• View in gallery

    Epidemiologic curve for 2009 pandemic influenza A/H1N1 (H1N1pdm09) in Kampala, Uganda, 2009–2015. Non-H1N1pdm09 refers to influenza A/H3N2, seasonal A/H1N1, and B viruses. This figure appears in color at www.ajtmh.org.

  • View in gallery

    Map of Kampala, Uganda, displaying influenza cases identified at five sentinel sites from 2009 to 2015. This figure appears in color at www.ajtmh.org.

  • 1.

    Girard MP, Tam JS, Assossou OM, Kieny MP, 2010. The 2009 A (H1N1) influenza virus pandemic: a review. Vaccine 28: 48954902.

  • 2.

    Bell DM, Weisfuse IB, Hernandez-Avila M, Del Rio C, Bustamante X, Rodier G, 2009. Pandemic influenza as 21st century urban public health crisis. Emerg Infect Dis 15: 19631969.

    • Search Google Scholar
    • Export Citation
  • 3.

    World Health Organization, 2010. Limiting Spread: Limiting the Spread of Pandemic, Zoonotic, and Seasonal Epidemic Influenza. Available at: http://www.who.int/influenza/resources/research/ research_agenda_influenza_stream_2_limiting_spread.pdf. Accessed June 5, 2017.

  • 4.

    Cummings MJ 2016. Epidemiologic and spatiotemporal characterization of influenza and severe acute respiratory infection in Uganda, 2010–2015. Ann Am Thorac Soc 13: 21592168.

    • Search Google Scholar
    • Export Citation
  • 5.

    Uganda Bureau of Statistics, 2014. The State of Uganda Population Report 2014. Available at: library.health.go.ug/download/file/fid/580910. Accessed June 27, 2017.

  • 6.

    Alirol E, Getaz L, Stoll B, Chappuis F, Loutan L, 2011. Urbanisation and infectious diseases in a globalised world. Lancet Infect Dis 11: 131141.

  • 7.

    Lutwama JJ, Bakamutumaho B, Kayiwa JT, Chiiza R, Namagambo B, Katz MA, Geissler AL, 2012. Clinic- and hospital-based sentinel influenza surveillance, Uganda 2007–2010. J Infect Dis 206 (Suppl 1): S87S93.

    • Search Google Scholar
    • Export Citation
  • 8.

    Keeling MJ, Rohani P, 2007. Modeling Infectious Diseases in Humans and Animals. Princeton, NJ: Princeton University Press.

  • 9.

    Carrat F, Vergu E, Ferguson NM, Lemaitre M, Cauchemez S, Leach S, Valleron AJ, 2008. Time lines of infection and disease in human influenza: a review of volunteer challenge studies. Am J Epidemiol 167: 775785.

    • Search Google Scholar
    • Export Citation
  • 10.

    World Health Organization, 2016. Zika Situation Report. Zika Virus Microcephaly Guillain-Barré Syndrome. Available at: http://www.who.int/emergencies/zika-virus/situation-report/1-september-2016/en/. Accessed June 27, 2017.

  • 11.

    Wong VK 2015. Phylogeographical analysis of the dominant multidrug-resistant H58 clade of Salmonella typhi identifies inter- and intracontinental transmission events. Nat Genet 47: 632639.

    • Search Google Scholar
    • Export Citation
  • 12.

    Conway DJ, 2007. Molecular epidemiology of malaria. Clin Microbiol Rev 20: 188204.

  • 13.

    Phillips H, Killingray D, 2003. The Spanish Influenza Pandemic of 1918–19: New Perspectives. New York, NY: Routledge.

  • 14.

    Van Kerkhove MD, Hirve S, Koukounari A, Mounts AW; H1N1pdm Serology Working Group, 2013. Estimating age-specific cumulative incidence for the 2009 influenza pandemic: a meta-analysis of A(H1N1)pdm09 serological studies from 19 countries. Influenza Other Respi Viruses 7: 872886.

    • Search Google Scholar
    • Export Citation
  • 15.

