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 (R 0 ). The R 0 for novel influenza A (H1N1) has recently been estimated to be between 1.4 and 1.6. This value is below values of R 0 estimated for the 1918-1919 pandemic strain (mean R 0~2 : range 1.4 to 2.8) and is comparable to R 0 values estimated for seasonal strains of influenza (mean R 0 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 resourceconstrained 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.

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.

UNAIDS, and the WHO, plan to use “treatment as prevention” (TasP) to eliminate HIV. The rationale is that treating HIV-infected individuals reduces their infectivity. We present a novel geostatistical framework for implementing the rollout of TasP in sub-Saharan Africa. We focus on Lesotho, because UNAIDS has identified their epidemic as a priority for elimination. Our framework is based on a density of infection (DoI) map that we generate by gridding high-resolution demographic data, and spatially smoothing georeferenced HIV-testing data. The map reveals the geographic dispersion pattern of HIV-infected individuals, both diagnosed and undiagnosed. We use the map to design a treatment allocation strategy that optimizes, under resource constraints, the efficiency of resource utilization. Using this strategy (rather than the current treatment allocation strategy) would make it easier to find, diagnose and treat individuals. We also use our framework to evaluate the feasibility of UNAIDS’ elimination plan. We show the feasibility of reaching specific treatment coverage levels depends upon the geographic dispersion pattern of HIV-infected individuals, and that this pattern reflects the spatial demographics of Lesotho. Only 20% of HIV-infected individuals live in urban areas; most live in rural settlements where the DoI is less than six infected individuals/km2. Given these conditions, it will be almost impossible to reach a very high coverage of treatment. Therefore, the UNAIDS elimination plan is unlikely to succeed in Lesotho. Taken together, our results show that the spatial demographics of populations will significantly hinder, and may even prevent, the elimination of HIV in sub-Saharan Africa.

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 couple’s 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.

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