View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Old Dominion University Old Dominion University ODU Digital Commons Biological Sciences Faculty Publications Biological Sciences 2011 Estimating the Reproductive Numbers for the 2008-2009 Cholera Outbreaks in Zimbabwe Zindoga Mukandavire Shu Liao Jin Wang Old Dominion University Holly Gaff Old Dominion University, [email protected] David L. Smith See next page for additional authors Follow this and additional works at: https://digitalcommons.odu.edu/biology_fac_pubs Part of the Bacterial Infections and Mycoses Commons, Immunology and Infectious Disease Commons, Public Health Commons, and the Statistics and Probability Commons Repository Citation Mukandavire, Zindoga; Liao, Shu; Wang, Jin; Gaff, Holly; Smith, David L.; and Morris, J. Glenn Jr., "Estimating the Reproductive Numbers for the 2008-2009 Cholera Outbreaks in Zimbabwe" (2011). Biological Sciences Faculty Publications. 273. https://digitalcommons.odu.edu/biology_fac_pubs/273 Original Publication Citation Mukandavire, Z., Liao, S., Wang, J., Gaff, H., Smith, D. L., & Morris, J. G. (2011). Estimating the reproductive numbers for the 2008-2009 cholera outbreaks in Zimbabwe. Proceedings of the National Academy of Sciences of the United States of America, 108(21), 8767-8772. doi:10.1073/pnas.1019712108 This Article is brought to you for free and open access by the Biological Sciences at ODU Digital Commons. It has been accepted for inclusion in Biological Sciences Faculty Publications by an authorized administrator of ODU Digital Commons. For more information, please contact [email protected]. Authors Zindoga Mukandavire, Shu Liao, Jin Wang, Holly Gaff, David L. Smith, and J. Glenn Morris Jr. This article is available at ODU Digital Commons: https://digitalcommons.odu.edu/biology_fac_pubs/273 Estimating the reproductive numbers for the SEE COMMENTARY 2008–2009 cholera outbreaks in Zimbabwe Zindoga Mukandavirea, Shu Liaob, Jin Wangc, Holly Gaffd, David L. Smitha, and J. Glenn Morris, Jr.a,1 aEmerging Pathogens Institute, University of Florida, Gainesville, FL 32610; bSchool of Mathematics and Statistics, Chongqing Technology and Business University, Chongqing 400067, China; and Departments of cMathematics and Statistics and dBiological Sciences, Old Dominion University, Norfolk, VA 23529 Edited* by G. Balakrish Nair, National Institute of Cholera and Enteric Diseases, Kolkata, India, and approved March 25, 2011 (received for review January 4, 2011) Cholera remains an important global cause of morbidity and Mozambique and also in Kariba (in Mashonaland West prov- mortality, capable of causing periodic epidemic disease. Beginning ince) on the border with Zambia (5) (Fig. 2). Cholera cases in in August 2008, a major cholera epidemic occurred in Zimbabwe, early 2008 were first noted during a small outbreak in Masho- with 98,585 reported cases and 4,287 deaths. The dynamics of such naland East, Mashonaland Central, Mashonaland West, Harare, outbreaks, particularly in nonestuarine regions, are not well un- and Manicaland between January and April 2008. The disease derstood. We explored the utility of mathematical models in under- resurfaced in mid-August from St. Mary’s and Zengeza wards standing transmission dynamics of cholera and in assessing the of Chitungwiza (5, 6). Between September and October 2008, magnitude of interventions necessary to control epidemic disease. cholera cases were reported in Mashonaland West, Mashona- Weekly data on reported cholera cases were obtained from the land East, and Harare provinces and a full cholera epidemic Zimbabwe Ministry of Health and Child Welfare (MoHCW) for the wave that swept across the country emerged between November period from November 13, 2008 to July 31, 2009. A mathematical 1 and 15, affecting 9 of the 10 provinces, with disease reported fi model was formulated and tted to cumulative cholera cases to from all 10 provinces by the end of December 2008 (6). The R estimate the basic reproductive numbers 0 and the partial repro- Zimbabwe Minister of Health and Child Welfare declared – ductive numbers from all 10 provinces for the 2008 2009 Zimbabwe a state of emergency on December 3, 2008 and launched an cholera epidemic. Estimated basic reproductive numbers were appeal for international humanitarian aid (7, 8). WHO then set highly heterogeneous, ranging from a low value of just above unity up a Cholera Command and Control Centre in Harare to co- to 2.72. Partial reproductive numbers were also highly heteroge- ordinate international groups that were distributing medication neous, suggesting that the transmission routes varied by province; and helping in the treatment of water in the country (9). human-to-human transmission accounted for 41–95% of all trans- A good understanding of the transmission dynamics and ecology mission. Our models suggest that the underlying patterns of cholera of cholera in emergent epidemic regions like Zimbabwe can help