1 confirm that pandemic seasons had higher velocities than inter-pandemic years. This higher velocity was maintained even compared with inter-pandemic influenza seasons of similar intensity levels as pandemic seasons. Moreover, pandemic seasons appeared to be of greater spatial intensity (larger R_0A) and were slower to clear (lower following edge velocities).

The plots of the susceptible and recovered integrals in Figure 6.11B show rising trends over the twentieth century, and this is consistent with the generally declining trend in the infective integral. As noted in Figure 6.10B, these trends imply declining intensity of influenza epidemics over the century-long study period. Again the pandemic years bucked the trend with raised infective integral values. The main other variations in the long term fall in the infectives integral occurred following the arrival of Asian influenza in 1957, when the infected integral remained above the trend (shaded in Figure 10.6B) for the next quarter of a century, and then, from the mid-1980s, when the integral remained resolutely below the trend line. The extended period of higher values for the infectives integral post-1957 may be attributed to the combined action of three effects: (i) the long interval since the last major strain shift (40 years since 1918); (ii) the shift from H2N2 to H3N2 in 1968–69; and (iii) the re-emergence in 1976 of Russian influenza (strain of H1N1) which has been co-circulating with the Hong Kong strain ever since. Together these effects meant that a greater proportion of the French population was likely to be susceptible to one or other of the mix of circulating strains than if just a single strain had been present over the period. After a generation, with no new major strains emerging, herd immunity appears to have caught up from the mid-1980s, leading to a general collapse of nationwide epidemics.

Iceland: An Island Location

This subsection is based upon (Cliff, Smallman-Raynor, Haggett, et al. 2009, pp. 566–72). Since 1895, Iceland has required direct notification of influenza cases by physicians. This concern for data collection stems from the island’s early history which was marked by disastrous externally-introduced epidemics, including the 1843 influenza outbreak which, although lasting only two (p. 158) months, doubled the expected death rate for the year (Schleisner, 1851). Annual totals and other summary data have been published in that country’s annual public health reports (Heilbrigðisskýrslur) since that date with, for influenza, national monthly morbidity time series available from 1913 (Figure 6.12A). For the 61-year period spanning the middle of the twentieth century from 1915 (Figure 6.12B), monthly data are broken down to a local level for some 50 medical districts (Figure 6.12C). This allows application of the swash model to estimate the changing spatial and temporal velocity of influenza epidemics over the period.

• Click to view larger

Figure 6.12

Reported influenza morbidity in Iceland. (A) Monthly records for total reported influenza cases from January 1913 to December 1976. (B) Monthly record of the number of Icelandic medical districts reporting one or more cases of influenza, January 1915 to December 1976. (C) Map of boundaries of Icelandic medical districts for the year 1945. (D) Cumulative total of reported monthly influenza cases for each Icelandic medical district in the period January 1915 to December 1976. Circles are drawn proportional to the cumulative number of cases but note the special position of the capital city, Reykjavík.

Source: All influenza data based on Heilbrigðisskýrslur (Public Health in Iceland)

Over the whole 61 years studied, Iceland’s doctors reported 530,276 cases of influenza, half of them from Reykjavík and immediately surrounding areas (Figure 6.12D). Although reported cases are likely to be under-estimates, the broad shape of outbreaks in both space and time is readily discernible. The distribution throughout the year shows clear peaks in March–April with the low periods (p. 159) in August–September, a pattern which, as we have seen for France in Figure 6.10A is typical of many northern latitude countries. However, the sub-Arctic climate of Iceland pushes the peak influenza months slightly later in the influenza season than in France, and so we use for Iceland a slightly different definition of season, running from September 1 through to August 31 of the following year.

The swash–backwash model was applied to Iceland as a whole. The time series of both edges is shown in Figure 6.13A. Despite marked year to year variation, the average trend shown by the linear regression line for the leading edge is distinctly upward implying that waves have speeded up over time, i.e. influenza waves moved around the island faster at the end than at the beginning of the study period. By contrast the position of the following edge when influenza incidence ceased in any influenza season has remained essentially unchanged. This implies that the duration of reported influenza incidence grew slowly longer, from around 2.5 months in 1915–16 to nearly 4.0 months in 1975–76 (Figure 10.8B), a finding which differs from that for France.

