An epidemiologist's life on the edge (of the science-policy interface)

Christl Donnelly

MRC Centre for Outbreak Analysis and Modelling

Department of Infectious Disease Faculty of Medicine Perspectives on epidemics: individuals Another view: populations

H1N1, 1918-19 SARS, Hong Kong, 2003

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Doubling time, H1N1, 2009 attack rate The bridge: contacts

Variola minor, England, 1966 SARS, Singapore, 2003

Secondary attack rate, offspring distribution, reproduction number, generation time H1N1, UK, 2009 My academic habitat

http://www1.imperial.ac.uk/publichealth/departments/ide/outbreaks/background/ My background

• : BA Maths 1988 • HSPH: MSc & ScD 1988-1992

• Lecturer in Statistics 1992-1995 • Head of Statistics Unit 1995-2000 • Imperial College London Reader then Professor 2000- The shoulders of giants

Daniel Bernoulli (1700-1782)

On smallpox inoculation:

“I simply wish that, in a matter which so closely concerns the well-being of mankind, no decision shall be made without all the knowledge which a little analysis and calculation can provide.” Modelling the contact process

8 Epidemic as 7 chain reaction: 6 5 Y 4 3 2 1 0 1 2 3 4 t Governed by Reproduction Number R.

Need R0 >1 for a large outbreak.

Contagion The process of emergence

• Exposure to animal Viral ‘chatter’ pathogens.

• Only a few break through to cause human epidemics.

• Want to predict and detect emergence.

• Both hard, but detection easier.

Antia et al. Nature 2003 Being rigorous

• Easy to hand-wave about increasing risk. • Much harder to quantify risk, prove hypotheses. • First start is cataloguing emerging .

Jones et al. Nature 2008 Drivers for changing risk

• EIDs appear to be increasing in frequency. Number of EID events per decade • Correlation between human population density and frequency. • And with wildlife species richness. • Causal relationships unproven though. • Vector-borne diseases have clearer link to climate.

Multivariable logistic regression coefficients

Jones et al. Nature 2008 Role of travel restrictions

• 80% drop in travel to affected countries seen in SARS epidemic – mostly spontaneous.

• Key problem – growth rate of flu pandemic – 10 fold in 7-14 days

• So stopping 90% of travel buys 1-2 weeks, 99% buys 2-4 weeks.

• Very disruptive, expensive.

• So probably only useful while containment is attempted.

SARS 2003

• Was controlled when hospital procedures intensified. • Fortunately only sick people transmitted, and universally severe. • Modelling gave epidemiological insight:  basic parameters (incubation period, mortality)  rate of spread [R=2.7] and impact of controls.  general insight.

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10-May 17-May 24-May 31-May Case fatality ratio

• Proportion of cases who eventually die from the disease; • Often estimated by using aggregated numbers of cases and deaths at a single time point: • E.g.: case fatality ratios compiled daily by WHO during the SARS outbreak: estimate of the case fatality ratio: number of deaths / total number of cases. • Simple estimates of these reports can be misleading if, at the time of the analysis, the outcome (death or recovery) is unknown for an important proportion of patients. Proportion of observations censored in the SARS outbreak

We do not know the outcome (death or recovery) yet. [Ghani et al. AJE, 2005] Simple methods

• Method 1: D D = Number of deaths CFR  C C = Total number of cases

• Method 2:

D D = Number of deaths CFR  (D  R) R = Number recovered Adapted Kaplan-Meier method

To extrapolate 1 incomplete survivor 0.9 functions, assume that 0.8 death/discharge rate at 0.7 the tail occurs at the 0.6 same rate as previously: 0.5 0.4 0.3 Proba death 0.2 at the tail 0.1

ˆ 0 0 0 20 40 60 0  Non-parametricdays from probability admission of survival ˆˆ Non-parametric probability of discharge 01  K-M like estimate

Proba discharge at the tail Comparison of the estimates

(deaths/cases) deaths/(deaths+recoveries)

An example: influenza

• Flu principally a bird virus.

• But some mammals can also be infected, with difficulty.

• Virus has to adapt (e.g. mutate) to transmit in mammals.

• Perhaps easier via intermediate hosts (e.g. pigs).

• Transmissible virus  pandemic limited human immunity.