    Biggerstaff M, Cauchemez S, Reed C, Gambhir M, Finelli L, 2014. Estimates of the reproduction number for seasonal, pandemic, and zoonotic influenza: a systematic review of the literature. BMC Infect Dis 14: 480.

    • Search Google Scholar
    • Export Citation
  • 16.

    Bedford T 2015. Global circulation patterns of seasonal influenza viruses vary with antigenic drift. Nature 523: 217220.

  • 17.

    Shek LP, Lee BW, 2003. Epidemiology and seasonality of respiratory tract virus infections in the tropics. Paediatr Respir Rev 4: 105111.

  • 18.

    Tamerius JD, Shaman J, Alonso WJ, Bloom-Feshbach K, Uejio CK, Comrie A, Viboud C, 2013. Environmental predictors of seasonal influenza epidemics across temperate and tropical climates. PLoS Pathog 9: e1003194.

    • Search Google Scholar
    • Export Citation
  • 19.

    Cohen C, Simonsen L, Sample J, Kang JW, Miller M, Madhi SA, Campsmith M, Viboud C, 2012. Influenza-related mortality among adults aged 25–54 years with AIDS in South Africa and the United States of America. Clin Infect Dis 55: 9961003.

    • Search Google Scholar
    • Export Citation

 

 

 

 

Emergence, Epidemiology, and Transmission Dynamics of 2009 Pandemic A/H1N1 Influenza in Kampala, Uganda, 2009–2015

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  • 1 Division of Pulmonary, Allergy, and Critical Care Medicine, Columbia University Medical Center, New York;
  • 2 National Influenza Center, Uganda Virus Research Institute, Entebbe, Uganda;
  • 3 Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, New York;
  • 4 Epidemiology and Surveillance Division, Ministry of Health, Kampala, Uganda;
  • 5 South Sudan Country Office, World Health Organization, Juba, South Sudan;
  • 6 Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, New York

In sub-Saharan Africa, little is known about the epidemiology of pandemic-prone influenza viruses in urban settings. Using data from a prospective sentinel surveillance network, we characterized the emergence, epidemiology, and transmission dynamics of 2009 pandemic A/H1N1 influenza (H1N1pdm09) in Kampala, Uganda. After virus introduction via international air travel from England in June 2009, we estimated the basic reproductive number in Kampala to be 1.06–1.13, corresponding to attack rates of 12–22%. We subsequently identified 613 cases of influenza in Kampala from 2009 to 2015, of which 191 (31.2%) were infected with H1N1pdm09. Patients infected with H1N1pdm09 were more likely to be older adult (ages 35–64) males with illness onset during rainy season months. Urban settings in sub-Saharan Africa are vulnerable to importation and intense transmission of pandemic-prone influenza viruses. Enhanced surveillance and influenza pandemic preparedness in these settings is needed.

INTRODUCTION

After its emergence in Mexico City in March 2009, pandemic A/H1N1 influenza virus (H1N1pdm09) quickly spread to major cities worldwide.1,2 Although sub-Saharan Africa accounted for a substantial proportion of global mortality during the pandemic, little remains known about the epidemiology and transmission of H1N1pdm09 in urban settings in the region.2,3 In these settings, high population density and increased amounts of shared space are likely conducive to influenza transmission, whereas surveillance and control measures are concomitantly challenged.2,4

Similar to the remainder of sub-Saharan Africa, Uganda has experienced rapid urbanization over the past 40 years, with the urban population at 15–20%.5 In the event of a recurrent influenza pandemic, continued growth and increasing density of nonimmune urban populations represents a significant challenge to containment and mitigation.6 Here, we characterize the emergence, epidemiology, and transmission dynamics of H1N1pdm09 in Kampala, Uganda, from 2009 to 2015.