• Click to view larger

Figure 6.13

Velocity of epidemic waves in Iceland for the influenza seasons 1915–16 to 1975–76. (A) Velocities of the leading edge (LE, solid lines) and following edge (FE, pecked lines) are shown as the velocity ratios, V_LE and V_FE, defined in Appendix 6.1 equation (A2). V is in the range, 0 ≤ V ≤ 1. The larger the value of V, the faster the edge. (B) The widening time gap between the two edges illustrated in (A) is confirmed in this graph which plots the average duration of each district’s epidemic wave in months. In both (A) and (B) the trend lines were determined by OLS linear regression.

Source: All influenza data based on Heilbrigðisskýrslur (Public Health in Iceland)

Three of the 61 seasons studied were associated with pandemics of influenza A (the Spanish, Asian and Hong Kong pandemics). Figure 6.14 uses data on the spatial extent of influenza in each season to plot the position of the pandemic front with reference to the three seasons which immediately preceded or followed it. In the Spanish and Asian pandemics the front stands out clearly but in the third (Figure 10.9C) the Hong Kong front appears, as in France, to have been spread over two seasons.

• Click to view larger

Figure 6.14

Pandemic seasons in the Icelandic records. (A)–(C) Profiles showing the cumulative number (scaled to unity) of medical districts reporting influenza cases by month as a measure of the spatial extent of influenza in Iceland for the three shift seasons of 1918–19, 1957–58 and 1968–69. In each case the profile for the shift season is compared with the profiles of the three preceding and three following non-shift seasons. (D) Value of the spatial version of the basic reproduction number, R_0A, for Iceland for the influenza seasons 1915–16 to 1975–76. Peaks are associated with the three viral shift years for influenza A (1918–19, 1957–58 and 1968–69) and for three other non-shift seasons (1927–28, 1943–44, and 1972–73).

Source: All influenza data based on Heilbrigðisskýrslur (Public Health in Iceland)

Although pandemic years had large numbers of influenza cases, they were not the largest recorded over the period. The 1937–38 season had the largest number of cases (21,977) and the highest monthly rate and, as Figure 6.12A shows, monthly case numbers in several inter-pandemic years exceeded those with pandemics. For Iceland in Table 6.2, as for France in Table 6.1, we have divided the 61 seasons into three groups: (a) pandemic (3); (b) high-intensity inter-pandemic (24), with case rates greater than the lowest pandemic season; and (c) low-intensity inter-pandemic (34), with case rates lower than the lowest pandemic season. The average velocity of the leading edges (LE) for the three groups is (a) = 2.83 months, (b) = 5.53 months and (c) = 6.03 months. Echoing the results for France, this suggests that pandemic seasons have higher velocities than inter-pandemic years, and that this higher velocity is maintained even compared with inter-pandemic influenza seasons of similar intensity levels as pandemic seasons. Table 6.2 also shows that values for R_0A, when calculated for the three categories of influenza season, (a), (b) and (c), defined above produced the same differentials as the leading and following edge parameters.

Thus application of the swash–backwash model to Iceland’s influenza morbidity records has shown that (i) the onset of waves speeded up over the period 1914 to 1975 and (ii) waves in three viral shift (pandemic) seasons spread significantly faster and were of longer duration than other equally large waves in non-shift (inter-pandemic) seasons. These are identical to the results reported for France. It suggests that both the swash model and the findings are robust across the transfer of geographical scales from a large country (France) to a small country (Iceland), and from a continental to an island setting. To test transferability across geographical scales further, we now apply the model at the smallest of our spatial scales by looking at influenza waves between 1947–48 and 1975–76 in the English market town of Cirencester.

Cirencester: A Small English Town

Cirencester is a small market town lying between Gloucester and Swindon in the English Cotswolds. It had a population of about 12,000 in 1957, rising to 15,000 by 1970 and 18,000 in 2011 (Figure 6.15B). Based in the town, R.E. Hope-Simpson ran a general practice covering an area of about 210 km² from a centrally located surgery. In this practice, individual patients were identified and influenza was diagnosed and studied by a single doctor and his partner over a 30-year period following the end of the Second World War. The panel consisted of between three and four thousand patients and Hope-Simpson kept very detailed records of the incidence of several infectious diseases, but especially influenza, for each patient. Working with the help of his wife and later with the support of the UK’s Medical Research Council and the Public Health Laboratory Service he set up an Epidemiological Research Unit in Cirencester. By converting cottage rooms at his surgery in Dyer Street, he established a laboratory to permit identification of the viruses isolated from his patients. As a result, this unique practice became (p. 160) internationally known and it provides an unrivalled window into the behaviour of influenza epidemics at the micro-scale.