• But what is the risk? What is the epidemic potential of H3N2v?

• H3N2v – new swine variant of influenza A/H3N2 causing cases in people (2011-), associated with animal fairs etc. • Key questions:  Is H3N2v more transmissible in humans than other swine strains?  Can H3N2v generate sustained epidemics in humans?

[Cauchemez S, Epperson S, Biggerstaff M., et al., 2013, PLoS Medicine, 10:e1001399] Challenge

Data we would like to have Data we have complete and representative chains of transmission low detection rate selection bias incomplete outbreak investigations

In general, we know the source of infection of detected cases. Proportion of first detected cases that were infected by swine

Length of the chain of Proportion of first detected cases infected by swine transmission 100 1 1/1=100% 80

60 2 1/2=50%

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was infected by pig)by wasinfected P(first reported case reported P(first 1/3=33%

3 20 Proportion Proportion infected pig by 1 2 3 4 5 6 Length of chain Size of lineage

• From proportion, can estimate length of transmission chain. • From length of chain, can estimate the reproduction number. Inferring R

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20 Proportion infected infected by swineProportion 0 0.0 0.2 0.4 0.6 0.8 1.0 Reproduction number R R for H3N2v and for other strains

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Other strains: 81% (17/21) infected by swine 80 Case detection rate R=0.2 (95% CI: 0.1,0.4) 1% 0.4% 60 0.01% H3N2v: 50% (3/6) infected by swine R=0.5 (95% CI: 0.2,0.8) 40

20 Proportion Proportion infected by swine

• Significantly <1 if detection rate=0.4%; 0 but not if detection rate=1%. 0.0 0.20.2 0.4 0.6 0.8 1.0 0.5 • Can’t reject the hypothesis of equality Reproduction number R (p=0.15).

BSE (Mad Cow Disease)

• First diagnosed in November 1986 • By the end of 1995 there had been over 100,000 clinical cases in British cattle. 40 Clinical cases 35 peaked in 1992 in 30 Great Britain despite 25 a ban on meat-and- 20 000’s bonemeal-containing 15 feed being 10 introduced in 1989. 5 0 1986 1988 1990 1992 1994 1996 1998 2000 Backcalculation

Fit the predicted age distribution of case notifications over time to the case reports data using maximum likelihood methods:

Probability that a Age-related exposure / Probability that cow susceptibility distribution case gets reported is maternally infected

 u  c(u t )= ρ( +u)S(u) (1-π(t )) K( + a) g(a) f(u - a)da+π(t ) f(u) 0 t0  0  t0 0   a0 

Density Probability that cow for onset survives to age u Incubation period at age u Feed risk function among distribution those . born at t0 (to first order in K g) BSE Backcalculation Results

12000 10 % maternal transmission assumed

10000 OTM ban introduced April 1996 8000

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in last year of BSE incubation 2000

Animals slaughtered for human consumption 0

1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 Year

OTMS has by far the largest effect on human risk A European View Conceptual Model for vCJD Epidemic

vCJD cases

Incubation • Dose/age/genotype dependent • Survivorship

• Infectivity - by tissue & incubation stage Infection • Dose response - linear/non-linear/cumulative • Susceptibility heterogeneity

• Consumption rates - per individual/per product Consumption patterns • Heterogeneity - by age/time - consumers per bovine

Meat production • Tissue types used for food - by time/type of bovine • SBO ban effectiveness

BSE • Estimation of infected animals epidemic slaughtered through time vCJD Incidence

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Annual incidence 84 deaths by the end of 2000 vCJD Total Epidemic Size (2000)

95% consistency with marginal time and age distributions

10 Cases to 2040 1 55 - 100 100 - 200 0.1 200 - 500 500 - 1000 1000 - 2000 0.01 ` 2000 - 5000 5000 - 10000

infectious bovine 10000 - 20000 0.001 20000 - 50000 50000 - 150000

from from consumption of one maximally

Mean number of vCJD cases Meanarising number cases of vCJD 0.0001 5 15 25 35 45 55 65 75 85 Mean incubation period (years) vCJD Incidence

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Annual incidence 177 deaths to date (as of 7 April 2015) http://www.cjd.ed.ac.uk/documents/figs.pdf

MERS-CoV: scale & severity • Assessing under-reporting from international cases: FMD in UK, 2001  4 non-resident traveller cases returning from Middle East;  Given passenger flows from/to Middle East (and if we assume visitors and locals have same risk of infection), how many cases in Middle East?  Estimated number of severe cases in Middle East: 940 (290-2,200).

