Sourya Shrestha, PhDJohns Hopkins Bloomberg School of Public Health

Characterizing the role of influenza in the epidemiology of pneumonia

Sun & Metzger, 2008; McCullers & Rehg, 2002

Background: animal challenge experiment

JID 2002;186 (1 August) Synergy between Influenza and Pneumococcus 343

Days after second challenge

Figure 1. Synergistic mortality. Groups of mice (n = 20) were in- fected with either influenza virus, pneumococcus, or PBS (mock in- fection) at day -7 then challenged with a second one of these at day 0. Combinations illustrated here include pneumococcus followed by influenza virus (4), PBS followed by pneumococcus (D), PBS followed by influenza virus (M), PBS followed by pneumococcus and influenza virus together (A), and influenza virus followed by pneumococcus (0). Control mice receiving only PBS at both time points had 10007o survival (data not shown).

or 3+5 on the basis of degree of involvement. Lung parenchyma involvement of ^507o received a score of 1 + , lung parenchyma involvement of 2507a-5007o received a score of 2+1 and lung paren- chyma involvement of ^007o received a score of 3 -H. Each airway in the lung was scored from 0 to 3H- , with the morphologic changes of inflammation, necrosis, and hyperplasia having equal weight of 14- each. An overall pathologic score of pulmonary alteration for each mouse was determined by dividing the sum score of the pa- renchymal fields and of the airways by the maximum combined score possible for the 2 parameters.

Statistical analysis. Comparison of survival between groups was done with the Mantel-Cox x2 test on the Kaplan-Meier survival data. Comparison of quantitative bacterial counts in blood and lungs between groups was done with the Wilcoxon rank sum test. P < .05 was considered to be significant for these comparisons. Evaluation of differences in lung pathologic analysis scores was done by pairwise comparisons with the exact Wilcoxon rank sum test and Student's t test. Because of the small sample size (3 animals/ group), significance in the pathologic comparisons could be claimed only when P < .10. Because results for both tests were similar, only those for the Student's t test are reported.

Results

Lethal synergism. To determine whether synergistic mortality could be seen in a mouse model of dual infection with influenza virus and pneumococcus, mice were given influenza virus and pneu- mococcus either alone, simultaneously, or in sequence 1 week apart. Groups of 20 mice were infected on day -7 and then again on day 0 with either 0.3 MLD50 of influenza virus, 0.2 MLD50 of pneumococcus, both together, or sterile PBS (mock infection) in various combinations. These doses were chosen so that mice would be ill but mortality would be predicted to be infrequent in the

absence of synergism. Mice were monitored for 21 days after the second challenge (day 0); all mice survived for at least the initial 7 days between infections. Survival after challenge with the second agent is plotted in figure 1. Lung virus titers peaked at days 2-3 after inoculation with PR8 at ~2 X 107 TCID50 and decreased by only ~1 log on day 7 at the time of pneumococcal challenge (data not shown).

Mice infected with either influenza virus or pneumococcus alone at day 0 after mock infection exhibited mortality rates of 3507o and 1507o, respectively. Infection with both agents si- multaneously (in a total volume of 100 /xL) at day 0 after mock infection gave an additive effect, with 6007o of the animals dying 2-11 days after the dual infection. Mice infected with pneu- mococcus at day -7 and challenged with influenza virus at day 0 were protected from influenza virus infection with no mor- tality, no clinical symptoms, and no weight loss (weight loss and morbidity data not shown), which is identical to the day 0 results seen with control mice challenged only with PBS at both time points (P*c.001, for difference in survival, vs. the group treated with virus alone). In contrast, all mice infected with influenza virus at day -7 and pneumococcus at day 0 uniformly succumbed to infection in ^4 h (i^ .001, for dif- ference in survival, vs. all other groups), demonstrating that synergistic mortality occurs in the model.

Timing of infection. After the demonstration that synergistic mortality was seen when influenza virus infection preceded pneu- mococcal challenge by 1 week, the number of days between ad- ministration of the 2 infectious agents was altered to determine the optimal timing necessary to achieve this effect. Groups of 6 mice were infected with 0.3 MLD50 of influenza virus at day 0 and challenged with 0.2 of MLD50 pneumococcus at days -7, 0

Day of pneumococcal challenge relative to influenza infection at day 0

Figure 2. Timing of synergism. Groups of mice (n = 6) were chal- lenged with pneumococcus at different times relative to influenza infection at day 0. Percentage survival 21 days after pneumococcal challenge is plotted (bars). The mean no. of days of survival after pneumococcal challenge, counting only those mice that died, is indicated (line with black squares).

Survival,

07o

Survival,

96

Mean

duration

of survival,

days

Animal challenge experiments find presence of interaction betweeninfluenza and pneumococcus and other bacterial pathogen associatedwith pneumonia.

344 McCullers and Rehg JID 2002;186 (1 August)

Table 1. Relationship of synergism to dose of in- fluenza virus.

No. of mice Duration of survival Dose of influenza surviving/ after the second virus, MID50 total (%) infection, mean daysa

140 0/10 (0)b 0.5 70 0/10 (0)b 1.4 35 1/10 (10)b 2.1 18 0/10 (0)b 2.3 9 2/10 (20)b 2.5 4 3/10 (30)b 3.7 2 3/10 (30)b 3.3 1 3/10 (30)b 3.6 0.5 6/10 (60) 4.8 0 9/10 (90) 4.0

NOTE. MID50, the dose infectious for 500Zo of mice, delivered in the 100 fxh inoculum. a Only data for mice that died were considered.

bi^.OS for survival, vs. the control group (Mantel-Cox X2 test on the Kaplan-Meier survival data).

(simultaneously), 3, 5, 7, 9, 14, or 21 and were monitored for 21 days after the second infection. The percentage of mice surviving and the mean duration of survival (i.e., the number of days the mice survived after the second infection, considering only mice who died) are shown in figure 2.

As in the first experiment, an additive mortality was seen with simultaneous infection, no mortality was observed when pneumococcal infection preceded influenza virus infection, and 10007o lethality occurred when pneumococcal challenge followed influenza virus infection by 7 days. One hundred percent mor- tality was also observed in the day 3 and day 5 groups, although death was most rapid in the day 7 group, occurring in ^4 h in all mice, compared with a mean duration of survival of 3.3 days for the group challenged on day 3 after influenza virus infection and 2.5 days for the group challenged on day 5. The synergistic effect was lost by day 21. These data suggest that there is a temporal sequence of events that must occur to prime for synergistic lethality and indicate that, in this model, pneu- mococcal challenge 7 days after influenza virus infection results in the most rapid and complete mortality.

Dose response for influenza. One potential contributor to the synergistic mortality seen with pneumococcal challenge after in- fluenza virus infection was the debilitation of the mice at the doses of PR8 used in the previous experiments (0.3 MLD50), which may have been most severe at 7 days after influenza virus infection. We therefore infected groups of 10 mice with sequen- tially 2-fold-lower doses of influenza virus, from 140 MID50 (equivalent to 1.0 MLD50) to 0.5 MID50, then challenged with 0.2 MLD50 of pneumococcus (1 X 105 cfu) at day 7 after infection. The dose at which 5007o of mice exhibit morbidity (i.e., clinical signs of infection such as weight loss, ruffled fur, huddling, hunched posture, shivering, and tachypnea) for this PR8 stock is 10 MID50. High mortality was observed at doses of influenza virus as low as 1 MID50 (table 1), with only 3007o survival, com- pared with 9007o in control mice, indicating that synergism occurs at doses below that needed to engender clinical symptoms or

weight loss. Therefore, other synergistic mechanisms besides de- bilitation from influenza must be operative in the model. The mean duration of survival (among mice that died) was directly related to the influenza virus dose (table 1).

Dose response for pneumococcus. After the demonstration that low doses of influenza virus can be used in the model with maintenance of the synergistic effect, an experiment was un- dertaken to determine the minimum dose of pneumococci needed for synergistic killing. Groups of 10 mice were infected with 0.13 MLD50 of influenza virus (equivalent to 18 MID50, the lowest dose at which 10007o mortality was observed in the prior experiment) and challenged at day 7 with sequentially 4- fold-lower doses of pneumococcus ranging from 1 X 105 cfu (equivalent to 0.2 MLD50) to 100 cfu. Data in table 2 indicate that synergistic mortality can be observed with low doses of both influenza virus and pneumococcus, although 10007o mor- tality was seen only at the highest dose tested. Of interest, although only 5007o mortality was observed in the group re- ceiving 100 cfu of pneumococcus, the mean duration of survival (among mice that died) was similar to that for the highest group. Thus, it is seems apparent that the rapidity of demise is related to the timing of administration of the 2 agents and the dose of influenza virus but not the dose of pneumococcus, whereas all 3 parameters factor into the percent mortality.

Pathologic abnormalities in the synergism model. The pul- monary alterations in the mouse lungs involved the pulmonary airways and/or the pulmonary parenchyma, but the degree of tissue involvement depended on the treatment received. Airway involvement was primarily limited to mice infected with virus and was characterized by inflammatory cells in the lumen and/ or mucosa, as well as epithelial necrosis and epithelial hyper- plasia. Parenchymal involvement was focal, and the percentage of infected tissue was variable. There were no significant dif- ferences, compared with controls, in the lungs of mice infected with pneumococcus alone (figures 3A and ZB; control mice not shown). However, virus-infected mice at 48 h after challenge at day 0 (i.e., 9 days after infection with influenza virus) had multiple parenchymal foci with alveolar inflammation, alveolar epithelial cell hypertrophy and hyperplasia, and occasional al-

Table 2. Relationship of synergism to dose of pneumococcus. No. of mice Duration of survival

Dose of pneumococcus surviving/ after the second D39, cfua total (%) infection, mean daysb

100,000 0/10 (0)c 2.8 25,000 2/10 (20)c 2.6 6400 2/10 (20)c 2.6 1600 3/10 (30)c 2.6 400 3/10 (30)c 3.1 100 5/10 (50)c 3.4 0 10/10 (100) NC

NOTE. NC, not calculated (because no mice died). a The dose indicated was that delivered in the 100 fiL inoculum. bOnly data for mice that died were considered. cZ^ .05 for survival, vs. the control group (Mantel-Cox x2 test on the

Kaplan-Meier survival data).