• Click to view larger

Figure 6.15

Influenza waves in an English GP practice. The country practice of a local doctor, Edgar Hope-Simpson, near Cirencester, England. Hope-Simpson made a special study of influenza in the area for some 50 years. (A) Boundaries of the 22 geographical units used by Hope-Simpson for data collection. (B) Location map of Cirencester. (C) Block diagram of cases of clinically-diagnosed influenza A weekly, 1946–74. The earlier appearance of influenza in the pandemic seasons of 1957–58 and 1968–69 is striking, as is the greater number of cases reported.

Source: Data from Cliff et al, Spatial Aspects of Influenza Epidemics, Figures 3.1 and 3.12, p. 48 and 65, Pion, London, UK, Copyright © 1986.

Hope-Simpson recorded every case of influenza identified in the practice, along with the causative strain of the virus and the geo-coordinates of the patient. Figure 6.15A maps the geographical framework used by Hope-Simpson for data collection, along with a block diagram (Figure 6.15C) of the cases reported in each influenza season, 1946–74. The earlier appearance of influenza in the town in the pandemic seasons of 1957–58 and 1968–69 is striking. This appearance of an early peak at times of antigenic shift in the causative virus can be related to the larger stock of susceptibles available for immediate infection on such occasions. (p. 161)

In Figure 6.16, the results of applying the swash–backwash model to the Hope-Simpson data from 1963–75 are summarised. They echo those already obtained for France and Iceland, with a sharp upturn in R_0A and an increase in the leading edge velocity in the second season of Hong Kong influenza (1969). Thereafter R_0A fell slowly to reach pre-1968 levels by 1973.

• Click to view larger

Figure 6.16

Cirencester, England: the 1968–69 pandemic of influenza in the Hope-Simpson general practice. Values of the reproductive number, and leading and following edge velocities for the swash–backwash model, 1963–75. As in France and Iceland, influenza moved more rapidly and struck earlier in 1968 than in non-pandemic seasons.

United States: The Swine Flu Outbreak of 2009

As noted in Section 6.2, the first cases of influenza caused by a novel influenza virus, subsequently typed 2009 swine A/H1N1 influenza, were reported in the state of Veracruz, Mexico, in March 2009, although the evidence suggests that there had been an ongoing epidemic for months before it was officially recognised. The Mexican government closed most of Mexico City’s public and private facilities in a futile attempt to contain the spread of the virus. The first cases in the United States were reported from California in April 2009. Rapid global diffusion of the new strain continued from the Mexican epicentre so that, on 11 June 2009, WHO declared the world to be at stage six of its six-phase pandemic alert scale. The crisis continued through into 2010, although the new strain had largely run its course by the turn of the year. The US Public Health Emergency for 2009 H1N1 influenza expired on June 23 2010 and, on 10 August 2010, WHO declared an end to the 2009 H1N1 pandemic globally. The new strain has subsequently behaved like normal seasonal influenza and is likely to be around for many years. In the US, the CDC estimates that between 43 million and 89 million cases (mid-range 61 million) of 2009 H1N1 occurred between April 2009 and April 10 2010; that there were between about 195,000 and 403,000 (mid-range 274,000) H1N1-related hospitalisations; and that there were between 8,870 and 18,300 (mid-range 12,470) 2009 H1N1-related deaths. US cases surged when schools reopened for the autumn term. The global impact of the pandemic is illustrated in Figure 6.17 and for the United States in Figures 6.18 and 6.19.

• Click to view larger

Figure 6.17

Pandemic (A/swine/H1N1) influenza, 2009. Countries, territories and areas with laboratory confirmed cases and cumulative number of deaths reported to the World Health Organization, Geneva, from the start of the pandemic. (A) 27 April 2009. (B) 22 July 2009. (C) 25 October 2009. (D) 21 March 2010.

