Estimating the contribution of human-to-human. transmission to Lassa fever

Transcription

1 Dynamic Drivers of Disease in Africa Integrating our understandings of zoonoses, ecosystems and wellbeing Estimating the contribution of human-to-human transmission to Lassa fever Gianni Lo Iacono Department Veterinary Medicine University of Cambridge, UK

2 Background Viral hemorrhagic fever caused by Arenavirus Lassa. False-colour transmission electron micrograph of the Lassa fever virus. Photo from Science Photo Library

3 Background Viral hemorrhagic fever caused by Arenavirus Lassa. Vector and reservoir: Mastomys Natalensis. Mastomys Nataliensis Photo from Lina Moses

4 Background Viral hemorrhagic fever caused by Arenavirus Lassa. Vector and reservoir: Mastomys Natalensis. Endemic in West Africa Map of the Mano River Union countries (Sierra Leone, Guinea, and Liberia). The approximate known endemic area for Lassa fever is shown by the dotted oval. From Khan et al. (2008)

5 Background Viral hemorrhagic fever caused by Arenavirus Lassa. Vector and reservoir: Mastomys Natalensis. Endemic in West Africa Unclear routes of transmission (contaminated excreta, aerosol..)

6 Background Viral hemorrhagic fever caused by Arenavirus Lassa. Vector and reservoir: Mastomys Natalensis. Endemic in West Africa Unclear routes of transmission (contaminated excreta, aerosol..) Unclear role of human-to-human transmission

7 O O O O,= Background Humans Cases: Available Data The Modelling Approach References Background Nosocomial outbreaks. From Carey et al. (1972) D. E. CAREY~ G. E. KEMP ET AL. 405 ~ ~0 -d.,.,i 2. s~ ~ o~ o ~4 ot Serological studies 140 family contacts of patients were bled and their sera examined by CF test in an effort to detect additional recent Lassa infections. 4 sera gave positive reactions. 2 of

8 Background Nosocomial outbreaks. From Carey et al. (1972)

9 Existing Data: Kenema Governament Hospital (KGH). The Kenema Government Hospital Laboratory. From Khan et al. (2008)

10 Existing Data: Kenema Governament Hospital (KGH).

11 Existing Data: Kenema Governament Hospital (KGH).

12 Existing Data: Kenema Governament Hospital (KGH).

13 Existing Data: Kenema Governament Hospital (KGH).

14 Kenema Governament Hospital (KGH) Shaffer et al. (2014) 12 9 No of Cases /04/08 01/10/08 01/04/09 01/10/09 01/04/10 01/10/10 01/04/11 01/10/11 Date of admission

15 Kenema Governament Hospital (KGH) Shaffer et al. (2014) 9 Zoonotic origin Human origin No of Cases /04/08 01/10/08 01/04/09 01/10/09 01/04/10 01/10/10 01/04/11 01/10/11 Date of admission

16 Kenema Governament Hospital (KGH) Shaffer et al. (2014) 9 Zoonotic origin Human origin No of Cases 6 How to disentangle the two contributions? /04/08 01/10/08 01/04/09 01/10/09 01/04/10 01/10/10 01/04/11 01/10/11 Date of admission

17 Kenema Governament Hospital (KGH) Shaffer et al. (2014) 9 Zoonotic origin Human origin No of Cases 6 How to disentangle the two contributions? Outbreaks where only human-to-human transmission occur /04/08 01/10/08 01/04/09 01/10/09 01/04/10 01/10/10 01/04/11 01/10/11 Date of admission

18 Kenema Governament Hospital (KGH) Shaffer et al. (2014) 9 Zoonotic origin Human origin No of Cases 6 How to disentangle the two contributions? Outbreaks where only human-to-human transmission occur 3 Calculate the corresponding Reproductive Numbers 0 01/04/08 01/10/08 01/04/09 01/10/09 01/04/10 01/10/10 01/04/11 01/10/11 Date of admission

19 Kenema Governament Hospital (KGH) Shaffer et al. (2014) 9 Zoonotic origin Human origin No of Cases 6 How to disentangle the two contributions? Outbreaks where only human-to-human transmission occur 3 Calculate the corresponding Reproductive Numbers Compare with the Reproductive Numbers from 0 01/04/08 01/10/08 01/04/09 01/10/09 01/04/10 01/10/10 01/04/11 01/10/11 KGH data Date of admission

20 Effective Reproductive number for nosocomial cases The relative likelihood q ij that case i has been infected by case j (based on Wallinga & Teunis (2004)):

21 Effective Reproductive number for nosocomial cases The relative likelihood q ij that case i has been infected by case j (based on Wallinga & Teunis (2004)): q ij = τ ij k τ ik τ mn = t n t m is the time of exposure of case n to case m

