Predicting Trends in HIV-1 Sexual Transmission in Sub-Saharan Africa Through the Drug Resource Enhancement Against AIDS and Malnutrition Model: Antiretrovirals for 5 Reduction of Population Infectivity, Incidence and Prevalence at the District Level

Palombi, Leonardo; Bernava, Giuseppe Massimo; Nucita, Andrea; Giglio, Pietro; Liotta, Giuseppe; Nielsen‐Saines, Karin; Orlando, Stefano; Mancinelli, Sandro; Buonomo, E; Scarcella, P; Altan, Anna Maria Doro; Guidotti, Giovanni; Ceffa, Susanna; Jere, Haswell; Zimba, I; Magid, Nurja Abdul; Marazzi, Maria Cristina

doi:10.1093/cid/cis380

Cited by 17 publications

(17 citation statements)

References 20 publications

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“…Incidence of a disease can be used for predicting the number of new cases in the years to come [18], which will provide decision references for planning prevention and control. Herein, time series analysis [19], [20], D-R model, GM(1,1) model [21], [22], Markov chain prediction model [23] and multi-variate linear regression [24] have been used to predict future trends in some infectious diseases. However, these published forecasting methods mostly aim at the incidence, prevalence, or mortality rate (or the number of people) of a disease, rather than the time when an epidemic peak may occur.…”

Section: Introductionmentioning

confidence: 99%

The Application of the Grey Disaster Model to Forecast Epidemic Peaks of Typhoid and Paratyphoid Fever in China

Shen

Chen

et al. 2013

PLoS ONE

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ObjectiveThe objectives of this study were to forecast epidemic peaks of typhoid and paratyphoid fever in China using the grey disaster model, to evaluate its feasibility of predicting the epidemic tendency of notifiable diseases.MethodsAccording to epidemiological features, the GM(1,1) model and DGM model were used to build the grey disaster model based on the incidence data of typhoid and paratyphoid fever collected from the China Health Statistical Yearbook. Model fitting accuracy test was used to evaluate the performance of these two models. Then, the next catastrophe date was predicted by the better model.ResultsThe simulation results showed that DGM model was better than GM(1,1) model in our data set. Using the DGM model, we predicted the next epidemic peak time will occur between 2023 to 2025.ConclusionThe grey disaster model can predict the typhoid and paratyphoid fever epidemic time precisely, which may provide valuable information for disease prevention and control.

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Section: Introductionmentioning

confidence: 99%

The Application of the Grey Disaster Model to Forecast Epidemic Peaks of Typhoid and Paratyphoid Fever in China

Shen

Chen

et al. 2013

PLoS ONE

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“…Modelling studies have explored the widespread use of ART but mainly in sub-Saharan settings [72][73][74][75][76][77][78][79][80][81][82][83][84][85][86][87][88][89], in the United States [90;91], in China [92][93][94], Canada [95] and for some specific groups, such as MSM in Australia [36;96;97], in Peru, Ukraine, Kenya and Thailand [98] and in different cities in the United States [99][100][101][102][103]; only a few of them model the HIV epidemic in European countries (MSM in Amsterdam, the Netherlands [104] and in the UK [105][106][107] and people who inject drugs in Russia [108]). They varied in their conclusions, although most have suggested potential appreciable beneficial effects on HIV incidence of introducing ART initiation at a higher CD4 count as a policy at a population level.…”

Section: European Population-level Impactmentioning

confidence: 99%

Antiretroviral therapy for prevention of HIV transmission: implications for Europe

et al. 2013

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The aim of this review is to summarise the evidence on the population-level effect of antiretroviral therapy (ART) in preventing HIV infections, and to discuss potential implications in the European context of recommending starting ART when the CD4 count is above 350 cells/mm 3 . The ability of ART to reduce the risk of HIV transmission has been reported in observational studies and in a randomised controlled trial (HPTN 052), in which ART initiation reduced HIV transmission by 96% within serodiscordant couples. As yet, there is no direct evidence for such an effect among men having sex with men or people who inject drugs. HPTN 052 led international organisations to develop recommendations with a higher CD4 threshold for ART initiation. However, there remains a lack of strong evidence of clinical benefit for HIV-positive individuals starting ART with CD4 count above 350 cells/mm 3 . The main goal of ART provision should be to increase ART coverage for all those in need, based on the current guidelines, and the offer of ART to those who wish to reduce infectivity; increased HIV testing is therefore a key requirement. Other proven prevention means such as condom use and harm reduction for people who inject drugs remain critical.

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“…Given the evidence for variability in network properties like concurrency and mixing patterns (Morris et al, 2007), it is plausible that communities differ in other network properties that are not observed. For example, a broad range of parameter values that characterize mixing by sexual activity level have been used in mathematical models that simulate HIV spreading in a population (Eaton et al, 2012; Palombi et al, 2012; Morris et al, 2009). In this paper, we focus on development of methods to sample dynamic networks to aid in the interpretation of conclusions regarding the potential impact of intervening on network features like concurrency in the presence of unmeasured network properties.…”

Section: Introductionmentioning

confidence: 99%

Sampling dynamic networks with application to investigation of HIV epidemic drivers

Goyal

Gruttola

2015

Mathematical Biosciences

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We propose a method for randomly sampling dynamic networks that permits isolation of the impact of different network features on processes that propagate on networks. The new methods permit uniform sampling of dynamic networks in ways that ensure that they are consistent with both a given cumulative network and with specified values for constraints on the dynamic network properties. Development of such methods is challenging because modifying one network property will generally tend to modify others as well. Methods to sample constrained dynamic networks are particularly useful in the investigation of network-based interventions that target and modify specific dynamic network properties, especially in settings where the whole network is unobservable and therefore many network properties are unmeasurable. We illustrate this method by investigating the incremental impact of changes in networks properties that are relevant for the spread of infectious diseases, such as concurrency in sexual relationships. Development of the method is motivated by the challenges that arise in investigating the role of HIV epidemic drivers due to the often limited information available about contact networks. The proposed methods for randomly sampling dynamic networks facilitate investigation of the type of network data that can best contribute to an understanding of the HIV epidemic dynamics as well as of the limitations of conclusions drawn in the absence of such information. Hence, the methods are intended to aid in the design and interpretation of studies of network-based interventions.

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Predicting Trends in HIV-1 Sexual Transmission in Sub-Saharan Africa Through the Drug Resource Enhancement Against AIDS and Malnutrition Model: Antiretrovirals for 5 Reduction of Population Infectivity, Incidence and Prevalence at the District Level

Abstract: Our model, based on patient data, supports the hypothesis that treatment of all infected individuals translates into a drastic reduction in incident HIV infections. A targeted implementation strategy with massive population coverage is feasible in sub-Saharan Africa.

Cited by 17 publications

References 20 publications

The Application of the Grey Disaster Model to Forecast Epidemic Peaks of Typhoid and Paratyphoid Fever in China

The Application of the Grey Disaster Model to Forecast Epidemic Peaks of Typhoid and Paratyphoid Fever in China

Antiretroviral therapy for prevention of HIV transmission: implications for Europe

Sampling dynamic networks with application to investigation of HIV epidemic drivers

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