THE STUDY

Kampala is the largest city in Uganda with a population of approximately 1.5 million and a population density of 7,900 persons/km2.5 Nearly 60% of Kampala’s population resides in slums or informal communities, where population density reaches 20,000–30,000 persons/km2.5 From 2009 to 2015, prospective surveillance for influenza was conducted by the Uganda Virus Research Institute (UVRI) among patients with influenza-like illness (ILI) and severe acute respiratory infection (SARI) identified at five health facilities in Kampala. During the 2009–2010 pandemic period, screening was also conducted at Entebbe International Airport, from where disembarking travelers with suspected ILI and SARI were referred to sentinel site health facilities. The full UVRI protocol for SARI and ILI case definition, identification, and enrollment has been published.4,7 At each facility, clinicians collected clinical and demographic data and naso- and/or oropharyngeal swab samples from ILI and SARI cases. Samples were tested at UVRI by real-time reverse transcription polymerase chain reaction using primers provided by U.S. Centers for Disease Control and Prevention. For influenza A–positive samples, subtyping was done for seasonal A/H1, A/H3, A/H5, and H1N1pdm09.

To identify risk factors for infection with H1Npdm09, we compared demographic and clinical characteristics of patients infected with H1N1pdm09 to those infected with other influenza types and subtypes. Fischer’s exact or χ2 tests were used to compare categorical variables, and medians were compared using the Mann–Whitney U test. Univariate analysis and multivariate logistic regression models were used to calculate odds ratios and 95% confidence intervals. Variables were included in the multivariate model if their corresponding P value in the univariate analysis was ≤ 0.10.

We also estimated the basic reproductive number (R0, i.e., the average number of secondary cases arising from one primary case in an entirely susceptible population) for H1N1pdm09 in Kampala. To estimate R0, we aggregated incidence into monthly intervals, and fitted a linear function to the logarithm of cumulative incidence over the first 4 months of the pandemic (i.e., June–September 2009). R0 was calculated per the formula: R0=1+rD, where r is the slope of the aforementioned linear fit, and D is the infectious period for influenza.8 Based on the calculated R0, we then estimated the attack rate by solving the equation 1z=e(R0z), where z is the final attack rate.9

In the context of routine surveillance, verbal consent was obtained from patients ≥ 18 years and from parents or legal guardians for patients < 18 years. The study was approved by institutional review boards at UVRI, the Uganda National Council for Science and Technology, and Columbia University.

The first case of H1N1pdm09 in Uganda was identified on June 30, 2009 in a 40-year-old businessman returning to Kampala from England where he had developed ILI on June 29th. Upon arrival to Entebbe International Airport on June 30th, the patient was identified through screening procedures and was transferred to a sentinel site facility. Three sequential nasopharyngeal swabs were all positive for H1N1pdm09.

After introduction into Kampala, H1N1pdm09 rapidly became the dominant circulating subtype in the city. The epidemic peaked in October 2009 with the majority of cases observed in older children and young adults (Figure 1, Supplemental data Supplemental Figure 1). During the remainder of the 2009–2015 surveillance period, clinicians identified 613 cases of influenza, of which 191 (31.2%) were infected with H1N1pdm09 (Figure 2).

Figure 1.
Figure 1.

Epidemiologic curve for 2009 pandemic influenza A/H1N1 (H1N1pdm09) in Kampala, Uganda, 2009–2015. Non-H1N1pdm09 refers to influenza A/H3N2, seasonal A/H1N1, and B viruses. This figure appears in color at www.ajtmh.org.

Citation: The American Journal of Tropical Medicine and Hygiene 98, 1; 10.4269/ajtmh.17-0524

Figure 2.
Figure 2.

Map of Kampala, Uganda, displaying influenza cases identified at five sentinel sites from 2009 to 2015. This figure appears in color at www.ajtmh.org.