Time and dose-dependence at the host-level.

Shrestha et al, 2013; Handel et al, 2010; Smith et al, 2011; Sun & Metzger 2008

Background: within-host models

Flu. (V )

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Timing of pneumococcal inoculation (days post influenza)

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> 20% lung autopsies showed presenceof streptococcus and other pneumopathogens

Morens et al, 2008

individuals who survived such severe pneumonia, severe chronic

pulmonary damage was apparently uncommon [37, 38].

Bacteriologic studies in autopsy series during the 1918 –

1919 influenza pandemic. Negative lung culture results were

uncommon in the 96 identified military and civilian autopsy

series, which examined 5266 subjects (4.2% of results overall)

(table 1; full bibliographic list available at http://www3

.niaid.nih.gov/topics/Flu/1918/bibliography.htm). In the 68

higher-quality autopsy series, in which the possibility of unre-

ported negative cultures could be excluded, 92.7% of autopsy

lung cultures were positive for !1 bacterium (table 1). Of these

96 series, 82 reported pneumopathogens in !50% of lungs ex-

amined, either alone or in mixed culture results that included

other bacteria (table 1). Outbreaks of meningococcal pneumo-

nia complicating influenza also were documented [39]. Despite

higher military case-fatality rates, the differences in the fre-

quency with which specific bacteria were isolated from lung tis-

sue cultures (table 1) and from culture of blood and pleural or

empyema fluids (data not shown) were minimal. Many of the

series were methodologically rigorous: in one study of approxi-

mately 9000 subjects who were followed from clinical presenta-

tion with influenza to resolution or autopsy [40], researchers

obtained, with sterile technique, cultures of either pneumococci

or streptococci from 164 of 167 lung tissue samples. There were

89 pure cultures of pneumococci; 19 cultures from which only

streptococci were recovered; 34 that yielded mixtures of

pneumococci and/or streptococci; 22 that yielded a mixture

of pneumococci, streptococci, and other organisms (promi-

nently pneumococci and nonhemolytic streptococci); and 3 that

yielded nonhemolytic streptococci alone. There were no nega-tive lung culture results.

In the 14 of 96 autopsy series that did not report the predomi-nance of lung pneumopathogens [29, 36, 41–53], pneumopatho-gens accounted collectively for 37.4% of pneumonia deaths. Therest of the deaths were associated collectively with either culture ofnonpneumopathogenic “other bacteria,” such as nonhemolyticand viridans streptococci, “green-producing streptococci” [54],probably largely corresponding to !-hemolytic streptococci, un-characterized diplostreptococci, Micrococcus (Moraxella) catarrha-lis, Bacillus (Escherichia) coli, Klebsiella species, and complex mixedbacteria (36.1% of cultures). Cultures also yielded Bacillus influen-zae (18.8%) and no bacterial growth (7.7%). These findings reflectrates of bacterial isolation similar to those of the series that reportedthe predominance of pneumopathogens (above and table 1), butwith higher isolation rates for “other bacteria” offsetting the lowerisolation rates for pneumococci, streptococci and staphylococci. Itis noteworthy that pneumococcal typing antisera were unavailablein 11 of these 14 studies, and that many of the cultured “other”bacteria were reported as “gram-positive diplococci,” “strepto-cocci,” or “diplostreptococci” (data not shown), consistent with thepossibility that in this early era of bacterial typing, some of the un-identified organisms in the culture may have been pneumopatho-gens.

The predominant coinfecting microorganism in lung tissuecultures containing !1 pneumopathogen was Bacillus influen-zae (largely corresponding to the modern Hemophilus influen-zae), an upper respiratory–tract organism not commonly foundin pure culture of samples from any anatomical compartment[20, 36, 55]. Bacillus influenzae tended to appear early in symp-

Table 1. Bacterial culture results in autopsy series involving 96 postmortem cultures of lung tissue from victims of the 1918 –1919influenza pandemic.

Type ofautopsy series

No. ofresults

No. (%) of cultures from which organism was recovered, by organism

Nogrowth

Streptococcuspneumoniae

Streptococcushemolyticus

Staphylococcusaureus

Diplococcusintracellularemeningitidis

Mixedpneumopathogens

Bacillusinfluenzae

Otherbacteria

All military (n " 60) 3515 855 (24.3) 615 (17.5) 263 (7.5) 40 (1.1) 707 (20.1) 387 (11.0) 484 (13.8) 164 (4.7)

All civilian (n " 36) 1751 380 (21.7) 281 (16.0) 164 (9.4) 1 (#0.1) 398 (22.7) 132 (7.5) 339 (19.4) 56 (3.2)

All military andcivilian (n " 96) 5266 1235 (23.5) 896 (17.0) 427 (8.1) 41 (0.8) 1105 (21.0) 519 (9.9) 823 (15.6) 220 (4.2)

All higher- qualitymilitary andciviliana (n " 68) 3074 712 (23.2) 553 (18.0) 238 (7.7) 21 (0.7) 828 (26.9) 144 (4.7) 353 (11.5) 225 (7.3)

Predominance ofpneumopathogensnot confirmed (n " 14) 1115 209 (18.7) 132 (11.8) 52 (4.7) 0 (0.0) 24 (2.2) 210 (18.8) 402 (36.1) 86 (7.7)

NOTE. The bacteria are listed by their common names in 1918. Streptococcus pneumoniae was cultured and (sometimes) typed with antisera into types I,II, IIa, III, and IV; type IV was generally regarded as containing a number of “untypeable types.” Streptococcus hemolyticus probably corresponds to Streptococcuspyogenes in most cases; most observers distinguished Staphylococcus aureus from Staphylococcus albus, but in some cases observers noted only “Staphylo-coccus,” which we categorized as “aureus” if the context suggested a pathogenic organism. Diplococcus intracellulare meningitidis corresponds to Neisseriameningitidis. Bacillus influenzae corresponds to Haemophilus influenzae. See Results for details about the “mixed pneumopathogens” and “other bacteria”categories. Many “other” organisms were undoubtedly untyped pneumococci and streptococci. Bold type indicates greatest percentage.

a A higher quality series was defined as a series in which lung tissue culture results reported, for all autopsies, both the presence and absence of negativeculture results and the bacterial components of mixed culture results.

4 ● JID 2008:198 (1 October) ● Morens et al.

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1918 Pandemic

Background: association during pandemic influenza

Age-groups with higher than normal

pneumococcal pneumonia were also the

age group with high influenza

Weinberger et al, 2012

2009 Pandemic, USA

Background: association during pandemic influenza

Background: association during non-pandemic influenza

over another and thus, rather than prove causation, adds avaluable piece to the overall interpretation of our analyses towardspotential causation. With respect to ecological effects, it should benoted that we effectively treat influenza as an aggregate‘‘environmental’’ exposure, in that we don’t have influenza statuson the individuals who develop invasive bacterial disease, whichcreates the risk of ecological effects, including ecological fallacy, inour study. There are practical barriers to formulating anadequately powered study that actually captured data on thetemporal relationship between influenza and invasive bacterialdisease at the level of the individual (e.g., through ongoingevaluation of individuals in the population for influenza infection,with subsequent identification of the [rare] individuals whodeveloped invasive bacterial disease).

Finally, with respect to measurement issues, for influenza A andB, we had to rely on public health surveillance data that areincomplete both because of under-reporting, and also because amajority of individuals with influenza never undergo virologicaltesting [37]. Other exposures may have been misclassified becauseof measurement or population mobility. This misclassificationshould not have introduced bias into our study in the absence ofcorrelation between pneumococcal risk and influenza or meteoro-logical reporting but may have reduced our statistical efficiency.Nondifferential misclassification of exposures would mean that theeffects reported here are likely to be lower-bound effects, with trueeffects being larger. Furthermore, the strong degree of agreement inour findings, across multiple methodological approaches, suggeststhat they are robust and not artifacts of the analytic approach taken.

In conclusion, our data support the hypothesis that influenzainfluences IPD risk by enhancing pneumococcal invasion incolonized individuals, but has little effect on the transmissiondynamics of pneumococcal infection. We suggest that themechanism for such an effect might be influenza-related alterationsat the level of the respiratory epithelium [27]. These effects appeardistinct from those of ambient environmental effects, although the

effect of increased UV radiation in reducing IPD risk that we hadpreviously reported in a study performed in Philadelphia needsfurther study [22]. These findings have important implications fordisease control policy, and suggest that improved efficacy ofinfluenza vaccines [38], and novel vaccination strategies that moreeffectively control influenza by vaccinating younger individuals[33], could have important effects in reducing IPD as well.

Acknowledgments

Collaborating investigators in the Toronto Invasive Bacterial DiseaseNetwork are as follows: S. O’Grady, Bridgepoint Health (Toronto,Canada); I. Armstrong and B. Yaffe, City of Toronto Public Health(Toronto, Canada); A. Sarabia, Credit Valley Hospital (Mississauga,Canada); J. Kapala, Dynacare Laboratories (Brampton, Canada); M.Loeb, Hamilton Health Sciences Centre (Hamilton, Canada); A. Matlow,S. Richardson, and D. Tran, Hospital for Sick Children (Toronto,Canada); K. Lee, Humber River Regional Hospital (Toronto, Canada); J.Allard, Joseph Brant Memorial Hospital (Burlington, Canada); M.Silverman, Lakeridge Health (Oshawa, Canada); D. Yamamura, Life-LabsMedical Laboratory Services (Toronto, Canada); P. Shokry, Mark-ham Stouffville Hospital (Markham, Canada); K. Green, A. Plevneshi, S.Pong-Porter, and B. Willey, Mount Sinai Hospital (Toronto, Canada); M.Lovgren and G. Tyrrell, National Center for Streptococcus (Edmonton,Canada); K. Katz and B. Mederski, North York General Hospital (NorthYork, Canada); N. Rau, Halton Healthcare (Oakville, Canada); J. Gubbay,F. Jamieson, and D. Low, Ontario Agency for Health Protection andPromotion (Toronto, Canada); E. de Villa, Peel Public Health (Brampton,Canada); I. Kitai, Rouge Valley Health System (Toronto, Canada); J.Rodgers, Royal Victoria Hospital (Barrie, Canada); C. Wigston, Soldier’sMemorial Hospital (Orillia, Canada); S. Krajden, St. Joseph’s HealthCentre (Toronto, Canada); M. Muller and R. Devlin, St. Michael’sHospital (Toronto, Canada); A. Simor and M. Vearncombe, SunnybrookHealth Sciences Centre (Toronto, Canada); R. Lovinsky and D. Rose, TheScarborough Hospital (Toronto, Canada); J. Downey, Toronto EastGeneral Hospital (Toronto, Canada) and Headwaters Healthcare Centre(Orangeville/Shelburne, Canada); J. Powis, Toronto East GeneralHospital (Toronto, Canada); K. Ostrowska, Trillium Health Centre(Mississauga, Canada); W.L. Gold and S. Walmsley, University HealthNetwork (Toronto, Canada); H. Dick, Vita-Tech Canada (Toronto,Canada); M. Baqi and D. Richardson, William Osler Health Centre(Brampton, Canada); D. Chen, Southlake Regional Hospital (Newmarket,Canada) and York Central Hospital (Richmond Hill, Canada). Allmembers of the Toronto Invasive Bacterial Diseases Network haveacquired data for this study and revised the article critically for importantintellectual content.