• Assessing selection bias towards more severe cases:  Are cases detected by surveillance more severe?  Risk of overestimating Case Fatality Ratio (CFR).  Comparison of first case in cluster with secondary cases:  First case CFR: 74% (49-91%).  Secondary case CFR: 20% (7-42%).

Cauchemez et al. Lancet Infect Dis 14: 50-56, 2014. (Published online 13 Nov 2013.) Sustained transmission in animals, not sustained in humans

Human case Animal case Detected case Cross-species transmission Within-species transmission

Sustained transmission in humans

Ferguson and Van Kerkhove Lancet 2013 Incubation period and generation time

• Incubation period FMD in UK, 2001  Derived from travel related clusters in the UK, France, Italy, Tunisia.  7 secondary cases with known times of exposures to index case.  Mean 5.5 (95% CI: 3.6-10.2) and SD 2.5 (95% CI: 1.2-11.6) days.

• Generation time (GT)  Lower bound: delay from onset in index case to onset in following case in cluster: o Mean: 10.7 (95% CI: 6.5-19.4) days o SD 6.3 (95% CI: 3.5-16.9) days  Sensitivity analysis GT=7 and 12 days.

• Analysis of human clusters indicated that R – in the absence of controls – was in the range 0·8 to 1·3. MERS – where are we today?

http://www.who.int/csr/disease/coronavirus_infections/mers-5-february-2015.pdf?ua=1

Imperial College members of WHO Response Team

Neil Christophe Pierre Thibaut Steven Maria Ferguson Christl Fraser Nouvellet Jombert Van Kerkhove Riley Donnelly

Gemma Wes Harriet Ilaria Isobel Nedjati‐ Hinsley Tini Garske Mills Dorigatti Anne Cori Blake Gilani

Projections

Figure S7 – WHO Ebola Response Team – NEJM – 23 Sep 2014

WHO Ebola Response Team – NEJM – 24 Dec 2014 Projections

Figure 1 – WHO Ebola Response Team – NEJM – 24 Dec 2014 WHO Ebola Response Team NEJM – 26 Mar 2015 Acknowledgements – co-authors of Dec NEJM • Junerlyn Agua-Agum, M.Ph., Archchun Ariyarajah, M.Sc., Bruce Aylward, M.D. Isobel M. Blake, Ph.D., Richard Brennan, M.D., Anne Cori, Ph.D., Christl A Donnelly, Sc.D., Ilaria Dorigatti, Ph.D., Christopher Dye, D.Phil., Tim Eckmanns, M.D., Neil M Ferguson, D.Phil., Pierre Formenty, M.D., Christophe Fraser, Ph.D., Erika Garcia, M.Ph., Tini Garske, Ph.D., Wes Hinsley, Ph.D., David Holmes, B.Sc., Stéphane Hugonnet, M.D., Swathi Iyengar M.Sc., Thibaut Jombart, Ph.D., Ravi Krishnan, M.Sc., Sascha Meijers, B.Sc., Harriet L. Mills, Ph.D., Yasmine Mohamed, B.Sc., Gemma Nedjati‐Gilani, Ph.D., Emily Newton, B.Sc., Pierre Nouvellet, Ph.D., Louise Pelletier, M.D., Devin Perkins, B.A., Steven Riley, D.Phil., Maria Sagrado, M.Sc., Johannes Schnitzler, M.D., Dirk Schumacher, M.Sc., Anita Shah, M.Ph., Maria D Van Kerkhove, Ph.D., Olivia Varsaneux, M.Sc., Niluka Wijekoon Kannangarage, M.B.B.S.

• World Health Organization (J.A.-A., A.A., B.A., R.B., C.D., T.E., P.F., E.G., D.H., S.H., S.I., R.K., S.M., E.N., D.P., L.P., I.S., M.S., D.S., A.S., O.V., N.W.K.). • Robert Koch Institute (T.E., D.S.). • MRC Centre for Outbreak Analysis and Modelling, WHO Collaborating Centre for Infectious Disease Modelling, Department of Infectious Disease Epidemiology, Imperial College London (I.M.B., A.C., C.A.D., I.D., N.M.F., C.F., T.G., W.H., T.J., H.L.M., G.N.‐G., P.N., S.R., M.D.V.K.)