22 Effective Reproductive number for nosocomial cases The relative likelihood q ij that case i has been infected by case j (based on Wallinga & Teunis (2004)): q ij = R Nos j τ ij k τ ik = i q ij τ mn = t n t m is the time of exposure of case n to case m

23 Human-to-human transmission From Carey et al. (1972) TS HR Diagrammatic representation Sister in law of SI Wife of Case HR2. Cared for child KD DG Ward Cleaner L Visited the ward PI Not located RA GD AK MA AA AH YB FT SI LM HR2 HY HA EE TI EE2 SE Hospital staff Cared for child Friend of HR. Visited the ward Scrub nurse Ward cleaner Not clear if she visited the ward Mother of LM On ward for other illness. Wife of TI, aunt of EE,EE2,SE Cared for TS On ward for other illness. Daughter of YB Husband of HR Not located Not located Nephew of FT and TI, brother of EE2, SE Husband of FT Sister of EE2 Brother of EE No of days from onset

24 Human-to-human transmission From Carey et al. (1972) TS HR Sister in law of SI Diagrammatic representation Wife of Case HR2. Cared for child KD DG L PI RA GD AK MA AA AH YB FT SI LM HR2 HY HA EE TI EE2 SE Ward Cleaner Visited the ward Not located Hospital staff Cared for child Friend of HR. Visited the ward Scrub nurse Ward cleaner Not clear if she visited the ward Mother of LM On ward for other illness. Wife of TI, aunt of EE,EE2,SE Cared for TS On ward for other illness. Daughter of YB Husband of HR Not located Not located Nephew of FT and TI, brother of EE2, SE Husband of FT Sister of EE2 Brother of EE No of days from onset

25 Effective Reproductive number for nosocomial cases 12 9 R eff TS HR KD DG PI RA GD AK MA AA AH YB Case ID FT SI LM HR2 HY HA EE TI EE2 SE NA

26 Generation time for nosocomial cases 0.10 Density Generation time (days)

27 Effective Reproductive number for data from (KGH) 12 9 No of Cases /04/08 01/10/08 01/04/09 01/10/09 01/04/10 01/10/10 01/04/11 01/10/11 Date of admission

28 Effective Reproductive number for data from (KGH)

29 Effective Reproductive number for data from (KGH)

30 Effective Reproductive number for data from KGH The relative likelihood that case i has been infected by case j, given their difference in time of symptom onset τ ij, is given by (Wallinga & Teunis (2004)):

31 Effective Reproductive number for data from KGH The relative likelihood that case i has been infected by case j, given their difference in time of symptom onset τ ij, is given by (Wallinga & Teunis (2004)): p ij = w(τ ij ) i k w(τ ik) where w(τ ij ) is the distribution for the generation interval

32 Effective Reproductive number for data from KGH The relative likelihood that case i has been infected by case j, given their difference in time of symptom onset τ ij, is given by (Wallinga & Teunis (2004)): p ij = w(τ ij ) i k w(τ ik) R j = n i=1 p ij where w(τ ij ) is the distribution for the generation interval

33 Effective Reproductive number for data from KGH The relative likelihood that case i has been infected by case j, given their difference in time of symptom onset τ ij, is given by (Wallinga & Teunis (2004)): w(τ ij ) p ij = i k w(τ ik) R j (Q) = n(1 Q) where w(τ ij ) is the distribution for the generation interval, Q fraction of imported cases i=1 p ij

34 Effective Reproductive number for data from KGH

35 Effective Reproductive number for data from KGH Reproductive Number from human to human transmission for all Sierra Leonean cases based on admission to KGH hospital R Proportion of cases due to human to human transmission

36 Effective Reproductive number for data from KGH 0.4 Reproductive Number for nosocomial cases (Jos and Zorzor) 0.3 Reproductive Number from human to human transmission for all Sierra Leonean cases based on admission to KGH hospital R Proportion of cases due to human to human transmission

37 Effective Reproductive number for data from KGH 0.4 Reproductive Number for nosocomial cases (Jos and Zorzor) 0.3 Reproductive Number from human to human transmission for all Sierra Leonean cases based on admission to KGH hospital R Reproductive Number for extra nosocomial cases (family from Jos) Proportion of cases due to human to human transmission

38 Effective Reproductive number for data from KGH 0.4 Reproductive Number for nosocomial cases (Jos and Zorzor) 0.3 Reproductive Number from human to human transmission for all Sierra Leonean cases based on admission to KGH hospital R 0.2 Proportion when the two Reproductive Numbers are equal 0.1 Reproductive Number for extra nosocomial cases (family from Jos) Proportion of cases due to human to human transmission