Source: World Health Organization Map Production: Public Health Information and Geographic Information System (GIS). (Map A) Reproduced from Pandemic (H1N1) 2009: Countries, territories and areas with lab confirmed cases and numbers of deaths reported to WHO, Status as of 27 April 2009, available from http://gamapserver.who.int/mapLibrary/Files/Maps/GlobalSubnationalMasterGradcolour_weekly_20090427.png; (Map B) Reproduced from Pandemic (H1N1) 2009: Countries, territories and areas with lab confirmed cases and numbers of deaths reported to WHO, Status as of 22 July 2009, available from http://gamapserver.who.int/mapLibrary/Files/Maps/GlobalSubnationalMasterGradcolour_20090722_weekly.png; (Map C) Reproduced from Pandemic (H1N1) 2009: Countries, territories and areas with lab confirmed cases and numbers of deaths reported to WHO, Status as of 25 October 2009, available from http://gamapserver.who.int/mapLibrary/Files/Maps/GlobalSubnationalMasterGradcolour_20091029_weekly.png and (Map D) Reproduced from Pandemic (H1N1) 2009: Countries, territories and areas with lab confirmed cases and numbers of deaths reported to WHO, Status as of 21 March 2010, available from http://gamapserver.who.int/mapLibrary/Files/Maps/GlobalSubnationalMasterGradcolour_20100321_weekly.png with permission from the World Health Organization.

• Click to view larger

Figure 6.18

Geographical development of the swine flu pandemic in the United States, 2009–10 influenza season. Each histogram shows the number of positive influenza virus isolates by Department of Health and Human Services regions reported by WHO/NREVSS Collaborating Laboratories. The preponderance of swine flu A(H1N1), shown in black, as opposed to normal seasonal strains (grey) is evident in all regions.

Source: Data from US Centers for Diseases Control, Influenza Division, available from http://www.cdc.gov/h1n1flu/update.htm and http://www.cdc.gov/flu/weekly/

• Click to view larger

Figure 6.19

Pneumonia and influenza mortality in the United States, 2007–11. (A) The percentage of all deaths due to pneumonia and influenza in 122 US cities is plotted against the US national seasonal influenza baseline and the 95% confidence band above which epidemics (shaded) are declared. While the 2009–10 season in the round does not appear exceptional compared with adjacent seasons, especially 2008, the epidemic of swine flu in the fall of 2009 stands out as the largest autumn influenza event in this time series. (B) Number of influenza-related paediatric deaths. The high infant mortality during the swine flu season as compared with normal seasonal influenza in adjacent seasons is evident.

Source: Data from US Centers for Diseases Control, Influenza Division, available from http://www.cdc.gov/flu/weekly/

Figure 6.20 plots the values of the spatial basic reproduction number, R_0A, and the leading edge parameter, , of the swash model for weeks 27–39 (second week of July–first week of October of the five normal influenza seasons, 2004–5 to 2008–9, and for the first swine flu influenza season, 2009–10). Note the substantially higher velocity through the US population for the new swine flu strain as compared with normal seasonal influenza at all three spatial scales as evidenced by the larger values of R_0A and smaller values of in the 2009–10 season. The time window plotted covers the start of each influenza season and the reopening of schools, colleges and universities in the Fall.

• Click to view larger

Figure 6.20

Swine flu, United States, 2009–10. Values of the swash model parameters, R_0A and t–_LE, for the first 39 weeks (July–October) of each of the influenza seasons, 2004–5 to 2008–9, and for the first swine flu season, 2009–10 at three geographical scales. (A) 122 cities comprising the ILINet (influenza-like illnesses) PI (pneumonia–influenza) mortality dataset. (B) Fifty states. (C) Ten United States Department of Health and Human Services regions.

Source: Data from Centers for Disease Control, available from http://www.cdc.gov/h1n1flu/update.htm and http://www.cdc.gov/flu/weekly/

Discussion

In this section, a spatial version of R_0A has been developed in the context of what we have called a swash–backwash model. This has been applied to records of influenza from France (1887–1999), Iceland (1916–75), Cirencester (1963–75) and the United States in 2009, locations of vastly different geographical scales and settings. But common threads have been found: pandemic influenza at all spatial scales appeared (i) earlier than normal in the influenza (p. 162) (p. 163) season and (ii) spread spatially more rapidly than normal. If our findings are confirmed elsewhere, this will have wider implications for public health measures. It would suggest that any new influenza pandemic in the twenty-first century, whether emerging from highly pathogenic avian influenza, swine influenza, or other sources, will give little lead time if the public health is to be protected. This conclusion underscores the role of surveillance and virus watch systems, issues which we considered in Chapter 2 and for which we now provide a practical example.