Author Contributions

ICMJE criteria for authorship read and met: SPK ART JCK AM TIBDNDNF. Agree with the manuscript’s results and conclusions: SPK ART JCKAM TIBDN DNF. Designed the experiments/the study: DNF. Analyzedthe data: SPK DNF. Contributed reagents/materials/analysis tools: ARTJCK AM TIBDN DNF. Enrolled patients: AM TIBDN. Wrote the firstdraft of the paper: SPK DNF. Contributed to the writing of the paper: SPKDNF.

References

1. Dushoff J, Plotkin JB, Levin SA, Earn DJ (2004) Dynamical resonance canaccount for seasonality of influenza epiemics. Proc Natl Acad Sci U S A 101:16915–16916.

2. Fisman DN (2007) Seasonality of infectious diseases. Annu Rev Public Health28: 127–143.

3. Kim PE, Musher DM, Glezen WP, Rodriguez-Barradas MC, Nahm WK, et al.(1996) Association of invasive pneumococcal disease with season, atmosphericconditions, air pollution, and the isolation of respiratory viruses. Clin Infect Dis22: 100–106.

4. Talbot TR, Poehling KA, Hartert TV, Arbogast PG, Halasa NB, et al. (2005)Seasonality of invasive pneumococcal disease: temporal relation to documentedinfluenza and respiratory syncytial viral circulation. Am J Med 118: 285–291.

5. Watson M, Gilmour R, Menzies R, Ferson M, McIntyre P (2006) Theassociation of respiratory viruses, temperature, and other climatic parameters

with the incidence of invasive pneumococcal disease in Sydney, Australia. ClinInfect Dis 42: 211–215.

6. Daneman N, McGeer A, Green K, Low DE (2006) Macrolide resistance inbacteremic pneumococcal disease: implications for patient management. ClinInfect Dis 43: 432–438.

7. McGeer A, Green KA, Plevneshi A, Shigayeva A, Siddiqi N, et al. (2007)Antiviral therapy and outcomes of influenza requiring hospitalization inOntario, Canada. Clin Infect Dis 45: 1568–1575.

8. Vanderkooi OG, Low DE, Green K, Powis JE, McGeer A (2005) Predictingantimicrobial resistance in invasive pneumococcal infections. Clin Infect Dis 40:1288–1297.

9. Ministry of the Environment (Canada). Historical data. Available: http://www.airqualityontario.com/reports/historical_data.cfm. Accessed 17 September2010.

Table 5. Short-term associations between influenza A and Band IPD according to case-crossover methods.

Lag (wk) Odds Ratio 95% Confidence Interval p-Value

0 1.05 0.97–1.13 0.20

21 1.10 1.02–1.18 0.01

22 1.00 0.93–1.09 0.90

23 0.93 0.86–1.01 0.07

24 1.02 0.94–1.11 0.65

Data are presented as odds ratios with 95% confidence intervals for 1 IPD caseper 100 influenza cases.doi:10.1371/journal.pmed.1001042.t005

Influenza and Pneumococcal Disease

PLoS Medicine | www.plosmedicine.org 7 June 2011 | Volume 8 | Issue 6 | e1001042

Table 1. Estimates of Influenza-Associated Invasive Pneumococcal Pneumonia Made by Using Negative Binomial Regression Models Applied to Regional Influenza Circulation Dataand Invasive Pneumococcal Pneumonia Observed in Active Population-based Surveillance in 3 United States Regions during 1995–2006

Surveillance regionand lag, weeks Influenza parameter P

No ofinfluenza weeksa

Mean no of weeklyinfluenza-associated

casesb (95% CI)No of influenza-associ-ated casesc (95% CI)

Influenza-associatedfraction during influenza weeks,d %

Influenza-associatedfraction during year,e %

Northeast

0 0.0082 .01 138 … … … …

1 0.0073 .03 138 2.1 (1.9–2.3) 293 (268–318) 13.5 (12.3–14.7) 4.9 (4.5–5.3)

2 0.0045 .2 138 … … … …

3 0.0011 .7 138 … … … …

4 -0.0041 .2 138 … … … …

South

0 0.0034 .02 173 … … … …

1 0.0049 !.001 173 3.3 (3.0–3.6) 572 (524–620) 11.4 (10.7–12.7) 5.4 (5.0–5.9)

2 0.0045 .002 173 … … … …

3 0.0028 .05 173 … … … …

4 0.0019 .2 173 … … … …

West

0 0.0053 .02 163 … … … …

1 0.0059 .01 163 1.6 (1.4–1.7) 254 (227–280) 11.9 (11.1–13.6) 5.2 (4.8–6.0)

2 0.0051 .03 163 … … … …

3 0.004 .08 163 … … … …

4 0.0022 .3 163 … … … …

NOTE. CI, confidence interval.a Number of weeks in region during 1995–2006 study period classified as influenza weeks (beginning with the first week and ending with the last week in each season in which influenza percentage positive was !10%

for 2 consecutive weeks).b Model estimate of mean weekly number of influenza-associated cases of invasive pneumococcal pneumonia during influenza weeks.c Model estimate of total number of influenza-associated cases of invasive pneumococcal pneumonia during 1995–2006 (mean weekly cases ! number of influenza weeks).d Estimated fraction of invasive pneumococcal pneumonia during influenza weeks that is influenza associated (influenza-associated cases/total cases during influenza weeks ! 100).e Estimated fraction of invasive pneumococcal pneumonia during entire year that is influenza associated (influenza-associated cases/total cases in year ! 100).

at Media Union Library, University of Michigan on May 23, 2011 cid.oxfordjournals.org Downloaded from

No temporal association between influenzaoutbreaks and invasive pneumococcal infectionsAndre Michael Toschke,1,2 Stephan Arenz,1 Rudiger von Kries,1 Wolfram Puppe,3

Josef A I Weigl,3 Michael Hohle,4 Ulrich Heininger5

1 Institute for Social Pediatricsand Adolescent Medicine,Ludwig-Maximilians UniversityMunich, Munich, Germany;2 Division of Health and SocialCare Research, King’s CollegeLondon, London, UK; 3 PediatricInfectious Diseases, UniversityChildren’s Hospital Kiel, Kiel,Germany; 4 Department ofStatistics, Ludwig-MaximiliansUniversity Munich, Munich,Germany; 5 Division of PediatricInfectious Diseases, UniversityChildren’s Hospital Basel, Basel,Switzerland

Correspondence to:Professor Ulrich Heininger,University Children’s HospitalBasel, PO Box 4005, Basel,Switzerland;Ulrich.Heininger@unibas.ch

Accepted 19 March 2007Published Online First12 April 2007

ABSTRACTObjective: To assess whether the influenza peak inpopulations precedes the annual peak for invasivepneumococcal infections (IPI) in winter.Design: Ecological study. Active surveillance data oninfluenza A and IPI in children up to 16 years of agecollected from 1997 to 2003 were analysed.Setting: Paediatric hospitals in Germany.Patients: Children under 16 years of age.Results: In all years under study, the influenza A seasondid not appear to affect the IPI season (p = 0.49).Specifically, the influenza peak never preceded the IPIpeak.Conclusion: On a population level there was noindication that the annual influenza epidemic triggered thewinter increase in the IPI rate or the peak of the IPIdistribution in children.

A number of animal studies have shown thatpreceding influenza infection increased the leth-ality of invasive pneumococcal infections (IPI).1–3

In a French study, 12% of cases of communityacquired pneumonia in children were presumed tobe causally linked to preceding influenza infec-tions.4 Further, a small case-control study inhospitalised children showed a significant associa-tion between severe pneumococcal pneumonia andpreceding influenza.5

In this study we took advantage of establishedactive national surveillance systems and comparedthe time course of IPI in children 0–16 years of ageover a period of 7 years with the timing ofinfluenza A infections in the respective years andage group in order to assess whether influenza A isassociated with an increase in IPI.

METHODSIn this ecological study, IPI cases were reported byan active hospital-based surveillance system in theGerman population up to 16 years of age fromJanuary 1997 to June 2003.6 Patients were enrolledif they had been admitted to a paediatric hospitaland if Streptococcus pneumoniae had been isolatedfrom at least one culture of blood, CSF or a samplefrom any other normally sterile body site. Patientswith otitis media were not included. Cases werereported by paediatric hospitals on a monthly basisby means of postcards. Reports were validated bysubsequent questionnaires.