Deadly impacts of Ebola

EBOLA – WHO Situation report 5 April 2015

Cases 25,550 Deaths 10,587

http://apps.who.int/ebola/current-situation/ebola-situation-report-8-april-2015

MEASLES – Takahashi et al. Science 347 (6227): 1240-1242 – 15 March 2015 “We project that after 6 to 18 months of disruptions, a large connected cluster of children unvaccinated for measles will accumulate across Guinea, Liberia, and Sierra Leone. This pool of susceptibility increases the expected size of a regional measles outbreak from 127,000 to 227,000 cases after 18 months, resulting in 2000 to 16,000 additional deaths (comparable to the numbers of Ebola deaths reported thus far).” MALARIA – in press... Watch this space! Common themes

• Hidden events

• Complex systems (e.g. multiple modes of transmission, multiple species, varied healthcare-seeking and risk-taking behaviours)

• Missing data (often far from missing at random)

• Communication of uncertainty

• More mobile, more populous world – diseases spread faster than ever before.

• Statistical/mathematical analysis and modelling can help in:  Contingency planning  Characterising new threats  Informing surveillance design  Assessing control policy options

“Dream team: (L-r) Prof Roy Anderson, Christl Donnelly and Dr Neil Ferguson”

12 April 2001 Hansard – House of Commons Debate

25 Oct 2012 4.27 pm [Column 1171-1172]

Mary Creagh (Wakefield) (Lab): … The Secretary of State is not in his place, but he referred to Christl Donnelly as a “he” during his statement on Tuesday—Christl is a she.

The Minister of State, Department for Environment, Food and Rural Affairs (Mr David Heath): She is a she.

Mary Creagh: Well, that is a relief. I do not know why the Minister has not told the Secretary of State that, because he is reported in Hansard as saying that she is a he. [Interruption.] He appears not to have read his own Hansard record or corrected it. He obviously has not spoken to the scientists, who faced down the animal rights activists during Labour’s badger cull in order to carry out the Labour Government’s research into culling badgers. We are not talking about some animal rights activists; these are scientists in the field wanting to get the right outcome for farmers and for the nation. http://www.publications.parliament.uk/pa/cm201213/cmhansrd/cm121025/debtext/121025-0004.htm

“Dr Christopher Dye (left) and Professor Christl Donnelly address a press conference on the Ebola virus” 24 September 2014

Key SARS publications

1. Jewell NP, Lei X, Ghani AC, Donnelly CA, Leung GM, Ho LM, Cowling BJ, Hedley AJ. Non-parametric estimation of the case fatality ratio with competing risks data: an application to Severe Acute Respiratory Syndrome (SARS). Stat Med. 2007 Apr 30;26(9):1982-98.

2. Cauchemez S, Boelle PY, Donnelly CA, Ferguson NM, Thomas G, Leung GM, Hedley AJ, Anderson RM, Valleron AJ. Real-time estimates in early detection of SARS. Emerg Infect Dis. 2006 Jan;12(1):110-3.

3. Ghani AC, Donnelly CA, Cox DR, Griffin JT, Fraser C, Lam TH, Ho LM, Chan WS, Anderson RM, Hedley AJ and Leung GM. Methods for estimating the case fatality ratio for a novel emerging infectious disease. American Journal of Epidemiology 162, 479-486, 2005.

4. Donnelly CA, Fisher MC, Fraser C, Ghani AC, Riley S, Ferguson NM, Anderson RM. Epidemiological and genetic analysis of severe acute respiratory syndrome. Lancet Infect Dis. 2004 Nov;4(11):672-83.

5. Leung GM, Hedley AJ, Ho LM, Chau P, Wong IO, Thach TQ, Ghani AC, Donnelly CA, Fraser C, Riley S, Ferguson NM, Anderson RM, Tsang T, Leung PY, Wong V, Chan JC, Tsui E, Lo SV, Lam TH. The epidemiology of severe acute respiratory syndrome in the 2003 Hong Kong epidemic: an analysis of all 1755 patients. Ann Intern Med. 2004 Nov 2;141(9):662-73.