Poliomyelitis in England and Wales, 1919–71

The historical geography of poliomyelitis as a twentieth-century emerging disease is described in (Smallman-Raynor, et al. 2006). Figure 6.24 plots the monthly time series of reported cases of poliomyelitis per 100,000 population in England and Wales, 1919–71. The upsurge of epidemic poliomyelitis after the Second World War until the impact of mass vaccination against the disease kicked in from the 1960s is dramatic (see Smallman-Raynor, et al., 2006, pp. 317–51 and pp. 482–6). In this epidemic sequence, that of 1947 was by far the largest and most severe. From an apparent onset in the late spring of 1947, the epidemic spread across the entire country, attacking both urban and rural populations with force. At the height (p. 165) of the epidemic, in late August and early September, more than 600 cases of poliomyelitis were recorded each week. All told, the epidemic was associated with some 7,655 civilian and 735 military cases – a toll far in excess of the 1,600 or so notifications associated with the previous epidemic high of 1938. As (Gale 1948) observes, it was not simply that the local intensity of the 1947 epidemic was greater than ever before; the geographical reach of the epidemic, too, exceeded all previous experience of the disease.

• Click to view larger

Figure 6.24

Poliomyelitis in England and Wales, 1919–71. Monthly number of notified cases of poliomyelitis per 100,000 population.

Source: Data from General Register Office: Registrar General's Weely Returns, 1919–71.

Origin and course of the epidemic

The events of the summer and autumn of 1947 were not wholly unanticipated by the medical community of England and Wales, although some purveyed complacency. As early as 28 June – a full month before the usual seasonal upturn in poliomyelitis – The Medical Officer observed that notifications of poliomyelitis in the week ending 14 June were “higher than usual for the time of year,” adding that “an upward trend is expected” (Anonymous, 1947a, p. 260). A few weeks later, and with the count of poliomyelitis notifications still rising, readers of The Lancet were alerted to a Ministry of Health memorandum that warned of “an unprecedented prevalence” of poliomyelitis in the months ahead (Anonymous, 1947b, p. 155). The deepening fears of the medical community were underscored by an editorial comment in The Medical Officer. “The brisk rise in notification of poliomyelitis in the late spring of the present year,” the editorial noted, “gives us much uneasiness…Nobody can foretell what poliomyelitis is going to do, but an epidemic on an extensive scale appears to be due in this country” (Anonymous, 1947c, p. 23). “Unfortunately,” the editorial added, “we can do nothing about it, for there is no proof that any measures to control the disease have any value.”

Figure 6.25 confirms the pandemic-like qualities of the 1947 outbreak as compared with the previous three poliomyelitis upswings and the one which followed in 1948. It attacked the geographical mesh of boroughs and districts more completely, more rapidly (R_0A, LE), and persisted longer (time between LE and FE) than any of the other four outbreaks. Table 6.3 gives the swash parameters defined in Appendix 6.1 for the five polio seasons between 1944 and 1948. As with influenza, the swash model picks out the extraordinary poliomyelitis epidemic of 1947 from its neighbouring, less intense, polio years.

• Click to view larger

Figure 6.25

Poliomyelitis in England and Wales, 1944–8: leading and following edges of poliomyelitis activity in England and Wales. Graphs plot the cumulative percentage of administrative units (county, metropolitan and municipal boroughs, urban districts and rural districts) that first notified (leading edge, LE; graph A) and last notified (following edge, FE; graph B) cases of poliomyelitis in the epidemic year of 1947 (heavy line trace) and in the corresponding weeks of the four non-epidemic years of 1944–46 and 1948.