The Pediatric Infectious Diseases Network onAcute Respiratory Tract Infections (PID-ARI.net)has registered cases of influenza A in childrenunder the age of 16 years in a sentinel surveillancesystem comprising two (northern and central

regions of Germany up to June 2002) and three(northern, central and southern regions ofGermany since July 2002) areas of Germany.Patients have been recruited from three universitychildren’s hospitals, three municipal children’shospitals and eight private paediatric practices.7

This surveillance system covers about 500 000children per region (2% of the total Germanpopulation). Naso-pharyngeal aspirates of childrenwith acute respiratory infections were sent to thelaboratory of the university children’s hospital inKiel and were analysed by multiplex RT-PCR-ELISA.8 9

Statistical analysisThe seasonal distributions of IPI and influenza Acases observed from January 1997 to June 2003 wereseparately analysed with the R package ‘‘surveil-lance’’.10 The algorithm by Farrington et al was usedto identify possible outbreaks in the two timeseries.11 The random error level for outbreakidentification was set to a,0.001. Three previousyears with a 2-month time period were consideredfor definition of sensible thresholds enabling analysisfrom 2000 onwards with the original Farringtonalgorithm.11 Additional analyses for the years 1998and 1999 considered only one previous year forthreshold definition. Zero counts of influenza datawere replaced by 0.1 to ensure convergence of thegeneralised model algorithm. Both time series werecompared with respect to identified outbreaks.Additional multivariate time series analysis to assessa possible association between influenza and pneu-mococcal infections with time lag 1 was performedusing the ‘‘3h algorithm’’. The 3h algorithm is basedon a multivariate branching process with immigra-tion and also implemented in the ‘‘surveillance’’package.12

All calculations were carried out with thestatistical software package R version 2.4.0.13

RESULTSBetween January 1997 and June 2003, a total of 1474cases of IPI were reported in children 0–16 years ofage in Germany. Throughout the study period, thehighest proportion of cases was observed betweenOctober and May (fig 1). In the corresponding sametime period, 737 cases of influenza A were reported.Most of the cases occurred between January andApril. As can be seen in fig 1, in each study year theincrease in IPI preceded the onset of the influenzaseason by several months. One outbreak could beidentified for the influenza time series by theFarrington algorithm in 2003 (fig 2), whereas no

Original article

218 Arch Dis Child 2008;93:218–220. doi:10.1136/adc.2006.098996

group.bmj.com on May 23, 2011 - Published by adc.bmj.comDownloaded from

Walter et al, 2010; Kuster et al, 2011; Toschke et al, 2008

Background: summary

Animal challenge experiments are unequivocal onthe presence of the interaction; evidence at the population levelare variable from non-existent to model (during non-pandemic

periods) to high during pandemic periods.

Open Questions

*What is the epidemiological pathway of the interaction?— impact limited to clinical manifestation or are there dynamical consequences?

*What are the characteristic features of this interaction? — how strong is the interaction and how long do they last?— what kind of impact population-level impact does this have?

*Why do we see high variability in the population level manifestation interaction?— are the interactions strains/serotype specific? — are the interactions stronger with pandemic strains?

Approach

Population-level data +

Mechanistic model +

Statistical inference

Approachmore than two interacting pathogens will be a straightforwardmatter, though the resulting model’s complexity, eg, in terms of itsstate-space dimension, will increase geometrically with the numberof interacting pathogens.

As shown in Fig. 1, we assume that individuals are bornsusceptible to both pathogens. For each pathogen, infectiondynamics follow the S?I?C?R progression, where S, I and Rare the familiar susceptible, infectious and recovered classes,respectively. Compartment C has, in previous analyses [18,37],been used to incorporate a period of convalescence, but heremight also represent, either a temporary period of immuno-suppression or strain-transcending cross-immunity or a temporaryperiod of enhanced transmissibility associated with ADE, forexample.

In this model, pathogens interact when an individual currentlyor previously infected with strain i is exposed to pathogen j. Theconsequence of this exposure for individuals previously exposed tostrain i is determined by positive parameters w, j and x, whichmodulate the force of infection of strain j, lj experienced byindividuals in each of the I , C, and R classes, respectively. Hence,if all w~j~x~1, we have the null model in which the dynamics

of the two pathogens are mutually independent. A value smallerthan 1 reflects either temporary (as when wv1 or jv1) orpermanent (as when xv1) cross-immunity. Similarly, when thesemultipliers are greater than 1, current or previous infection withone pathogen increases susceptibility to the other, either in atemporary (ww1 or jw1) or permanent (xw1) fashion. Thismodel assumes that all pathogen interactions are via modulation ofhost susceptibility. In reality, interactions may also operate viatransmissibility. Here we ignore effects of heterotypic infections ontransmissibility, as explored by, for example, [28].

The model also accounts for host demography in that birthsreplenish the susceptible pool, and natural deaths remove hostsfrom each compartment. These rates are assumed independent ofdisease status and are both fixed at m. Thus the host populationsize is held constant.

Deterministic skeleton of the model. In a deterministicsetting, the model is described by 16 ordinary differentialequations. Equations for each state can be read directly fromFig. 1. In particular, each arrow is associated with aninstantaneous flux which is the product of a per capita rate andthe number of individuals in the source box. The per capita rates are

Figure 1. Schematics of a two pathogen model with various interaction mechanisms. Each box represents a possible host state, withindividuals Xij categorized according to their status with regards to the two pathogens. Letters S, I , C, and R stand for susceptible, infected,convalescent, and recovered, respectively. The horizontal arrows follow the progression of a host’s infection due to the first pathogen, and thevertical arrows follow the progression of the second. The diagonal arrows represent disease independent births and deaths. The transitions denotedby red arrows are affected by pathogen interaction.doi:10.1371/journal.pcbi.1002135.g001

Statistical Inference for Multi-Pathogen Systems

PLoS Computational Biology | www.ploscompbiol.org 3 August 2011 | Volume 7 | Issue 8 | e1002135

A more rigorous challenge arises when reporting bias is not knowna priori and must be inferred along with interaction parameters. Wetake one such scenario in which both reporting rate, r, and long-terminteraction, x, are unknown. The log-likelihood profiles (Fig. 8) showthat these parameters can be identified simultaneously.

Aggregated data. We have assumed in the foregoing that thedata are strain specific, ie, that accurate strain typing is possible. Itis frequently the case that strain identification is unavailable,impossible, or ambiguous. In the extreme case, incidence datarepresent an aggregate across strains. We now ask whether with

Figure 4. Inference under scenario II: Temporary cross-immunity. Inference is carried out for two separate data sets constructed from thesame set of parameter values – results are shown in [Left] and [Right] columns for each data set. [Top] Simulated case-data for the two infections areplotted in solid and dashed lines. Log-likelihood profiles for parameters describing the short (w,j) [Middle] and the long term (x) [Bottom]interactions. In the insets, we show close-ups of the profiles near the peaks. Plotted Dloglik are relative difference in the raw log-likelihood from thereference point set at Dloglik~0, indicated by the horizontal dashed line. Dloglik~0 represents the 95% confidence interval – parameter valuescorresponding to a positive Dloglik are within the confidence bound. The gray dots indicate the repeated likelihood estimates (5 replicate SMCcalculations for each profile point, 30,000 particles in each SMC calculation). The profiles are created by fitting a smooth line through the log of thearithmetic mean likelihoods (shown in black dots). The vertical red dashed line is plotted at the actual parameter value used to generate thesimulated case-data. Parameters not shown in the graph are taken from Table 1.doi:10.1371/journal.pcbi.1002135.g004

Statistical Inference for Multi-Pathogen Systems

PLoS Computational Biology | www.ploscompbiol.org 8 August 2011 | Volume 7 | Issue 8 | e1002135

Shrestha et al, 2011; King et al, 2008

Mechanistic model

Simulated Data

Likelihood profiles

Data

A

1920 1921 1922 1923 1924

01

23

4

New York city

Time

Influ

enza

and

pne

umon

ia c

ases

(log

10)

●●● ● ●

●

influenzapneumoniamissing data

Influenza and pneumonia case reports

Data

A

1920 1921 1922 1923 1924

01

23

4Chicago

Time

Wee

kly

influ

enza

and

pne

umon

ia c

ases

(log

10)

● ●●●●●●● ● ● ● ●● ●●●●●●● ●●

●

influenzapneumoniamissing data

B

1920 1921 1922 1923 1924

01

23

4

Philadelphia

Time

Wee

kly

influ

enza

and

pne

umon

ia c

ases

(log

10)

● ● ● ● ● ● ● ● ● ●●●●●●●●●●●●●● ●●●●●●●●●● ●●●●●●● ●● ●●●●●●●●●●●●

●

influenzapneumoniamissing data

D

1920 1921 1922 1923 1924

01

23

4

Los Angeles

Time

Wee

kly

influ

enza

and

pne

umon

ia c

ases

(log

10)

● ●● ● ●●●●●●●●● ● ● ● ●●●●●●●●●●●●●● ●● ●● ● ● ● ●

●

influenzapneumoniamissing data

C

1920 1921 1922 1923 1924

01

23

4

Baltimore

Time

Wee

kly

influ

enza

and

pne

umon

ia c

ases

(log

10)

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●

influenzapneumoniamissing data

Influenza and pneumonia case reports

Data

●●● ● ●

1920 1921 1922 1923−7

−6−5

−4−3

A D

1990 1991 1992 1993 1994 1995 1996 1997 1998

−7−6

−5−4

Influ

enza

, pne

umon

ia a

nd p

neum

ococ

cal p

neum

onia

inci

denc

e re

ports

(log

10)

●

influenzapneumoniapneumococcal−pneumoniamissing data

B

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

−7−6

−5−4

C

Illinois influenza, pneumococcal pneumonia, and pneumonia hospitalizationsbefore and after the introduction of PCV

Data

Kearney

Johnston

Jackson

Humphreys

Hancock

Greene

Grant

Gordon

Funston

Fremont

Dodge

Dix

Devens

Custer

Cody

Bowie

Beauregard

Wheeler

Wadsworth

Upton

Travis

Taylor

Sherman

Sheridan

Shelby

Sevier

Pike

Mills

Meade

McClellan

MacArthur

Logan

Lewis

Lee

M J J A S O N D M J J A S O N D

10% influenza 10% pneumonia 10% coinfections

US Army Camps, Fall of 1918

MODEL FOR PNEUMONIA EPIDEMIOLOGY

S I R

Cases�(t) = �(t)(I/N + !)

