Faculty Bibliography

We are currently faced with a global infectious disease crisis which has been anticipated for decades. While many promising biotherapeutics are being tested, the search for a small molecule has yet to deliver an approved drug or therapeutic for the Ebola or similar filoviruses that cause haemorrhagic fever. Two recent high throughput screens published in 2013 did however identify several hits that progressed to animal studies that are FDA approved drugs used for other indications. The current computational analysis uses these molecules from two different structural classes to construct a common features pharmacophore. This ligand-based pharmacophore implicates a possible common target or mechanism that could be further explored. A recent structure based design project yielded nine co-crystal structures of pyrrolidinone inhibitors bound to the viral protein 35 (VP35). When receptor-ligand pharmacophores based on the analogs of these molecules and the protein structures were constructed, the molecular features partially overlapped with the common features of solely ligand-based pharmacophore models based on FDA approved drugs. These previously identified FDA approved drugs with activity against Ebola were therefore docked into this protein. The antimalarials chloroquine and amodiaquine docked favorably in VP35. We propose that these drugs identified to date as inhibitors of the Ebola virus may be targeting VP35. These computational models may provide preliminary insights into the molecular features that are responsible for their activity against Ebola virus in vitro and in vivo and we propose that this hypothesis could be readily tested.

BACKGROUND: China's one-child-per-couple policy, introduced in 1979, led to profound demographic changes for nearly a quarter of the world's population. Several decades later, the consequences include decreased fertility rates, population aging, decreased household sizes, changes in family structure, and imbalanced sex ratios. The epidemiology of communicable diseases may have been affected by these changes since the transmission dynamics of infectious diseases depend on demographic characteristics of the population. Of particular interest is influenza because China and Southeast Asia lie at the center of a global transmission network of influenza. Moreover, changes in household structure may affect influenza transmission. Is it possible that the pronounced demographic changes that have occurred in China have affected influenza transmission? METHODS AND FINDINGS: To address this question, we developed a continuous-time, stochastic, individual-based simulation model for influenza transmission. With this model, we simulated 30 years of influenza transmission and compared influenza transmission rates in populations with and without the one-child policy control. We found that the average annual attack rate is reduced by 6.08% (SD 2.21%) in the presence of the one-child policy compared to a population in which no demographic changes occurred. There was no discernible difference in the secondary attack rate, -0.15% (SD 1.85%), between the populations with and without a one-child policy. We also forecasted influenza transmission over a ten-year time period in a population with a two-child policy under a hypothesis that a two-child-per-couple policy will be carried out in 2015, and found a negligible difference in the average annual attack rate compared to the population with the one-child policy. CONCLUSIONS: This study found that the average annual attack rate is slightly lowered in a population with a one-child policy, which may have resulted from a decrease in household size and the proportion of children in the population.

OBJECTIVE: Epidemiological studies have observed that genital schistosomiasis increases the risk of HIV infection in Africa. We analysed the correlation between Schistosoma haematobium prevalence and HIV prevalence across sub-Saharan African countries. DESIGN: Regression analysis of prevalence of HIV and S. haematobium across sub-Saharan African countries. METHODS: Using compiled country-level S. haematobium prevalence, HIV prevalence and other demographic and economic data from published sources, we applied univariate and multivariate regression models to assess the correlations between S. haematobium prevalence and HIV prevalence while controlling for risk factors associated with each infection. RESULTS: In 43 sub-Saharan African countries, the mean prevalence of S. haematobium was 22.4% [standard deviation (SD): 9.8%] and for HIV was 6.21% (SD: 5.71%). In multivariate analysis, adjusted for prevalence of male circumcision, years since a country's first HIV/AIDS diagnosis, geographical region and immunization coverage, each S. haematobium infection per 100 individuals was associated with a 2.9% (95% CI: 0.2-5.8%) relative increase in HIV prevalence. S. haematobium was not associated with Schistosoma mansoni, HSV-2, hepatitis C, malaria or syphilis. CONCLUSIONS: Schistosoma haematobium prevalence was associated with HIV prevalence in sub-Saharan Africa. Controlling S. haematobium may be an effective means of reducing HIV transmission in sub-Saharan Africa.

OBJECTIVE: To use observed data to develop a mathematical model that estimates the impact of migration on the spread of HIV in South Africa. METHODS: A deterministic mathematical model was designed to evaluate the dynamic interactions between mobility, sexual behaviour, HIV, and sexually transmitted infections. The model was based on a population study of 488 adults, which included male migrants, male non-migrants and their rural partners in KwaZulu/Natal, South Africa. RESULTS: The model predicted that the impact of migration depends upon the epidemic's stage and the pattern of migration. Early in the epidemic, frequent migration between populations with different HIV prevalence rates accelerated HIV spread; however, local sexual risk behaviour determined the eventual scale of the epidemic. If migration is coupled with increased sexual risk behaviour by migrant men, as has been reported in the South African communities studied, HIV prevalence would increase 10 times among migrants' female partners (1.8 to 19%). In contrast, if migration were to occur infrequently, with migration-associated risk behaviour assumed to be at current levels, the predicted epidemic would be one fifth that currently observed (2.8 versus 15.1%). CONCLUSIONS: Migration primarily influences HIV spread by increasing high-risk sexual behaviour, rather than by connecting areas of low and high risk. Frequent return of migrants is an important risk factor when coupled with increased sexual risk behaviour. Accordingly, intervention programmes in South Africa need to target the sexual behaviour of short-term migrants specifically, even though these individuals may be more difficult to identify.

The emergence and rapid global spread of the severe acute respiratory syndrome (SARS) coronavirus in 2002-2003 prompted efforts by modelers to characterize SARS epidemiology and inform control policies. We overview and discuss models for emerging infectious diseases (EIDs), provide a critical survey of SARS modeling literature, and discuss promising future directions for research. We reconcile discrepancies between published estimates of the basic reproductive number R0 for SARS (a crucial epidemiologic parameter), discuss insights regarding SARS control measures that have emerged uniquely from a modeling approach, and argue that high priorities for future modeling of SARS and similar respiratory EIDs should include informing quarantine policy and better understanding the impact of population heterogeneity on transmission patterns.

BACKGROUND: High rates of population movement may have helped spread human immunodeficiency virus (HIV) in southern Africa, including Zimbabwe, but whether mobility continues to influence the epidemic is unclear. METHODS: The relationship between movement, risk behaviors, and prevalence of HIV was assessed from a general population survey of >9800 adults in 12 rural communities in Manicaland province in eastern Zimbabwe. RESULTS: HIV prevalence varied with socioeconomic development. In community centers, prevalence among women was 49.9% (95% confidence interval [CI], 46.1%-53.6%), compared with 24.7% (95% CI, 22.6%-26.7%) in the least-developed subsistence-farming areas. Mobility was not associated with risk of HIV infection, except for those who migrated between rural locations. Among migrant agricultural workers, prevalence was 38.8% (95% CI, 33.1%-44.6%) for women and 26.4% (95% CI, 23.8%-28.9%) for men, compared with 29.7% (95% CI, 28.3%-31.1%) and 20.9% (95% CI, 19.3%-22.4%) for other sexually active women and men, respectively. Risk was increased if an individual traveled to Harare in the last month, without their spouse, but this risk was not transferred to the partner. CONCLUSION: Rural-urban migration does not appear to be responsible for maintaining the high HIV prevalence in rural Zimbabwe, but rates of HIV infection may be affected by rural-rural migration.