Table 6.3 Poliomyelitis in England and Wales, 1944–48. Values of the swash model parameters for the poliomyelitis outbreaks in each year at the county borough and district scale

		N of infected units	t LE	t FE	V LE	V FE	S A	I A	R A	R 0A
1944	All units	256	13.64	15.30	0.48	0.41	0.49	0.10	0.41	0.87
1945	All units	321	13.94	17.72	0.46	0.32	0.50	0.18	0.32	0.74
1946	All units	283	13.34	16.31	0.49	0.37	0.47	0.15	0.37	0.84
1947	All units	1113	10.29	18.50	0.60	0.29	0.36	0.35	0.29	0.90
1948	All units	532	13.66	18.41	0.47	0.29	0.49	0.22	0.29	0.72

1. Quality of Data

Iceland’s public health and epidemiological records are among the highest quality in the world in terms of their completeness, (p. 166) geographical granularity and temporal resolution. For many human communicable diseases, mortality and morbidity counts are available on a monthly basis for around 125 years in some 50 geographical areas; for some diseases the record goes back on a similar basis to the middle of the eighteenth century. Figure 6.26 summarises the general demographic history of Iceland from that date, along with the occurrence of the major epidemics of communicable diseases until the last quarter of the twentieth century by which time mass vaccination had all but eliminated them. Population size was stable at around 40–60,000 for the period 1751–1900, when a remarkable growth set in; the population approximately quadrupled between 1900 and 1970 to about 200,000. Up to 1950, this (p. 167) growth was concentrated mainly in the capital, Reykjavík. Since then, Reykjavík and the rest of Iceland have grown in population at about the same rate. The decline in infant mortality since 1850 and the higher birth-rates of the twentieth century (especially since 1940) are the main reasons for Iceland’s population growth.

• Click to view larger

Figure 6.26

Iceland demography and communicable disease history, 1751–1970. Annual population total, births, deaths and major epidemics of communicable diseases.

Source: Reproduced from Cliff et al, Spatial Diffusion: An Historical Geography of Epidemics in an Island Community, Figures 3.8 and 3.9, pp. 47-48, Cambridge University Press, Cambridge, UK, Copyright © 1981, by permission of the authors.

Throughout its history, neither the island as a whole nor any individual settlement approached the critical community size required to maintain endemically the spectrum of human infectious diseases (Section 4.3). As a result, epidemics of infectious diseases in Iceland have been spatially and temporally separated, with inter-epidemic intervals during which either no or only isolated cases occurred. Figure 6.27 illustrates the point by plotting Iceland’s twentieth-century time series for three diseases – measles, rubella and pertussis. Thus for a significant outbreak of one of these diseases to occur on an Icelandic farmstead, the disease agent had to cross several hundred miles of sea, travel from a seaport or airport to the rural community, and eventually to the farm itself. Once the susceptible population was exhausted, the agent disappeared from the island until the susceptible population grew again by births to a sufficient size to sustain a new outbreak. This temporal and spatial separation of outbreaks facilitates both the development and testing of forecasting models.

• Click to view larger

Figure 6.27

Epidemic diseases in Iceland, 1900–90. Monthly time series of reported cases (grey, log scale) and rate per thousand population (black, arithmetic scale) of measles, rubella and pertussis.

Source: Reproduced from Cliff et al, Island Epidemics, Figure 6.3, p. 245, Oxford University Press, Oxford, UK, Copyright © 2000 by permission of Oxford University Press.

2. Population Distribution

Iceland is a large island (about the size of southern England or the state of Indiana) located just south of the Arctic Circle. Its 1990 population was somewhat over 250,000. It is the least-densely settled country in Europe and the harsh environment of the interior plateau has restricted population to the peripheral lowlands (diagonal shading in Figure 6.28). This diagram maps the geographical distribution of Iceland’s population by medical districts using proportional circles and squares. The percentage change in population that has taken place over the last century is also shown. A pattern familiar across Western Europe – of rural depopulation on a large scale – is evident. The biggest single settlement is the capital, Reykjavík, which has been growing both absolutely and in its share of Iceland’s total population. In 1901 its 6,700 inhabitants accounted for less than a tenth of the island’s 78,000; by 1990, Reykjavík had grown to 100,000 out of a total of 256,000. The overall impression is of population distributed around the coast much like the beads on a necklace. The degree of isolation of settlements around the coast means that it is often possible to treat such communities as separate epidemiological cells from a modelling point of view, giving a spatial equivalent to the discrete epidemic waves in time evident in Figure 6.27.

• Click to view larger

Figure 6.28

Iceland population distribution. Settled areas of Iceland with 1990 medical district populations shown by proportional circles and squares. Medical district boundaries plotted as pecked lines. Percentage population change, 1900–90 indicated numerically.

