Brian J Coburn

Modeling influenza epidemics and pandemics: insights into the future of swine flu (H1N1)

Here we present a review of the literature of influenza modeling studies, and discuss how these models can provide insights into the future of the currently circulating novel strain of influenza A (H1N1), formerly known as swine flu. We discuss how the feasibility of controlling an epidemic critically depends on the value of the Basic Reproduction Number (R0). The R0 for novel influenza A (H1N1) has recently been estimated to be between 1.4 and 1.6. This value is below values of R0 estimated for the 1918–1919 pandemic strain (mean R0~2: range 1.4 to 2.8) and is comparable to R0 values estimated for seasonal strains of influenza (mean R0 1.3: range 0.9 to 2.1). By reviewing results from previous modeling studies we conclude it is theoretically possible that a pandemic of H1N1 could be contained. However it may not be feasible, even in resource-rich countries, to achieve the necessary levels of vaccination and treatment for control. As a recent modeling study has shown, a global cooperative strategy will be essential in order to control a pandemic. This strategy will require resource-rich countries to share their vaccines and antivirals with resource-constrained and resource-poor countries. We conclude our review by discussing the necessity of developing new biologically complex models. We suggest that these models should simultaneously track the transmission dynamics of multiple strains of influenza in bird, pig and human populations. Such models could be critical for identifying effective new interventions, and informing pandemic preparedness planning. Finally, we show that by modeling cross-species transmission it may be possible to predict the emergence of pandemic strains of influenza.

Calculating the potential for within-flight transmission of influenza A (H1N1)

BackgroundClearly air travel, by transporting infectious individuals from one geographic location to another, significantly affects the rate of spread of influenza A (H1N1). However, the possibility of within-flight transmission of H1N1 has not been evaluated; although it is known that smallpox, measles, tuberculosis, SARS and seasonal influenza can be transmitted during commercial flights. Here we present the first quantitative risk assessment to assess the potential for within-flight transmission of H1N1.MethodsWe model airborne transmission of infectious viral particles of H1N1 within a Boeing 747 using methodology from the field of quantitative microbial risk assessment.ResultsThe risk of catching H1N1 will essentially be confined to passengers travelling in the same cabin as the source case. Not surprisingly, we find that the longer the flight the greater the number of infections that can be expected. We calculate that H1N1, even during long flights, poses a low to moderate within-flight transmission risk if the source case travels First Class. Specifically, 0-1 infections could occur during a 5 hour flight, 1-3 during an 11 hour flight and 2-5 during a 17 hour flight. However, within-flight transmission could be significant, particularly during long flights, if the source case travels in Economy Class. Specifically, two to five infections could occur during a 5 hour flight, 5-10 during an 11 hour flight and 7-17 during a 17 hour flight. If the aircraft is only partially loaded, under certain conditions more infections could occur in First Class than in Economy Class. During a 17 hour flight, a greater number of infections would occur in First Class than in Economy if the First Class Cabin is fully occupied, but Economy class is less than 30% full.ConclusionsOur results provide insights into the potential utility of air travel restrictions on controlling influenza pandemics in the winter of 2009/2010. They show travel by one infectious individual, rather than causing a single outbreak of H1N1, could cause several simultaneous outbreaks. These results imply that, during a pandemic, quarantining passengers who travel in Economy on long-haul flights could potentially be an important control strategy. Notably, our results show that quarantining passengers who travel First Class would be unlikely to be an effective control strategy.

Modeling the impact on the HIV epidemic of treating discordant couples with antiretrovirals to prevent transmission.

Background:The HPTN 052 study demonstrated a 96% reduction in HIV transmission in discordant couples using antiretroviral therapy (ART). Objective:To predict the epidemic impact of treating HIV-discordant couples to prevent transmission. Design:Mathematical modeling to predict incidence reduction and the number of infections prevented. Methods:Demographic and epidemiological data from Ghana, Lesotho, Malawi and Rwanda were used to parameterize the model. ART was assumed to be 96% effective in preventing transmission. Results:Our results show there would be a fairly large reduction in incidence and a substantial number of infections prevented in Malawi. However, in Ghana a large number of infections would be prevented, but only a small reduction in incidence. Notably, the predicted number of infections prevented would be similar (and low) in Lesotho and Rwanda, but incidence reduction would be substantially greater in Lesotho than Rwanda. The higher the proportion of the population in stable partnerships (whether concordant or discordant), the greater the effect of a discordant couples intervention on HIV epidemics. Conclusion:The effectiveness of a discordant couples intervention in reducing incidence will vary among countries due to differences in HIV prevalence and the percentage of couples that are discordant (i.e. degree of discordancy). The number of infections prevented within a country, as a result of an intervention, will depend upon a complex interaction among three factors: population size, HIV prevalence and degree of discordancy. Our model provides a quantitative framework for identifying countries most likely to benefit from treating discordant couples to prevent transmission.