Hypothesis I: ✓ > 1Transmission Impact

Individuals coinfected with influenza

contribute more to transmission

Hypothesis II: � > 1Susceptibility Impact

Individuals infected with influenza

are more susceptible to pneumonia

Hypothesis III: ⇠ > 1Pathogenesis Impact

Individuals coinfected with influenza

develop more severe symptoms

✏

��

⇢p

SIRS model

8>>>>>>>>><

>>>>>>>>>:

dS

dt= µ(N � S) � � S + ✏ R

dI

dt= � S � � I � µ I

dR

dt= � I � µ R � ✏ R

Model

MODEL FOR PNEUMONIA EPIDEMIOLOGY

IF

ISU

SF

R

Cases�(t) = �(t)(I/N + !)

Hypothesis I: ✓ > 1Transmission Impact

Individuals coinfected with influenza

contribute more to transmission

Hypothesis III: ⇠ > 1Pathogenesis Impact

Individuals coinfected with influenza

develop more severe symptoms

Hypothesis II: � > 1Susceptibility Impact

Individuals infected with influenza

are more susceptible to pneumonia

��

✏

��

⇢p

SIRS model

8>>>>>>>>><

>>>>>>>>>:

dS

dt= µ(N � S) � � SU � �� SF + ✏ R

dI

dt= � SU + �� SF � � I � µ I

dR

dt= � I � µ R � ✏ R

flu as covariate

8>>>>><

>>>>>:

SF =F(t)

⇢F NS

SS = S � SF = S �F(t)

⇢F NS

Model

MODEL FOR PNEUMONIA EPIDEMIOLOGY

SU IU

SF IF

R

Cases�(t) = �(t)(IU/N + ✓ IF/N + !)

Hypothesis III: ⇠ > 1Pathogenesis Impact

Individuals coinfected with influenza

develop more severe symptoms

Hypothesis I: ✓ > 1Transmission Impact

Individuals coinfected with influenza

contribute more to transmission

Hypothesis II: � > 1Susceptibility Impact

Individuals infected with influenza

are more susceptible to pneumonia

��

✏

��

⇢p

SIRS model

8>>>>>>>>>>>>>><

>>>>>>>>>>>>>>:

dS

dt= µ(N � S) � � SU � �� SF + ✏ R

dIU

dt= � SU � � IU � µ IU

dIFdt

= �� SF � � IF � µ IF

dR

dt= � I � µ R � ✏ R

flu as covariate

8>>>>><

>>>>>:

SF =F(t)

⇢F NS

SS = S � SF = S �F(t)

⇢F NS

Model

MODEL FOR PNEUMONIA EPIDEMIOLOGY

SU IU

SF IF

R

Cases�(t) = �(t)(IU/N + ✓ IF/N + !)

Hypothesis I: ✓ > 1Transmission Impact

Individuals coinfected with influenza

contribute more to transmission

Hypothesis II: � > 1Susceptibility Impact

Individuals infected with influenza

are more susceptible to pneumonia

Hypothesis III: ⇠ > 1Pathogenesis Impact

Individuals coinfected with influenza

develop more severe symptoms

��

✏

��

�

⇢p

⇠⇢p

SIRS model

8>>>>>>>>>>>>>><

>>>>>>>>>>>>>>:

dS

dt= µ(N � S) � � SU � �� SF + ✏ R

dIU

dt= � SU � � IU � µ IU

dIFdt

= �� SF � � IF � µ IF

dR

dt= � I � µ R � ✏ R

flu as covariate

8>>>>><

>>>>>:

SF =F(t)

⇢F NS

SS = S � SF = S �F(t)

⇢F NS

Model

Results: Nature of interaction, pneumococcal pneumonia

A

Pre−

PCV:

1989−1

997

Post−P

CV:

2000−2

009

logl

iklo

glik

B C D

E F G

Transmission impact, θ Susceptibility impact, φ Pathogenesis impact, ξ

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MLE: 145 [83,277]

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−186

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MLE: 0.4 [0.38,1.4]

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●MLE: 115 [70,230]

with θ=1and ξ=1

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MLE: 85 [27,160]

with θ=1and ξ=1

A

Pre−

PCV:

1989−1

997

Post−P

CV:

2000−2

009

logl

iklo

glik

B C D

E F G

Transmission impact, θ Susceptibility impact, φ Pathogenesis impact, ξ

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MLE: 0.8 [0.41,4.7]

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MLE: 95 [53,230]

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810

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MLE: 2.5 [0.4,3.4]

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0−1

845

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MLE: 1.5 [0.3,1.9]

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MLE: 145 [83,277]

●●

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−186

0−1

845

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MLE: 0.4 [0.38,1.4]

●● ● ● ● ●

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−182

5−1

805

−178

5

0 1 5 10 20 50 200 500

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● ●●●●●

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●● ●

●

●MLE: 115 [70,230]

with θ=1and ξ=1

● ●● ●

●

● ● ● ●●●●●●●●●●●●●●

●●●●●●●●●●●●●

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−186

0−1

840

−182

0

0 1 5 10 20 50 200 500

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●

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●●●●●●●●●●●●●

●●

●

●●

●●

●

MLE: 85 [27,160]

with θ=1and ξ=1

Results: Nature of interaction, pneumococcal pneumonia

A

Pre−

PCV:

1989−1

997

Post−P

CV:

2000−2

009

logl

iklo

glik

B C D

E F G

Transmission impact, θ Susceptibility impact, φ Pathogenesis impact, ξ

●●

●

● ●

● ● ●● ●

●

● ●

●

● ●

●

●

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●

−182

5−1

810

−179

5

0 1 2 5 10 20 50

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MLE: 0.8 [0.41,4.7]

●

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MLE: 95 [53,230]

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−182

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810

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MLE: 2.5 [0.4,3.4]

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−186

0−1

845

−183

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MLE: 1.5 [0.3,1.9]

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−186

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845

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MLE: 145 [83,277]

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−186

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845

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MLE: 0.4 [0.38,1.4]

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−182

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805

−178

5

0 1 5 10 20 50 200 500

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●MLE: 115 [70,230]

with θ=1and ξ=1

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840

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MLE: 85 [27,160]

with θ=1and ξ=1

Results: Nature of interaction, pneumococcal pneumonia

A

Pre−

PCV:

1989−1

997

Post−P

CV:

2000−2

009

logl

iklo

glik

B C D

E F G

Transmission impact, θ Susceptibility impact, φ Pathogenesis impact, ξ

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MLE: 0.8 [0.41,4.7]

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MLE: 95 [53,230]

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810

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MLE: 2.5 [0.4,3.4]

●●

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−186

0−1

845

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0

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MLE: 1.5 [0.3,1.9]

●

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845

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MLE: 145 [83,277]

●●

●

●●

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●●

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● ● ●

●

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●

−186

0−1

845

−183

0

0 1 2 5 10 20

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●

MLE: 0.4 [0.38,1.4]

●● ● ● ● ●

● ●●●●●

●●●●●●●●●

●●●●●●●●●●●●●●●●●

●● ●

●

−182

5−1

805

−178

5

0 1 5 10 20 50 200 500

●● ● ● ● ●

● ●●●●●

●●●●●●●●●

●●●●●●●●●●●●●●●●●

●● ●

●

●MLE: 115 [70,230]

with θ=1and ξ=1

● ●● ●

●

● ● ● ●●●●●●●●●●●●●●

●●●●●●●●●●●●●

●●

●

●●

●●

−186

0−1

840

−182

0

0 1 5 10 20 50 200 500

● ●● ●

●

● ● ● ●●●●●●●●●●●●●●

●●●●●●●●●●●●●

●●

●

●●

●●

●

MLE: 85 [27,160]

with θ=1and ξ=1

Results: Nature of interaction, pneumococcal pneumonia

A

Pre−

PCV:

1989−1

997

Post−P

CV:

2000−2

009

logl

iklo

glik

B C D

E F G

Transmission impact, θ Susceptibility impact, φ Pathogenesis impact, ξ

●●

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−182

5−1

810

−179

5

0 1 2 5 10 20 50

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MLE: 0.8 [0.41,4.7]

●

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−182

5−1

810

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50 1 5 10 20 50 200 500

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MLE: 95 [53,230]

●

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5−1

810

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5

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●

MLE: 2.5 [0.4,3.4]

●●

● ●●●

●

●

●●

● ●●●

● ●● ●

●

●

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−186

0−1

845

−183

0

0 1 2 5 10 20 50

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MLE: 1.5 [0.3,1.9]

●

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−186

0−1

845

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● ● ●●●●●

●●●●●●●●

●●●●●●●●●●

●●●●●●● ●●

●

●

●

MLE: 145 [83,277]

●●

●

●●

●●●●

●●

●●

● ● ●

●

● ●

●●

●

●

−186

0−1

845

−183

0

0 1 2 5 10 20

●●

●

●●

●

●●●●

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●●●●●●●●●●

●

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● ● ●

●

● ●

●●

●

●

●

MLE: 0.4 [0.38,1.4]

●● ● ● ● ●

● ●●●●●

●●●●●●●●●

●●●●●●●●●●●●●●●●●

●● ●

●

−182

5−1

805

−178

5

0 1 5 10 20 50 200 500

●● ● ● ● ●

● ●●●●●

●●●●●●●●●

●●●●●●●●●●●●●●●●●

●● ●

●

●MLE: 115 [70,230]

with θ=1and ξ=1

● ●● ●

●

● ● ● ●●●●●●●●●●●●●●

●●●●●●●●●●●●●

●●

●

●●

●●

−186

0−1

840

−182

0

0 1 5 10 20 50 200 500

● ●● ●

●

● ● ● ●●●●●●●●●●●●●●

●●●●●●●●●●●●●

●●

●

●●

●●

●

MLE: 85 [27,160]

with θ=1and ξ=1

Results: Nature of interaction, pneumococcal pneumonia

Results: Nature of interaction

Carrying out this same analysis on all pneumonia cases.