Source: Reproduced from Cliff et al, Island Epidemics, Figure 6.2, p. 243, Oxford University Press, Oxford, UK, Copyright © 2000 by permission of Oxford University Press.

3. Physician Reports

Historically, Icelandic legislation required the principal physician of each of the country’s c. 50 medical districts to submit an annual report to the Chief Medical Officer of Health in Reykjavík. The reports contain contact tracing information for each epidemic of an infectious disease to have affected the district that year. The reports give, in varying detail, information on the origin of the outbreak (p. 168) and its local spread. They are much fuller prior to 1945 than thereafter. Figure 6.29 maps the results of this contact tracing for all epidemics of measles, rubella and pertussis which have affected Iceland since 1900. The last map, D (All), pools the results for the three diseases. The principal vectors fan out from the capital, Reykjavíkur (population 95,000 in 1990), to the three main regional towns in Iceland of Akureyrar (population 15,000), Ísafjarðar (population 3,600) and Seyðisfjarðar (population 1,000) – cascade or hierarchical diffusion. Local spread from these centres into their hinterlands (contagious diffusion) is illustrated by the vectors within the diagonally shaded areas on each map, and is most clearly seen on map All around Akureyrar.

• Click to view larger

Figure 6.29

Iceland: internal epidemic pathways. Observed transmission routes between Icelandic settlements for measles, rubella and pertussis, 1900–90. Vectors show the number of times the diseases were recorded in Heilbrigðisskýrslur (Public Health in Iceland) as passing between linked medical districts.

Source: Reproduced from Cliff et al, Island Epidemics, Figure 6.4, p. 247, Oxford University Press, Oxford, UK, Copyright © 2000 by permission of Oxford University Press.

Calculation of Lag Maps

For epidemic , code the first month in which a disease was reported anywhere in Iceland as month 1 and, for medical district i, note the month in which the disease was reported in that medical district as month 2, or 3, or 4, etc. Denote this month as. The desired quantities are then (6.1) where i is subscripted over the 47 medical districts.

Figure 6.30 plots the for three of the Icelandic diseases separately (measles, rubella and pertussis) and for an average map, where the average has been calculated from the results for (p. 169) the three separate diseases and influenza; data for 1900–90 have been used. Circle sizes are proportional to the 1990 populations of medical districts. Circles have been coloured black on the map in which they first appear and have been left as open circles thereafter. A similar pattern is seen across the diseases. Reykjavík and the Reykjavík region are reached early; the principal regional capitals of Akureyri, Ísafjörður and Seyðisfjörður are attacked at the second stage; and epidemic decay occurs in the smaller and more remote settlements of the fjord areas of eastern and northwest Iceland. This is consistent with the pathway diagrams in Figure 6.29, and yields a schematic space–time model like Figure 6.31.

• Click to view larger

Figure 6.30

Time lag maps for infectious diseases in Iceland, 1900–90. Average time in months from commencement of epidemics to reach medical districts in different parts of Iceland. On each map, medical districts reached for the first time are shown as solid circles. Districts appearing on earlier maps appear as open circles on later maps. Circle sizes are proportional to 1990 medical district populations. (A) Measles. (B) Rubella. (C) Pertussis. (D) Average across all three diseases and influenza.

Source: Reproduced from Cliff et al, Island Epidemics, Figure 6.12, p. 264, Oxford University Press, Oxford, UK, Copyright © 2000 by permission of Oxford University Press.

• Click to view larger

Figure 6.31

Communicable disease diffusion in Iceland. Hypothetical sketch of the spread of an infectious disease agent from an international source down to a farm family in rural Iceland. The arrows indicate possible contacts. At levels 1 and 2, the movements are likely to be young adults; at 3 and 4, children. The synchrony of Iceland with Europe and the United States as international sources which seed infectious diseases in Iceland is discussed in Weinberger, et al. (2012).

Source: Reproduced from Cliff et al, Spatial Diffusion: An Historical Geography of Epidemics in an Island Community, Figure 3.14, p. 54, Cambridge University Press, Cambridge, UK, Copyright © 1981, by permission of the authors.

