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1.  Current drivers and geographic patterns of HIV in Lesotho: implications for treatment and prevention in Sub-Saharan Africa 
BMC Medicine  2013;11:224.
The 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.
We 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.
HIV 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.
There 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.
PMCID: PMC4016528  PMID: 24131484
HIV; Epidemiology; Risk factors; Circumcision; Geography
2.  The importance of including dynamic social networks when modeling epidemics of airborne infections: does increasing complexity increase accuracy? 
BMC Medicine  2011;9:88.
Mathematical models are useful tools for understanding and predicting epidemics. A recent innovative modeling study by Stehle and colleagues addressed the issue of how complex models need to be to ensure accuracy. The authors collected data on face-to-face contacts during a two-day conference. They then constructed a series of dynamic social contact networks, each of which was used to model an epidemic generated by a fast-spreading airborne pathogen. Intriguingly, Stehle and colleagues found that increasing model complexity did not always increase accuracy. Specifically, the most detailed contact network and a simplified version of this network generated very similar results. These results are extremely interesting and require further exploration to determine their generalizability.
Please see related article BMC Medicine, 2011, 9:87
PMCID: PMC3158113  PMID: 21771292
3.  Calculating the potential for within-flight transmission of influenza A (H1N1) 
BMC Medicine  2009;7:81.
Clearly 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.
We model airborne transmission of infectious viral particles of H1N1 within a Boeing 747 using methodology from the field of quantitative microbial risk assessment.
The 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.
Our 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.
PMCID: PMC2813231  PMID: 20034378
4.  Modeling influenza epidemics and pandemics: insights into the future of swine flu (H1N1) 
BMC Medicine  2009;7:30.
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.
PMCID: PMC2715422  PMID: 19545404
5.  How far will we need to go to reach HIV-infected people in rural South Africa? 
BMC Medicine  2007;5:16.
The South African Government has outlined detailed plans for antiretroviral (ART) rollout in KwaZulu-Natal Province, but has not created a plan to address treatment accessibility in rural areas in KwaZulu-Natal. Here, we calculate the distance that People Living With HIV/AIDS (PLWHA) in rural areas in KwaZulu-Natal would have to travel to receive ART. Specifically, we address the health policy question 'How far will we need to go to reach PLWHA in rural KwaZulu-Natal?'.
We developed a model to quantify treatment accessibility in rural areas; the model incorporates heterogeneity in spatial location of HCFs and patient population. We defined treatment accessibility in terms of the number of PLWHA that have access to an HCF. We modeled the treatment-accessibility region (i.e. catchment area) around an HCF by using a two-dimensional function, and assumed that treatment accessibility decreases as distance from an HCF increases. Specifically, we used a distance-discounting measure of ART accessibility based upon a modified form of a two-dimensional gravity-type model. We calculated the effect on treatment accessibility of: (1) distance from an HCF, and (2) the number of HCFs.
In rural areas in KwaZulu-Natal even substantially increasing the size of a small catchment area (e.g. from 1 km to 20 km) around an HCF would have a negligible impact (~2%) on increasing treatment accessibility. The percentage of PLWHA who can receive ART in rural areas in this province could be as low as ~16%. Even if individuals were willing (and able) to travel 50 km to receive ART, only ~50% of those in need would be able to access treatment. Surprisingly, we show that increasing the number of available HCFs for ART distribution ~ threefold does not lead to a threefold increase in treatment accessibility in rural KwaZulu-Natal.
Our results show that many PLWHA in rural KwaZulu-Natal are unlikely to have access to ART, and that the impact of an additional 37 HCFs on treatment accessibility in rural areas would be less substantial than might be expected. There is a great length to go before we will be able to reach many PLWHA in rural areas in South Africa, and specifically in KwaZulu-Natal.
PMCID: PMC1906822  PMID: 17577418

Results 1-5 (5)