NYC

:192

0−19

24Ill

inoi

s:19

89−1

997

Illin

ois:

2000−2

009

logl

iklo

glik

logl

ik

Hypothesis 1: θ > 1Transmission impact

Hypothesis 2: φ > 1Susceptibility impact

Hypothesis 3: ξ > 1Pathogenesis impact

A B C

D E F

G H I

Transmission impact, θ Susceptibility impact, φ Pathogenesis impact, ξ

● ● ● ●● ● ● ●

● ●●

●

● ●●●

●●

●

●

●

−117

0−1

150

−113

0

0 1 5 20 50

●

●

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●

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●

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● ● ● ●● ● ●

●● ●

●

●

● ●

●

●

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●

●

●

●

MLE: 0 [0,5]

●

●

●

● ●

●

● ●

●●●●●●●●●

●●●●●●●●●●●● ● ● ● ● ● ●

● ●

●

●

−117

0−1

150

−113

0

0 1 5 20 100 500 2000

●

●

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MLE: 45 [29,1100]

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−117

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−113

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MLE: 0.5 [0.34,1.4]

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−278

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765

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MLE: 2 [0.38,9.5]

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−278

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MLE: 180 [0.3,450]

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−278

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MLE: 15 [0.69,19]

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●MLE: 0.5 [0,1]

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MLE: 110 [59,310]

●

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5−3

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MLE: 1 [0.84,2.6]

●●

●

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● ● ● ●

● ●●●●●●●●●●●●●●●●●

●●●●●●●●●●●●●

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−117

0−1

145

−112

0

0 1 5 10 50 200 500

●●

●

●

● ● ● ●

● ●●●●●●●●●●●●●●●●●

●●●●●●●●●●●●●

●● ● ●

●

MLE: 80 [25,150]

with θ=1and ξ=1

●

● ● ● ●●

●

●●●●

●●●●

●●●●●●

●●●●●●●●●

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●

−278

5−2

760

−273

5

0 1 5 10 50 200 500

●● ● ● ●

●

●●●●

●●●●●

●●●●●●●●●●●●●●●

●●●●

● ●

●

●

MLE: 100 [1.2,410]

with θ=1and ξ=1

● ●

● ●

●

●

●

●●●●●●

●●●●

●●●

●●●

●●●●●●●●●

●●

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●

−324

5−3

220

−319

5

0 1 5 10 50 200 500

● ●

● ●

●

●

●

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●●●

●●●●●●●●●

●●

●● ●

●● ●

●

●MLE: 110 [63,240]

with θ=1and ξ=1

Results: Nature of interaction, pneumonia

Results: Nature of interaction, pneumonia

●●● ● ●

1920 1921 1922 1923

−7−6

−5−4

−3

A●

influenzapneumoniamissing data

max−peak to min−peakratio=170.15

1990 1991 1992 1993 1994 1995 1996 1997 1998

−7−6

−5−4

Influ

enza

and

pne

umon

ia in

cide

nce

repo

rts (l

og10

)

B

max−peak to min−peakratio=4.32

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

−7−6

−5−4

Cmax−peak to min−peak

ratio=12.5

Lower variability in influenza peaks

A BCity

(population)Out−of−fit Prediction

R2

NYC−nullR2

NYC−MLE

Inference of Susceptibility impactφ−profiles MLE

(95% CI)

New York City

Chicago

Philadelphia

Baltimore

Los Angeles

(5,620,048)

(2,701,705)

(1,823,779)

( 733,826)

( 576,673)

0.314

0.107

0.567

0.111

0.85

0.484

0.818

−2.956

NA NA

1 5 10 30 100 1000

●●

●

●

● ● ● ●

● ● ●●●●●●●●●●●●●●●●

●●●●●●●●●●●●●

●● ● ●

●

−40

−20

0∆

logl

ik

●

●●

●● ● ● ●●●●

●●

●●●●●●●●●●

●●●●●●

●●●

●●●● ●

●

●

●

−40

−20

0∆

logl

ik

●

●

● ● ● ● ● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●● ● ● ● ● ●●

−40

−20

0∆

logl

ik

●

● ● ●● ● ●

●●●●●●●●●●●●●●●●●

●●●●●●●●●●●● ●●

●

●

−40

−20

0∆

logl

ik

●

●● ● ● ●

● ●●●●●●●●●●●●●●

●●●●●●●●●●●●●●●

●

●

●

●

−40

−20

0∆

logl

ik

80

75

105

135

85

(25−153)

(32−108)

(13−757)

(11−401)

(14−112)

Results: Nature of interaction, pneumonia

Results: Time scale of interaction

●

●

●

01

510

2040

8020

050

0

0−1 1−2 2−3

Timing of interaction(in weeks post influenza)

Susc

eptib

ility

impa

cts,

φ,φ

1,φ2

A● MLE

95% CI

The susceptibility impact is limited to a week.

Results: Time scale of interaction

●

●

● ● ●

●

●

●

−7 0 3 5 7 9 14 21

0/6

1/6

2/6

3/6

4/6

5/6

6/6

pneumococcal challenge relative to influenza challenge (days)

surv

ival

Timescales are consistent with what is found in animal challenge experiments

~100 fold enhancement is consistent with strong dose dependence

McCullers & Rehg, 2002

Results: comparison with animal challenge experiment

344 McCullers and Rehg JID 2002;186 (1 August)

Table 1. Relationship of synergism to dose of in- fluenza virus.

No. of mice Duration of survival Dose of influenza surviving/ after the second virus, MID50 total (%) infection, mean daysa

140 0/10 (0)b 0.5 70 0/10 (0)b 1.4 35 1/10 (10)b 2.1 18 0/10 (0)b 2.3 9 2/10 (20)b 2.5 4 3/10 (30)b 3.7 2 3/10 (30)b 3.3 1 3/10 (30)b 3.6 0.5 6/10 (60) 4.8 0 9/10 (90) 4.0

NOTE. MID50, the dose infectious for 500Zo of mice, delivered in the 100 fxh inoculum. a Only data for mice that died were considered.

bi^.OS for survival, vs. the control group (Mantel-Cox X2 test on the Kaplan-Meier survival data).

(simultaneously), 3, 5, 7, 9, 14, or 21 and were monitored for 21 days after the second infection. The percentage of mice surviving and the mean duration of survival (i.e., the number of days the mice survived after the second infection, considering only mice who died) are shown in figure 2.

As in the first experiment, an additive mortality was seen with simultaneous infection, no mortality was observed when pneumococcal infection preceded influenza virus infection, and 10007o lethality occurred when pneumococcal challenge followed influenza virus infection by 7 days. One hundred percent mor- tality was also observed in the day 3 and day 5 groups, although death was most rapid in the day 7 group, occurring in ^4 h in all mice, compared with a mean duration of survival of 3.3 days for the group challenged on day 3 after influenza virus infection and 2.5 days for the group challenged on day 5. The synergistic effect was lost by day 21. These data suggest that there is a temporal sequence of events that must occur to prime for synergistic lethality and indicate that, in this model, pneu- mococcal challenge 7 days after influenza virus infection results in the most rapid and complete mortality.

Dose response for influenza. One potential contributor to the synergistic mortality seen with pneumococcal challenge after in- fluenza virus infection was the debilitation of the mice at the doses of PR8 used in the previous experiments (0.3 MLD50), which may have been most severe at 7 days after influenza virus infection. We therefore infected groups of 10 mice with sequen- tially 2-fold-lower doses of influenza virus, from 140 MID50 (equivalent to 1.0 MLD50) to 0.5 MID50, then challenged with 0.2 MLD50 of pneumococcus (1 X 105 cfu) at day 7 after infection. The dose at which 5007o of mice exhibit morbidity (i.e., clinical signs of infection such as weight loss, ruffled fur, huddling, hunched posture, shivering, and tachypnea) for this PR8 stock is 10 MID50. High mortality was observed at doses of influenza virus as low as 1 MID50 (table 1), with only 3007o survival, com- pared with 9007o in control mice, indicating that synergism occurs at doses below that needed to engender clinical symptoms or

weight loss. Therefore, other synergistic mechanisms besides de- bilitation from influenza must be operative in the model. The mean duration of survival (among mice that died) was directly related to the influenza virus dose (table 1).

Dose response for pneumococcus. After the demonstration that low doses of influenza virus can be used in the model with maintenance of the synergistic effect, an experiment was un- dertaken to determine the minimum dose of pneumococci needed for synergistic killing. Groups of 10 mice were infected with 0.13 MLD50 of influenza virus (equivalent to 18 MID50, the lowest dose at which 10007o mortality was observed in the prior experiment) and challenged at day 7 with sequentially 4- fold-lower doses of pneumococcus ranging from 1 X 105 cfu (equivalent to 0.2 MLD50) to 100 cfu. Data in table 2 indicate that synergistic mortality can be observed with low doses of both influenza virus and pneumococcus, although 10007o mor- tality was seen only at the highest dose tested. Of interest, although only 5007o mortality was observed in the group re- ceiving 100 cfu of pneumococcus, the mean duration of survival (among mice that died) was similar to that for the highest group. Thus, it is seems apparent that the rapidity of demise is related to the timing of administration of the 2 agents and the dose of influenza virus but not the dose of pneumococcus, whereas all 3 parameters factor into the percent mortality.

Pathologic abnormalities in the synergism model. The pul- monary alterations in the mouse lungs involved the pulmonary airways and/or the pulmonary parenchyma, but the degree of tissue involvement depended on the treatment received. Airway involvement was primarily limited to mice infected with virus and was characterized by inflammatory cells in the lumen and/ or mucosa, as well as epithelial necrosis and epithelial hyper- plasia. Parenchymal involvement was focal, and the percentage of infected tissue was variable. There were no significant dif- ferences, compared with controls, in the lungs of mice infected with pneumococcus alone (figures 3A and ZB; control mice not shown). However, virus-infected mice at 48 h after challenge at day 0 (i.e., 9 days after infection with influenza virus) had multiple parenchymal foci with alveolar inflammation, alveolar epithelial cell hypertrophy and hyperplasia, and occasional al-

Table 2. Relationship of synergism to dose of pneumococcus. No. of mice Duration of survival

Dose of pneumococcus surviving/ after the second D39, cfua total (%) infection, mean daysb

100,000 0/10 (0)c 2.8 25,000 2/10 (20)c 2.6 6400 2/10 (20)c 2.6 1600 3/10 (30)c 2.6 400 3/10 (30)c 3.1 100 5/10 (50)c 3.4 0 10/10 (100) NC

NOTE. NC, not calculated (because no mice died). a The dose indicated was that delivered in the 100 fiL inoculum. bOnly data for mice that died were considered. cZ^ .05 for survival, vs. the control group (Mantel-Cox x2 test on the

Kaplan-Meier survival data).