to transmission varied widely from province to province, with a cor- responding variation in the amenability of outbreaks in different improve the control of future epidemics. Mathematical models provinces to control measures such as immunization. These data provide a quantitative and potentially valuable tool for this purpose. underscore the heterogeneity of cholera transmission dynamics, Althoughmultiplemodelsfor cholera havebeencreated,most of the potentially linked to differences in environment, socio-economic earlier models focused on endemiccholera andinteractions between MEDICAL SCIENCES conditions, and cultural practices. The lack of traditional estuarine environmental variables and disease occurrence, building on data from areas (such as Bangladesh) where there is close contact be- reservoirs combined with these estimates of R0 suggest that mass vaccination against cholera deployed strategically in Zimbabwe and tween infected populations and the estuarine (or riverine) environ- surrounding regions could prevent future cholera epidemics and ment. Zimbabwe, as a land-locked country in the middle of Africa, eventually eliminate cholera from the region. presents a very different setting for cholera, presaging the future of cholera epidemics in a rapidly urbanizing world. We present here a APPLIED disease transmission | parameter estimate model, fitted to the Zimbabwe data, that provides insights into the MATHEMATICS nature of the epidemic in Zimbabwe and, on a broader scale, to fi he 2008–2009 cholera outbreak in Zimbabwe was the worst control of cholera at a global level. More speci cally, we used African cholera epidemic in the last 15 y. In addition to the Zimbabwe data to derive estimates of the basic reproductive number T R fi large number of cases, the outbreak was characterized by its high ( 0) of cholera on a regional basis, building on a modi ed version of case fatality ratio (CFR) and extensive spread. The outbreak the cholera model initially proposed by our group (10). The epi- R began in August 2008 and swept across the whole country by demic threshold 0 provides information for the occurrence of an R < December 2008, and by the end of July 2009 there were 98,585 epidemic. If 0 1, then the pathogen introduced into a wholly susceptible population will eventually die out, and when R0 > 1, reported cases and 4,287 deaths. Zimbabwe has experienced fi sporadic outbreaks of cholera since the introduction of seventh endemicity is possible. It also de nes the average time it takes for an pandemic El Tor strains in the 1970s. These outbreaks have in- epidemic to complete one generation and the larger it is, the shorter creased in frequency and severity since the early 1990s and have the generation andthe more explosive epidemictransmission will be. become increasingly difficult to control as a result of deterioration of the health system and its associated infrastructure, related to Author contributions: Z.M., D.L.S., and J.G.M. designed research; Z.M., S.L., and J.W. the national economic crisis. Zimbabwe had cholera outbreaks in performed research; Z.M., S.L., J.W., H.G., and D.L.S. contributed new reagents/analytic 1992 and 1993 with 2,048 and 5,385 reported cases, respectively tools; Z.M., S.L., J.W., and H.G. analyzed data; and Z.M., D.L.S., and J.G.M. wrote the (1): Outbreaks were linked to influx of refugees from Mozambi- paper. que (a cholera endemic area) and drought (2, 3). There were no The authors declare no conflict of interest. cholera outbreaks recorded from 1994 to 1997; however, since *This Direct Submission article had a prearranged editor. 1998, a period that coincides with the start of the economic crisis Freely available online through the PNAS open access option. in the country, cholera has been reported every year (4) (Fig. 1). See Commentary on page 8529. Cholera outbreaks in Zimbabwe occurred previously in com- 1To whom correspondence should be addressed. E-mail: [email protected]fl.edu. munities that border endemic regions, particularly in the prov- This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10. inces of Manicaland and Mashonaland East on the border with 1073/pnas.1019712108/-/DCSupplemental. www.pnas.org/cgi/doi/10.1073/pnas.1019712108 PNAS | May 24, 2011 | vol. 108 | no. 21 | 8767–8772 Year Cases Deaths CFR % 1992 2048 57 2.8 12 12 1993 5385 323 6 Cholera Cases Cholera Deaths 1994 3 0 0 CFR% 1995 0 0 0 10 10 1996 0 0 0 1997 1 0 0 8 8 1998 883 46 5.2 1999 4081 240 5.9 6 6 2000 1911 71 3.7 % CRF 2001 649 13 2 Log [Cholera cases/deaths] Fig. 3. Model flow diagram. 2002 3684 354 9.6 4 4 2003 879 19 2.2 2004 125 10 8 the basis for the critical importance of the fast human-to-human versus the 2 2 2005 231 15 6.5 slower environment-to-human transmission in the explosive nature of cholera fi 2006 789 63 8 epidemics. These simpli cations result in a model with very similar dynamics, including the model’s ability to describe the explosive cholera outbreaks (17).