Citation: The American Journal of Tropical Medicine and Hygiene 98, 1; 10.4269/ajtmh.17-0524

In multivariate models, patients infected with H1N1pdm09 were more likely to be male, aged 35–64, and had illness onset during rainy season months (Table 1). However, these trends were driven predominantly by data collected during the 2009 pandemic period (Supplemental Tables 1 and 2 in Supplemental Data). Assuming D = 2–4 days, R0 was estimated to be 1.06 (D = 2 days), 1.10 (D = 3 days), and 1.13 (D = 4 days), with corresponding attack rates of 12%, 17%, and 22%, respectively (Supplemental data Supplemental Figure 2).9

Table 1

Risk factors for infection with 2009 influenza A/H1N1 in Kampala, Uganda, 2009–2015

Patient characteristicsH1N1pdm09 influenzaNon-H1N1pdm09 influenzaUnivariate OR (95% CI)Multivariate OR (95% CI)
N (% total)N (% total)
N = 191N = 422
SexMale114 (60.0)203 (48.1)1.62 (1.14–2.29)1.72 (1.19–2.47)
Female76 (40.0)219 (51.9)
Age2 months to 4 years92 (50.3)259 (62.3)0.61 (0.43–0.87)0.74 (0.49–1.11)
5–14 years55 (30.1)112 (26.9)1.17 (0.80–1.71)
15–34 years25 (13.7)36 (8.7)1.67 (0.97–2.87)1.34 (0.72–2.49)
35–64 years11 (6.0)8 (1.9)3.26 (1.29–8.25)3.17 (1.17–8.58)
Median age, years (IQR)4 (2–11)3 (1-7)
Illness onset during rainy seasonYes134 (70.5)223 (53.3)2.09 (1.45–3.02)1.99 (1.36–2.91)
No56 (29.5)195 (46.7)
Signs and symptomsSore throat8 (6.5)23 (6.5)0.99 (0.43–2.27)
Dyspnea64 (51.6)175 (49.6)1.09 (0.72–1.63)
Diarrhea35 (28.5)99 (28.4)1.00 (0.63–1.58)
Vomiting48 (38.7)108 (30.7)1.43 (0.93–2.19)
Headache88 (71.0)268 (76.4)0.76 (0.48–1.20)
Myalgia45 (36.6)136 (38.9)0.91 (0.59–1.39)

CI = confidence interval; OR = odds ratio.

CONCLUSIONS

In this study, we identified the introduction of H1N1pdm09 in Uganda via international air travel and characterized the epidemiology and transmission dynamics of the virus in Uganda’s largest city. Although international outbreaks of Ebola, Zika, and Chikungunya virus have highlighted the emergence of high-priority pathogens out of sub-Saharan Africa, our findings emphasize the equal importance of pandemic-prone virus introduction into the region. Our data further suggest that the transmission potential of H1N1pdm09 in Kampala was comparable to that reported from other large cities.

We identified the introduction of H1N1pdm09 into Uganda among a traveler returning to Uganda from England.1 Historically, limited attention has been paid to importation of emerging pathogens into sub-Saharan Africa. However, Asian lineage Zika virus was imported to Cape Verde in 2016, and drug-resistant Plasmodium falciparum and Salmonella typhi species were introduced to the region from Asia nearly two decades ago.1012 Evidence also suggests that 1918 H1N1 virus was introduced into sub-Saharan African seaports from Europe and Asia, with similar importation trends likely responsible for the 1957 H2N2 and 1968 H3N2 pandemics.13

We calculated the R0 of H1N1pdm09 in Kampala to be 1.06–1.13. Although slightly below the median R0 reported for H1N1pdm09 globally (1.46), our results are comparable to R0 reported from other urban settings (range 1.1–2.8).14,15 Urban influenza transmission in sub-Saharan Africa poses a considerable global health challenge. Together with a high-risk environment for human-to-human transmission, live poultry markets are common and may facilitate zoonotic introduction of novel influenza viruses into concentrated populations. Surveillance and control measures may also be challenged in crowded schools and slums.2,4,6

The high incidence of H1N1pdm09 in 2009 most likely saturated the herd immunity in Kampala, with few H1N1pdm09 cases observed from 2010 to 2013. However, after 3 years of accumulating naive hosts, a recurrent epidemic of H1N1pdm09 occurred again in 2014, mainly in young children. Consistent with a prior analysis, this supports the slower drift rate of H1N1 viruses and its dependence on new naive hosts.16 Seasonally, H1N1pdm09 incidence peaked during the rainy seasons, and patients infected with H1N1pdm09 were more likely to have illness onset during these time periods. Studies from the tropics have demonstrated increased influenza incidence during rainy seasons, potentially due to a relationship between virus survival, humidity, and precipitation.17,18 Given implications for vaccine deployment, future investigations are needed to better characterize these environmental transmission dynamics.