Results: influenza-attributable pneumococcal pneumoniaestimated mean 95% confidence

1990 1991 1992 1993 1994 1995 1996 1997 1998

0.0

0.1

0.2

0.3

0.4

0.5

0.6

influ

enza−a

ttrib

utab

le e

tiolo

gica

l fra

ctio

nof

pne

umoc

occa

l pne

umon

ia c

ases

A

0.00

0.10 B

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

0.0

0.1

0.2

0.3

0.4

0.5

0.6 C

0.00

0.10 D

Result: Model Simulations

1920 1921 1922 1923 1924

12

34

1920 1921 1922 1923 1924pn

eum

onia

cas

es (l

og10

)time

pneumonia−datainfluenza−datapneumonia−prediction

95% confidence

A

1920 1921 1922 1923 1924

12

34

1920 1921 1922 1923 1924

pneu

mon

ia c

ases

(log

10) B

1920 1921 1922 1923 1924

01

23

45

1920 1921 1922 1923 1924

influ

enza

cas

es (l

og10

)

time

C

MLE-model

Null model

Data

Kearney

Johnston

Jackson

Humphreys

Hancock

Greene

Grant

Gordon

Funston

Fremont

Dodge

Dix

Devens

Custer

Cody

Bowie

Beauregard

Wheeler

Wadsworth

Upton

Travis

Taylor

Sherman

Sheridan

Shelby

Sevier

Pike

Mills

Meade

McClellan

MacArthur

Logan

Lewis

Lee

M J J A S O N D M J J A S O N D

10% influenza 10% pneumonia 10% coinfections

US Army Camps, Fall of 1918

Results: Predictions in US Army Camps, 1918 pandemic

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A Data Prediction: Pneumonia Prediction: Coinfections Incidencescale

0%

10%

Kearney

Johnston

Jackson

Humphreys

Hancock

Greene

Grant

Gordon

Funston

Fremont

Dodge

Dix

Devens

Custer

Cody

Bowie

Beauregard

Wheeler

Wadsworth

Upton

Travis

Taylor

Sherman

Sheridan

Shelby

Sevier

Pike

Mills

Meade

McClellan

MacArthur

Logan

Lewis

Lee

M J J A S O N D M J J A S O N D M J J A S O N D M J J A S O N D

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BeauregardBowie

Cody

Custer

Devens

Dix

Dodge

Fremont

Funston

Gordon

GrantGreene

Hancock

HumphreysJackson

Johnston

Kearney

Lee

Lewis

Logan

MacArthur

McClellan

Meade

Mills

Pike Sevier

Shelby

Sheridan

Sherman

Taylor

Travis

Upton

Wadsworth

Wheeler

0 1 2 3 4 5 6

00.

51

1.5

22.

5

Data: Pneumonia (%)

Pred

ictio

n: P

neum

onia

(%)

B

R2=0.52

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BeauregardBowieCody

Custer

Devens

Dix

Dodge

Fremont

Funston

Gordon

GrantGreene

Hancock

HumphreysJackson

Johnston

Kearney

Lee

Lewis

Logan

MacArthur

McClellan

Meade

Mills

PikeSevier

Shelby

Sheridan

Sherman

Taylor

TravisUpton

Wadsworth

Wheeler

0 1 2 3 4

00.

51

1.5

22.

5

Data: Coinfections (%)

Pred

ictio

n: C

oinf

ectio

ns (%

)

C

R2=0.6

Results: Predictions in US Army Camps, 1918 pandemic

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● ● ● ● ● ● ● ● ● ● ● ● ●

A Data Prediction: Pneumonia Prediction: Coinfections Incidencescale

0%

10%

Kearney

Johnston

Jackson

Humphreys

Hancock

Greene

Grant

Gordon

Funston

Fremont

Dodge

Dix

Devens

Custer

Cody

Bowie

Beauregard

Wheeler

Wadsworth

Upton

Travis

Taylor

Sherman

Sheridan

Shelby

Sevier

Pike

Mills

Meade

McClellan

MacArthur

Logan

Lewis

Lee

M J J A S O N D M J J A S O N D M J J A S O N D M J J A S O N D

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●

●

●

●

●

BeauregardBowie

Cody

Custer

Devens

Dix

Dodge

Fremont

Funston

Gordon

GrantGreene

Hancock

HumphreysJackson

Johnston

Kearney

Lee

Lewis

Logan

MacArthur

McClellan

Meade

Mills

Pike Sevier

Shelby

Sheridan

Sherman

Taylor

Travis

Upton

Wadsworth

Wheeler

0 1 2 3 4 5 6

00.

51

1.5

22.

5

Data: Pneumonia (%)

Pred

ictio

n: P

neum

onia

(%)

B

R2=0.52

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●

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●

●

BeauregardBowieCody

Custer

Devens

Dix

Dodge

Fremont

Funston

Gordon

GrantGreene

Hancock

HumphreysJackson

Johnston

Kearney

Lee

Lewis

Logan

MacArthur

McClellan

Meade

Mills

PikeSevier

Shelby

Sheridan

Sherman

Taylor

TravisUpton

Wadsworth

Wheeler

0 1 2 3 4

00.

51

1.5

22.

5

Data: Coinfections (%)

Pred

ictio

n: C

oinf

ectio

ns (%

)

C

R2=0.6

Results: Detectability of interaction1 2 3 4 5 6 7

Relative Pneumococcal pneumonia Peaks

020

4060

8010

012

014

016

0

0.5 1 1.5 2 3.2 4.6 6.4 10 12 20 50 Relative Influenza Peaks

Susc

eptib

ility

Impa

ct, φ

● ● ● ● ● ● ● ●●● ●● ●●● ●● ● ● ● ●

I II III IV V

A

2001 1996 1991 2003 1993 2007 1997 1995 2002 1994 1990 2006 1992 2005 2008 2004

influ

enza

pneu

moc

occa

lpn

eum

onia

∆ lo

glik

I

2000 2001 2002 2003 2004

−7−6

−5−4 B

2000 2001 2002 2003 2004

−6−5 G

● ● ● ● ● ● ● ● ● ● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●

●

●

Susceptibility Impact, φ

−25

−20

−15

−10

−50

5

1 2 5 10 25 55 125 500

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●

●

L

II

2000 2001 2002 2003 2004

−7−6

−5−4 C

2000 2001 2002 2003 2004

−6−5 H

● ● ● ● ● ● ● ●● ● ●●●●●●●●●●●●●●●●

●●●●●●●●●●●

●●

●

Susceptibility Impact, φ

−25

−20

−15

−10

−50

5

1 2 5 10 25 55 125 500

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●

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●●

●

M

III

2000 2001 2002 2003 2004−7

−6−5

−4 D

2000 2001 2002 2003 2004

−6−5 I

● ● ● ● ● ●● ●

● ● ●●●●●●

●●●●●●●●●●●●●●●●●●●●●●●

●

●Susceptibility Impact, φ

−25

−20

−15

−10

−50

5

1 2 5 10 25 55 125 500

●●●●●●●●●●

●●●

●

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●

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●

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●

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●

●

N

IV

2000 2001 2002 2003 2004

−7−6

−5−4 E

2000 2001 2002 2003 2004

−6−5 J

● ● ●

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●

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●

●

Susceptibility Impact, φ

−25

−20

−15

−10

−50

5

1 2 5 10 25 55 125 500

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O

V

2000 2001 2002 2003 2004

−7−6

−5−4 F

2000 2001 2002 2003 2004

−6−5 K

●●

●●

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●

●

●

●

●

●

●

●

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●

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●

●

Susceptibility Impact, φ

−25

−20

−15

−10

−50

5

1 2 5 10 25 55 125 500

●

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●

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●

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●

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●

P

Results: Time scale of interaction1 2 3 4 5 6 7

Relative Pneumococcal pneumonia Peaks

020

4060

8010

012

014

016

0

0.5 1 1.5 2 3.2 4.6 6.4 10 12 20 50 Relative Influenza Peaks

Susc

eptib

ility

Impa

ct, φ

● ● ● ● ● ● ● ●●● ●● ●●● ●● ● ● ● ●

I II III IV V

A

2001 1996 1991 2003 1993 2007 1997 1995 2002 1994 1990 2006 1992 2005 2008 2004

influ

enza

pneu

moc

occa

lpn

eum

onia

∆ lo

glik

I

2000 2001 2002 2003 2004

−7−6

−5−4 B

2000 2001 2002 2003 2004

−6−5 G

● ● ● ● ● ● ● ● ● ● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●

●

●

Susceptibility Impact, φ

−25

−20

−15

−10

−50

5

1 2 5 10 25 55 125 500

●

●●●

●

●●

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●

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●

●

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●

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●

●

L

II

2000 2001 2002 2003 2004

−7−6

−5−4 C

2000 2001 2002 2003 2004

−6−5 H

● ● ● ● ● ● ● ●● ● ●●●●●●●●●●●●●●●●

●●●●●●●●●●●

●●

●

Susceptibility Impact, φ

−25

−20

−15

−10

−50

5

1 2 5 10 25 55 125 500

●●●●●●●●●● ●●

●

●

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●●●●●●●●●●●

●●

●

M

III

2000 2001 2002 2003 2004−7

−6−5

−4 D

2000 2001 2002 2003 2004

−6−5 I

● ● ● ● ● ●● ●

● ● ●●●●●●

●●●●●●●●●●●●●●●●●●●●●●●

●

●Susceptibility Impact, φ

−25

−20

−15

−10

−50

5

1 2 5 10 25 55 125 500

●●●●●●●●●●

●●●

●

●●●●●●●●●●●●●

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●

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● ● ● ● ● ●● ●

● ● ●●●●●●

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●

●

N

IV

2000 2001 2002 2003 2004

−7−6

−5−4 E

2000 2001 2002 2003 2004

−6−5 J

● ● ●

●

● ●

●

●

●

●

●●

●●●●●

●●●●●●●●●●●●●●

●●●●●

●●

●

●

Susceptibility Impact, φ

−25

−20

−15

−10

−50

5

1 2 5 10 25 55 125 500

●

●

●

●

●●

●

●

●

●

●

●

●

●

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O

V

2000 2001 2002 2003 2004

−7−6

−5−4 F

2000 2001 2002 2003 2004

−6−5 K

●●

●●

●●

●

●

●

●

●

●

●

●

●●●●●●●●●●●●●●●●●●

●●●●

●

●

●

●

Susceptibility Impact, φ

−25

−20

−15

−10

−50

5

1 2 5 10 25 55 125 500

●

●

●

●

●●●●●●

●

●

●

●

●

●

●

●

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●

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●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●