6. Leung GM, Chung PH, Tsang T, Lim W, Chan SK, Chau P, Donnelly CA, Ghani AC, Fraser C, Riley S, Ferguson NM, Anderson RM, Law YL, Mok T, Ng T, Fu A, Leung PY, Peiris JS, Lam TH, Hedley AJ. SARS-CoV antibody prevalence in all Hong Kong patient contacts. Emerg Infect Dis. 2004 Sep;10(9):1653-6. Erratum in: Emerg Infect Dis. 2004 Oct;10(10):1890.

7. Anderson RM, Fraser C, Ghani AC, Donnelly CA, Riley S, Ferguson NM, Leung GM, Lam TH, Hedley AJ. Epidemiology, transmission dynamics and control of SARS: the 2002-2003 epidemic. Philos Trans R Soc Lond B Biol Sci. 2004 Jul 29;359(1447):1091-105. Review.

8. Donnelly CA, Ghani AC, Leung GM, Hedley AJ, Fraser C, Riley S, Abu-Raddad LJ, Ho LM, Thach TQ, Chau P, Chan KP, Lam TH, Tse LY, Tsang T, Liu SH, Kong JH, Lau EM, Ferguson NM, Anderson RM. Epidemiological determinants of spread of causal agent of severe acute respiratory syndrome in Hong Kong. Lancet. 2003 May 24;361(9371):1761-6. Erratum in: Lancet. 2003 May 24;361(9371):1832.

9. Riley S, Fraser C, Donnelly CA, Ghani AC, Abu-Raddad LJ, Hedley AJ, Leung GM, Ho LM, Lam TH, Thach TQ, Chau P, Chan KP, Lo SV, Leung PY, Tsang T, Ho W, Lee KH, Lau EM, Ferguson NM, Anderson RM. Transmission dynamics of the etiological agent of SARS in Hong Kong: impact of public health interventions. Science. 2003 Jun 20;300(5627):1961-6. Epub 2003 May 23. Key Influenza publications

