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Early mathematical models varied in their predictions of the impact of HIV/AIDS on population growth from minimal impact to reductions in growth, in pessimistic scenarios, from positive to negative values over a period of 25 years. Models predicting negative rates of natural increase forecast little effect on the dependency ratio. Twenty years later, HIV prevalence in small towns, estates, and rural villages in eastern Zimbabwe, has peaked within the intermediate range predicted by the early models, but the demographic impact has been more acute than was predicted. Despite concurrent declines in fertility, fueled in part by HIV infections (total fertility is now 8% lower than expected without an epidemic), and a doubling of the crude death rate because of HIV/AIDS, the rate of natural population increase between 1998 and 2005 remained positive in each socioeconomic stratum. In the worst-affected areas (towns with HIV prevalence of 33%), HIV/AIDS reduced growth by two-thirds from 2.9% to 1.0%. The dependency ratio fell from 1.21 at the onset of the HIV epidemic to 0.78, the impact of HIV-associated adult mortality being outweighed by fertility decline. With the benefit of hindsight, the more pessimistic early models overestimated the demographic impact of HIV epidemics by over-extrapolating initial HIV growth rates or not allowing for heterogeneity in key parameters such as transmissibility and sexual risk behavior. Data collected since the late 1980s show that there was a mismatch between the observed growth in the HIV epidemic and assumptions made about viral transmission. dependency ratio | population growth

It is imperative to advance our understanding of heterogeneities in the transmission of SARS-CoV-2 such as age-specific infectiousness and superspreading. To this end, it is important to exploit multiple data streams that are becoming abundantly available during the pandemic. In this paper, we formulate an individual-level spatiotemporal mechanistic framework to integrate individual surveillance data with geolocation data and aggregate mobility data, enabling a more granular understanding of the transmission dynamics of SARS-CoV-2. We analyze reported cases, between March and early May 2020, in five (urban and rural) counties in the state of Georgia. First, our results show that the reproductive number reduced to below one in about 2 wk after the shelter-in-place order. Superspreading appears to be widespread across space and time, and it may have a particularly important role in driving the outbreak in rural areas and an increasing importance toward later stages of outbreaks in both urban and rural settings. Overall, about 2% of cases were directly responsible for 20% of all infections. We estimate that the infected nonelderly cases (<60 y) may be 2.78 [2.10, 4.22] times more infectious than the elderly, and the former tend to be the main driver of superspreading. Our results improve our understanding of the natural history and transmission dynamics of SARS-CoV-2. More importantly, we reveal the roles of age-specific infectiousness and characterize systematic variations and associated risk factors of superspreading. These have important implications for the planning of relaxing social distancing and, more generally, designing optimal control measures. [ABSTRACT FROM AUTHOR]

Understanding how immunity shapes the dynamics of multistrain pathogens is essential in determining the selective pressures imposed by vaccines. There is currently much interest in elucidating the strain dynamics of rotavirus to determine whether vaccination may lead to the replacement of vaccine-type strains. In developed countries, G1P[8] strains constitute the majority of rotavirus infections most years, but occasionally other genotypes dominate for reasons that are not well understood. We developed a mathematical model to examine the interaction of five common rotavirus genotypes. We explored a range of estimates for the relative strength of homotypic vs. heterotypic immunity and compared model predictions against observed genotype patterns from six countries. We then incorporated vaccination in the model to examine its impact on rotavirus incidence and the distribution of strains. Our model can explain the coexistence and cyclical pattern in the distribution of genotypes observed in most developed countries. The predicted frequency of cycling depends on the relative strength of homotypic vs. heterotypic immunity. Vaccination that provides strong protection against G1 and weaker protection against other strains will likely lead to an increase in the relative prevalence of non-G1 strains, whereas a vaccine that provides equally strong immunity against all strains may promote the continued predominance of G1. Overall, however, disease incidence is expected to be substantially reduced under both scenarios and remain below prevaccination levels despite the possible emergence of new strains. Better understanding of homotypic vs. heterotypic immunity, both natural and vaccine-induced, will be critical in predicting the impact of vaccination. [ABSTRACT FROM AUTHOR]