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●

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●

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●

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●

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●

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●

●

●

●

●

●

●

●●●●●●●●●●●●●●●●●●

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●

●

●

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P

Results: Time scale of interaction1 2 3 4 5 6 7

Relative Pneumococcal pneumonia Peaks

020

4060

8010

012

014

016

0

0.5 1 1.5 2 3.2 4.6 6.4 10 12 20 50 Relative Influenza Peaks

Susc

eptib

ility

Impa

ct, φ

● ● ● ● ● ● ● ●●● ●● ●●● ●● ● ● ● ●

I II III IV V

A

2001 1996 1991 2003 1993 2007 1997 1995 2002 1994 1990 2006 1992 2005 2008 2004

influ

enza

pneu

moc

occa

lpn

eum

onia

∆ lo

glik

I

2000 2001 2002 2003 2004

−7−6

−5−4 B

2000 2001 2002 2003 2004

−6−5 G

● ● ● ● ● ● ● ● ● ● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●

●

●

Susceptibility Impact, φ

−25

−20

−15

−10

−50

5

1 2 5 10 25 55 125 500

●

●●●

●

●●

●●●

●●●●●

●●●●●

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●

●

L

II

2000 2001 2002 2003 2004

−7−6

−5−4 C

2000 2001 2002 2003 2004

−6−5 H

● ● ● ● ● ● ● ●● ● ●●●●●●●●●●●●●●●●

●●●●●●●●●●●

●●

●

Susceptibility Impact, φ

−25

−20

−15

−10

−50

5

1 2 5 10 25 55 125 500

●●●●●●●●●● ●●

●

●

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●●

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●

M

III

2000 2001 2002 2003 2004−7

−6−5

−4 D

2000 2001 2002 2003 2004

−6−5 I

● ● ● ● ● ●● ●

● ● ●●●●●●

●●●●●●●●●●●●●●●●●●●●●●●

●

●Susceptibility Impact, φ

−25

−20

−15

−10

−50

5

1 2 5 10 25 55 125 500

●●●●●●●●●●

●●●

●

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●

●

N

IV

2000 2001 2002 2003 2004

−7−6

−5−4 E

2000 2001 2002 2003 2004

−6−5 J

● ● ●

●

● ●

●

●

●

●

●●

●●●●●

●●●●●●●●●●●●●●

●●●●●

●●

●

●

Susceptibility Impact, φ

−25

−20

−15

−10

−50

5

1 2 5 10 25 55 125 500

●

●

●

●

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●

●

●

●

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O

V

2000 2001 2002 2003 2004

−7−6

−5−4 F

2000 2001 2002 2003 2004

−6−5 K

●●

●●

●●

●

●

●

●

●

●

●

●

●●●●●●●●●●●●●●●●●●

●●●●

●

●

●

●

Susceptibility Impact, φ

−25

−20

−15

−10

−50

5

1 2 5 10 25 55 125 500

●

●

●

●

●●●●●●

●

●

●

●

●

●

●

●

●

●

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●

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●

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●

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●

●

●

●

●

●

●

●●●●●●●●●●●●●●●●●●

●●●●

●

●

●

●

P

Results: Time scale of interaction1 2 3 4 5 6 7

Relative Pneumococcal pneumonia Peaks

020

4060

8010

012

014

016

0

0.5 1 1.5 2 3.2 4.6 6.4 10 12 20 50 Relative Influenza Peaks

Susc

eptib

ility

Impa

ct, φ

● ● ● ● ● ● ● ●●● ●● ●●● ●● ● ● ● ●

I II III IV V

A

2001 1996 1991 2003 1993 2007 1997 1995 2002 1994 1990 2006 1992 2005 2008 2004

influ

enza

pneu

moc

occa

lpn

eum

onia

∆ lo

glik

I

2000 2001 2002 2003 2004

−7−6

−5−4 B

2000 2001 2002 2003 2004

−6−5 G

● ● ● ● ● ● ● ● ● ● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●

●

●

Susceptibility Impact, φ

−25

−20

−15

−10

−50

5

1 2 5 10 25 55 125 500

●

●●●

●

●●

●●●

●●●●●

●●●●●

●●●

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●

●

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● ● ● ● ● ● ● ● ● ● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●

●

●

L

II

2000 2001 2002 2003 2004

−7−6

−5−4 C

2000 2001 2002 2003 2004

−6−5 H

● ● ● ● ● ● ● ●● ● ●●●●●●●●●●●●●●●●

●●●●●●●●●●●

●●

●

Susceptibility Impact, φ

−25

−20

−15

−10

−50

5

1 2 5 10 25 55 125 500

●●●●●●●●●● ●●

●

●

●●

●●

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M

III

2000 2001 2002 2003 2004−7

−6−5

−4 D

2000 2001 2002 2003 2004

−6−5 I

● ● ● ● ● ●● ●

● ● ●●●●●●

●●●●●●●●●●●●●●●●●●●●●●●

●

●Susceptibility Impact, φ

−25

−20

−15

−10

−50

5

1 2 5 10 25 55 125 500

●●●●●●●●●●

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●

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●

●

N

IV

2000 2001 2002 2003 2004

−7−6

−5−4 E

2000 2001 2002 2003 2004

−6−5 J

● ● ●

●

● ●

●

●

●

●

●●

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●●●●●●●●●●●●●●

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●

●

Susceptibility Impact, φ

−25

−20

−15

−10

−50

5

1 2 5 10 25 55 125 500

●

●

●

●

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●

●

●

●

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O

V

2000 2001 2002 2003 2004

−7−6

−5−4 F

2000 2001 2002 2003 2004

−6−5 K

●●

●●

●●

●

●

●

●

●

●

●

●

●●●●●●●●●●●●●●●●●●

●●●●

●

●

●

●

Susceptibility Impact, φ

−25

−20

−15

−10

−50

5

1 2 5 10 25 55 125 500

●

●

●

●

●●●●●●

●

●

●

●

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P

Results: Time scale of interaction1 2 3 4 5 6 7

Relative Pneumococcal pneumonia Peaks

020

4060

8010

012

014

016

0

0.5 1 1.5 2 3.2 4.6 6.4 10 12 20 50 Relative Influenza Peaks

Susc

eptib

ility

Impa

ct, φ

● ● ● ● ● ● ● ●●● ●● ●●● ●● ● ● ● ●

I II III IV V

A

2001 1996 1991 2003 1993 2007 1997 1995 2002 1994 1990 2006 1992 2005 2008 2004

influ

enza

pneu

moc

occa

lpn

eum

onia

∆ lo

glik

I

2000 2001 2002 2003 2004

−7−6

−5−4 B

2000 2001 2002 2003 2004

−6−5 G

● ● ● ● ● ● ● ● ● ● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●

●

●

Susceptibility Impact, φ

−25

−20

−15

−10

−50

5

1 2 5 10 25 55 125 500

●

●●●

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●

●

L

II

2000 2001 2002 2003 2004

−7−6

−5−4 C

2000 2001 2002 2003 2004

−6−5 H

● ● ● ● ● ● ● ●● ● ●●●●●●●●●●●●●●●●

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Susceptibility Impact, φ

−25

−20

−15

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−50

5

1 2 5 10 25 55 125 500

●●●●●●●●●● ●●

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M

III

2000 2001 2002 2003 2004−7

−6−5

−4 D

2000 2001 2002 2003 2004

−6−5 I

● ● ● ● ● ●● ●

● ● ●●●●●●

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●Susceptibility Impact, φ

−25

−20

−15

−10

−50

5

1 2 5 10 25 55 125 500

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●

N

IV

2000 2001 2002 2003 2004

−7−6

−5−4 E

2000 2001 2002 2003 2004

−6−5 J

● ● ●

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Susceptibility Impact, φ

−25

−20

−15

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−50

5

1 2 5 10 25 55 125 500

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O

V

2000 2001 2002 2003 2004

−7−6

−5−4 F

2000 2001 2002 2003 2004

−6−5 K

●●

●●

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●

●

●

●

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●

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●

Susceptibility Impact, φ

−25

−20

−15

−10

−50

5

1 2 5 10 25 55 125 500

●

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●

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P

Results: Detectability of interaction

Since the interaction is strong and short-lived, it can be undetectable, especially during non-

pandemic period with limited data

Limitations

Model: — Model does not include heterogeneities such as age-dependent contact patterns, other associated risk factors, other viruses and interactions.— Bacterial carriage levels, and carriage-to-invasion dynamics are not considered.

Data: — Not based on microbiological confirmations.— Differences in datasets.— Quality of historical data

Summary

— Influenza increases individual’s susceptibility to pneumococcal and other bacterial pneumonia. —This susceptibility impact is strong (~100 folds), but short-lived (a week),

consistent with the timing and dose response in the animal challenge experiments.

—The population level impact can be modest, esp. during non-pandemics.— This pattern is consistent with epidemics patterns at different scales and during both pandemic and non-pandemic periods.—The signal of this interaction can be masked at the population level.

Acknowledgements

Betsy Foxman, Allison Aiello, Joshua BerusClaudia Steiner, Dan Weinberger, Cecile Viboud, Willem Van Panhuis Aaron A King & Pejman Rohani