1. *Ferguson NM, Fraser C, Donnelly CA, Ghani AC and Anderson RM. Public health risk from the avian H5N1 influenza epidemic. Science 304, 968- 969, 2004. DOI: 10.1126/science.1096898 2. van Boven M, Koopmans M, Du Ry van Beest Holle M, Meijer A, Klinkenberg D, Donnelly CA and Heesterbeek JAP. Detecting emerging transmissibility of avian influenza virus in human households. PLoS Computational Biology 3(7): e145 1394-1402, 2007. DOI: 10.1371/journal.pcbi.0030145 3. Bouma A, Claassen I, Natih K, Klinkenberg D, Donnelly CA, Koch G and van Boven M. Estimation of transmission parameters of H5N1 avian influenza virus in chickens. PLoS Pathogens 5(1): e1000281 2009. DOI:10.1371/journal.ppat.1000281 4. *Fraser C†, Donnelly CA†, Cauchemez S, Hanage WP, Van Kerkhove MD, Hollingsworth TD, Griffin J, Baggaley RF, Jenkins HE, Lyons EJ, Jombart T, Hinsley WR, Grassly NC, Balloux F, Ghani AC, Ferguson NM, Rambaut A, Pybus OG, Lopez-Gatell H, Alpuche-Aranda CM, Chapela IB, Zavala EP, Guevara DME, Checchi F, Garcia E, Hugonnet S, Roth C: The WHO Rapid Pandemic Assessment Collaboration. Pandemic Potential of a Strain of Influenza A (H1N1): Early Findings. Science 324, 1557-1561, 2009. (†These authors contributed equally to the work.) Published online 14 May 2009 DOI:10.1126/science.1176062. 5. Garske T, Legrand J, Donnelly CA, Ward H, Cauchemez S, Fraser C, Ferguson NM and Ghani AC. Assessing the severity of the novel influenza A/H1N1 pandemic. BMJ 339, 220-224, 2009. Published 14 July 2009, doi:10.1136/bmj.b2840 6. *Cauchemez S, Donnelly CA, Reed C, Ghani AC, Fraser C, Kent CK, Finelli L and Ferguson NM. Household transmission of 2009 pandemic influenza A (H1N1) virus in the United States. New England Journal of Medicine 361, 2619-27, 2009. 7. Gatewood Hoen A, Buckeridge DL, Chan EH, Freifeld CC, Keller M, Charland K, Donnelly CA and Brownstein JS. Characteristics of US public schools with reported cases of novel influenza A (H1N1). International Journal of Infectious Diseases 14 (Supplement 3), e6-e8, 2010. DOI:10.1016/j.ijid.2009.11.034 8. Donnelly CA, Finelli L, Cauchemez S, Olsen SJ, Doshi S, Jackson ML, Kennedy ED, Kamimoto L, Marchbanks TL, Morgan OW, Patel M, Swerdlow DL, Ferguson NM and the pH1N1 Household Investigations Working Group. Serial Intervals and the Temporal Distribution of Secondary Infections within Households of 2009 Pandemic Influenza A (H1N1): Implications for Influenza Control Recommendations. Clinical Infectious Diseases 52 (Supplement 1): S123-S130, 2011. DOI:10.1093/cid/ciq028 9. Van Kerkhove MD, Vandemaele KAH, Shinde V, Jaramillo-Gutierrez G, Koukounari A, Donnelly CA, Carlino LO, Owen R, Paterson B, Pelletier L, Vachon J, Gonzalez C, Yu Hongjie, Feng Zijian, Shuk Kwan Chuang, Au A, Buda S, Krause G, Haas W, Bonmarin I, Taniguichi K, Nakajima K, Shobayashi T, Takayama Y, Sunagawa T; Heraud JM, Orelle A, Palacios E, van der Sande MAB, Lieke Wielders CCH, Hunt D, Cutter J, Lee VJ, Thomas J, Santa-Olalla P, Sierra-Moros MJ, Hanshaoworakul W, Ungchusak K, Pebody R, Jain S, Mounts AW, on behalf of the WHO Working Group for Risk Factors for Severe H1N1pdm Infection. Risk factors for severe outcomes following 2009 Influenza A (H1N1) infection: A global pooled analysis. PLoS Medicine 8(7): e1001053, 2011. DOI:10.1371/journal.pmed.1001053. 10. Truscott J, Fraser C, Cauchemez S, Meeyai A, Hinsley W, Donnelly CA, Ghani A and Ferguson N. Essential epidemiological mechanisms underpinning the transmission dynamics of seasonal influenza. Journal of the Royal Society Interface 9: 304-312, 2012. Published online before print June 29 2011. DOI:10.1098/rsif.2011.0309 11. Yu H*, Cauchemez S*, Donnelly CA, Zhou L, Feng L, Xiang N, Zheng J, Ye M, Huai Y, Liao Q, Peng Z, Feng Y, Jiang H, Yang W, Wang Y, Ferguson NM and Feng Z. Transmission dynamics, border entry screening and school holidays during the 2009 influenza A(H1N1) pandemic, China. Emerging Infectious Diseases 18: 758-766, 2012. (*These authors contributed equally to the work.) DOI:10.3201/eid1805.110356 12. Van Kerkhove MD, Hirve S, Koukounari A and Mounts AW for the H1N1pdm serology working group (including Donnelly CA). Estimating Age-Specific Cumulative Incidence for the 2009 Influenza Pandemic: a Meta-Analysis of A(H1N1)pdm09 Serological Studies from 19 countries. Influenza and Other Respiratory Viruses 7(5): 872-886, 2013. DOI: 10.1111/irv.12074. Published online before print 21 January 2013. Key BSE/vCJD publications (1)

Donnelly CA and Ferguson NM. Statistical Aspects of BSE and vCJD: Models for Epidemics, Monographs on Statistics and Applied Probability, Chapman & Hall/CRC Press, 229pp, 1999.