Quantification of the role of discordant couples in driving incidence of HIV in sub-Saharan Africa

Data presented in the meta-analysis by Oghenowede Eyawo and colleagues sheds new light on the role of serodiscordant couples in driving the incidence of HIV in Africa. The HIV epidemic in Africa is driven by heterosexual transmission. Studies have shown transmission in stable discordant couples can be as low as 1·9 per 100 person-years to as high as 19·0 per 100 person-years; these couples are typically in stable relationships lasting at least a year. However, the extent to which they drive countrylevel incidence in sub-Saharan Africa percentage of the population in stable relationships (regardless of whether they are in discordant or concordant couples), the more discordant couples drive incidence. Notably, their role in driving incidence is country-specifi c. For example, if 40% of the population in Ghana is in stable relationships, stable discordant couples could account for 34% of the country-level incidence. However, if the same percentage in Rwanda is in stable relationships, they could only account for 18% of the country-level incidence. Results from a previous study suggest transmission in stable discordant couples could account for about 50% of the countrylevel incidence. Our results show that such transmission is possible, but is more likely in some countries than in others; for example, it seems possible in Kenya but unlikely in Guinea (fi gure). We agree that public health programmes aimed at preventing HIV infection in stable discordant couples could be very useful in reducing transmission. However, determination of the degree to which these couples drive incidence of HIV in specifi c countries is crucial, because this will determine the necessity and intensity of other interventions. Figure: The proportion of country-level incidence of HIV due to transmission by stable discordant couples versus the proportion of the population in stable relationships lasting a year or more. Data includes concordant and discordant couples. SDC=stable discordant couple. Burkina Faso Ethiopia Guinea Kenya Malawi Rwanda Tanzania

Current drivers and geographic patterns of HIV in Lesotho: implications for treatment and prevention in Sub-Saharan Africa

BackgroundThe most severe HIV epidemics worldwide occur in Lesotho, Botswana and Swaziland. Here we focus on the Lesotho epidemic, which has received little attention. We determined the within-country heterogeneity in the severity of the epidemic, and identified the risk factors for HIV infection. We also determined whether circumcised men in Lesotho have had a decreased risk of HIV infection in comparison with uncircumcised men. We discuss the implications of our results for expanding treatment (current coverage is only 60%) and reducing transmission.MethodsWe used data from the 2009 Lesotho Demographic and Health Survey, a nationally representative survey of 3,849 women and 3,075 men in 9,391 households. We performed multivariate analysis to identify factors associated with HIV infection in the sexually active population and calculated age-adjusted odds ratios (aORs). We constructed cartographic country-level prevalence maps using geo-referenced data.ResultsHIV is hyperendemic in the general population. The average prevalence is 27% in women and 18% in men, but shows substantial geographic variation. Throughout the country prevalence is higher in urban centers (31% in women; 21% in men) than in rural areas (25% in women; 17% in men), but the vast majority of HIV-infected individuals live in rural areas. Notably, prevalence is extremely high in women (18%) and men (12%) with only one lifetime sex partner. Women with more partners have a greater risk of infection: aOR 2.3 (2 to 4 partners), aOR 4.4 (≥5 partners). A less substantial effect was found for men: aOR 1.4 (3 to 6 partners), aOR 1.8 (≥7 partner). Medical circumcision protected against infection (aOR 0.5), traditional circumcision did not (aOR 0.9). Less than 5% of men in Lesotho have been medically circumcised; approximately 50% have been circumcised using traditional methods.ConclusionsThere is a substantial need for treatment throughout Lesotho, particularly in rural areas where there is the greatest burden of disease. Interventions aimed at reducing the number of sex partners may only have a limited effect on reducing transmission. Substantially increasing levels of medical circumcision could be very effective in reducing transmission, but will be very difficult to achieve given the current high prevalence of traditional circumcision.