Although influenza incidence is typically concentrated among infants and young children, we observed a high frequency of H1N1pdm09 infection among older children and young adults during the early pandemic period in 2009. Patients infected with H1N1pdm09 were also more likely to be older adults. Data from South Africa suggest that this age group may be at higher risk for severe outcomes from influenza, likely due to a higher burden of human immunodeficiency virus (HIV) infection.19

Our study had limitations. Sentinel surveillance programs are reflective of their catchment populations, and our findings are limited to data collected at five facilities. For our estimates of R0, we chose to target the early pandemic period, potentially limiting the power of our estimates. Finally, our epidemiologic analyses were limited to data captured by the UVRI surveillance program, and we lacked detailed clinical data such as HIV infection status.

Urban settings in sub-Saharan Africa are vulnerable to importation and intense transmission of pandemic influenza viruses. Further elucidation of the epidemiology and transmission dynamics of influenza in these settings is needed to enhance containment and mitigation strategies for these unique environments.

Supplementary Material

Acknowledgments:

We thank sentinel site clinicians for their assistance with data and sample collection.

REFERENCES

  • 1.

    Girard MP, Tam JS, Assossou OM, Kieny MP, 2010. The 2009 A (H1N1) influenza virus pandemic: a review. Vaccine 28: 48954902.

  • 2.

    Bell DM, Weisfuse IB, Hernandez-Avila M, Del Rio C, Bustamante X, Rodier G, 2009. Pandemic influenza as 21st century urban public health crisis. Emerg Infect Dis 15: 19631969.

    • Search Google Scholar
    • Export Citation
  • 3.

    World Health Organization, 2010. Limiting Spread: Limiting the Spread of Pandemic, Zoonotic, and Seasonal Epidemic Influenza. Available at: http://www.who.int/influenza/resources/research/ research_agenda_influenza_stream_2_limiting_spread.pdf. Accessed June 5, 2017.

  • 4.

    Cummings MJ 2016. Epidemiologic and spatiotemporal characterization of influenza and severe acute respiratory infection in Uganda, 2010–2015. Ann Am Thorac Soc 13: 21592168.

    • Search Google Scholar
    • Export Citation
  • 5.

    Uganda Bureau of Statistics, 2014. The State of Uganda Population Report 2014. Available at: library.health.go.ug/download/file/fid/580910. Accessed June 27, 2017.

  • 6.

    Alirol E, Getaz L, Stoll B, Chappuis F, Loutan L, 2011. Urbanisation and infectious diseases in a globalised world. Lancet Infect Dis 11: 131141.

  • 7.

    Lutwama JJ, Bakamutumaho B, Kayiwa JT, Chiiza R, Namagambo B, Katz MA, Geissler AL, 2012. Clinic- and hospital-based sentinel influenza surveillance, Uganda 2007–2010. J Infect Dis 206 (Suppl 1): S87S93.

    • Search Google Scholar
    • Export Citation
  • 8.

    Keeling MJ, Rohani P, 2007. Modeling Infectious Diseases in Humans and Animals. Princeton, NJ: Princeton University Press.

  • 9.

    Carrat F, Vergu E, Ferguson NM, Lemaitre M, Cauchemez S, Leach S, Valleron AJ, 2008. Time lines of infection and disease in human influenza: a review of volunteer challenge studies. Am J Epidemiol 167: 775785.

    • Search Google Scholar
    • Export Citation
  • 10.

    World Health Organization, 2016. Zika Situation Report. Zika Virus Microcephaly Guillain-Barré Syndrome. Available at: http://www.who.int/emergencies/zika-virus/situation-report/1-september-2016/en/. Accessed June 27, 2017.

  • 11.