1. Anderson RM, Donnelly CA, Ferguson NM, Woolhouse MEJ, Watt CJ, Udy HJ, MaWhinney S, Dunstan SP, Southwood TRE, Wilesmith JW, Ryan JBM, Hoinville LJ, Hillerton JE, Austin AR and Wells GAH. Transmission dynamics and epidemiology of BSE in British cattle. Nature 382, 779-788, 1996. 2. Donnelly CA, Ferguson NM, Ghani AC, Woolhouse MEJ, Watt CJ, and Anderson RM. The epidemiology of BSE in GB cattle herds: I. Epidemiological processes, demography of cattle and approaches to control by culling. Philosophical Transactions of the Royal Society London B 352, 781-801, 1997. 3. Ferguson NM, Donnelly CA, Woolhouse MEJ and Anderson RM. The epidemiology of BSE in GB cattle herds: II. Model construction and analysis of transmission dynamics. Philosophical Transactions of the Royal Society London B 352, 803-838, 1997. 4. Donnelly CA, Gore SM, Curnow RN and Wilesmith JW. The bovine spongiform encephalopathy maternal cohort study: its purpose and findings. Applied Statistics 46, 299-304, 1997. 5. Donnelly CA, Ghani AC, Ferguson NM, Wilesmith JW and Anderson RM. Analysis of the bovine spongiform encephalopathy maternal cohort study: evidence of direct maternal transmission. Applied Statistics 46, 321-344, 1997. 6. Donnelly CA, Ghani AC, Ferguson NM and Anderson RM. Recent trends in the BSE epidemic. Nature 389, 903, 1997. 7. Ferguson NM, Donnelly CA, Woolhouse MEJ and Anderson RM. A genetic interpretation of heightened risk of BSE in offspring of affected dams. Proceedings of the Royal Society London B 264, 1445-1455, 1997. 8. Donnelly CA, Ferguson NM, Ghani AC, Wilesmith JW and Anderson RM. Analysis of the dam-calf pairs of BSE cases: confirmation of a maternal risk enhancement. Proceedings of the Royal Society London B 264, 1647-1656, 1997. 9. Ferguson NM, Ghani AC, Donnelly CA, Denny GO and Anderson RM. BSE in Northern Ireland: epidemiological patterns past, present and future. Proceedings of the Royal Society London B 265, 545-554, 1998. 10. Donnelly CA. Maternal transmission of BSE: interpretation of the data on the offspring of BSE-affected pedigree suckler cows. Veterinary Record 142, 579-580, 1998. 11. Ghani AC, Ferguson NM, Donnelly CA, Hagenaars TJ and Anderson RM. Estimation of the number of people incubating variant CJD. Lancet 352, 1353-1354, 1998. 12. Ghani AC, Ferguson NM, Donnelly CA, Hagenaars TJ and Anderson RM. Epidemiological determinants of the pattern and magnitude of the vCJD epidemic in Great Britain. Proceedings of the Royal Society London B 265, 2443-2452, 1998. 13. Ferguson NM, Donnelly CA, Woolhouse MEJ and Anderson RM. Estimation of the basic reproduction number of BSE: the intensity of transmission in British cattle. Proceedings of the Royal Society London B 266, 23-32, 1999. Key BSE/vCJD publications (2)