Years of Potential Life Lost (YPLL)

Agencies such as the World Health Organization and the US Centers for Disease Control and Prevention have been experimenting with statistics which measure years of potential life lost (YPLL). This takes into account the year at which a death occurs as compared with their life expectancy at, say, birth, as given in the life tables published by most national population agencies. If an individual dies before their normal life expectancy, the difference between date of death and life expectancy in years represents one measure of YPLL. More complicated versions of this simple model exist because life expectancy itself changes with age. Thus, in England in 2000, a 20-year-old male could expect on average to live for another 55 years (60 for a female) whereas an 80-year-old male could expect to live on average for another seven years. So under YPLL, the two deaths would be weighted differently, the younger death having a weight much higher than the older.

The effect of measures of this kind is to change the order of some leading causes of death, as compared with the list order based on death rates alone, all tending to give greater weight to the infectious diseases (which especially affect children) and to injuries (which especially affect young people); where these conditions cause mortality, it is most commonly in the first two decades of life. YPLL also reduces the relative importance of heart disease and cancers (which tend to affect the old).

Disability-Adjusted Life Years (DALYs) and Healthy Life Expectancy (HALE)

With illness and disability, rather than mortality, similar arguments may be used, giving special weight to diseases which cause a lifetime of disability (so-called disability-adjusted life years, DALYs) rather than a brief illness. The World Health Organization makes frequent use of healthy life expectancy or HALE. HALE at birth sums expectation of life for different health states, adjusted for severity distribution, making it sensitive to changes over time or differences between countries in the severity distribution of health states. It gives the average number of years that a person can expect (p. 175) to live in ‘full health’ by taking into account years lived in less than full health due to disease and/or injury. A full account of the many different measures which have been proposed to quantify what is meant by ‘full health’ is given in (Murray, et al. 2002). Yet other weighting systems attempt to weight morbidity data by quality of life measures. But the difficulty with all these approaches is defining unambiguous and generally accepted sets of weights.

Figure 6.35 shows the YPLL, DALY and HALE maps for the countries of the European Union for infectious and parasitic diseases in the first decade of the new millennium. All show essentially the same pattern of higher loss of life and poorer health primarily in southern Europe and the new republics of Eastern Europe which were originally part of the former USSR. The YPLL map focuses on death before age 65, so the figures give an indication of the premature loss of economically productive life. In the EU-27, the average YPLL is 116 per 100,000 men, as compared with 47 YPLL per 100,000 women. The increased resilience of women against infectious and parasitic diseases is a well known biological fact. The highest losses of potential life due to infectious diseases among men were found in Portugal (538 YPLL per 100,000 men) and in Romania (444 per 100,000 men). The variance is high in the highest quintile, showing the patchy nature of local epidemics in people under 65: in Lisboa the number of YPLL increased to over 1,000 among men. The lowest figures were found in the Czech Republic (23.1 YPLL). The same geographical pattern is found among women, with the highest YPLL again found in Romania (190) and Portugal (157, with 300 in Lisboa). The lowest figures were returned by Malta (only 1.5 years, which may be an artefact of Malta’s small population) and the Czech Republic (10.3 YPLL).

• Click to view larger

Figure 6.35

YPLL, DALY and HALE maps for the countries of the European Union. (A) Male YPLL. (B) Female YPLL. The YPLL is estimated against an age of death of 65. (C) Age-standardised DALYs per 100,000 population. (D) HALE. The YPLL and DALY maps are for infectious and parasitic diseases at the beginning of the new millennium. The HALE map gives the estimated healthy life expectancy for all persons at birth. In the British Isles, although the total burden of deaths from infectious and parasitic diseases was small at the millennium, the map shows that the greatest YPLL and lowest expectations of good health were in Scotland and the London area. The conurbations in South and West Yorkshire, Greater Manchester, and in the West Midlands also experienced comparatively high risks of premature mortality from infectious and parasitic diseases as did the Welsh Marches.

Sources: Figures (A) and (B) reproduced from Eurostat Statistical Books, Health Statistics – Atlas on Mortality in the European Union, Figures 6.3 and 6.4, pp. 46–47, Office for Official Publications of the European Communities, Luxembourg, Copyright © European Communities, 2009, with permission from the European Union; (C) Data from WHO Global Burden of Disease data by member countries, 2004, available from http://www.who.int/healthinfo/global_burden_disease/estimates_country/en/index.html and (D) Data from World Health Organization, World Health Report, World Health Organization, Geneva, Switzerland, Copyright © 2004.