39 Super-spreading An illustration of Typhoid Mary (Mary Mallon) that appeared in the New York American article of June 20, See e.g.??here

40 Effective Reproductive number for nosocomial cases 10 5 Density Hospital Zorzor Jos R eff

41 The rule 13.7% 11.7% 9.7% Proportion of R > 1 7.8% 6% 4.5% 3.2% 1.8% 1% 0.3% 0% 0% 10% 20% 30% 40% 50% 60% 70% Human to human contribution % 80% 90% 100%

42 The rule

43 The rule

44 The rule 77% 72% 67% 60% Proportional Impact 52% 44% 38% 27% 21% 14% 0.3% (10%) 1.8% (30%) 3.2% 4.5% 6% 7.8% 9.7% (40%) (50%) (60%) (70%) (80%) Proportion of R > 1 (Human to human contribution %) 11.7% (90%) 13.7% (100%)

45 The rule

46 Acknowledgments Andrew A. Cunningham 2, Elisabeth Fichet-Calvet 3, Robert F. Garry 4,5,6, Donald S. Grant 7, Sheik Humarr Khan 7, Melissa Leach 8, Lina M. Moses 4, John S. Schieffelin 10, Jeffrey G. Shaffer 11, Collen Webb 12, James L. N. Wood 1 1 Department of Veterinary Medicine, Disease Dynamics Unit, University of Cambridge, Cambridge, United Kingdom. 2 Institute of Zoology, Zoological Society of London. United Kingdom 3 Bernhard-Nocht Institute of Tropical Medicine. Hamburg, Germany 4 Department of Microbiology and Immunology, Tulane University, New Orleans, Louisiana, USA 5 Broad Institute, Cambridge, Massachusetts, USA 6 Zalgen Labs, LLC, Germantown, MD 7 Lassa Fever Program, Kenema Government Hospital, Kenema, Sierra Leone 8 Institute of Development Studies, University of Sussex. Brighton, United Kingdom. 10 Sections of Infectious Disease, Departments of Pediatrics and Internal Medicine, School of Medicine, Tulane University, New Orleans, LA, USA 11 Department of Biostatistics and Bioinformatics, Tulane School of Public Health and Tropical Medicine, New Orleans, LA, USA 12 Department of Biology, Colorado State University, Fort Collins, USA This work for the Dynamic Drivers of Disease in Africa Consortium was funded with support from the Ecosystem Services for Poverty Alleviation (ESPA) programme. The ESPA programme is funded by the Department for International Development (DFID), the Economic and Social Research Council (ESRC) and the Natural Environment Research Council (NERC). See more at:

47 Carey, D., Kemp, G., White, H., Pinneo, L., Addy, R., Fom, A., Stroh, G., Casals, J., & Henderson, B. (1972). Lassa fever Epidemiological aspects of the 1970 epidemic, Jos, Nigeria. Transactions of the Royal Society of Tropical Medicine and Hygiene, 66(3), URL Khan, M. A. I., Luo, X. Y., Nicolleau, F. C. G. A., Tucker, P. G., & Lo Iacono, G. (2008). Effects of LES sub-grid flow structure on particle deposition in a plane channel with a ribbed wall. International Journal for Numerical Methods in Biomedical Engineering, 26(8), URL Shaffer, J. G., Grant, D. S., Schieffelin, J. S., Boisen, M. L., Goba, A., Hartnett, J. N., Levy, D. C., Yenni, R. E., Moses, L. M., Fullah, M., Momoh, M., Fonnie, M., Fonnie, R., Kanneh, L., Koroma, V. J., Kargbo, K., Ottomassathien, D., Muncy, I. J., Jones, A. B., Illick, M. M., Kulakosky, P. C., Haislip, A. M., Bishop, C. M., Elliot, D. H., Brown, B. L., Zhu, H., Hastie, K. M., Andersen, K. G., Gire, S. K., Tabrizi, S., Tariyal, R., Stremlau, M., Matschiner, A., Sampey, D. B., Spence, J. S., Cross, R. W., Geisbert, J. B., Folarin, O. a., Happi, C. T., Pitts, K. R., Geske, F. J., Geisbert, T. W., Saphire, E. O., Robinson, J. E., Wilson, R. B., Sabeti, P. C., Henderson, L. a., Khan, S. H., Bausch, D. G., Branco, L. M., & Garry, R. F. (2014). Lassa Fever in post-conflict sierra leone. PLoS neglected tropical diseases, 8(3), e2748. Wallinga, J., & Teunis, P. (2004). Different epidemic curves for severe acute respiratory syndrome reveal similar impacts of control measures. American journal of epidemiology, 160(6), URL