14. Donnelly CA, Santos R, Ramos M, Galo A and Simas JP. BSE in Portugal: Anticipating the decline of an epidemic. Journal of Epidemiology and Biostatistics 4, 277-283, 1999. 15. Ghani AC, Donnelly CA, Ferguson NM and Anderson RM. Assessment of the prevalence of vCJD through testing tonsils and appendices for abnormal prion protein. Proceedings of the Royal Society London B 267, 23-29, 2000. 16. Hagenaars TJ, Ferguson NM, Donnelly CA, Ghani AC and Anderson RM. Feed-borne transmission of BSE and case clustering: a model study. Proceedings of the Royal Society London B 267, 205-215, 2000. 17. Ghani AC, Ferguson NM, Donnelly CA and Anderson RM. Predicted vCJD mortality in Great Britain. Nature 406, 583-584, 2000. 18. Donnelly CA. Likely size of the French BSE epidemic - Epidemiological analysis helps in evaluating the potential risks of eating French beef. Nature 408, 787-788, 2000. 19. Ferguson NM, Ghani AC, Donnelly CA, Hagenaars TJ and Anderson RM. Estimating the risk to human health posed by possible entry of BSE infection into the GB sheep flock. Nature 415, 420-424, 2002. Published online 9 January 2002; DOI: 10.1038/nature709. 20. Ghani AC, Donnelly CA, Ferguson NM and Anderson RM. The transmission dynamics of BSE and vCJD. Comptes Rendus Biologies 325, 37-47, 2002. 21. Donnelly CA. BSE in France: Epidemiological analysis and predictions. Comptes Rendus Biologies 325, 793-806, 2002. 22. Donnelly CA, Ferguson NM, Ghani AC and Anderson RM. Implications of BSE infection screening data for the scale of the British BSE epidemic and current European infection levels. Proceedings of the Royal Society London B 269, 2179-2190, 2002. Published online 9 October 2002. DOI: 10.1098/rspb.2002.2156. 23. Ghani AC, Ferguson NM, Donnelly CA and Anderson RM. Factors determining the pattern of the variant Creutzfeldt-Jakob disease (vCJD) epidemic in Great Britain. Proceedings of the Royal Society of London Series B 270, 689–698, 2003. Published online 28 February 2003. DOI: 10.1098/rspb.2002.2313. 24. Ghani AC, Ferguson NM, Donnelly CA and Anderson RM. Updated projections of future vCJD deaths in the UK. BMC Infectious Diseases 3:4, (27 April) 2003. URL: http://www.biomedcentral.com/1471-2334/3/4 25. Donnelly CA, Ferguson NM, Ghani AC and Anderson RM. Extending backcalculation to analyse BSE data. Statistical Methods in Medical Research 12, 177-190, 2003. 26. Ghani AC, Ferguson NM, Donnelly CA and Anderson RM. Short-term projections for variant Creutzfeldt-Jakob disease onsets. Statistical Methods in Medical Research 12, 191-201, 2003. 27. Ferguson NM and Donnelly CA. Assessment of risk posed by BSE in GB cattle and the impact of potential changes to current control measures. Proceedings of the Royal Society of London Series B 270, 1579-1584, 2003. Published online 20 June 2003. DOI: 10.1098/rspb.2003.2484 28. Harney MS, Ghani AC, Donnelly CA, Walsh RM, Walsh M, Howley R, Brett F and Farrell M. vCJD risk in The Republic of Ireland. BMC Infectious Diseases 3:28 (26 November) 2003. DOI:10.1186/1471-2334-3-28 URL: http://www.biomedcentral.com/content/pdf/1471-2334-3-28.pdf

Key MERS publications

1. Lessler J, Rodriguez-Barraquer I, Cummings DA, Garske T, Van Kerkhove M, Mills H, Truelove S, Hakeem R, Albarrak A, Ferguson NM; MERS-CoV Scenario Modeling Working Group; MERS-CoV Scenario Modeling Working Group. Estimating Potential Incidence of MERS-CoV Associated with Hajj Pilgrims to Saudi Arabia, 2014. PLoS Curr. 2014 Nov 24;6. pii: ecurrents.outbreaks.c5c9c9abd636164a9b6fd4dbda974369. doi: 10.1371/currents.outbreaks.c5c9c9abd636164a9b6fd4dbda974369.

2. Cauchemez S, Fraser C, Van Kerkhove MD, Donnelly CA, Riley S, Rambaut A, Enouf V, van der Werf S, Ferguson NM. Middle East respiratory syndrome coronavirus: quantification of the extent of the epidemic, surveillance biases, and transmissibility. Lancet Infect Dis. 2014 Jan;14(1):50-6. doi: 10.1016/S1473-3099(13)70304-9. Epub 2013 Nov 13.

3. Cauchemez S, Van Kerkhove MD, Riley S, Donnelly CA, Fraser C, Ferguson NM. Transmission scenarios for Middle East Respiratory Syndrome Coronavirus (MERS-CoV) and how to tell them apart. Euro Surveill. 2013 Jun 13;18(24). pii: 20503. Key Ebola publications

1. WHO Ebola Response Team. Ebola virus disease among children in West Africa. N Engl J Med. 2015 Mar 26;372(13):1274-7. doi: 10.1056/NEJMc1415318.

2. WHO Ebola Response Team. West African Ebola epidemic after one year--slowing but not yet under control. N Engl J Med. 2015 Feb 5;372(6):584-7. doi: 10.1056/NEJMc1414992. Epub 2014 Dec 24.

3. WHO Ebola Response Team. Ebola virus disease in West Africa--the first 9 months of the epidemic and forward projections. N Engl J Med. 2014 Oct 16;371(16):1481-95. doi: 10.1056/NEJMoa1411100. Epub 2014 Sep 22.