A major HIV risk factor for young men who have sex with men is sex with older partners.

More than 50% of new HIV infections in the United States occur in men who have sex with men (MSM). Recent data from the Centers for Disease Control and Prevention has shown a resurgence of the HIV epidemic particularly in young MSM. The factors driving this recent rise in HIV infections have not yet been identified. In this issue, Hurt et al present striking data indicating that many young MSM in North Carolina are becoming infected with HIV because they have sex with older MSM. In their study, they found the MSM participants with recent HIV infection reported having sex with older partners. After stratifying on the age of the older partner Hurt et al found the risk for HIV infection was doubled when the partner was 5 years older [odds ratio (OR): 2.2, 95% confidence interval (CI): 1.2 to 3.3] and quadrupled when the partner was 10 years older (OR: 4.1, 95% CI: 1.5 to 11); the risk was calculated in comparison to the risk of HIV infection when the partner was the same (approximate) age as the participant. The study by Hurt et al is particularly significant as it highlights the need to address the overlooked issue that age-mixing patterns can be very important in driving HIV epidemics. The potential effect of age mixing on HIV epidemiology was first suggested by Morris et al who analyzed data collected in the late 1980s from MSM who participated in the Longitudinal AIDS Project in New York City. Morris et al found that only 45% of sexual partnerships were with men in the same age class. They used the agemixing network they identified to model the transmission of HIV, and they found that young MSM who had sex with older MSM were the leading edge of the epidemic in New York City. A few years later, a series of studies on HIVepidemics and age mixing was carried out by Blower et al. They used data which had been collected in the early 1990s for the San Francisco Men’s Health Study, and they analyzed age-based sexual mixing patterns for young MSM under 30 years of age. Their analysis showed the risk of infection doubled if the participant had sex with men who were older than 30 years and with men who were (approximately) their same age (OR: 2.3, 95% CI: 0.9 to 6.2). Furthermore, they found the risk of infection was more than 5 times greater if all of the participants partners were older than 30 years (OR: 5.4, 95% CI: 1.8 to 15.8). Taken together, the studies by Morris et al, Blower et al, and Hurt et al indicate that age mixing has been, and continues to be, a significant factor in driving HIV epidemics in MSM communities throughout the United States. Selecting older sex partners is an important risk factor for HIV infection for young MSM because HIV prevalence is very age stratified. Figure 1 shows the prevalence data for MSM communities in Baltimore, Los Angeles, Miami, New York City, and San Francisco collected for the National HIV Behavioral Surveillance surveys in 2004–2005 by the Centers for Disease Control. Prevalence increases significantly with age; prevalence in older men (.30 years old) is almost twice as high as in younger MSM (,30 years old) (Fig. 1). Young MSM may seek older partners for a variety of reasons. However, it is important to note that age-mixing patterns are not simply a function of partner selection preferences but also reflect the age distribution of the MSM community. Even if age preferences do not exist, young MSMs are more likely to have sex with older men simply because a high proportion of MSM are older than 30 years. For example, Service and Blower calculated that young MSM in San Francisco in the mid-1990s were approximately 4 times more likely to choose an older partner than a partner their own age simply based on the age distribution of MSM. The number of new infections is greatest in minorities; in their study, Hurt et al found although 40% of the participants in their study were nonwhite, 60% of the recent infections they found occurred in this group. Identification of age mixing as a significant risk factor for HIV infection in young MSMs has significant implications for public health and prevention strategies. Education and prevention programs should move beyond discussing traditional risk factors and concentrating only on safer sex messages. Programs should be tailored to different age classes and focus on both negatives and positives. Public health officials need to make MSM aware that age mixing is a risk factor for HIV infection. Furthermore, young MSM need to be told the magnitude of the risk of HIV infection due to age mixing in comparison with the magnitude of risk due to other traditional risk factors. It should be noted that in San Francisco in the mid-1990s young MSM who had only a few partners (but older partners) were more likely to be infected with HIV than young MSM who had multiple partners. All MSM, Received for publication January 5, 2010; accepted January 14, 2010. From the Center for Biomedical Modeling, Semel Institute of Neuroscience and Human Behavior, David Geffen School of Medicine at UCLA, Los Angeles, CA. Conflicts: We declare that no conflicts of interest exist. The article was jointly written by the authors. Correspondence to: Sally Blower, PhD, 10940 Wilshire Boulevard, Suite 1450, Los Angeles, CA 90024 (e-mail: sblower@mednet.ucla.edu). Copyright 2010 by Lippincott Williams & Wilkins