    Wong VK 2015. Phylogeographical analysis of the dominant multidrug-resistant H58 clade of Salmonella typhi identifies inter- and intracontinental transmission events. Nat Genet 47: 632639.

    • Search Google Scholar
    • Export Citation
  • 12.

    Conway DJ, 2007. Molecular epidemiology of malaria. Clin Microbiol Rev 20: 188204.

  • 13.

    Phillips H, Killingray D, 2003. The Spanish Influenza Pandemic of 1918–19: New Perspectives. New York, NY: Routledge.

  • 14.

    Van Kerkhove MD, Hirve S, Koukounari A, Mounts AW; H1N1pdm Serology Working Group, 2013. Estimating age-specific cumulative incidence for the 2009 influenza pandemic: a meta-analysis of A(H1N1)pdm09 serological studies from 19 countries. Influenza Other Respi Viruses 7: 872886.

    • Search Google Scholar
    • Export Citation
  • 15.

    Biggerstaff M, Cauchemez S, Reed C, Gambhir M, Finelli L, 2014. Estimates of the reproduction number for seasonal, pandemic, and zoonotic influenza: a systematic review of the literature. BMC Infect Dis 14: 480.

    • Search Google Scholar
    • Export Citation
  • 16.

    Bedford T 2015. Global circulation patterns of seasonal influenza viruses vary with antigenic drift. Nature 523: 217220.

  • 17.

    Shek LP, Lee BW, 2003. Epidemiology and seasonality of respiratory tract virus infections in the tropics. Paediatr Respir Rev 4: 105111.

  • 18.

    Tamerius JD, Shaman J, Alonso WJ, Bloom-Feshbach K, Uejio CK, Comrie A, Viboud C, 2013. Environmental predictors of seasonal influenza epidemics across temperate and tropical climates. PLoS Pathog 9: e1003194.

    • Search Google Scholar
    • Export Citation
  • 19.

    Cohen C, Simonsen L, Sample J, Kang JW, Miller M, Madhi SA, Campsmith M, Viboud C, 2012. Influenza-related mortality among adults aged 25–54 years with AIDS in South Africa and the United States of America. Clin Infect Dis 55: 9961003.

    • Search Google Scholar
    • Export Citation

Author Notes

Address correspondence to Max R. O’Donnell, Columbia University Medical Center, 622 West 168th Street, PH8-E, Room 101, New York, NY 10032. E-mail: mo2130@columbia.edu

Financial support: Surveillance activities carried out by UVRI were funded through Ministry of Health, Uganda, and the World Health Organization Country Office and Regional Office for Africa and a cooperative agreement with the U.S. Centers for Disease Control and Prevention. Additional support was provided by the Irving Institute for Clinical and Translational Research at Columbia University through the National Center for Advancing Translational Sciences, National Institutes of Health (UL1TR000040) and the David R. Nalin’65 Fund for International Research at Albany Medical College.

Authors’ addresses: Matthew J. Cummings, Allison Wolf, and Max R. O’Donnell, Division of Pulmonary, Allergy, and Critical Care Medicine, Columbia University Medical Center, New York, NY, E-mails: mjc2244@columbia.edu, aw2565@columbia.edu, and mo2130@columbia.edu. Barnabas Bakamutumaho, John Kayiwa, Nicholas Owor, Barbara Namagambo, Timothy Byaruhanga, and Julius J. Lutwama, National Influenza Center, Uganda Virus Research Institute, Entebbe, Uganda, E-mails: bbarnabas2001@yahoo.com, jkayiwa@uvri.go.ug, nicowor@gmail.com, barb.namax@gmail.com, tbyaruhanga@uvri.go.ug, and jjlutwama03@yahoo.com. Wan Yang, Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, NY, E-mail: wy2202@columbia.edu. Joseph F. Wamala, South Sudan Country Office, World Health Organization, Juba, South Sudan, E-mail: wamalaj@who.int. Jeffrey Shaman, Mailman School of Public Health, Columbia University, New York, NY, E-mail: jls106@columbia.edu.

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