Using geospatial mapping to design HIV elimination strategies for sub-Saharan Africa

Mapping the geographic dispersion pattern of HIV-infected individuals in a sub-Saharan African country reveals the challenge to eliminating HIV. Mapping a path to HIV elimination About ~25 million individuals in sub-Saharan Africa are living with HIV. In new work, Coburn et al. design HIV elimination strategies for this region. The authors focused on Lesotho, where ~25% of the population is infected with HIV. They combined several large data sets and constructed a map that revealed the countrywide geographic dispersion pattern of HIV-infected individuals. They estimated that ~20% of the population lives in urban areas, and almost all rural communities have at least one HIV-infected individual. Their analyses showed that the spatial dispersion of Lesotho’s population hinders, and may even prevent, the elimination of HIV. This may hold true for other predominantly rural countries in sub-Saharan Africa. Treatment as prevention (TasP) has been proposed by the World Health Organization and the Joint United Nations Programme on HIV/AIDS (UNAIDS) as a global strategy for eliminating HIV. The rationale is that treating individuals reduces their infectivity. We present a geostatistical framework for designing TasP-based HIV elimination strategies in sub-Saharan Africa. We focused on Lesotho, where ~25% of the population is infected. We constructed a density of infection map by gridding high-resolution demographic data and spatially smoothing georeferenced HIV testing data. The map revealed the countrywide geographic dispersion pattern of HIV-infected individuals. We found that ~20% of the HIV-infected population lives in urban areas and that almost all rural communities have at least one HIV-infected individual. We used the map to design an optimal elimination strategy and identified which communities should use TasP. This strategy minimized the area that needed to be covered to find and treat HIV-infected individuals. We show that UNAIDS’s elimination strategy would not be feasible in Lesotho because it would require deploying treatment in areas where there are ~4 infected individuals/km2. Our results show that the spatial dispersion of Lesotho’s population hinders, and may even prevent, the elimination of HIV.

Predicting the potential for within-flight transmission and global dissemination of MERS

www.thelancet.com/infection Vol 14 February 2014 99 3 Cauchemez S, Fraser C, Van Kerkhove MD, et al. Middle East respiratory syndrome coronavirus: quantifi cation of the extent of the epidemic, surveillance biases, and transmissibility. Lancet Infect Dis 2014; 14: 50–56. 4 Mangili A, Gendreau MA. Transmission of infectious diseases during commercial air travel. Lancet 2005; 365: 989–96. 5 Wagner BG, Coburn BJ, Blower S. Calculating the potential for within-fl ight transmission of infl uenza A (H1N1). BMC Med 2009, 7: 81. 6 Wells WF. On air-borne infection: study II—droplets and droplet nuclei. Am J Hyg 1934; 20: 611–18. Predicting the potential for within-fl ight transmission and global dissemination of MERS

Mapping HIV epidemics in sub-Saharan Africa with use of GPS data

WHO and many other organisations are very interested in implementing treatment-as-prevention as a global policy to control the HIV pandemic.1 Widespread treatment of HIV-infected individuals with antiretroviral therapy will reduce HIV transmission, because it decreases viral load and hence infectiousness. To implement the rollout of treatment-as-prevention in an efficient manner, estimation of the number of HIV-infected individuals and where they live is needed. This assessment will be difficult to accomplish, particularly in areas of sub-Saharan Africa with severe HIV epidemics. We propose a solution to this problem by using geospatial statistical techniques and global positioning system (GPS) data. To estimate the number of HIV-infected individuals in a particular area, a predictive map of the prevalence of infection could be constructed. This map would then be overlaid on a grid map that shows the geographical dispersion of the population. The size of the grid would determine the degree of spatial resolution of the overlay map (ie, the density-of-infection map). The density map would show the estimated number of HIV-infected individuals per km2 and their geographical distribution over the area of interest. The total number of HIV-infected individuals could be obtained by summing the estimates in each grid over the entire area. All of the geospatial techniques needed to construct density-of-infection maps for HIV are techniques that have been used in studies of other infectious diseases— eg, dengue fever, influenza, malaria, rotavirus, and tuberculosis.2–8 Predictive prevalence maps have been constructed by using georeferenced prevalence data and spatial interpolation techniques. The most commonly used techniques are Bayesian geostatistical modelling and Kriging.7,8 Bayesian geostatistical models are constructed in the same manner as are Bayesian statistical models, but include additional parameters to allow for spatial dependency in the data. Bayesian geostatistical models have been used to generate predictive prevalence and risk maps for malaria and tuberculosis.7,8 Kriging uses semivariograms to model spatial dependency. The standard error of the estimated prevalence at any specific location is usually calculated, irrespective of whether Bayesian geostatistical modelling or Kriging is used for spatial interpolation. The standard error is then mapped to visualise the uncertainty in the prediction at any geographical location. The standard error is always largest in areas with the lowest density of sample sites. Kriging was developed by Danie Krige9 in the 1950s to identify the locations of gold mines by using georeferenced samples of mineral deposits. In 1992, Carrat and Valleron2 were the first to apply Kriging to the spatial analysis of an infectious disease. They used surveillance data from specific geographical locations and generated predictive surfaces to identify the spatial and temporal spread of the 1989–90 influenza epidemic in France. Kriging has since been used to generate predictive prevalence maps for dengue fever,3 rotavirus,4 and malaria.5–7 We propose that the same geospatial statistical techniques can be applied to HIV. We used these techniques to estimate the number of HIV-infected individuals in Maseru (a health-care district in Lesotho) and establish their geographical location. The district of Maseru is a relatively large area, about 4300 km2, and Lesotho has one of the most severe HIV epidemics in the world. We used HIV prevalence data collected in the 2009–10 Lesotho Demographic and Health Survey, which was based on cluster sampling.10 Handheld GPS devices were used to establish the geographical coordinates at each sample site. Of the Demographic and Health Survey sample sites in Maseru, 31 were in urban areas and 28 were in rural areas (figure part A). Figure 1 Geospatial mapping of Maseru, Lesotho A map of Kriging estimates (ie, prevalence predictions) for individuals aged 15–49 years, based on the georeferenced prevalence data, is shown in figure part B; spatial resolution is 100m2. The predictive map shows that prevalence is high (on average >20%) throughout Maseru, but that prevalence varies substantially with geography. Prevalence is predicted to be highest along the northwest border of the Maseru district where the city of Maseru (the capital of Lesotho) is located, and also in the centre of the district around the city of Roma. The standard error of the prediction estimates (figure part C) ranges from 2.4% (black shading) to 6.8% (white shading). Figure part D shows the geographical distribution of HIV-infected individuals and the density of infection; density ranges from 4.2 HIV-infected individuals per 100 m2 (red shading) to less than 0.05 HIV-infected individuals per 100 m2 (white shading). The map was used to determine that about 46 000 HIV-infected individuals aged 15–49 years live in the Maseru district. Geospatial statistical techniques have been used for more than 40 years in studies of many infectious diseases. They have provided important new insights into epidemics and, more recently, have assisted in the design of health policies for dengue, influenza, malaria, rotavirus, and tuberculosis.2–8 Their use could greatly assist the design of health policies to fight HIV epidemics. We recommend that—to maximise efficiency and cost-effectiveness—a geospatial approach should be used in decisions about how to roll out treatment-as-prevention and other public-health interventions in sub-Saharan Africa. At a minimum, this geospatial approach could be used to find HIV-infected individuals in high-prevalence epidemics, establish where they live, and estimate the burden of disease.

A feasibility analysis of implementing interventions for discordant couples in 14 African countries: implications for epidemic control.

We find interventions targeting serodiscordant couples (SDC) may not be feasible in countries where HIV prevalence is less than 5%, because only 3–19/1000 individuals are HIV-positive/negative and in SDC. Interventions may be feasible in countries where prevalence is greater than 10%, because 34–48/1000 individuals are HIV-positive/negative and in SDC. We calculated that 20–27% of all HIV-positive individuals, but less than 6% of all HIV-negative individuals, are in SDC. Consequently, targeting HIV-positive partners could significantly reduce transmission, whereas targeting HIV-negative partners may have little impact.